Determinants of well-being factors mediated by teacher commitment

The purpose of this study is to investigate the variables that affect students' academic well-being. This study found that individualized teaching, family support for learning and teacher dedication are some of the variables that have an impact on children's well-being. The study used a quantitative methodology with 57 students as the sample size. A questionnaire was used to collect the data and random sampling is used in the sampling process. Some of the data analysis methods used in PLS-SEM include path diagram models, validity tests, reliability tests, model fits, R squares and modified R squares, predictive relevance, collinearity, direct and  indirect effects and hypothesis testing. The findings of the study demonstrate that well-being is significantly impacted by directly differentiated instruction that is customized to each student's needs and traits. The well-being of students is significantly impacted by parental learning support. Evaluation of the indirect effects reveals that the differentiated learning variable through teacher devotion significantly affects students' well-being. Parental support's indirect influence on the learning variable has a significant effect on children's wellbeing similar to how teacher dedication serves as a mediator. The research contribution to the development of literature is related to how the role of parent support in learning has led to increased teacher commitment. Effective implementation of individualized instruction and integration of parental assistance in learning into the learning process are accessible to teachers who have a strong dedication to their career. A committed teacher will create an inclusive learning environment that will ultimately realize the overall well-being of students

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

Assessment of Energy-Population-Urbanization Nexus with Changing Energy Industry Scenario in India

The demand for energy has been growing worldwide, especially in India partly due to the rapid population growth and urbanization of the country. To meet the ever-increasing energy requirement while maintaining an ecological balance is a challenging task. However, the energy industry-induced effect on population and urbanization has not been addressed before. Therefore, this study investigates the linkages between energy, population, and urbanization. The study also aims to find the quantifiable indicators for the population growth and rate of urbanization due to the expanding energy industry. The integrated framework uses a multi-temporal Landsat data to analyze the urbanization pattern, a census data for changes in population growth, night time light (NTL) data as an indicator for economic development and energy production and consumption data for energy index. Multi-attribute model is used to calculate a unified metric, termed as the energy-population-urbanization (EPU) nexus index. The proposed approach is demonstrated in the National Thermal Power Corporation (NTPC) Dadri power plant located in Uttar Pradesh, India. Landsat and NTL data clearly shows the urbanization pattern, economic development, and electrification in the study area. A comparative analysis based on various multi-attribute decision model assessment techniques suggests that the average value of EPU nexus index is 0.529, which significantly large compared to other studies and require special attention by policymakers because large EPU index indicates stronger correlation among energy, population, and urbanization. The authors believe that it would help the policymakers in planning and development of future energy projects, policies, and long-term strategies as India is expanding its energy industry

Population–Urbanization–Energy Nexus: A Review

Energy expansion and security in the current world scenario focuses on increasing the energy generation capacity and if possible, adopting cleaner and greener energy in that development process. However, too often this expansion and planning alters the landscape and human influence on its surroundings through a very complex mechanism. Resource extraction and land management activity involved in energy infrastructure development and human management of such development systems have long-term and sometimes unforeseen consequences. Although alternative energy sources are being explored, energy production is still highly dependent on fossil fuel, especially in most developing countries. Further, energy production can potentially affect land productivity, land cover, human migration, and other factors involved in running an energy production system, which presents a complex integration of these factors. Thus, land use, energy choices, infrastructure development and the population for which such facilities are being developed must be cognizant of each other, and the interactions between them need to be studied and understood closely. This study strives to analyze the implications of linkages between the energy industry, urbanization, and population and especially highlights processes that can be affected by their interaction. It is found that despite advancement in scientific tools, each of the three components, i.e., population growth, urbanization, and energy production, operates in silos, especially in developing countries, and that this complex issue of nexus is not dealt with in a comprehensive way

Deep Learning-Based Spatiotemporal Fusion of Unmanned Aerial Vehicle and Satellite Reflectance Images for Crop Monitoring

Spatiotemporal fusion (STF) techniques play important roles in Earth observation analysis as they enable the generation of images with high spatial and temporal resolution. However, existing STF models often fuse images from various satellites, not satisfying the demand for precise crop monitoring. In contrast, unmanned aerial vehicle (UAV) images can deliver detailed data, and deep learning (DL)-based STF models have the potential to automatically extract abstract features. To this end, this study proposed a novel end-to-end DL-based STF model named UAV-Net, which can produce centimeter-scale UAV images. UAV-Net has an encoder-decoder architecture with Modified ResNet (MResNet), Feature Pyramid Network (FPN), and decoder modules. The encoder uses MResNet modules to extract input features, while the FPN module performs a multiscale fusion of these features before reconstructing UAV images using transposed convolution in the decoder module. Through comparative and ablation experiments, this study evaluated the efficacies of MResNet modules with 18, 34, and 50 layers, along with the FPN module of UAV-Net. The experimental results on real-world datasets demonstrated that UAV-Net adequately produces UAV images both visually and quantitatively. Furthermore, a comparison with state-of-the-art STF models highlights the innovation and effectiveness of UAV-Net in producing centimeter-scale images. The predicted centimeter-scale images using UAV-Net have great potential for various environmental monitoring applications