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Both electrical and thermal efficiencies combine in determining and evaluating the performance of a PV/T collector. In this study, two PV/T systems consisting of poly and monocrystalline PV panels were used, which are connected from the bottom by a heat exchanger consisting of a spiral tube through which a nanofluid circulates. In this study, a base fluid, water, and ethylene glycol were used, and iron oxide nanoparticles (nano-Fe2O3) were used as an additive. The mixing was carried out according to the highest specifications adopted by the researchers, and the thermophysical properties of the fluid were carefully examined. The prepared nanofluid properties showed a limited effect of the nanoparticles on the density and viscosity of the resulting fluid. As for the thermal conductivity, it increased by increasing the mass fraction added to reach 140% for the case of adding 2% of nano-Fe2O3. The results of the zeta voltage test showed that the supplied suspensions had high stability. When a mass fraction of 0.5% nano-Fe2O3 was added the zeta potential was 68 mV, while for the case of 2%, it reached 49 mV. Performance tests showed a significant increase in the efficiencies with increased mass flow rate. It was found when analyzing the performance of the two systems for nanofluid flow rates from 0.08 to 0.17 kg/s that there are slight differences between the monocrystalline, and polycrystalline systems operating in the spiral type of exchanger. As for the case of using monocrystalline PV the electrical, thermal, and total PV/T efficiencies with 2% added Fe2O3 ranged between 10% to 13.3%, 43–59%, and 59 to 72%, respectively, compared to a standalone PV system. In the case of using polycrystalline PV, the electrical, thermal, and total PV/T efficiencies ranged from 11% to 13.75%, 40.3% to 63%, and 55.5% to 77.65%, respectively, compared to the standalone PV system. It was found that the PV/T electrical exergy was between 45, and 64 W with thermal exergy ranged from 40 to 166 W, and total exergy from 85 to 280 W, in the case of using a monocrystalline panel. In the case of using polycrystalline, the PV/T electrical, thermal, and total exergy were between 45 and 66 W, 42–172 W, and 85–238 W, respectively. The results showed that both types of PV panels can be used in the harsh weather conditions of the city of Baghdad with acceptable, and efficient productivity.

Closed-loop supply chain networks are gaining research popularity due to environmental, economic and social concerns. Such networks are primarily designed to overcome carbon footprints and to retrieve end of life products from customers. This study considers a multi echelon closed-loop supply chain in the presence of machine disruption. A multi-objective model is presented to optimize the total cost, the total time and emissions in a closed-loop supply chain network. The aim is to analyze the trade-off between the objectives of cost, time, and emissions and how these decisions are impacted by the selection of different available machines. A number of solution approaches are tested on a case study from the tire industry. The results suggest the improved performance of the hybrid heuristic and the importance of controlling disruption in a closed-loop supply chain network. Furthermore, there is a trade-off between the different objective functions which can help the decision maker to choose a particular solution according to the preference of an organization. Finally, conclusion and future research avenues are provided.

Effectively reducing climate change requires marked, global behavior change. However, it is unclear which strategies are most likely to motivate people to change their climate beliefs and behaviors. Here, we tested 11 expert-crowdsourced interventions on four climate mitigation outcomes: beliefs, policy support, information sharing intention, and an effortful tree-planting behavioral task. Across 59,440 participants from 63 countries, the interventions’ effectiveness was small, largely limited to nonclimate skeptics, and differed across outcomes: Beliefs were strengthened mostly by decreasing psychological distance (by 2.3%), policy support by writing a letter to a future-generation member (2.6%), information sharing by negative emotion induction (12.1%), and no intervention increased the more effortful behavior—several interventions even reduced tree planting. Last, the effects of each intervention differed depending on people’s initial climate beliefs. These findings suggest that the impact of behavioral climate interventions varies across audiences and target behaviors.

This research deals with the process of reviving urban heritage, especially in old cities after the war, due to their symbolic and moral values in society. By studying the reality of the city and its destructed heritage features, there was a local need to study the integrated strategies of the urban heritage revival process to reach indicators that are drawn from international experiences and the curricula and studies of international organizations (ICOMOS, World Bank, UN-Habitat, IOM) that interested in cultural heritage and to use these indicators in evaluating local projects in the center of the old city of Mosul which was chosen as a case study for this research, due to its historical and civilizational importance, which has contributed greatly to the formation of the city of Mosul through the ages, and as this city has witnessed deliberate sabotage of its urban heritage due to the terrorist acts witnessed before its liberation.

Abstract Mitigating the effects of environmental deterioration requires a focus on not just CO2 emissions from energy consumption, but also environmental pollution from industry sectors. To reach this goal, recent studies have extended ecological footprint (EF) analysis to identify the ecological drivers of various key industry sectors. The role of the phosphorus (P) industry on the EF within the environmental Kuznets curve (EKC) framework for China is the emphasis of this study. Autoregressive distributive lag (ARDL) as well as the impulse response function and robustness analysis were used to consider a time from 1985 to 2018. The study verifies the EKC hypothesis for China in both the long and the short run, and indispensable determinants are proposed to be included to assure the model’s fitness and robustness when conducting EF analysis of industry sectors. Energy consumption–based carbon emissions have been verified as the dominant contributor to EF, but P use and urbanization have a significant lagged positive influence on EF in the short run. P exports, in particular, have been highlighted as a critical driver of the EF of China’s P industry. The conducted frequency domain causality test reinforced the above findings and demonstrated bidirectional causality at different frequencies. This work suggests that formulating plausible P export policies to alleviate the conflict between the output of China’s P industry and the environmental sustainability of this industry are necessary. In this context, “multidisciplinary, multidimensional, and practical solutions” are most desirable for sustainable P management.

Avec l'augmentation de la demande d'énergie et les sources d'énergie limitées, le système fonctionne à sa capacité maximale. Par conséquent, la capacité de déterminer la stabilité de la tension avant l'effondrement de la tension a fait l'objet d'une grande attention en raison de la complexité du système d'alimentation. Dans cet article, une prédiction de l'indice de stabilité de la tension (VSI) basée sur le réseau neuronal à fonction de base radiale (RBFNN) pour le réseau Super Grid irakien, 400KV. Des données d'apprentissage ont été obtenues pour divers paramètres de variables de charge à l'aide du flux de charge et de la méthode FVSI conventionnelle. L'entrée les données ont été effectuées en utilisant un test de 135 échantillons avec différentes tensions de bus (V b ), puissances active et réactive de bus (P b , Q b ), angle de charge de bus (δ b ) et FVSI ij. Le modèle RBFNN a quatre entrées représentant le (V b , P b , Q b et δ b ), seize nœuds à la couche cachée et un nœud de sortie représentant FVSI ij ont été utilisés pour évaluer la sécurité en ligne. La méthode proposée a été testée dans l'IEEE 30 et un système pratique. Les résultats de simulation montrent que le le procédé proposé est plus approprié pour l'évaluation de la stabilité de la tension en ligne en termes de détection automatique de la ligne de transmission critique lorsque des charges réelles ou réactives supplémentaires sont ajoutées. Con el aumento de la demanda de energía y las fuentes de alimentación limitadas, el sistema funciona a su máxima capacidad. Por lo tanto, la capacidad de determinar la estabilidad de la tensión antes del colapso de la tensión ha recibido una gran atención debido a la complejidad del sistema de alimentación. En este documento, se ha realizado una predicción del índice de estabilidad de la tensión (VSI) basada en la red neuronal de función de base radial (RBFNN) para la red iraquí Super Grid, 400KV. Se han obtenido datos de aprendizaje para diversas configuraciones de variables de carga utilizando el flujo de carga y el método FVSI convencional. La entrada los datos se realizaron mediante el uso de una prueba de 135 muestras con diferentes voltajes de bus (V b ), potencia activa y reactiva del bus (P b , Q b ), ángulo de carga del bus (δ b ) y FVSI ij. El modelo RBFNN tiene cuatro entradas que representan (V b , P b , Q b y δ b ), dieciséis nodos en la capa oculta y un nodo de salida que representa FVSI ij se han utilizado para evaluar la seguridad en línea. El método propuesto se ha probado en el IEEE 30 y un sistema práctico. En los resultados de la simulación muestran que el método propuesto es más adecuado para la evaluación de la estabilidad de la tensión en línea en términos de detección automática de la línea de transmisión crítica cuando se añaden cargas reales o reactivas adicionales. With the increase in power demand and limited power sources has caused the system to operate at its maximum capacity.Therefore, the ability of determine voltage stability before voltage collapse has received a great attention due to the complexity of power system.In this paper a prediction of voltage stability index (VSI) based on radial basis function neural network (RBFNN) for the Iraqi Super Grid network, 400KV.Learning data has been obtained for various settings of load variables using load flow and conventional FVSI method.The input data was performed by using a 135 samples test with different bus voltage (V b ), Bus active and reactive power (P b , Q b ), bus load angle (δ b ) and FVSI ij .The RBFNN model has four input representing the (V b , P b , Q b and δ b ), sixteen nodes at hidden layer and one output node representing FVSI ij have been used to assess the security on line.The proposed method has been tested in the IEEE 30 and a practical system.In Simulation results show that the proposed method is more suitable for on-line voltage stability assessment in term of automatically detection of critical transmission line when additional real or reactive loads are added. مع زيادة الطلب على الطاقة ومحدودية مصادر الطاقة، تسبب النظام في العمل بأقصى طاقته. لذلك، حظيت قدرة تحديد استقرار الجهد قبل انهيار الجهد باهتمام كبير بسبب تعقيد نظام الطاقة. في هذه الورقة، تم التنبؤ بمؤشر استقرار الجهد (VSI) بناءً على الشبكة العصبية الوظيفية الشعاعية (RBFNN) للشبكة الفائقة العراقية، 400 كيلو فولت. تم الحصول على بيانات التعلم لإعدادات مختلفة من متغيرات الحمل باستخدام تدفق الحمل وطريقة FVSI التقليدية. تم إجراء البيانات باستخدام اختبار 135 عينة بجهد ناقل مختلف (V b )، وناقل نشط وقوة تفاعلية (P b ، Q b )، وزاوية تحميل الناقل (δ b ) و FVSI ij. يحتوي نموذج RBFNN على أربعة مدخلات تمثل (V b ، P b ، Q b و δ b )، وستة عشر عقدة في طبقة مخفية وعقدة خرج واحدة تمثل FVSI ij تم استخدامها لتقييم الأمان على الخط. تم اختبار الطريقة المقترحة في IEEE 30 ونظام عملي. في نتائج المحاكاة تظهر أن الطريقة المقترحة أكثر ملاءمة لتقييم استقرار الجهد عبر الإنترنت من حيث الكشف التلقائي عن خط النقل الحرج عند إضافة أحمال حقيقية أو تفاعلية إضافية.

Confocal microscope imaging has become popular in biotechnology labs. Confocal imaging technology utilizes fluorescence optics, where laser light is focused onto a specific spot at a defined depth in the sample. A considerable number of images are produced regularly during the process of research. These images require methods of unbiased quantification to have meaningful analyses. Increasing efforts to tie reimbursement to outcomes will likely increase the need for objective data in analyzing confocal microscope images in the coming years. Utilizing visual quantification methods to quantify confocal images with naked human eyes is an essential but often underreported outcome measure due to the time required for manual counting and estimation. The current method (visual quantification methods) of image quantification is time-consuming and cumbersome, and manual measurement is imprecise because of the natural differences among human eyes’ abilities. Subsequently, objective outcome evaluation can obviate the drawbacks of the current methods and facilitate recording for documenting function and research purposes. To achieve a fast and valuable objective estimation of fluorescence in each image, an algorithm was designed based on machine vision techniques to extract the targeted objects in images that resulted from confocal images and then estimate the covered area to produce a percentage value similar to the outcome of the current method and is predicted to contribute to sustainable biotechnology image analyses by reducing time and labor consumption. The results show strong evidence that t-designed objective algorithm evaluations can replace the current method of manual and visual quantification methods to the extent that the Intraclass Correlation Coefficient (ICC) is 0.9.

The growing number of electric vehicles in recent years is observable in almost all countries. The country’s energy transition should accompany this rise in electromobility if it is currently generated from non-renewable sources. Only electric vehicles powered by renewable energy sources can be considered zero-emission. Therefore, it is essential to conduct interdisciplinary research on the feasibility of combining energy recovery/generation structures and testing the energy consumption of electric vehicles under real driving conditions. This work presents a comprehensive approach for evaluating the energy consumption of a modern public building–electric vehicle system within a specific location. The original methodology developed includes surveys that demonstrate the required mobility range to be provided to occupants of the building under consideration. In the next step, an energy balance was performed for a novel near-zero energy building equipped with a 199.8 kWp photovoltaic installation, the energy from which can be used to charge an electric vehicle. The analysis considered the variation in vehicle energy consumption by season (winter/summer), the actual charging profile of the vehicle, and the parking periods required to achieve the target range for the user.