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The global community has set intensive targets in Sustainable Development Goals (SDGs) to better people’s lives after closing the Millennium Development Goals (MDGs). It corresponds to the 2030 aspirations of the United Nations to enhance and promote the sustainable development of human society. The current paper explores the impact of fiscal hedging and R&D in energy Using a green-energy system in SDGs. To do this, we used TOPSIS and QARDL methodologies on a 21-year dataset of South and Southeast Asian economies from 2000 to 2020. The study results show that fiscal hedging contributes favourably to the environmental degradation of the underlying economy. Research and development (R&D) in renewables has contributed negatively to ecological degradation and SDGs in the economies of South & Southeast Asia. This study suggests policy guidelines for advanced and developing economies based on fiscal stability and technical innovation through R&D to meet SDG.

Global, spectral isolation data measured every minute over a four-year period at Dhahran (26° 32′ N, 50° 13′ E) Saudi Arabia, using five Schott filters with cutoff at “285, 500, 530, 630, and 695” nm, and ultraviolet radiation sensor, 295–385nm, are analyzed. Monthly averages of the diurnal variations of these bands are presented. The rainfall, dust/sand storm, cloud and air mass effects on the band radiation are also investigated. Comparisons of the yearly average of band radiation measured at Dhahran and those reported in Goldberg and Klein (1977) for Jerusalem (32° N) and Rockville (39° N) are presented. Monthly average band radiation are also presented. The data showed that the ratio of the monthly average of the diurnal band radiations to the total radiation for winter and summer are nearly the same. The data also showed that the rainfall increases the percentage of radiation in the bands 385–500nm, 500–530nm, and 630–690nm, and it decreases the percentage radiation of the band 690–2800nm and that the opposite is true for dust/sand storm effect. The change in the band radiation due to cloud cover is small. The data also showed that the monthly and yearly averages of the radiation in the bands “630–695, and 695–2800” nm are latitude independent. The monthly radiation values at each band over the year are almost constant.

The air-conditioning and refrigeration systems in the Kingdom of Saudi Arabia consume more than 60% of the electrical energy of the building sector. Most of the used systems are of the vapor-compression type. Using solar energy to power such systems will save a large amount of energy (primary or electrical) that can be utilized by the production sectors such as industries. Therefore in the present study, alternate designs for a 24-h operating solar-powered absorption refrigeration technology have been developed in detail. The development includes an in-depth review of the design and operation of the conventional and solar-assisted absorption refrigeration systems coming-up with new alternative designs, detailed thermodynamic analysis of some of the new alternative designs and selection of the most suitable alternative design. The analysis indicates that continuously operating solar-powered aqua-ammonia absorption system with refrigerant storage is the most suitable alternative design for an uninterrupted supply of cooling effect.

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Sustainable food supply has gained considerable consumer concern due to the high percentage of spoilage microorganisms. Food industries need to expand advanced technologies that can maintain the nutritive content of foods, enhance the bio-availability of bioactive compounds, provide environmental and economic sustainability, and fulfill consumers’ requirements of sensory characteristics. Heat treatment negatively affects food samples’ nutritional and sensory properties as bioactives are sensitive to high-temperature processing. The need arises for non-thermal processes to reduce food losses, and sustainable developments in preservation, nutritional security, and food safety are crucial parameters for the upcoming era. Non-thermal processes have been successfully approved because they increase food quality, reduce water utilization, decrease emissions, improve energy efficiency, assure clean labeling, and utilize by-products from waste food. These processes include pulsed electric field (PEF), sonication, high-pressure processing (HPP), cold plasma, and pulsed light. This review describes the use of HPP in various processes for sustainable food processing. The influence of this technique on microbial, physicochemical, and nutritional properties of foods for sustainable food supply is discussed. This approach also emphasizes the limitations of this emerging technique. HPP has been successfully analyzed to meet the global requirements. A limited global food source must have a balanced approach to the raw content, water, energy, and nutrient content. HPP showed positive results in reducing microbial spoilage and, at the same time, retains the nutritional value. HPP technology meets the essential requirements for sustainable and clean labeled food production. It requires limited resources to produce nutritionally suitable foods for consumers’ health.

Abstract In this paper, a hybrid features based support vector machine (SVM) model is proposed using infrared thermography technique for hotspots detection and classification of photovoltaic (PV) panels. A novel hybrid feature vector consisting of RGB, texture, the histogram of oriented gradient (HOG), and local binary pattern (LBP) as features is formed using a data fusion approach. A machine learning algorithm SVM is employed to classify the obtained thermal images of PV panels into three different classes (i.e., healthy, non-faulty hotspot, and faulty). The comparison of different machine learning algorithms and datasets is also carried out to validate the superiority of the proposed model and hybrid feature dataset. The experimental results reveal that the proposed hybrid features with SVM resulted in 96.8% training accuracy and 92% testing accuracy with lesser computational complexity and storage space than other machine learning algorithms. The proposed approach is easily implementable for efficient monitoring and fault diagnosis of PV panels.

ABSTRACTWind power forecasts are useful tools for power load balancing, energy trading and wind farm operations. Long range monthly‐to‐seasonal forecasting allows the prediction of departures from average weather conditions beyond traditional weather forecast timescales, months in advance. However, it has not yet been demonstrated how these forecasts can be optimally transformed to wind power. The predictable part of a seasonal forecast is for longer monthly averages, not daily averages, but to use monthly averages misses information on variability. To investigate, here a model relating average weather conditions to average wind power output was built, based on the relationship between instantaneous wind speed and power production and incorporating fluctuations in air density due to temperature and wind speed variability. Observed monthly average power output from UK stations was used to validate the model and to investigate the optimal temporal resolution for the data used to drive the model. Multiple simulations of wind power were performed based on reanalysis data, making separate simulations based on monthly, daily and sub‐daily averages, using a distribution defined by the mean across the period to incorporate information on variability. Basing the simulation on monthly averages alone is sub‐optimal: using daily average winds gives the highest correlation against observations. No improvement over this is gained by using sub‐daily averages or including temperature variability. This signifies that to transform seasonal forecasts to wind power a compromise must be made between using the daily averages with debatable skill and the more predictable monthly averages, losing information on day‐to‐day variability.