The increased awareness of the environmental effects of petroleum based plastics has stimulated the coffee price emergence of biodegradable polymers such as polylactic acid (PLA). In a bid to increase the sustainability of PLA agricultural residues of animal feeds (corn stover, rice straw, and soybean hulls) have been explored and examined as reinforcing fillers to PLA composites. The consideration of such applications is suitable to the goals of the circular economy as it recycles low-value agricultural products. The current review critically evaluates lately carried out life cycle assessment (LCA) studies on PLA composites that have implemented such waste fillers with the full focus being on their environmental performance as well as methodological consistency. The review shows that these fillers have a potential of reducing the amount of greenhouse emission, energy usage, and other environmental effects, compared to pure PLA. However, unevenness in LCA methodology, especially in functional units, the system boundaries, and impacts categories obstructs direct LCA comparisons. The 1997 State of the Market report also has limited options of feedstocks and the lack of appraisals in the socio-economic front, so the overall sustainability analysis is restricted. Some of the remaining limitations that can be critical are to have generalized LCA frameworks, extended exploration of waste-based fillers, as well as combination of techno-economic analysis and social impact. Future inquiries ought to devise design considerations that would optimize both the functional characteristics and the performance of the environment and improve the reliability of sustainability measures. This review is evidence to the potential of agricultural waste reinforced PLA composites in the progress towards environmentally friendly materials and the need of integrative evaluation in the sustainable maturation of bioplastics.

Background: The hotel industry is labor-intensive. Both technical and behavioral aspects of quality are considered to ensure service quality and customer satisfaction among the internal and external customers as a whole, creating a competitive advantage. Significance: Recruiting and selecting the right people is paramount to the success of the hospitality industry in the sense that the best delivery will be enhanced if proper procedures are used and the right people are selected who can handle their tasks to the best satisfaction of the customer. Method: The goal of the research was to explore the recruiting and selection practices/methods used in the hotel industry, as well as their employability. The study aims to explore the differences in the mix of recruitment and selection methods implemented in 4- and 5-star and different category hotels. As an enterprise, HRD comprises change, learning, and performance. Results: Based on the findings, it is imperative to invest in human resources as a capital asset to boost staff entities in terms of knowledge and capabilities, thereby contributing to better service quality and enhanced customer satisfaction. This would help fulfil the organizations’ objectives. Conclusion: The study concludes that the selected candidates are being analyzed properly and effectively. It is very important to note that the results of this study cannot be generalized as it deals with a restricted clientele, and this could only add on variables and instances to form a common standpoint for the other hotel managers.

The destructive geohazard of landslides produces significant economic and environmental damages and social effects. State-of-the-art advances in landslide detection and monitoring are made possible through the integration of increased Earth Observation (EO) technologies and Deep Learning (DL) methods with traditional mapping methods. This assessment examines the EO and DL union for landslide detection by summarizing knowledge from more than 500 scholarly works. The research included examinations of studies that combined satellite remote sensing information, including Synthetic Aperture Radar (SAR) and multispectral imaging, with up-to-date Deep Learning models, particularly Convolutional Neural Networks (CNNs) and their U-Net versions. The research categorizes the examined studies into groups based on their methodological development, spatial extent, and validation techniques. Real-time EO data monitoring capabilities become more extensive through their use, but DL models perform automated feature recognition, which enhances accuracy in detection tasks. The research faces three critical problems: the deficiency of training data quantity for building stable models, the need to improve understanding of AI's predictions, and its capacity to function across diverse geographical landscapes. We introduce a combined approach that uses multi-source EO data alongside DL models incorporating physical laws to improve the evaluation and transferability between different platforms. Incorporating explainable AI (XAI) technology and active learning methods reduces the uninterpretable aspects of deep learning models, thereby improving the trustworthiness of automated landslide maps. The review highlights the need for a common agreement on datasets, benchmark standards, and interdisciplinary team efforts to advance the research topic. Research efforts in the future must combine semi-supervised learning approaches with synthetic data creation and real-time hazardous event predictions to optimise EO-DL framework deployments regarding landslide danger management. This study integrates EO and AI analysis methods to develop future landslide surveillance systems that aid in reducing disasters amid the current acceleration of climate change.

This study comprehensively evaluates the system performance by considering the thermodynamic and exergy analysis of hydrogen production by the water electrolysis method. Energy inputs, hydrogen and oxygen production capacities, exergy balance, and losses of the electrolyzer system were examined in detail. In the study, most of the energy losses are due to heat losses and electrochemical conversion processes. It has also been observed that increased electrical input increases the production of hydrogen and oxygen, but after a certain point, the rate of efficiency increase slows down. According to the exergy analysis, it was determined that the largest energy input of the system was electricity, hydrogen stood out as the main product, and oxygen and exergy losses were important factors affecting the system performance. The results, in line with other studies in the literature, show that the integration of advanced materials, low-resistance electrodes, heat recovery systems, and renewable energy is critical to increasing the efficiency of electrolyzer systems and minimizing energy losses. The modeling results reveal that machine learning programs have significant potential to achieve high accuracy in electrolysis performance estimation and process view. This study aims to contribute to the production of growth generation technologies and will shed light on global and technological regional decision-making for sustainable energy policies as it expands.