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AbstractThis study presents a reconstruction of the seasonal mass balance of Glacier No. 354, located in the Akshiirak range, Kyrgyzstan, from 2003 to 2014. We use a distributed accumulation and temperature-index melt model driven by daily air temperature and precipitation from a nearby meteorological station. The model is calibrated with in situ measurements of the annual mass balance collected from 2011 to 2014. The snow-cover depletion pattern observed using satellite imagery provides additional information on the dynamics of mass change throughout the melting season. Two digital elevation models derived from high-resolution satellite stereo images acquired in 2003 and 2012 are used to calculate glacier volume change for the corresponding period. The geodetic mass change thus derived is used to validate the modelled cumulative glacier-wide balance. For the period 2003–12 we find a cumulative mass balance of –0.40±10mw.e.a-1. This result agrees well with the geodetic balance of –0.48±0.07mw.e.a-1over the same period.

Accurate estimation of grassland leaf area index (LAI), fractional vegetation cover (FVC), and aboveground biomass (AGB) is fundamental in grassland studies. The newly launched Ocean and Land Color Imager (OLCI) sensor onboard Sentinel-3 (S3) provides images with comparable spatial and spectral resolution with MODIS data. However, the use of S3 OLCI imageries for vegetation variable estimation is rarely evaluated. This study evaluated the potential of S3 OLCI and MODIS data for estimating grassland LAI, FVC, and AGB in the eastern Eurasian steppe. A Bayesian spatial model (Integrated Nested Laplace Approximation with Stochastic Partial Differential Equation, INLA-SPDE) was used to address spatial autocorrelation of in-situ observation data and to enhance our predictions. Our results showed that the models based on S3 OLCI data presented higher accuracy than models with MODIS data. The RMSEs decreased by 3.7-10.8 %, 3.7-7.5 %, and 1.6-14.2 % for LAI, FVC, and AGB predictions, respectively. Through combinations of multiple predictors, we confirmed the robustness of red edge bands for grassland variable estimation, the models employing red edge variables yielded 3.5 %, 3.2 %, and 0.4 % lower RMSEs than models with conventional visible and NIR bands for LAI, FVC, and AGB prediction, respectively. INLA-SPDE spatial model produced lower bias and higher prediction accuracy than random forest and random forests kriging method in most of the models; the INLA-SPDE predicted LAI and FVC maps also showed a better agreement with ground observations than MODIS and PROBA-V land products.

Les progrès des appareils électroniques ont augmenté la demande de maisons intelligentes basées sur l'Internet des objets (IoT), où les appareils de connexion se développent à un rythme rapide. Les appareils électroniques connectés sont plus courants dans les bâtiments intelligents, les villes intelligentes, les réseaux intelligents et les maisons intelligentes. Les progrès des technologies de réseau intelligent ont permis de surveiller chaque moment de la consommation d'énergie dans les bâtiments intelligents. Le problème avec les appareils intelligents est une plus grande consommation d'énergie par rapport aux bâtiments ordinaires. En raison des taux de croissance des villes intelligentes et des maisons intelligentes, la demande pour une gestion efficace des ressources augmente également de jour en jour. L'énergie est une ressource vitale, et son coût de production est très élevé. Pour cette raison, les scientifiques et les chercheurs travaillent sur l'optimisation de l'utilisation de l'énergie, en particulier dans les villes intelligentes, en plus de fournir un environnement confortable. L'objectif central de cet article est l'optimisation de la consommation d'énergie dans les bâtiments intelligents ou les maisons intelligentes. Pour l'indice de confort (qualité thermique, visuelle et de l'air), nous avons utilisé trois paramètres, à savoir la température (°F), l'éclairage (lx) et le CO2 (ppm). Le problème majeur avec les méthodes précédentes dans la littérature est les paramètres statiques de l'utilisateur (température, éclairage et CO2) ; lorsqu'ils (paramètres) sont attribués au début, ils ne peuvent pas être modifiés. Dans cet article, le filtre Alpha Beta a été utilisé pour prédire la température intérieure, l'éclairage et la qualité de l'air et éliminer le bruit des données. Nous avons appliqué une approche machine d'apprentissage extrême en profondeur pour prédire les paramètres utilisateur. Nous avons utilisé l'algorithme Bat et la logique floue pour optimiser la consommation d'énergie et la gestion des indices de confort. Les paramètres utilisateur prévus ont amélioré les performances globales du système en termes de facilité d'utilisation des systèmes intelligents, de consommation d'énergie et de gestion de l'indice de confort. L'indice de confort après optimisation est resté proche de 1, ce qui prouve l'importance du système. Après optimisation, la consommation d'énergie a également diminué et est restée autour du maximum de 15-18wh. Los avances en los dispositivos electrónicos han aumentado la demanda de hogares inteligentes basados en Internet de las cosas (IoT), donde los dispositivos de conexión están creciendo a un ritmo rápido. Los dispositivos electrónicos conectados son más comunes en edificios inteligentes, ciudades inteligentes, redes inteligentes y hogares inteligentes. Los avances en las tecnologías de redes inteligentes han permitido monitorear cada momento de consumo de energía en edificios inteligentes. El problema con los dispositivos inteligentes es un mayor consumo de energía en comparación con los edificios ordinarios. Debido a las tasas de crecimiento de las ciudades inteligentes y los hogares inteligentes, la demanda de una gestión eficiente de los recursos también está creciendo día a día. La energía es un recurso vital, y su coste de producción es muy elevado. Debido a eso, los científicos e investigadores están trabajando para optimizar el uso de la energía, especialmente en las ciudades inteligentes, además de proporcionar un entorno cómodo. El enfoque central de este documento es la optimización del consumo de energía en edificios inteligentes u hogares inteligentes. Para el índice de confort (calidad térmica, visual y del aire), hemos utilizado tres parámetros, es decir, Temperatura (°F), iluminación (lx) y CO2 (ppm). El principal problema con los métodos anteriores en la literatura son los parámetros estáticos del usuario (Temperatura, iluminación y CO2); cuando se asignan (parámetros) al principio, no se pueden cambiar. En este documento, el filtro Alfa Beta se ha utilizado para predecir la temperatura interior, la iluminación y la calidad del aire y eliminar el ruido de los datos. Aplicamos un enfoque de máquina de aprendizaje extremo profundo para predecir los parámetros del usuario. Hemos utilizado el algoritmo Bat y la lógica difusa para optimizar el consumo de energía y la gestión del índice de confort. Los parámetros de usuario previstos han mejorado el rendimiento general del sistema en términos de facilidad de uso de sistemas inteligentes, consumo de energía y gestión del índice de confort. El índice de confort después de la optimización se mantuvo cerca de 1, lo que demuestra la importancia del sistema. Después de la optimización, el consumo de energía también se redujo y se mantuvo alrededor del máximo de 15-18wh. The advancements in electronic devices have increased the demand for the internet of things (IoT) based smart homes, where the connecting devices are growing at a rapid pace. Connected electronic devices are more common in smart buildings, smart cities, smart grids, and smart homes. The advancements in smart grid technologies have enabled to monitor every moment of energy consumption in smart buildings. The issue with smart devices is more energy consumption as compared to ordinary buildings. Due to smart cities and smart homes' growth rates, the demand for efficient resource management is also growing day by day. Energy is a vital resource, and its production cost is very high. Due to that, scientists and researchers are working on optimizing energy usage, especially in smart cities, besides providing a comfortable environment. The central focus of this paper is on energy consumption optimization in smart buildings or smart homes. For the comfort index (thermal, visual, and air quality), we have used three parameters, i.e., Temperature (°F), illumination (lx), and CO2 (ppm). The major problem with the previous methods in the literature is the static user parameters (Temperature, illumination, and CO2); when they (parameters) are assigned at the beginning, they cannot be changed. In this paper, the Alpha Beta filter has been used to predict the indoor Temperature, illumination, and air quality and remove noise from the data. We applied a deep extreme learning machine approach to predict the user parameters. We have used the Bat algorithm and fuzzy logic to optimize energy consumption and comfort index management. The predicted user parameters have improved the system's overall performance in terms of ease of use of smart systems, energy consumption, and comfort index management. The comfort index after optimization remained near to 1, which proves the significance of the system. After optimization, the power consumption also reduced and stayed around the maximum of 15-18wh. أدت التطورات في الأجهزة الإلكترونية إلى زيادة الطلب على المنازل الذكية القائمة على إنترنت الأشياء (IoT)، حيث تنمو أجهزة الاتصال بوتيرة سريعة. الأجهزة الإلكترونية المتصلة أكثر شيوعًا في المباني الذكية والمدن الذكية والشبكات الذكية والمنازل الذكية. مكنت التطورات في تقنيات الشبكة الذكية من مراقبة كل لحظة من استهلاك الطاقة في المباني الذكية. المشكلة مع الأجهزة الذكية هي استهلاك الطاقة أكثر مقارنة بالمباني العادية. نظرًا لمعدلات نمو المدن الذكية والمنازل الذكية، يتزايد الطلب على الإدارة الفعالة للموارد يومًا بعد يوم. الطاقة مورد حيوي، وتكلفة إنتاجها مرتفعة للغاية. ونتيجة لذلك، يعمل العلماء والباحثون على تحسين استخدام الطاقة، خاصة في المدن الذكية، إلى جانب توفير بيئة مريحة. ينصب التركيز الرئيسي لهذه الورقة على تحسين استهلاك الطاقة في المباني الذكية أو المنازل الذكية. بالنسبة لمؤشر الراحة (الجودة الحرارية والبصرية وجودة الهواء)، استخدمنا ثلاثة معلمات، أي درجة الحرارة (درجة فهرنهايت) والإضاءة (lx) وثاني أكسيد الكربون (جزء في المليون). المشكلة الرئيسية في الأساليب السابقة في الأدبيات هي معلمات المستخدم الثابتة (درجة الحرارة والإضاءة وثاني أكسيد الكربون) ؛ عندما يتم تعيينها (المعلمات) في البداية، لا يمكن تغييرها. في هذه الورقة، تم استخدام فلتر ألفا بيتا للتنبؤ بدرجة الحرارة الداخلية والإضاءة وجودة الهواء وإزالة الضوضاء من البيانات. طبقنا نهج آلة التعلم المتطرف العميق للتنبؤ بمعلمات المستخدم. لقد استخدمنا خوارزمية الخفافيش والمنطق الغامض لتحسين استهلاك الطاقة وإدارة مؤشر الراحة. أدت معلمات المستخدم المتوقعة إلى تحسين الأداء العام للنظام من حيث سهولة استخدام الأنظمة الذكية واستهلاك الطاقة وإدارة مؤشر الراحة. بقي مؤشر الراحة بعد التحسين قريبًا من 1، مما يثبت أهمية النظام. بعد التحسين، انخفض استهلاك الطاقة أيضًا وبقي حول الحد الأقصى 15-18 واط.

AbstractTwenty-five years since foundational publications on valuing ecosystem services for human well-being1,2, addressing the global biodiversity crisis3 still implies confronting barriers to incorporating nature’s diverse values into decision-making. These barriers include powerful interests supported by current norms and legal rules such as property rights, which determine whose values and which values of nature are acted on. A better understanding of how and why nature is (under)valued is more urgent than ever4. Notwithstanding agreements to incorporate nature’s values into actions, including the Kunming-Montreal Global Biodiversity Framework (GBF)5 and the UN Sustainable Development Goals6, predominant environmental and development policies still prioritize a subset of values, particularly those linked to markets, and ignore other ways people relate to and benefit from nature7. Arguably, a ‘values crisis’ underpins the intertwined crises of biodiversity loss and climate change8, pandemic emergence9 and socio-environmental injustices10. On the basis of more than 50,000 scientific publications, policy documents and Indigenous and local knowledge sources, the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) assessed knowledge on nature’s diverse values and valuation methods to gain insights into their role in policymaking and fuller integration into decisions7,11. Applying this evidence, combinations of values-centred approaches are proposed to improve valuation and address barriers to uptake, ultimately leveraging transformative changes towards more just (that is, fair treatment of people and nature, including inter- and intragenerational equity) and sustainable futures.

Abstract Background Animal husbandry is one of the agricultural activities that generates economic benefits for agriculture. We detected significant development of these activities in Morocco. Currently, it is stuck between the increase of organic waste polluting the farm environment and the energy needed to ensure the activities. Faced with this challenge, we determined all physical, chemical, and microbiological characteristics for livestock wastes most spread in Morocco. We evaluated also their ability to be used as bioresources for the anaerobic digestion and incineration ways for energy production to agricultural units. Methods We worked on four organic wastes (cow dung, horse manure, broiler droppings, and the excrement of laboratory mouse). The physical, chemical, and microbiological characteristics: moisture, total solids, volatile solids, organic carbon, nitrogen, ions and heavy metals, staphylococci, coliforms, yeasts and fungi and total aerobic mesophilic bacteria are determined by standard methods. The determination of lower heating value is performed with calorimetric bomb. The biogas production is determined by four batch types of digesters. All digesters are incubated at 35 ± 1 ℃ for 40 days. The volumes of biogas produced are corrected under standard pressure and temperature conditions. Results We noticed that the four agricultural wastes have a lower heating value closer to each other. When comparing the physicochemical composition of our wastes with that of Tanner’s theoretical waste, we have found that the valorization of these organic wastes by incineration is without energy and economic benefits. The microbiological content reflects the presence of a reservoir of pathogenic bacteria. On the other hand, the biogas potential shows that cow waste produces the largest amount of biogas. The co-digestion is necessary for horse manure, chicken manure, and excrement of laboratory mouse in order to increase their biogas potential. The mineral composition shows the possibility of using digestate of these wastes as an organic amendment to plants. Conclusions The comparison of the physicochemical and microbiological characteristics of the four organic wastes in Morocco reflects some important points. Firstly, there is an urgency to intervene to treat and valorize these wastes before putting them in the open air. Secondly, the incineration of this waste is inadequate from an energy point of view. In the third position, these wastes present a great ability to be used as feed substrates of farm digesters. Finally, the biogas potential and the mineral composition of these wastes demonstrates the ability to use them as bioresources capable of producing green energy and an organic amendment to Moroccan farms.

The paper focuses on the relationship between businesses and digital transformation, and how digital transformation has changed manufacturing in several ways. Aspects like Cloud Computing, vertical and horizontal integration, data communication, and the internet have contributed to sustainable manufacturing by decentralizing supply chains. In addition, digital transformation inventions such as predictive analysis and big data analytics have helped optimize sustainable manufacturing by reducing overproduction or underproduction through predicting customer demands. It integrates digital technology to enhance business operations, consumer engagement, supply chains, and coordination, the manufacturing process, energy conservation, efficiency, and environmental conservation and culture to satisfy business needs. Businesses’ failure to embrace digital transformation in this era contributes to their demise. This research paper will analyze and contrast several businesses and the extent of digital transformation’s influence on them during COVID-19. A two-stage study is conducted, the first stage assesses a chosen exemplary business success over three years. The second stage investigates the reasons for success, or otherwise, and the connection to digitalization in the business. Our outcomes suggest that digital transformation strongly influences firms’ effectiveness and survival from a technology-centric and business model standpoint. Some essential generic recommendations are suggested based on the results obtained.