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This work investigates the endurance and performance of aluminum-doped zinc oxide (AZO) thin films fabricated on flexible polyethylene terephthalate (PET) substrates, providing new insights into their degradation linked to mechanical flexing and accelerated thermal cycling (ATC). The current study uniquely combines cyclic bending fatigue at 23 °C and 70 °C with ATC between 0 °C and 100 °C, simulating operational stresses in real-world environments, in contrast to previous research that has focused primarily on either isolated mechanical or thermal effects. The 425 nm thick films showed high transparency and conductivity, making them suitable materials for flexible electronics and optoelectronic devices. In this work, electrical resistivity, one of the most important performance parameters, was investigated after each mechanical or thermal cycle. The results indicate that mechanical cycling at high temperatures can drastically enhance the crack formation and electrical degradation, with an over 250% change in the electrical resistance (PCER) after 12,000 cycles at 70 °C and more than 300% after 500 thermal cycles. The highly deleterious effects of combined stressors on the structural integrity and electrical properties of AZO films are underlined by these observations. This study further suggests that the design of more robust AZO-based materials/coatings would contribute toward achieving better durability in flexible electronic applications. These findings also go hand in glove with the ninth goal of the United Nations' Sustainable Development Goals, specifically Target 9.5: Enhance Research and Upgrade Industrial Technologies. [ABSTRACT FROM AUTHOR]

Traffic crashes are a prominent cause of fatalities around the world. This study focuses on the dynamics of traffic safety by analysing factors influencing crash frequency during both nighttime and daytime conditions. Four years' data derived from the Fatality Analysis Reporting System (FARS) database were analysed statistically based on the negative binomial regression model, to identify the contributions of several factors affecting crash outcomes. The findings reveal significant related factors, including area type, roadway alignment, speeding-related factors, sex of driver, number of lanes, grade, surface type and weather conditions, that contributed to the expected crash frequency during both daytime and nighttime conditions. Through quantitative analysis, the extent to which each factor contributes to the expected crash frequency was determined, offering effective insights for policymaking to boost roadway safety. The findings highlight the necessity of implementing targeted strategies to minimize the risk of crashes by creating safer road environments. [ABSTRACT FROM AUTHOR]

Policymakers have become eager to move towards sustainability recently due to the growing costs of electricity and concerns about the environment. Environment friendly and economical energy sources, such as solar power, are being introduced at increasing rates. Photovoltaic (PV) panels are considered an important method of harnessing solar power. Although solar energy is one of the most efficient renewable and sustainable sources of energy, the accumulation of dust and debris on even one panel in a PV array reduces the efficiency of energy generation, thus highlighting the need to keep the surfaces of PV panels clean. Several methods can be used to clean PV panels, such as Heliotex technology, electrostatic cleaning, the use of self-cleaning glass, automatic cleaning and manual cleaning. The Preference Selection Index (PSI) multicriteria decision-making approach is used in this study to compare these cleaning methods. Data were collected via a survey of solar energy experts in Jordan to enable a comparison of these cleaning methods, and several attributes were considered. After the initial PSI analysis, a follow-up sensitivity analysis was conducted that involved removing the cost attributes. The results showed that the best method was manual cleaning. The results of the sensitivity analysis confirmed that manual cleaning is the method most often preferred by experts. [ABSTRACT FROM AUTHOR]

A huge variation exists among countries in terms of freshwater availability. Water scarcity is considered a global problem that continues to grow with the fast increase in population and depleting sources of freshwater. Water desalination can be a professional solution for addressing such scarcity of water supply, but the selection of the suitable desalination method can be hard in some cases and depends on several criteria. Multi-criteria decision-making methods could be used to simplify the decision-making problem. In this paper, the preference selection index (PSI) will be used to rank seven desalination technologies; these include reverse osmosis, electrodialysis, multiflash desalination, multi-effect desalination, mechanical vapor compression, nanofiltration and ion exchange. Twelve criteria were used in evaluating the desalination methods, including the capital cost, operating cost, the energy required, recovery ratio, product water quality, adaptability, lifetime, environmental impact, simplicity, reliability, maturity and applicability. The results of the PSI model showed that the ion exchange technology is the optimal desalination technology, followed by nanofiltration technology. [ABSTRACT FROM AUTHOR]

Policymakers have become eager to move towards sustainability recently due to the growing costs of electricity and concerns about the environment. Environment friendly and economical energy sources, such as solar power, are being introduced at increasing rates. Photovoltaic (PV) panels are considered an important method of harnessing solar power. Although solar energy is one of the most efficient renewable and sustainable sources of energy, the accumulation of dust and debris on even one panel in a PV array reduces the efficiency of energy generation, thus highlighting the need to keep the surfaces of PV panels clean. Several methods can be used to clean PV panels, such as Heliotex technology, electrostatic cleaning, the use of self-cleaning glass, automatic cleaning and manual cleaning. The Preference Selection Index (PSI) multicriteria decision-making approach is used in this study to compare these cleaning methods. Data were collected via a survey of solar energy experts in Jordan to enable a comparison of these cleaning methods, and several attributes were considered. After the initial PSI analysis, a follow-up sensitivity analysis was conducted that involved removing the cost attributes. The results showed that the best method was manual cleaning. The results of the sensitivity analysis confirmed that manual cleaning is the method most often preferred by experts. [ABSTRACT FROM AUTHOR]