Introduction: Many detrimental effects on employees’ health and wellbeing might result from inadequate illumination in the workplace. Headaches and trouble focusing can result from eye strain brought on by inadequate illumination. The purpose of this study was to simulate and optimize workplace illumination in the ceramic industry. Materials and methods: A common Luxmeter ST-1300 was used to measure the illumination in seven workplaces at a height of 100 cm above the floor. DIALux evo version 7.1 software was used to simulate the illumination of workplaces. To optimize the illumination conditions, a numerical experiment design consisting of 16 scenarios was used for each of the workplaces. Four factors were considered for each scenario: luminaire height, number of luminaires, luminous flux, and light loss factor. The Design-Expert program version 13.0.5.0 was applied for developing the scenarios. Finally, by developing quadratic models for each workplace, the optimization process was implemented. Results: Every workplace had illumination levels that were measured to be between 250 and 300 lux. Instead of using compact fluorescent luminaires, LED technology was recommended to maximize the illumination conditions for the workers. Following optimization, 376 lux of illumination were visible at each workstation in every workspace. For the majority of the workspaces, the simulated illumination was expected to have a desirability degree greater than 0.9. The uniformity and illumination of the workplace were significantly impacted by the two factors of luminaire height and luminaire count. Conclusion: The primary outcomes of this optimization were the environmental, political, and socioeconomic ones, including reduced consumption power, high light flux, and environmental compatibility. Nonetheless, the optimization technique applied in this work can be applied to the design of similar situations, such as residential infrastructure.

The existing studies on the association between the built environment and health mainly concentrates on urban areas, while rural communities in China have a huge demand for a healthy built environment, and research in this area remains insufficient. There is a lack of research on the health impact of the built environment in rural communities in China, where there is a significant demand for advancements in the healthy built environment. Exploring the Influence of built environment satisfaction on self-rated health outcomes in New-type village communities has positive significance for advancing research on healthy village community. This paper selects four new-type village communities as typical cases, which are located in the far suburbs of Shanghai, China. A questionnaire survey was conducted on individual villagers, and 223 valid questionnaire samples were obtained. A PLS-SEM model was developed using survey data to examine how built environment satisfaction influences dwellers’ self-rated health while taking into account the mediating function of the perceived social environment. Moreover, multi-group analysis was performed based on age. The results show that built environment satisfaction indirectly influences residents self-rated health through its impact on perceived social environment. The research also discovered that the relationship between built environment satisfaction, social environment satisfaction and self-rated health is not influenced by age as a moderating factor. The research offers new insights for the planning and design of new-type village community from a health perspective.

The area of lake surface water is shrinking rapidly in Central Asia. We explore anthropogenic and climate factors driving this trend in Shalkar Lake, located in the Aral Sea region in Kazakhstan, Central Asia. We employ the Landsat satellite archive to map interannual changes in surface water between 1986 and 2021. The high temporal resolution of our dataset allows us to analyze the water surface data to investigate the time series of surface water change, economic and agricultural activities, and climate drivers like precipitation, evaporation, and air temperature. Toward this end, we utilize dynamic linear models (DLM). Our findings suggest that the shrinking of Shalkar Lake does not exhibit a systemic trend that could be associated with climate factors. Our empirical analysis, adopted to address local conditions, reveals that water reduction in the area is related to human interventions, particularly agricultural activities during the research period. On the other hand, the retrospectively fitted values indicate a semi-regular periodicity despite anthropogenic factors. Our results demonstrate that climate factors still play an essential role and should not be disregarded. Additionally, considering long-term climate projections in environmental impact assessment is crucial. The projected increase in temperatures and the corresponding decline in lake size highlights the need for proactive measures in managing water resources under changing climatic conditions.

This research focuses on the construction of the competency of “Double-qualified” teachers in higher vocational colleges. Through comprehensive literature analysis, in-depth interviews and questionnaire surveys, a competency model covering three dimensions, namely personality charm, teaching literacy and practical skills, has been successfully established. This model provides a scientific basis for higher vocational colleges in teacher selection, performance evaluation and professional training, and particularly emphasizes the importance of teachers’ cultivation of students’ practical abilities and professional qualities in the context of vocational education. The research reveals that these three competency dimensions are interdependent and jointly influence teachers’ educational and teaching achievements as well as students’ career development.

Uncontrolled economic development often leads to land degradation, a decline in ecosystem services, and negative impacts on community welfare. This study employs water yield (WY) modeling as a method for environmental management, aiming to provide a comprehensive understanding of the relationship between Land Use Land Cover (LULC), Land Use Intensity (LUI), and WY to support sustainable natural resource management in the Cisadane Watershed, Indonesia. The objectives include: (1) analyzing changes in WY for 2010, 2015, and 2021; (2) predicting WY for 2030 and 2050 under two scenarios—Business as Usual (BAU) and Protected Forest Area (PFA); (3) assessing the impacts of LULC and climate change on WY; and (4) exploring the relationship between LUI and WY. The Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model calculates actual and predicted WY conditions, while the Coupling Coordination Degree (CCD) analyzes the LULC-WY relationship. Results indicate that the annual WY in 2021 was 215.8 × 108 m³, reflecting a 30.42% increase from 2010. Predictions show an increasing trend in WY under both scenarios for 2030 and 2050 with different magnitudes. Rainfall contributes 88.99% more dominantly to WY than LULC. Additionally, around 50% of districts exhibited unbalanced coordination between LUI and WY in 2010 and 2020. This study reveals the importance of ESs in sustainable watershed management amidst increasing demand for natural resources due to population growth.

The telecommunications services market faces essential challenges in an increasingly flexible and customer-adaptable environment. Research has highlighted that the monopolization of the spectrum by one operator reduces competition and negatively impacts users and the general dynamics of the sector. This article aims to present a proposal to predict the number of users, the level of traffic, and the operators’ income in the telecommunications market using artificial intelligence. Deep Learning (DL) is implemented through a Long-Short Term Memory (LSTM) as a prediction technique. The database used corresponds to the users, revenues, and traffic of 15 network operators obtained from the Communications Regulation Commission of the Republic of Colombia. The ability of LSTMs to handle temporal sequences, long-term dependencies, adaptability to changes, and complex data management makes them an excellent strategy for predicting and forecasting the telecom market. Various works involve LSTM and telecommunications. However, many questions remain in prediction. Various strategies can be proposed, and continued research should focus on providing cognitive engines to address further challenges. MATLAB is used for the design and subsequent implementation. The low Root Mean Squared Error (RMSE) values and the acceptable levels of Mean Absolute Percentage Error (MAPE), especially in an environment characterized by high variability in the number of users, support the conclusion that the implemented model exhibits excellent performance in terms of precision in the prediction process in both open-loop and closed-loop.