There is a global trend towards adopting green energy, with solar energy being the primary source derived from solar panel technologies. Solar panels can generate enough power for general and household use. However, to effectively function and serve their purpose, they require regular cleaning and effective maintenance, and robotic cleaning is among the current applicable technologies. This research aims to determine the intention of using solar panel cleaning robots in Thailand for individual solar panel users. The study was hinged on the extended C-TAM-TPB model. The quantitative survey study design was employed using primary data collected from individual solar panel users in their households. 419 respondents were used to collect the data. The C-TAM-TPB model proposed using reliability, validity, and model fitness which employed confirmatory factor analysis (CFA). They adopted structural equation modeling (SEM) in the evaluation of the variables' relationships and study hypotheses. Subjective norms and trust in technology, individual control perception, and awareness of renewable energy significantly and positively affected behavioral intention to use solar panel cleaning robots as indicated by the study. Trust in technology, awareness of renewable energy, and environmental concerns were found to be pivotal mediators to the attitude effect on individual users' intention to act in using solar panel cleaning robots. The authors recommend that to improve the adoption of solar panel cleaning robots; the concerned stakeholders should consider, firstly, enhancing trust in the technology of these robots, which is crucial, focusing on aspects like reliability, privacy, security, and reputation. Secondly, considering the influence of subjective norms, including perceptions from family, friends, colleagues, and experts, is essential. Perceived behavioral control should also be a focal point, encompassing self-efficacy, resources, and complexity. Moreover, increasing awareness of renewable energy and environmental benefits is vital to encourage individual adoption. The research also recommended that to encourage the adoption and use of solar panel cleaning robots, the aspects that should be emphasized include subjective norm, perceived behavior control, trust in technology, and awareness of renewable energy.

The six GCC states share similar economic, geographic and socio-cultural characteristics and also face with similar challenges in terms of energy perspective. This study plans to focus on the eco-nomic and energy efficiency of the six GCC states. In the process, the study ranks the GCC states in terms of their efficiency scores. These efficiencies are computed through Data Envelopment Analysis. The economic efficiency is calculated for all six GCC states. Capital and labor are the inputs and GDP is the output. In this survey, Saudi Arabia maintains the highest efficiency score of 0.94, closely followed by Qatar (0.92), Kuwait (0.89), Bahrain (0.83), Oman (0.81) and UAE (0.67). There is a huge gap between the economic efficiency scores of Saudi Arabia and UAE. The environmental efficiency scores are calculated using CO2 emissions as output and electric power consumption and energy as input. Again, the highest efficiency score is for Saudi Arabia (0.91) followed by Oman (0.87), Kuwait and Bahrain have a tie for the 3rd position with a score of 0.74. Finally, the laggards are UAE (0.65) and Qatar (0.62). Again, there is a huge gap between the best and the worst performers. The case of two countries is worth mentioning. Qatar is ranked second in terms of economic efficiency while it was ranked sixth in terms of economic efficiency. Oman was ranked fifth in terms of economic efficiency while it was ranked second in terms of environmental efficiency. Finally, an average of economic and environmental efficiency are taken to compute the composite index. Saudi Arabia has the first place followed by Oman, Kuwait, Bahrain, Qatar and UAE.

Energy efficiency is one of the critical aspects, because the hotel sector has significant energy consumption and has an impact on environmental information. Energy efficiency helps reduce operational costs. By reducing excessive energy consumption, hotels can set a good example of responsible and sustainable business practices. It is important to understand the factors that affect energy efficiency. Some of the relevant factors are knowledge sharing, risk perception, and environmental information. This study aims to analyze the effect of knowledge sharing, risk perception, and environmental information factors on energy efficiency in the hospitality industry. The research method used in this study is a quantitative method with a survey approach. The number of samples used in this study were 176 hotel managers in Indonesia. Questionnaires were distributed to respondents using a Likert scale of 1 to 7. Data were analyzed using the Partial Least Square (PLS) method using SmartPLS software. The research results conclude that knowledge sharing, perceived risk, and environmental information have a significant influence on energy efficiency in the hospitality industry. Well-informed consumers tend to be more aware of the impact of their decisions and actions on environmental information, so they are more likely to choose hotels that adopt sustainable and energy efficient practices. In addition, the perception of risk also affects energy efficiency in the hospitality industry. Consumers who perceive risks associated with low sustainability or negative environmental information impacts are less likely to choose the hotel. Environmental information factors also play an important role in energy efficiency. Hotels that have an environment that supports and facilitates sustainable practices have the potential to achieve higher energy efficiency.

This paper presents a new basic clustering algorithm to increase the wireless sensor networks energy and longevity, efficiently. In each period, cluster heads are selected based on the maximum energy and the minimum distance through the nodes. Therefore, the protocol to obtain good performance in terms of longevity is achieved by balancing the energy load among all nodes. The simulation results obtained from the MATLAB software indicate that the suggested approach is effective in prolonging the network longevity.

Wearable and Internet of Medical Things devices increasingly support continuous health monitoring, but cloud-dependent analytics remain constrained by latency, connectivity, privacy, and battery requirements. Tiny machine learning shifts inference toward resource-constrained microcontrollers and edge processors; however, its clinical value depends not only on predictive accuracy but also on energy efficiency, real-time responsiveness, and understandable decision logic. This scoping review mapped the evidence on explainability and energy efficiency in TinyML-based health monitoring. Following the Joanna Briggs Institute approach and PRISMA guidance for scoping reviews, Scopus was searched in the title, abstract, and keyword fields for studies published from 2020 to 2025. The search combined TinyML and embedded or edge artificial intelligence terms with wearable or Internet of Medical Things concepts, health-monitoring applications, and explainability or efficiency terms. The supplied export contained 293 records. After screening, 39 reports underwent eligibility assessment and 36 studies were included. Publication activity accelerated sharply, with 18 studies published in 2025. The evidence covered cardiac monitoring, human activity and fall detection, neurological and affective assessment, respiratory monitoring, signal-quality control, glucose sensing, gait analysis, and smart textiles. Convolutional neural networks and hybrid deep models were common, while deployment platforms ranged from microcontrollers to field-programmable gate arrays, application-specific integrated circuits, and neuromorphic hardware. Quantization, pruning, binary or ternary computation, feature reduction, event-driven processing, and local transmission control were frequently used to reduce resource demand. In contrast, only a small minority of studies explicitly evaluated explainability through model-based feature selection, feature importance, or class activation maps. The field is therefore energy-aware but not yet consistently explanation-aware. Future research should adopt standardized hardware reporting, clinician-centered explanation evaluation, external and longitudinal validation, and multiobjective optimization that jointly considers clinical accuracy, energy, latency, memory, robustness, and interpretability.