In order to protect the safety and health of laborers and to achieve the goal of zero occupational accidents at work, the study takes the top three industries with the highest number of laborers inspections from 2010 to 2019, namely, construction, manufacturing, wholesale, and retail as the research object. Using three major indicators of disability injury including Disabling Frequency Rate, Disabling Severity Rate, and Frequency Severity Indicator as parameters, it applies grey theory to establish a GM (1,1) rolling forecast model. It further predicts the trend of disability injuries from 2020 to 2025. Based on the optimized GM (1,1) rolling model, the results show that there has the highest accuracy rate in the prediction of Disabling Frequency Rate (accuracy is 95.235% in K7) in construction. Disabling Severity Rate and Frequency Severity Indicator are both in wholesale and retail industries (accuracy is 97.044% in K6 and accuracy is 99.906% in K5). Therefore, Disabling Severity Rate has an upward trend, which is due to the common type of traffic accidents in the wholesale and retail industry. The study further proposes that relevant actual disaster cases could be the training materials and strengthen the communication in education to improve workers' safety awareness for occupational disaster prevention.

The global industries are progressively transitioning towards low-carbon development; however, construction remains a significant contributor to energy consumption and carbon emissions. In promoting industrialized construction, the use of prefabricated buildings emerges as a crucial strategy for achieving environmental sustainability. This study initially examines the development and current status of prefabricated concrete component factories in Fujian Province, focusing on regional distribution and production conditions. It also gathers data on carbon emissions, time, and costs to formulate a multi-objective optimization model. Utilizing ant colony algorithms, the model aims to minimize costs while adhering to low-carbon principles and fostering the sustainable development of prefabricated buildings. The optimization results for slabs indicate a minimum production cost of RMB 5.7023 million, with associated carbon emissions of 1154.85 tons. Notably, a cost variation of RMB 10,000 can lead to a maximum difference of 50 tons in carbon emissions, emphasizing the importance of cost minimization as a primary objective. In comparison to conventional production, the optimization of collaborative production demonstrates reductions in both costs and carbon emissions. Furthermore, when focusing on normal and rush modes, costs can be reduced by over 20%, resulting in a potential decrease of up to 50% in carbon emissions. Consequently, effectively mitigating carbon emissions in component production is essential for enhancing the sustainability of the construction industry.

Access to energy is among the key pillars to socioeconomic and improved life style. The East African Community (EAC) countries, also members of sub-Saharan Africa, are among countries with enough energy resources but still struggling with low electricity access, and the lower proportion of citizens with electricity access challenges such as expensive tariff, frequent blackouts, and unreliable service still persists. Diesel technology is among the easy and fast installation technologies for a location with an urgent need of electricity while solar is a clean technology with free fuel. Considering the diversity of electricity tariffs, cost of diesel fuel, and suitability to solar energy exploitation in EAC, this paper intends to provide a technoeconomic analysis for reliable, affordable, and sustainable energy system in the region. A daily load of 94.44 kWh recorded from averaging electricity bills of a luxury house in Kigali, Rwanda, is used as research object, and HOMER simulations are carried on considering the level of such daily load being supplied by either (a) diesel generator, (b) technology, (c) storage, or (d) system in each member country of the EAC. The results show that (a) solar energy is a feasible and applicable technology for energy generation for the whole six EAC countries; (b) for South Sudan, if it is a standalone system, the diesel technology is less costly than solar technology; however, solar energy can still be recommended to be adopted as it has no gas emissions; (c) except South Sudan, storage technology is found to be more affordable and cleaner than any technology including diesel; and (d) the option of connecting storage to the grid is found more economical for locations where grid interaction is possible because their levelized electricity costs (LCOE) are lower than the real electricity tariffs currently in use within each of the six EAC countries. The solar energy system with battery storage (both off-grid and grid connected) proposed in this research can lead to an efficient increase of national energy resource exploitation in the EAC countries, resulting in reliable, affordable, and sustainable energy access to all the citizenry of the EAC.

With the advancement of building digitalization and safety management, Working-at-Heights presents challenges due to its complex scenarios and dispersed regulations, making it difficult for on-site personnel to quickly and accurately query and assess compliance. While general-purpose large language models possess text capabilities, they lack professional knowledge adaptation, are not updated in a timely manner, and provide answers without sufficient evidence, thus failing to directly meet the safety and compliance requirements of Working-at-Heights. This paper deploys a localized retrieval-augmented generation (RAG) system based on RAGFlow and Docker. Addressing the safety consultation needs of Working-at-Heights, it transforms regulations and standards into a vector knowledge base. The system integrates keyword and semantic retrieval for precise positioning and aims to reduce hallucinations through source tracing and prompt constraints. Simultaneously, it connects to online retrieval to supplement the latest regulations, achieving a dual-track question–answering (Q&A) mechanism of “local priority, online supplementation.” A typical scenario test set of 100 questions was constructed to evaluate three system configurations across multiple dimensions, including accuracy, traceability, timeliness, and latency. The results show that faithfulness scores increased from 0.45 for the pure large model to 0.91 for local RAG and 0.97 for full-functionality integration, while context recall improved from 0.78 to 0.93 between the latter two configurations. However, response latency also increased from 2.1 s to 4.5 s and 6.8 s, respectively. These findings indicate that local RAG provides more reliable and traceable responses to basic compliance inquiries, whereas full-functionality integration offers superior performance in dynamic information supplementation and handling of unsafe requests. Overall, the integrated approach demonstrates potential for supporting on-site safety compliance consultation and risk assessment, providing a foundation for intelligent applications in construction safety management.

The global industries are gradually moving towards the development of low-carbon, however, construction industry has significant energy consumption and carbon emissions. To making the promotion of industrialized construction with low-carbon and energy-efficient, prefabricated buildings are one of important ways to achieve environmental sustainability. This study first investigated the development and status of prefabricated concrete component factories in Fujian Province about regional distribution and production conditions and collecting data of carbon emissions, time and costs to create a multi-objective optimization model for intelligent and collaborative production. All while keeping costs as low as possible, using ant colony algorithms meets low-carbon and promotes sustainable development of prefabricated buildings. With the number of iterations, the overall cost shows a downward during the optimization process. According to optimization modes, the result of slabs indicates the minimum production cost (5.7023 million with carbon emissions 1154.85 tons), and a cost difference of 10000 can result in a maximum carbon emissions difference of 50 tons while minimizing costs to be the primary goal. Compared with general production, the optimization of collaborative production reduces both costs and carbon emissions. Meanwhile, focused on normal and rush mod, the cost reduced more than 20% can be down to 50% on carbon emissions. Therefore, effectively reducing carbon emissions in the component production will contribute to the sustainability of the construction industry.

Thermal printing is a prevalent method due to its advantages of rapid output, cost effectiveness, and ease of use. However, the quality of thermal printing is influenced by the printing speed, the temperature, and the concentration and characteristics of the materials. This research employs a BP neural network to forecast print quality, utilizing two activation functions. The findings indicate that a dual-layer hidden configuration utilizing the GeLU activation function yields a lower root-mean-square error (RMSE). The optimal configuration identified consists of six neurons in the first hidden layer and three neurons in the second hidden layer. To enhance the predictive performance, a PSO algorithm was integrated with the PSO-BP model to refine the parameter selection, which included ambient temperature, printing speed, and printing concentration, with iterative training and validation conducted via the gradient descent algorithm. The PSO-BP network achieved an MAE of 0.1108, an RMSE of 0.145, an MSE of 0.021, and an R² value of 0.9916 in predicting print quality. These results substantiate the stability and reliability of the neural network model developed with the PSO algorithm. Further validation with ten sets of test samples demonstrated that the model attained an average absolute error of 2.77% in print quality predictions, indicating robust generalization capabilities and precise forecasting.