- home
- Advanced Search
- Energy Research
- Energy Research
description Publicationkeyboard_double_arrow_right Article 2024 AustraliaPublisher:MDPI AG Bidhan Nath; Les Bowtell; Guangnan Chen; Elizabeth Graham; Thong Nguyen-Huy;doi: 10.3390/en17153693
The study of the thermokinetics of two types of wheat straw pellets, T1 (100% wheat straw) and T2 (70% wheat straw, 10% each of bentonite clay, sawdust, and biochar), under a nitrogen atmosphere (31–800 °C and 5, 10, and 20 °C/min heating rates) using model-free and model-based approaches by TG/DTG data, revealed promising results. While model-free methods were not suitable, model-based reactions, particularly Fn (nth-order phase interfacial) and F2 (second-order) models, effectively described the three-phase consecutive thermal degradation pathway (A→B, C→D, and D→E). The activation energy (Eα) for phases 2 and 3 (Fn model) averaged 136.04 and 358.11 kJ/mol for T1 and 132.86 and 227.10 kJ/mol for T2, respectively. The pre-exponential factor (lnA) varied across heating rates and pellets (T2: 38.244–2.9 × 109 1/s; T1: 1.2 × 102–5.45 × 1014 1/s). Notably, pellets with additives (T2) exhibited a higher degradable fraction due to lower Eα. These findings suggest a promising potential for utilizing wheat straw pellet biomass as a bioenergy feedstock, highlighting the practical implications of this research.
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2024License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en17153693&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
more_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2024License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en17153693&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022 Australia, France, FrancePublisher:Springer Science and Business Media LLC Kath, Jarrod; Craparo, Alessandro; Fong, Youyi; Byrareddy, Vivekananda; Davis, Aaron P.; King, Rachel; Nguyen-Huy, Thong; Asten, Piet J.A. van; Marcussen, Torben; Mushtaq, Shahbaz; Stone, Roger; Power, Scott;Our understanding of the impact of climate change on global coffee production is largely based on studies focusing on temperature and precipitation, but other climate indicators could trigger critical threshold changes in productivity. Here, using generalized additive models and threshold regression, we investigate temperature, precipitation, soil moisture and vapour pressure deficit (VPD) effects on global Arabica coffee productivity. We show that VPD during fruit development is a key indicator of global coffee productivity, with yield declining rapidly above 0.82 kPa. The risk of exceeding this threshold rises sharply for most countries we assess, if global warming exceeds 2 °C. At 2.9 °C, countries making up 90% of global supply are more likely than not to exceed the VPD threshold. The inclusion of VPD and the identification of thresholds appear critical for understanding climate change impacts on coffee and for the design of adaptation strategies.
CGIAR CGSpace (Consu... arrow_drop_down CGIAR CGSpace (Consultative Group on International Agricultural Research)Article . 2022Full-Text: https://hdl.handle.net/10568/125539Data sources: Bielefeld Academic Search Engine (BASE)University of Southern Queensland: USQ ePrintsArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/s43016-022-00614-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 46 citations 46 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
more_vert CGIAR CGSpace (Consu... arrow_drop_down CGIAR CGSpace (Consultative Group on International Agricultural Research)Article . 2022Full-Text: https://hdl.handle.net/10568/125539Data sources: Bielefeld Academic Search Engine (BASE)University of Southern Queensland: USQ ePrintsArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/s43016-022-00614-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 Australia, Australia, SpainPublisher:Elsevier BV Sujan Ghimire; Thong Nguyen-Huy; Mohanad S. AL-Musaylh; Ravinesh C. Deo; David Casillas-Pérez; Sancho Salcedo-Sanz;handle: 10115/24758
The authors thank the data providers, all the reviewers and the Editor for their thoughtful comments, suggestions and the review process. Partial support of this study is through the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure in the power generation network. To deliver a high-quality electricity demand prediction, this paper proposes a hybrid combination technique, based on a deep learning model of Convolutional Neural Networks and Echo State Networks, named as CESN. Daily electricity demand data from four sites (Roderick, Rocklea, Hemmant and Carpendale), located in Southeast Queensland, Australia, have been used to develop the proposed hybrid prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, deep neural network, and Light Gradient Boosting) to compare and evaluate the outcomes of the proposed deep learning approach. The results obtained in the experimental study showed that the proposed hybrid deep learning model is able to obtain the highest performance compared to other existing models developed for daily electricity demand data forecasting. Based on the statistical approaches utilized in this study, the proposed hybrid approach presents the highest prediction accuracy among the compared models. The obtained results showed that the proposed hybrid deep learning algorithm is an excellent and accurate electricity demand forecasting method, which outperformed the state of the art algorithms that are currently used in this problem.
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BY NC NDData sources: Bielefeld Academic Search Engine (BASE)Universidad Rey Juan Carlos, Madrid: Archivo Abierto InstitucionalArticle . 2023License: CC BY NC NDFull-Text: https://hdl.handle.net/10115/24758Data sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2023License: CC BY NC NDData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2023.127430&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 39 citations 39 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
more_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BY NC NDData sources: Bielefeld Academic Search Engine (BASE)Universidad Rey Juan Carlos, Madrid: Archivo Abierto InstitucionalArticle . 2023License: CC BY NC NDFull-Text: https://hdl.handle.net/10115/24758Data sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2023License: CC BY NC NDData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2023.127430&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 AustraliaPublisher:Wiley Loc Cao; Shahbaz Mushtaq; Laurent Bossolasco; Vivekananda Byrareddy; Thong Nguyen-Huy; Thong Nguyen-Huy; Alessandro Craparo; Alessandro Craparo; Jarrod Kath;doi: 10.1111/gcb.15097
pmid: 32223007
AbstractCoffea canephora(robusta coffee) is the most heat‐tolerant and ‘robust’ coffee species and therefore considered more resistant to climate change than other types of coffee production. However, the optimum production range of robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22–30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high‐resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify robusta's optimal temperature range for production. Our climate‐based models explained yield variation well across the study area with a cross‐validated meanR2 = .51. We demonstrate that robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5–9°C than current estimates. In the middle of robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350–460 kg/ha (95% credible interval). Our results suggest that robusta coffee is far more sensitive to temperature than previously thought. Current assessments, based on robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi‐billion dollar coffee industry and the livelihoods of millions of farmers.
Global Change Biolog... arrow_drop_down Global Change BiologyArticle . 2020 . Peer-reviewedLicense: Wiley Online Library User AgreementData sources: CrossrefUniversity of Southern Queensland: USQ ePrintsArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/gcb.15097&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 79 citations 79 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert Global Change Biolog... arrow_drop_down Global Change BiologyArticle . 2020 . Peer-reviewedLicense: Wiley Online Library User AgreementData sources: CrossrefUniversity of Southern Queensland: USQ ePrintsArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/gcb.15097&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024 AustraliaPublisher:Wiley Authors: Bidhan Nath; Guangnan Chen; Les Bowtell; Thong Nguyen‐Huy;doi: 10.1002/ese3.1833
AbstractPyrolysis of two types of pellets (T1: 100% wheat straw, and T2: 70% wheat straw; 10% sawdust, 10% biochar, and 10% bentonite clay) was performed in a pilot‐scale reactor under a nitrogen environment at 20°C to 700°C. This was to investigate slow pyrolysis yields and gas composition as a function of temperature and residence time. The experimental data were obtained between 300°C and 600°C, with a residence time of 90 min, a nitrogen flow rate of 50 cm3/min, and a heating rate of 20°C/min. The results indicated that the maximum pyrolysis temperature is 605°C with a residence time of 55 min. The product analysis showed that the proportion of gas was higher than that of biochar and bio‐oil. The conversion efficiency increased with higher temperatures and varied between 66% and 76%. The results showed that carbon dioxide was the main component in the produced gas, and the maximum gas concentration was 63.6% at 300°C for T1. The higher temperature and longer residence time increased the syngas (CO + H2) composition for both T1 and T2 treatments. Nevertheless, the produced biochar had a high carbon content and retained a high calorific value, indicating slow pyrolysis is the ideal utilization route of wheat straw pellet biomass for biochar.
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2024License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Energy Science & EngineeringArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1002/ese3.1833&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 6 citations 6 popularity Average influence Average impulse Top 10% Powered by BIP!
more_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2024License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Energy Science & EngineeringArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1002/ese3.1833&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Journal 2021 AustraliaPublisher:SPIE-Intl Soc Optical Eng Thy T M Pham; The-Duoc Nguyen; Han T N Tham; Thi N. K. Truong; Nguyen Lam-Dao; Thong Nguyen‐Huy;On pense que le changement de l'utilisation des terres/de la couverture terrestre (LULC) et le changement climatique sont étroitement liés et qu'ils s'influencent mutuellement, en particulier dans les contextes où les terres sont converties pour l'expansion urbaine ou l'agriculture. Nous représentons une première tentative de préciser la relation entre le changement LULC et la sécheresse dans une région du Vietnam profondément affectée par le changement climatique. À l'aide de l'indice de sécheresse de la végétation et de la température (TVDI), nous avons spécifié les relations et les changements en cours dans le bassin de la rivière Ba au Vietnam, l'un des plus grands systèmes fluviaux de la côte centre-sud. À l'aide de Google Earth Engine, nous avons extrait les données d'utilisation des terres des images Landsat et calculé les valeurs TVDI à partir des données du spectroradiomètre d'imagerie à résolution modérée (MODIS) pour 2000 à 2019. Nous avons constaté, tout d'abord, que la superficie agricole et la déforestation ont augmenté de 7,2 % et 2,4 % par an, respectivement. Ces changements ont été motivés par le développement économique, la hausse des prix des cultures, l'exploitation forestière illégale, les incendies de forêt et l'émergence de nouvelles zones agricoles. Deuxièmement, les zones classées dans les intervalles TVDI les plus secs (secs et très secs) occupaient 57 % du bassin en 2019, dont 70 % de terres agricoles et 20 % d'autres (principalement des terres urbaines et dénudées). Ces catégories de terres les plus sèches ont augmenté à un taux moyen de 0,08% à 0,1% par an. En outre, 90 % des zones classées comme « très humides » et « humides » étaient des forêts. Nous avons observé une forte relation entre le changement LULC et TVDI. Le changement climatique et le changement LULC semblent donc propulser le climat du bassin vers une sécheresse croissante, suggérant la nécessité de politiques visant à réduire la superficie agricole et à étendre les forêts tout en développant des moyens de subsistance plus adaptatifs et durables. Se cree que el cambio en el uso de la tierra/cobertura de la tierra (LULC) y el cambio climático están estrechamente relacionados y se influyen mutuamente, especialmente en contextos donde la tierra se convierte para la expansión urbana o la agricultura. Representamos un primer intento de especificar la relación entre el cambio de LULC y la sequedad en una región de Vietnam que está profundamente afectada por el cambio climático. Utilizando el índice de sequedad temperatura-vegetación (TVDI), especificamos las relaciones y los cambios en curso en la cuenca del río Ba de Vietnam, uno de los sistemas fluviales más grandes de la costa centro-sur. Usando Google Earth Engine, extrajimos datos de uso del suelo de imágenes Landsat y calculamos valores TVDI de datos de espectrorradiómetro de imágenes de resolución moderada (MODIS) para 2000 a 2019. Encontramos, en primer lugar, que la superficie agrícola y la deforestación aumentaron un 7,2% y un 2,4% anual, respectivamente. Estos cambios fueron impulsados por el desarrollo económico, el aumento de los precios de los cultivos, la tala ilegal, los incendios forestales y la aparición de nuevas áreas agrícolas. En segundo lugar, las áreas clasificadas en los intervalos TVDI más secos (secas y muy secas) ocuparon el 57% de la cuenca en 2019, el 70% de las cuales eran tierras agrícolas y el 20% otras (principalmente urbanas y desnudas). Estas categorías de tierras más secas se expandieron a una tasa promedio de 0.08% a 0.1% por año. Además, el 90% de las áreas clasificadas como "muy húmedas" y "húmedas" eran bosques. Observamos una fuerte relación entre el cambio de LULC y TVDI. Por lo tanto, el cambio climático y el cambio de LULC parecen estar impulsando el clima de la cuenca hacia una mayor sequedad, lo que sugiere la necesidad de políticas para reducir el área agrícola y expandir los bosques mientras se desarrollan medios de vida más adaptables y sostenibles. Land use/land cover (LULC) change and climate change are thought to be closely related and mutually influential, especially in contexts where land is converted for urban expansion or agriculture. We represent a first attempt to specify the relationship between LULC change and dryness in a region of Vietnam that is profoundly affected by climate change. Using the temperature–vegetation dryness index (TVDI), we specified the relationships and changes underway in Vietnam's Ba river basin, one of the largest river systems in the South Central Coast. Using Google Earth Engine, we extracted land use data from Landsat images and calculated TVDI values from Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000 to 2019. We found, first, that agricultural area and deforestation rose by 7.2% and 2.4% annually, respectively. These changes were driven by economic development, rising crop prices, illegal logging, wildfires, and emergence of new agricultural areas. Second, areas classified in the driest TVDI intervals (dry and very dry) occupied 57% of the basin in 2019, 70% of which was agricultural lands and 20% other (mainly urban and bare lands). These driest land categories expanded at an average rate of 0.08% to 0.1% per year. Moreover, 90% of areas classified as "very wet" and "wet" were forest. We observed a strong relationship between LULC change and TVDI. Climate change and LULC change thus appear to be propelling the basin's climate toward increasing dryness, suggesting the need for policies to reduce the agricultural area and expand forests while developing more adaptive and sustainable livelihoods. يُعتقد أن تغير استخدام الأراضي/الغطاء الأرضي (LULC) وتغير المناخ مرتبطان ارتباطًا وثيقًا ومؤثران بشكل متبادل، خاصة في السياقات التي يتم فيها تحويل الأراضي للتوسع الحضري أو الزراعة. نحن نمثل محاولة أولى لتحديد العلاقة بين تغير استخدام الأراضي وتغير استخدام الأراضي والجفاف في منطقة من فيتنام تتأثر بشدة بتغير المناخ. باستخدام مؤشر جفاف درجة الحرارة والغطاء النباتي (TVDI)، حددنا العلاقات والتغيرات الجارية في حوض نهر با في فيتنام، أحد أكبر أنظمة الأنهار في الساحل الجنوبي الأوسط. باستخدام Google Earth Engine، استخرجنا بيانات استخدام الأراضي من صور Landsat وقيم TVDI المحسوبة من بيانات مقياس الطيف التصويري متوسط الدقة (MODIS) للفترة من 2000 إلى 2019. وجدنا، أولاً، أن المساحة الزراعية وإزالة الغابات ارتفعت بنسبة 7.2 ٪ و 2.4 ٪ سنويًا، على التوالي. كانت هذه التغييرات مدفوعة بالتنمية الاقتصادية، وارتفاع أسعار المحاصيل، وقطع الأشجار غير القانوني، وحرائق الغابات، وظهور مناطق زراعية جديدة. ثانيًا، احتلت المناطق المصنفة في فترات TVDI الأكثر جفافًا (الجافة والجافة جدًا) 57 ٪ من الحوض في عام 2019، 70 ٪ منها أراضي زراعية و 20 ٪ أخرى (بشكل رئيسي الأراضي الحضرية والعارية). توسعت فئات الأراضي الأكثر جفافًا هذه بمعدل متوسط يتراوح بين 0.08 ٪ و 0.1 ٪ سنويًا. علاوة على ذلك، فإن 90 ٪ من المناطق المصنفة على أنها "رطبة جدًا" و "رطبة" كانت غابات. لاحظنا وجود علاقة قوية بين تغيير LULC و TVDI. وبالتالي، يبدو أن تغير المناخ وتغير استخدام الأراضي واستهلاك الأراضي يدفعان مناخ الحوض نحو زيادة الجفاف، مما يشير إلى الحاجة إلى سياسات للحد من المساحة الزراعية وتوسيع الغابات مع تطوير سبل عيش أكثر تكيفًا واستدامة.
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1117/1.jrs.15.024503&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 10 citations 10 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1117/1.jrs.15.024503&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2023 AustraliaPublisher:Elsevier BV Nu Quy Linh Tran; Hong H. T. C. Le; Cong Tuan Pham; Xuan Huong Nguyen; Tran Ngoc Dang; Tuyet-Hanh Thi Tran; Son Nghiem; Thi Mai Ly Luong; Vinh Bui; Thong Nguyen‐Huy; Quang‐Van Doan; Kim Anh Dang; Thi Hoai Thuong; Hieu K.T. Ngo; Truong Vien Nguyen; Ngoc Huy Nguyen; Manh Cuong; Tuan Nghia Ton; Thi Anh Thu Dang; Kien Trung Nguyen; Xuan Bach Tran; Phong K. Thai; Dung Phung;Cette étude vise à étudier l'impact du changement climatique sur la santé et l'adaptation au Vietnam grâce à un examen systématique et à des analyses supplémentaires de l'exposition à la chaleur, de la vulnérabilité à la chaleur, de la sensibilisation et de l'engagement, ainsi que des coûts de santé prévus. Sur 127 études examinées, les résultats ont indiqué une propagation plus large des maladies infectieuses et une augmentation des risques de mortalité et d'hospitalisation associés à la chaleur extrême, aux sécheresses et aux inondations. Cependant, il existe peu d'études portant sur les coûts de la santé, la sensibilisation, l'engagement, l'adaptation et les politiques. Des analyses supplémentaires ont montré une exposition croissante aux vagues de chaleur au Vietnam et une vulnérabilité mondiale supérieure à la moyenne à la chaleur. D'ici 2050, le changement climatique devrait coûter jusqu'à 1 à 3 milliards de dollars en coûts de soins de santé, 3 à 20 milliards de dollars en décès prématurés et 6 à 23 milliards de dollars en perte de travail. Malgré l'attention accrue des médias sur le climat et la santé, un écart entre les publications publiques et gouvernementales a souligné la nécessité d'un engagement gouvernemental plus important. Les politiques climatiques du Vietnam ont été confrontées à des défis de mise en œuvre, notamment des approches descendantes, un manque de coopération, une faible capacité d'adaptation et des ressources limitées. Este estudio tiene como objetivo investigar el impacto del cambio climático en la salud y la adaptación en Vietnam a través de una revisión sistemática y análisis adicionales de la exposición al calor, la vulnerabilidad al calor, la conciencia y el compromiso, y los costos de salud proyectados. De los 127 estudios revisados, los hallazgos indicaron la propagación más amplia de enfermedades infecciosas y el aumento de la mortalidad y los riesgos de hospitalización asociados con el calor extremo, las sequías y las inundaciones. Sin embargo, hay pocos estudios que aborden el costo de la salud, la concienciación, el compromiso, la adaptación y la política. Los análisis adicionales mostraron una mayor exposición a las olas de calor en Vietnam y una vulnerabilidad global superior a la media al calor. Para 2050, se proyecta que el cambio climático costará hasta USD1-3B en costos de atención médica, USD3-20B en muertes prematuras y USD6-23B en pérdida de trabajo. A pesar de un mayor enfoque de los medios en el clima y la salud, una brecha entre las publicaciones públicas y gubernamentales destacó la necesidad de una mayor participación gubernamental. Las políticas climáticas de Vietnam han enfrentado desafíos de implementación, incluidos enfoques de arriba hacia abajo, falta de cooperación, baja capacidad de adaptación y recursos limitados. This study aims to investigate climate change's impact on health and adaptation in Vietnam through a systematic review and additional analyses of heat exposure, heat vulnerability, awareness and engagement, and projected health costs.Out of 127 reviewed studies, findings indicated the wider spread of infectious diseases, and increased mortality and hospitalisation risks associated with extreme heat, droughts, and floods. However, there are few studies addressing health cost, awareness, engagement, adaptation, and policy.Additional analyses showed rising heatwave exposure across Vietnam and global above-average vulnerability to heat. By 2050, climate change is projected to cost up to USD1-3B in healthcare costs, USD3-20B in premature deaths, and USD6-23B in work loss.Despite increased media focus on climate and health, a gap between public and government publications highlighted the need for more governmental engagement. Vietnam's climate policies have faced implementation challenges, including top-down approaches, lack of cooperation, low adaptive capacity, and limited resources. تهدف هذه الدراسة إلى التحقيق في تأثير تغير المناخ على الصحة والتكيف في فيتنام من خلال مراجعة منهجية وتحليلات إضافية للتعرض للحرارة، والضعف الحراري، والوعي والمشاركة، والتكاليف الصحية المتوقعة. من بين 127 دراسة تمت مراجعتها، أشارت النتائج إلى الانتشار الأوسع للأمراض المعدية، وزيادة مخاطر الوفيات والاستشفاء المرتبطة بالحرارة الشديدة والجفاف والفيضانات. ومع ذلك، هناك القليل من الدراسات التي تتناول التكلفة الصحية والوعي والمشاركة والتكيف والسياسة. أظهرت التحليلات الإضافية ارتفاع التعرض لموجة الحر في جميع أنحاء فيتنام والتعرض العالمي للحرارة فوق المتوسط. بحلول عام 2050، من المتوقع أن يكلف تغير المناخ ما يصل إلى 1-3 مليار دولار أمريكي في تكاليف الرعاية الصحية، و 3-20 مليار دولار أمريكي في الوفيات المبكرة، و 6-23 مليار دولار أمريكي في فقدان العمل. على الرغم من زيادة تركيز وسائل الإعلام على المناخ والصحة، سلطت الفجوة بين المنشورات العامة والحكومية الضوء على الحاجة إلى مزيد من المشاركة الحكومية. واجهت سياسات فيتنام المناخية تحديات في التنفيذ، بما في ذلك النهج التنازلية، ونقص التعاون، وانخفاض القدرة على التكيف، ومحدودية الموارد.
Griffith University:... arrow_drop_down Griffith University: Griffith Research OnlineArticle . 2023License: CC BYFull-Text: http://hdl.handle.net/10072/427875Data sources: Bielefeld Academic Search Engine (BASE)University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)The Lancet Regional Health. Western PacificArticle . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.lanwpc.2023.100943&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!
more_vert Griffith University:... arrow_drop_down Griffith University: Griffith Research OnlineArticle . 2023License: CC BYFull-Text: http://hdl.handle.net/10072/427875Data sources: Bielefeld Academic Search Engine (BASE)University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)The Lancet Regional Health. Western PacificArticle . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.lanwpc.2023.100943&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 Australia, Spain, SpainPublisher:Elsevier BV Sujan Ghimire; Thong Nguyen-Huy; Mohanad S. AL-Musaylh; Ravinesh C. Deo; David Casillas-Pérez; Sancho Salcedo-Sanz;handle: 10017/63099
Este artículo desarrolla un modelo de aprendizaje profundo para la predicción de demanda de electricidad a partir de datos y variables climáticas locales. El modelo utiliza un algoritmo conocido como Multi-Head Self-Attention Transformer (TNET) para capturar información crítica de la demanda de electricidad, y lograr así predicciones fiables con datos de variables atmosféricas locales como lluvia, radiación solar, humedad, evaporación y temperaturas máximas y mínimas de las subestaciones de Energex en Queensland, Australia. Posteriormente, el modelo TNET se evalúa con modelos de aprendizaje profundo (LSTM, LSTM Bidireccional, y redesd GRU, Redes Neuronales Convolucionales CNN y Redes Neuronales Profundas DNN) basados en métricas de evaluación de modelos robustos. El método de Estimación de Densidad Kernel se utiliza asimismo para generar el intervalo de predicción (PI) de los pronósticos de demanda de electricidad y derivar métricas de probabilidad y resultados para demostrar que el modelo TNET desarrollado es preciso para todas las subestaciones. El estudio concluye que el modelo TNET propuesto es una herramienta muy fiable para predecir la demanda de electricidad, con alta precisión y bajos errores de predicción, y podría ser empleado como estrategia por gestores de demanda eléctrica, así como gestores de políticas energéticas que deseen incorporar factores climáticos en los patrones de demanda de electricidad, y desarrollar sistemas de análisis e información del mercado energético nacional. This paper develops a trustworthy deep learning model that considers electricity demand ( ) and local climate conditions. The model utilises Multi-Head Self-Attention Transformer (TNET) to capture critical information from , to attain reliable predictions with local climate (rainfall, radiation, humidity, evaporation, and maximum and minimum temperatures) data from Energex substations in Queensland, Australia. The TNET model is then evaluated with deep learning models (Long-Short Term Memory LSTM, Bidirectional LSTM BILSTM, Gated Recurrent Unit GRU, Convolutional Neural Networks CNN, and Deep Neural Network DNN) based on robust model assessment metrics. The Kernel Density Estimation method is used to generate the prediction interval (PI) of electricity demand forecasts and derive probability metrics and results to show the developed TNET model is accurate for all the substations. The study concludes that the proposed TNET model is a reliable electricity demand predictive tool that has high accuracy and low predictive errors and could be employed as a stratagem by demand modellers and energy policy-makers who wish to incorporate climatic factors into electricity demand patterns and develop national energy market insights and analysis systems. Agencia Estatal de Investigación
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2023License: CC BY NC NDData sources: Recolector de Ciencia Abierta, RECOLECTABiblioteca Digital de la Universidad de AlcaláArticle . 2023License: CC BY NC NDData sources: Biblioteca Digital de la Universidad de Alcaláadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egyai.2023.100302&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 20 citations 20 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
visibility 88visibility views 88 download downloads 13 Powered bymore_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2023License: CC BY NC NDData sources: Recolector de Ciencia Abierta, RECOLECTABiblioteca Digital de la Universidad de AlcaláArticle . 2023License: CC BY NC NDData sources: Biblioteca Digital de la Universidad de Alcaláadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egyai.2023.100302&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2025 AustraliaPublisher:Elsevier BV Sujan Ghimire; Mohanad S. AL-Musaylh; Thong Nguyen-Huy; Ravinesh C. Deo; Rajendra Acharya; David Casillas-Pérez; Zaher Mundher Yaseen; Sancho Salcedo-Sanz;Electricity consumption has stochastic variabilities driven by the energy market volatility. The capability to predict electricity demand that captures stochastic variances and uncertainties is significantly important in the planning, operation and regulation of national electricity markets. This study has proposed an explainable deeply-fused nets electricity demand prediction model that factors in the climate-based predictors for enhanced accuracy and energy market insight analysis, generating point-based and confidence interval predictions of daily electricity demand. The proposed hybrid approach is built using Deeply Fused Nets (FNET) that comprises of Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (BILSTM) Network with residual connection. The study then contributes to a new deep fusion model that integrates intermediate representations of the base networks (fused output being the input of the remaining part of each base network) to perform these combinations deeply over several intermediate representations to enhance the demand predictions. The results are evaluated with statistical metrics and graphical representations of predicted and observed electricity demand, benchmarked with standalone models i.e., BILSTM, LSTMCNN, deep neural network, multi-layer perceptron, multivariate adaptive regression spline, kernel ridge regression and Gaussian process of regression. The end part of the proposed FNET model applies residual bootstrapping where final residuals are computed from predicted and observed demand to generate the 95% prediction intervals, analysed using probabilistic metrics to quantify the uncertainty associated with FNETS objective model. To enhance the FNET model’s transparency, the SHapley Additive explanation (SHAP) method has been applied to elucidate the relationships between electricity demand and climate-based predictor variables. The suggested model analysis reveals that the preceding hour’s electricity demand and evapotranspiration were the most influential ...
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2024.124763&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 4 citations 4 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2024.124763&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2023 Australia, Australia, IndonesiaPublisher:Informa UK Limited Authors: Haerani Haerani; Armando Apan; Thong Nguyen‐Huy; Badri Basnet;Affichage des formules : ?Les formules mathématiques ont été codées en MathML et sont affichées dans cette version HTML à l'aide de MathJax afin d'améliorer leur affichage. Décochez la case pour désactiver MathJax. Cette fonctionnalité nécessite Javascript. Cliquez sur une formule pour zoomer. Visualización de fórmulas: Las fórmulas matemáticas se han codificado como MathML y se muestran en esta versión HTML utilizando MathJax para mejorar su visualización. Desmarque la casilla para desactivar MathJax. Esta función requiere Javascript. Haz clic en una fórmula para hacer zoom. Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. عرض الصيغ:? تم ترميز الصيغ الرياضية كـ MathML ويتم عرضها في إصدار HTML هذا باستخدام MathJax لتحسين عرضها. قم بإلغاء تحديد المربع لإيقاف تشغيل MathJax. تتطلب هذه الميزة جافا سكريبت. انقر على صيغة للتكبير/التصغير.
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1080/10095020.2022.2155255&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 9 citations 9 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1080/10095020.2022.2155255&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu
description Publicationkeyboard_double_arrow_right Article 2024 AustraliaPublisher:MDPI AG Bidhan Nath; Les Bowtell; Guangnan Chen; Elizabeth Graham; Thong Nguyen-Huy;doi: 10.3390/en17153693
The study of the thermokinetics of two types of wheat straw pellets, T1 (100% wheat straw) and T2 (70% wheat straw, 10% each of bentonite clay, sawdust, and biochar), under a nitrogen atmosphere (31–800 °C and 5, 10, and 20 °C/min heating rates) using model-free and model-based approaches by TG/DTG data, revealed promising results. While model-free methods were not suitable, model-based reactions, particularly Fn (nth-order phase interfacial) and F2 (second-order) models, effectively described the three-phase consecutive thermal degradation pathway (A→B, C→D, and D→E). The activation energy (Eα) for phases 2 and 3 (Fn model) averaged 136.04 and 358.11 kJ/mol for T1 and 132.86 and 227.10 kJ/mol for T2, respectively. The pre-exponential factor (lnA) varied across heating rates and pellets (T2: 38.244–2.9 × 109 1/s; T1: 1.2 × 102–5.45 × 1014 1/s). Notably, pellets with additives (T2) exhibited a higher degradable fraction due to lower Eα. These findings suggest a promising potential for utilizing wheat straw pellet biomass as a bioenergy feedstock, highlighting the practical implications of this research.
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2024License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en17153693&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 1 citations 1 popularity Average influence Average impulse Average Powered by BIP!
more_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2024License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.3390/en17153693&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2022 Australia, France, FrancePublisher:Springer Science and Business Media LLC Kath, Jarrod; Craparo, Alessandro; Fong, Youyi; Byrareddy, Vivekananda; Davis, Aaron P.; King, Rachel; Nguyen-Huy, Thong; Asten, Piet J.A. van; Marcussen, Torben; Mushtaq, Shahbaz; Stone, Roger; Power, Scott;Our understanding of the impact of climate change on global coffee production is largely based on studies focusing on temperature and precipitation, but other climate indicators could trigger critical threshold changes in productivity. Here, using generalized additive models and threshold regression, we investigate temperature, precipitation, soil moisture and vapour pressure deficit (VPD) effects on global Arabica coffee productivity. We show that VPD during fruit development is a key indicator of global coffee productivity, with yield declining rapidly above 0.82 kPa. The risk of exceeding this threshold rises sharply for most countries we assess, if global warming exceeds 2 °C. At 2.9 °C, countries making up 90% of global supply are more likely than not to exceed the VPD threshold. The inclusion of VPD and the identification of thresholds appear critical for understanding climate change impacts on coffee and for the design of adaptation strategies.
CGIAR CGSpace (Consu... arrow_drop_down CGIAR CGSpace (Consultative Group on International Agricultural Research)Article . 2022Full-Text: https://hdl.handle.net/10568/125539Data sources: Bielefeld Academic Search Engine (BASE)University of Southern Queensland: USQ ePrintsArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/s43016-022-00614-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen 46 citations 46 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
more_vert CGIAR CGSpace (Consu... arrow_drop_down CGIAR CGSpace (Consultative Group on International Agricultural Research)Article . 2022Full-Text: https://hdl.handle.net/10568/125539Data sources: Bielefeld Academic Search Engine (BASE)University of Southern Queensland: USQ ePrintsArticle . 2022Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1038/s43016-022-00614-8&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 Australia, Australia, SpainPublisher:Elsevier BV Sujan Ghimire; Thong Nguyen-Huy; Mohanad S. AL-Musaylh; Ravinesh C. Deo; David Casillas-Pérez; Sancho Salcedo-Sanz;handle: 10115/24758
The authors thank the data providers, all the reviewers and the Editor for their thoughtful comments, suggestions and the review process. Partial support of this study is through the project PID2020-115454GB-C21 of the Spanish Ministry of Science and Innovation (MICINN). Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure in the power generation network. To deliver a high-quality electricity demand prediction, this paper proposes a hybrid combination technique, based on a deep learning model of Convolutional Neural Networks and Echo State Networks, named as CESN. Daily electricity demand data from four sites (Roderick, Rocklea, Hemmant and Carpendale), located in Southeast Queensland, Australia, have been used to develop the proposed hybrid prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, deep neural network, and Light Gradient Boosting) to compare and evaluate the outcomes of the proposed deep learning approach. The results obtained in the experimental study showed that the proposed hybrid deep learning model is able to obtain the highest performance compared to other existing models developed for daily electricity demand data forecasting. Based on the statistical approaches utilized in this study, the proposed hybrid approach presents the highest prediction accuracy among the compared models. The obtained results showed that the proposed hybrid deep learning algorithm is an excellent and accurate electricity demand forecasting method, which outperformed the state of the art algorithms that are currently used in this problem.
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BY NC NDData sources: Bielefeld Academic Search Engine (BASE)Universidad Rey Juan Carlos, Madrid: Archivo Abierto InstitucionalArticle . 2023License: CC BY NC NDFull-Text: https://hdl.handle.net/10115/24758Data sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2023License: CC BY NC NDData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2023.127430&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 39 citations 39 popularity Top 10% influence Top 10% impulse Top 1% Powered by BIP!
more_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BY NC NDData sources: Bielefeld Academic Search Engine (BASE)Universidad Rey Juan Carlos, Madrid: Archivo Abierto InstitucionalArticle . 2023License: CC BY NC NDFull-Text: https://hdl.handle.net/10115/24758Data sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2023License: CC BY NC NDData sources: Recolector de Ciencia Abierta, RECOLECTAadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.energy.2023.127430&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Journal 2020 AustraliaPublisher:Wiley Loc Cao; Shahbaz Mushtaq; Laurent Bossolasco; Vivekananda Byrareddy; Thong Nguyen-Huy; Thong Nguyen-Huy; Alessandro Craparo; Alessandro Craparo; Jarrod Kath;doi: 10.1111/gcb.15097
pmid: 32223007
AbstractCoffea canephora(robusta coffee) is the most heat‐tolerant and ‘robust’ coffee species and therefore considered more resistant to climate change than other types of coffee production. However, the optimum production range of robusta has never been quantified, with current estimates of its optimal mean annual temperature range (22–30°C) based solely on the climatic conditions of its native range in the Congo basin, Central Africa. Using 10 years of yield observations from 798 farms across South East Asia coupled with high‐resolution precipitation and temperature data, we used hierarchical Bayesian modeling to quantify robusta's optimal temperature range for production. Our climate‐based models explained yield variation well across the study area with a cross‐validated meanR2 = .51. We demonstrate that robusta has an optimal temperature below 20.5°C (or a mean minimum/maximum of ≤16.2/24.1°C), which is markedly lower, by 1.5–9°C than current estimates. In the middle of robusta's currently assumed optimal range (mean annual temperatures over 25.1°C), coffee yields are 50% lower compared to the optimal mean of ≤20.5°C found here. During the growing season, every 1°C increase in mean minimum/maximum temperatures above 16.2/24.1°C corresponded to yield declines of ~14% or 350–460 kg/ha (95% credible interval). Our results suggest that robusta coffee is far more sensitive to temperature than previously thought. Current assessments, based on robusta having an optimal temperature range over 22°C, are likely overestimating its suitable production range and its ability to contribute to coffee production as temperatures increase under climate change. Robusta supplies 40% of the world's coffee, but its production potential could decline considerably as temperatures increase under climate change, jeopardizing a multi‐billion dollar coffee industry and the livelihoods of millions of farmers.
Global Change Biolog... arrow_drop_down Global Change BiologyArticle . 2020 . Peer-reviewedLicense: Wiley Online Library User AgreementData sources: CrossrefUniversity of Southern Queensland: USQ ePrintsArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/gcb.15097&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess Routesbronze 79 citations 79 popularity Top 1% influence Top 10% impulse Top 1% Powered by BIP!
more_vert Global Change Biolog... arrow_drop_down Global Change BiologyArticle . 2020 . Peer-reviewedLicense: Wiley Online Library User AgreementData sources: CrossrefUniversity of Southern Queensland: USQ ePrintsArticle . 2020Data sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1111/gcb.15097&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2024 AustraliaPublisher:Wiley Authors: Bidhan Nath; Guangnan Chen; Les Bowtell; Thong Nguyen‐Huy;doi: 10.1002/ese3.1833
AbstractPyrolysis of two types of pellets (T1: 100% wheat straw, and T2: 70% wheat straw; 10% sawdust, 10% biochar, and 10% bentonite clay) was performed in a pilot‐scale reactor under a nitrogen environment at 20°C to 700°C. This was to investigate slow pyrolysis yields and gas composition as a function of temperature and residence time. The experimental data were obtained between 300°C and 600°C, with a residence time of 90 min, a nitrogen flow rate of 50 cm3/min, and a heating rate of 20°C/min. The results indicated that the maximum pyrolysis temperature is 605°C with a residence time of 55 min. The product analysis showed that the proportion of gas was higher than that of biochar and bio‐oil. The conversion efficiency increased with higher temperatures and varied between 66% and 76%. The results showed that carbon dioxide was the main component in the produced gas, and the maximum gas concentration was 63.6% at 300°C for T1. The higher temperature and longer residence time increased the syngas (CO + H2) composition for both T1 and T2 treatments. Nevertheless, the produced biochar had a high carbon content and retained a high calorific value, indicating slow pyrolysis is the ideal utilization route of wheat straw pellet biomass for biochar.
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2024License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Energy Science & EngineeringArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1002/ese3.1833&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 6 citations 6 popularity Average influence Average impulse Top 10% Powered by BIP!
more_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2024License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Energy Science & EngineeringArticle . 2024 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1002/ese3.1833&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type , Journal 2021 AustraliaPublisher:SPIE-Intl Soc Optical Eng Thy T M Pham; The-Duoc Nguyen; Han T N Tham; Thi N. K. Truong; Nguyen Lam-Dao; Thong Nguyen‐Huy;On pense que le changement de l'utilisation des terres/de la couverture terrestre (LULC) et le changement climatique sont étroitement liés et qu'ils s'influencent mutuellement, en particulier dans les contextes où les terres sont converties pour l'expansion urbaine ou l'agriculture. Nous représentons une première tentative de préciser la relation entre le changement LULC et la sécheresse dans une région du Vietnam profondément affectée par le changement climatique. À l'aide de l'indice de sécheresse de la végétation et de la température (TVDI), nous avons spécifié les relations et les changements en cours dans le bassin de la rivière Ba au Vietnam, l'un des plus grands systèmes fluviaux de la côte centre-sud. À l'aide de Google Earth Engine, nous avons extrait les données d'utilisation des terres des images Landsat et calculé les valeurs TVDI à partir des données du spectroradiomètre d'imagerie à résolution modérée (MODIS) pour 2000 à 2019. Nous avons constaté, tout d'abord, que la superficie agricole et la déforestation ont augmenté de 7,2 % et 2,4 % par an, respectivement. Ces changements ont été motivés par le développement économique, la hausse des prix des cultures, l'exploitation forestière illégale, les incendies de forêt et l'émergence de nouvelles zones agricoles. Deuxièmement, les zones classées dans les intervalles TVDI les plus secs (secs et très secs) occupaient 57 % du bassin en 2019, dont 70 % de terres agricoles et 20 % d'autres (principalement des terres urbaines et dénudées). Ces catégories de terres les plus sèches ont augmenté à un taux moyen de 0,08% à 0,1% par an. En outre, 90 % des zones classées comme « très humides » et « humides » étaient des forêts. Nous avons observé une forte relation entre le changement LULC et TVDI. Le changement climatique et le changement LULC semblent donc propulser le climat du bassin vers une sécheresse croissante, suggérant la nécessité de politiques visant à réduire la superficie agricole et à étendre les forêts tout en développant des moyens de subsistance plus adaptatifs et durables. Se cree que el cambio en el uso de la tierra/cobertura de la tierra (LULC) y el cambio climático están estrechamente relacionados y se influyen mutuamente, especialmente en contextos donde la tierra se convierte para la expansión urbana o la agricultura. Representamos un primer intento de especificar la relación entre el cambio de LULC y la sequedad en una región de Vietnam que está profundamente afectada por el cambio climático. Utilizando el índice de sequedad temperatura-vegetación (TVDI), especificamos las relaciones y los cambios en curso en la cuenca del río Ba de Vietnam, uno de los sistemas fluviales más grandes de la costa centro-sur. Usando Google Earth Engine, extrajimos datos de uso del suelo de imágenes Landsat y calculamos valores TVDI de datos de espectrorradiómetro de imágenes de resolución moderada (MODIS) para 2000 a 2019. Encontramos, en primer lugar, que la superficie agrícola y la deforestación aumentaron un 7,2% y un 2,4% anual, respectivamente. Estos cambios fueron impulsados por el desarrollo económico, el aumento de los precios de los cultivos, la tala ilegal, los incendios forestales y la aparición de nuevas áreas agrícolas. En segundo lugar, las áreas clasificadas en los intervalos TVDI más secos (secas y muy secas) ocuparon el 57% de la cuenca en 2019, el 70% de las cuales eran tierras agrícolas y el 20% otras (principalmente urbanas y desnudas). Estas categorías de tierras más secas se expandieron a una tasa promedio de 0.08% a 0.1% por año. Además, el 90% de las áreas clasificadas como "muy húmedas" y "húmedas" eran bosques. Observamos una fuerte relación entre el cambio de LULC y TVDI. Por lo tanto, el cambio climático y el cambio de LULC parecen estar impulsando el clima de la cuenca hacia una mayor sequedad, lo que sugiere la necesidad de políticas para reducir el área agrícola y expandir los bosques mientras se desarrollan medios de vida más adaptables y sostenibles. Land use/land cover (LULC) change and climate change are thought to be closely related and mutually influential, especially in contexts where land is converted for urban expansion or agriculture. We represent a first attempt to specify the relationship between LULC change and dryness in a region of Vietnam that is profoundly affected by climate change. Using the temperature–vegetation dryness index (TVDI), we specified the relationships and changes underway in Vietnam's Ba river basin, one of the largest river systems in the South Central Coast. Using Google Earth Engine, we extracted land use data from Landsat images and calculated TVDI values from Moderate Resolution Imaging Spectroradiometer (MODIS) data for 2000 to 2019. We found, first, that agricultural area and deforestation rose by 7.2% and 2.4% annually, respectively. These changes were driven by economic development, rising crop prices, illegal logging, wildfires, and emergence of new agricultural areas. Second, areas classified in the driest TVDI intervals (dry and very dry) occupied 57% of the basin in 2019, 70% of which was agricultural lands and 20% other (mainly urban and bare lands). These driest land categories expanded at an average rate of 0.08% to 0.1% per year. Moreover, 90% of areas classified as "very wet" and "wet" were forest. We observed a strong relationship between LULC change and TVDI. Climate change and LULC change thus appear to be propelling the basin's climate toward increasing dryness, suggesting the need for policies to reduce the agricultural area and expand forests while developing more adaptive and sustainable livelihoods. يُعتقد أن تغير استخدام الأراضي/الغطاء الأرضي (LULC) وتغير المناخ مرتبطان ارتباطًا وثيقًا ومؤثران بشكل متبادل، خاصة في السياقات التي يتم فيها تحويل الأراضي للتوسع الحضري أو الزراعة. نحن نمثل محاولة أولى لتحديد العلاقة بين تغير استخدام الأراضي وتغير استخدام الأراضي والجفاف في منطقة من فيتنام تتأثر بشدة بتغير المناخ. باستخدام مؤشر جفاف درجة الحرارة والغطاء النباتي (TVDI)، حددنا العلاقات والتغيرات الجارية في حوض نهر با في فيتنام، أحد أكبر أنظمة الأنهار في الساحل الجنوبي الأوسط. باستخدام Google Earth Engine، استخرجنا بيانات استخدام الأراضي من صور Landsat وقيم TVDI المحسوبة من بيانات مقياس الطيف التصويري متوسط الدقة (MODIS) للفترة من 2000 إلى 2019. وجدنا، أولاً، أن المساحة الزراعية وإزالة الغابات ارتفعت بنسبة 7.2 ٪ و 2.4 ٪ سنويًا، على التوالي. كانت هذه التغييرات مدفوعة بالتنمية الاقتصادية، وارتفاع أسعار المحاصيل، وقطع الأشجار غير القانوني، وحرائق الغابات، وظهور مناطق زراعية جديدة. ثانيًا، احتلت المناطق المصنفة في فترات TVDI الأكثر جفافًا (الجافة والجافة جدًا) 57 ٪ من الحوض في عام 2019، 70 ٪ منها أراضي زراعية و 20 ٪ أخرى (بشكل رئيسي الأراضي الحضرية والعارية). توسعت فئات الأراضي الأكثر جفافًا هذه بمعدل متوسط يتراوح بين 0.08 ٪ و 0.1 ٪ سنويًا. علاوة على ذلك، فإن 90 ٪ من المناطق المصنفة على أنها "رطبة جدًا" و "رطبة" كانت غابات. لاحظنا وجود علاقة قوية بين تغيير LULC و TVDI. وبالتالي، يبدو أن تغير المناخ وتغير استخدام الأراضي واستهلاك الأراضي يدفعان مناخ الحوض نحو زيادة الجفاف، مما يشير إلى الحاجة إلى سياسات للحد من المساحة الزراعية وتوسيع الغابات مع تطوير سبل عيش أكثر تكيفًا واستدامة.
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1117/1.jrs.15.024503&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 10 citations 10 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2021License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1117/1.jrs.15.024503&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2023 AustraliaPublisher:Elsevier BV Nu Quy Linh Tran; Hong H. T. C. Le; Cong Tuan Pham; Xuan Huong Nguyen; Tran Ngoc Dang; Tuyet-Hanh Thi Tran; Son Nghiem; Thi Mai Ly Luong; Vinh Bui; Thong Nguyen‐Huy; Quang‐Van Doan; Kim Anh Dang; Thi Hoai Thuong; Hieu K.T. Ngo; Truong Vien Nguyen; Ngoc Huy Nguyen; Manh Cuong; Tuan Nghia Ton; Thi Anh Thu Dang; Kien Trung Nguyen; Xuan Bach Tran; Phong K. Thai; Dung Phung;Cette étude vise à étudier l'impact du changement climatique sur la santé et l'adaptation au Vietnam grâce à un examen systématique et à des analyses supplémentaires de l'exposition à la chaleur, de la vulnérabilité à la chaleur, de la sensibilisation et de l'engagement, ainsi que des coûts de santé prévus. Sur 127 études examinées, les résultats ont indiqué une propagation plus large des maladies infectieuses et une augmentation des risques de mortalité et d'hospitalisation associés à la chaleur extrême, aux sécheresses et aux inondations. Cependant, il existe peu d'études portant sur les coûts de la santé, la sensibilisation, l'engagement, l'adaptation et les politiques. Des analyses supplémentaires ont montré une exposition croissante aux vagues de chaleur au Vietnam et une vulnérabilité mondiale supérieure à la moyenne à la chaleur. D'ici 2050, le changement climatique devrait coûter jusqu'à 1 à 3 milliards de dollars en coûts de soins de santé, 3 à 20 milliards de dollars en décès prématurés et 6 à 23 milliards de dollars en perte de travail. Malgré l'attention accrue des médias sur le climat et la santé, un écart entre les publications publiques et gouvernementales a souligné la nécessité d'un engagement gouvernemental plus important. Les politiques climatiques du Vietnam ont été confrontées à des défis de mise en œuvre, notamment des approches descendantes, un manque de coopération, une faible capacité d'adaptation et des ressources limitées. Este estudio tiene como objetivo investigar el impacto del cambio climático en la salud y la adaptación en Vietnam a través de una revisión sistemática y análisis adicionales de la exposición al calor, la vulnerabilidad al calor, la conciencia y el compromiso, y los costos de salud proyectados. De los 127 estudios revisados, los hallazgos indicaron la propagación más amplia de enfermedades infecciosas y el aumento de la mortalidad y los riesgos de hospitalización asociados con el calor extremo, las sequías y las inundaciones. Sin embargo, hay pocos estudios que aborden el costo de la salud, la concienciación, el compromiso, la adaptación y la política. Los análisis adicionales mostraron una mayor exposición a las olas de calor en Vietnam y una vulnerabilidad global superior a la media al calor. Para 2050, se proyecta que el cambio climático costará hasta USD1-3B en costos de atención médica, USD3-20B en muertes prematuras y USD6-23B en pérdida de trabajo. A pesar de un mayor enfoque de los medios en el clima y la salud, una brecha entre las publicaciones públicas y gubernamentales destacó la necesidad de una mayor participación gubernamental. Las políticas climáticas de Vietnam han enfrentado desafíos de implementación, incluidos enfoques de arriba hacia abajo, falta de cooperación, baja capacidad de adaptación y recursos limitados. This study aims to investigate climate change's impact on health and adaptation in Vietnam through a systematic review and additional analyses of heat exposure, heat vulnerability, awareness and engagement, and projected health costs.Out of 127 reviewed studies, findings indicated the wider spread of infectious diseases, and increased mortality and hospitalisation risks associated with extreme heat, droughts, and floods. However, there are few studies addressing health cost, awareness, engagement, adaptation, and policy.Additional analyses showed rising heatwave exposure across Vietnam and global above-average vulnerability to heat. By 2050, climate change is projected to cost up to USD1-3B in healthcare costs, USD3-20B in premature deaths, and USD6-23B in work loss.Despite increased media focus on climate and health, a gap between public and government publications highlighted the need for more governmental engagement. Vietnam's climate policies have faced implementation challenges, including top-down approaches, lack of cooperation, low adaptive capacity, and limited resources. تهدف هذه الدراسة إلى التحقيق في تأثير تغير المناخ على الصحة والتكيف في فيتنام من خلال مراجعة منهجية وتحليلات إضافية للتعرض للحرارة، والضعف الحراري، والوعي والمشاركة، والتكاليف الصحية المتوقعة. من بين 127 دراسة تمت مراجعتها، أشارت النتائج إلى الانتشار الأوسع للأمراض المعدية، وزيادة مخاطر الوفيات والاستشفاء المرتبطة بالحرارة الشديدة والجفاف والفيضانات. ومع ذلك، هناك القليل من الدراسات التي تتناول التكلفة الصحية والوعي والمشاركة والتكيف والسياسة. أظهرت التحليلات الإضافية ارتفاع التعرض لموجة الحر في جميع أنحاء فيتنام والتعرض العالمي للحرارة فوق المتوسط. بحلول عام 2050، من المتوقع أن يكلف تغير المناخ ما يصل إلى 1-3 مليار دولار أمريكي في تكاليف الرعاية الصحية، و 3-20 مليار دولار أمريكي في الوفيات المبكرة، و 6-23 مليار دولار أمريكي في فقدان العمل. على الرغم من زيادة تركيز وسائل الإعلام على المناخ والصحة، سلطت الفجوة بين المنشورات العامة والحكومية الضوء على الحاجة إلى مزيد من المشاركة الحكومية. واجهت سياسات فيتنام المناخية تحديات في التنفيذ، بما في ذلك النهج التنازلية، ونقص التعاون، وانخفاض القدرة على التكيف، ومحدودية الموارد.
Griffith University:... arrow_drop_down Griffith University: Griffith Research OnlineArticle . 2023License: CC BYFull-Text: http://hdl.handle.net/10072/427875Data sources: Bielefeld Academic Search Engine (BASE)University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)The Lancet Regional Health. Western PacificArticle . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.lanwpc.2023.100943&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 3 citations 3 popularity Average influence Average impulse Average Powered by BIP!
more_vert Griffith University:... arrow_drop_down Griffith University: Griffith Research OnlineArticle . 2023License: CC BYFull-Text: http://hdl.handle.net/10072/427875Data sources: Bielefeld Academic Search Engine (BASE)University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)The Lancet Regional Health. Western PacificArticle . 2023 . Peer-reviewedLicense: CC BYData sources: Crossrefadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.lanwpc.2023.100943&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2023 Australia, Spain, SpainPublisher:Elsevier BV Sujan Ghimire; Thong Nguyen-Huy; Mohanad S. AL-Musaylh; Ravinesh C. Deo; David Casillas-Pérez; Sancho Salcedo-Sanz;handle: 10017/63099
Este artículo desarrolla un modelo de aprendizaje profundo para la predicción de demanda de electricidad a partir de datos y variables climáticas locales. El modelo utiliza un algoritmo conocido como Multi-Head Self-Attention Transformer (TNET) para capturar información crítica de la demanda de electricidad, y lograr así predicciones fiables con datos de variables atmosféricas locales como lluvia, radiación solar, humedad, evaporación y temperaturas máximas y mínimas de las subestaciones de Energex en Queensland, Australia. Posteriormente, el modelo TNET se evalúa con modelos de aprendizaje profundo (LSTM, LSTM Bidireccional, y redesd GRU, Redes Neuronales Convolucionales CNN y Redes Neuronales Profundas DNN) basados en métricas de evaluación de modelos robustos. El método de Estimación de Densidad Kernel se utiliza asimismo para generar el intervalo de predicción (PI) de los pronósticos de demanda de electricidad y derivar métricas de probabilidad y resultados para demostrar que el modelo TNET desarrollado es preciso para todas las subestaciones. El estudio concluye que el modelo TNET propuesto es una herramienta muy fiable para predecir la demanda de electricidad, con alta precisión y bajos errores de predicción, y podría ser empleado como estrategia por gestores de demanda eléctrica, así como gestores de políticas energéticas que deseen incorporar factores climáticos en los patrones de demanda de electricidad, y desarrollar sistemas de análisis e información del mercado energético nacional. This paper develops a trustworthy deep learning model that considers electricity demand ( ) and local climate conditions. The model utilises Multi-Head Self-Attention Transformer (TNET) to capture critical information from , to attain reliable predictions with local climate (rainfall, radiation, humidity, evaporation, and maximum and minimum temperatures) data from Energex substations in Queensland, Australia. The TNET model is then evaluated with deep learning models (Long-Short Term Memory LSTM, Bidirectional LSTM BILSTM, Gated Recurrent Unit GRU, Convolutional Neural Networks CNN, and Deep Neural Network DNN) based on robust model assessment metrics. The Kernel Density Estimation method is used to generate the prediction interval (PI) of electricity demand forecasts and derive probability metrics and results to show the developed TNET model is accurate for all the substations. The study concludes that the proposed TNET model is a reliable electricity demand predictive tool that has high accuracy and low predictive errors and could be employed as a stratagem by demand modellers and energy policy-makers who wish to incorporate climatic factors into electricity demand patterns and develop national energy market insights and analysis systems. Agencia Estatal de Investigación
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2023License: CC BY NC NDData sources: Recolector de Ciencia Abierta, RECOLECTABiblioteca Digital de la Universidad de AlcaláArticle . 2023License: CC BY NC NDData sources: Biblioteca Digital de la Universidad de Alcaláadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egyai.2023.100302&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 20 citations 20 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
visibility 88visibility views 88 download downloads 13 Powered bymore_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)Recolector de Ciencia Abierta, RECOLECTAArticle . 2023License: CC BY NC NDData sources: Recolector de Ciencia Abierta, RECOLECTABiblioteca Digital de la Universidad de AlcaláArticle . 2023License: CC BY NC NDData sources: Biblioteca Digital de la Universidad de Alcaláadd ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.egyai.2023.100302&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article 2025 AustraliaPublisher:Elsevier BV Sujan Ghimire; Mohanad S. AL-Musaylh; Thong Nguyen-Huy; Ravinesh C. Deo; Rajendra Acharya; David Casillas-Pérez; Zaher Mundher Yaseen; Sancho Salcedo-Sanz;Electricity consumption has stochastic variabilities driven by the energy market volatility. The capability to predict electricity demand that captures stochastic variances and uncertainties is significantly important in the planning, operation and regulation of national electricity markets. This study has proposed an explainable deeply-fused nets electricity demand prediction model that factors in the climate-based predictors for enhanced accuracy and energy market insight analysis, generating point-based and confidence interval predictions of daily electricity demand. The proposed hybrid approach is built using Deeply Fused Nets (FNET) that comprises of Convolutional Neural Network (CNN) and Bidirectional Long-Short Term Memory (BILSTM) Network with residual connection. The study then contributes to a new deep fusion model that integrates intermediate representations of the base networks (fused output being the input of the remaining part of each base network) to perform these combinations deeply over several intermediate representations to enhance the demand predictions. The results are evaluated with statistical metrics and graphical representations of predicted and observed electricity demand, benchmarked with standalone models i.e., BILSTM, LSTMCNN, deep neural network, multi-layer perceptron, multivariate adaptive regression spline, kernel ridge regression and Gaussian process of regression. The end part of the proposed FNET model applies residual bootstrapping where final residuals are computed from predicted and observed demand to generate the 95% prediction intervals, analysed using probabilistic metrics to quantify the uncertainty associated with FNETS objective model. To enhance the FNET model’s transparency, the SHapley Additive explanation (SHAP) method has been applied to elucidate the relationships between electricity demand and climate-based predictor variables. The suggested model analysis reveals that the preceding hour’s electricity demand and evapotranspiration were the most influential ...
add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2024.124763&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen hybrid 4 citations 4 popularity Average influence Average impulse Average Powered by BIP!
more_vert add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1016/j.apenergy.2024.124763&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eudescription Publicationkeyboard_double_arrow_right Article , Other literature type 2023 Australia, Australia, IndonesiaPublisher:Informa UK Limited Authors: Haerani Haerani; Armando Apan; Thong Nguyen‐Huy; Badri Basnet;Affichage des formules : ?Les formules mathématiques ont été codées en MathML et sont affichées dans cette version HTML à l'aide de MathJax afin d'améliorer leur affichage. Décochez la case pour désactiver MathJax. Cette fonctionnalité nécessite Javascript. Cliquez sur une formule pour zoomer. Visualización de fórmulas: Las fórmulas matemáticas se han codificado como MathML y se muestran en esta versión HTML utilizando MathJax para mejorar su visualización. Desmarque la casilla para desactivar MathJax. Esta función requiere Javascript. Haz clic en una fórmula para hacer zoom. Formulae display:?Mathematical formulae have been encoded as MathML and are displayed in this HTML version using MathJax in order to improve their display. Uncheck the box to turn MathJax off. This feature requires Javascript. Click on a formula to zoom. عرض الصيغ:? تم ترميز الصيغ الرياضية كـ MathML ويتم عرضها في إصدار HTML هذا باستخدام MathJax لتحسين عرضها. قم بإلغاء تحديد المربع لإيقاف تشغيل MathJax. تتطلب هذه الميزة جافا سكريبت. انقر على صيغة للتكبير/التصغير.
University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1080/10095020.2022.2155255&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.euAccess RoutesGreen gold 9 citations 9 popularity Top 10% influence Average impulse Top 10% Powered by BIP!
more_vert University of Southe... arrow_drop_down University of Southern Queensland: USQ ePrintsArticle . 2023License: CC BYData sources: Bielefeld Academic Search Engine (BASE)add ClaimPlease grant OpenAIRE to access and update your ORCID works.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.This Research product is the result of merged Research products in OpenAIRE.
You have already added works in your ORCID record related to the merged Research product.All Research productsarrow_drop_down <script type="text/javascript"> <!-- document.write('<div id="oa_widget"></div>'); document.write('<script type="text/javascript" src="https://beta.openaire.eu/index.php?option=com_openaire&view=widget&format=raw&projectId=10.1080/10095020.2022.2155255&type=result"></script>'); --> </script>
For further information contact us at helpdesk@openaire.eu