The effectiveness and efficiency of e-learning system in industry significantly depend on users’ acceptance and adoption. This is specifically determined by external and internal factors represented by subjective norms (SN) and experience (XP), both believed to affect users’ perceived usefulness (PU) and perceived ease of use (PEOU). Users’ acceptance of e-learning system is influenced by the immensity of region, often hampered by inadequate infrastructure support. Therefore, this study aimed to investigate behavioral intention to use e-learning in the Indonesian insurance industry by applying Technology Acceptance Model (TAM). To achieve this objective, Jabotabek and Non-Jabotabek regions were used as moderating variables in all related hypotheses. An online survey was conducted to obtain data from 800 respondents who were Indonesian insurance industry employees. Subsequently, Structural Equation Model (SEM) was used to evaluate the hypotheses, and Multi-Group Analysis (MGA) to examine the role of region. The results showed that out of the seven hypotheses tested, only one was rejected. Furthermore, XP had no significant effect on PU, and the most significant correlation was found between PEOU and PU. In each relationship path model, the role of region (Jabodetabek and Non Jabodetabek) had no significant differences. These results were expected to provide valuable insights into the components of e-learning acceptability for the development of a user-friendly system in the insurance industry.

Studies show that Fourth Industrial Revolution (4IR) technologies can enhance compliance with COVID-19 guidelines within the parties in the construction industry in the future and mitigate job loss. It implies that mitigating job loss improves the achievement of Sustainable Development Goal 1 (SDG 1) (eliminate poverty). There is a paucity of literature concerning 4IR technologies application and COVID-19 impact on South Africa’s construction industry. Thus, this paper investigates the impacts of the pandemic on the sector and the roles of digital technologies in mitigating job loss in future pandemics. Data were collected via virtual semi-structured interviews. The participants proffered unexplored insights into the impact of the pandemic on the sector and the possible roles that 4IR technology can play in mitigating the spread of the virus within the sector. Findings show that the sector was hit, especially the low-income earners, threatens to achieve Goal 1, despite government institutions’ intervention, such as economic support programmes, health and safety guidelines awareness, and medical facilities. Findings group the emerged impacts into health and safety, environmental, economic, productivity, social, and legal and insurance issues in South Africa. The study shows that technology can be advantageous to improving achieving Goal 1 in a pandemic era due to limited job loss.

This project is carried out to assess the remediation effect on soil contaminated by molybdenum (Mo), one of heavy metals, through the use of an energy crop, sunflowers. This project explores the integration of phytohormones and chelates in the phytoremediation of soils contaminated by heavy metals, and further assesses the operational measures of remedying heavy-metal contaminated soil with sunflowers, in addition to the related environmental factors. Then the project explores phytohormones and heavy metals on the growth scenario explants (explants morphological analysis) through the experiment. The results indicate that GA₃ can increase the growth rate of the plants. The average incremental growth of the heavy-metal-added-only group is 21.0 cm; of the GA₃-added group it is 21.9 cm; of the EDDS-added group, it is 20.3 cm; of the GA₃+ EDDS-added group, it is 21.7 cm. Compared with the conventional methods of phytoremediation, these integrated measures can actually spur the growth of plants.

This project analyzes the evolution of the manufacturing sector in Portugal from 2009 to 2021, focusing on the variations in the number of active companies across various subcategories, such as food, textiles, and metal product industries. The goal of this analysis is to understand the dynamics of growth and contraction within each sector, providing insights for companies to adjust their market and operational strategies. Key objectives include analyzing the overall evolution in the number of companies, identifying subcategories with notable changes, and providing a comprehensive analysis of observed trends and patterns. The study is based on data from PORDATA 2024, and the research employs temporal trend analysis, linear and quadratic regression, and the Pareto representation to identify patterns of growth and decline. By comparing annual data, the project uncovers periods of growth and decline, allowing for a deeper understanding of the sector’s dynamics. The findings also highlight variations in periods of economic crises and during the Covid-19 pandemic, and recommendations for action are presented to support businesses resilience and continuity. These results are valuable for companies within the manufacturing sectors analyzed and policy makers, guiding strategic decisions to navigate the complexities of the market dynamics and to ensuring long-term organizational sustainable success.

The proportion of national logistics costs to Gross Domestic Product (NLC/GDP) serve as a valuable indicator for estimating a country’s overall macro-level logistics costs. In some developing nations, policies aimed at reducing the NLC/GDP ratio have been elevated to the national agenda. Nevertheless, there is a paucity of research examining the variables that can determine this ratio. The purpose of this paper is to offer a scientific approach for investigating the primary determinants of the NLC/GDP and to advice policy for the reduction of macro-level logistics costs. This paper presents a systematic framework for identifying the essential criteria for lowering the NLC/GDP score and employs co-integration analysis and error correction models to evaluate the impact of industrial structure, logistics commodity value, and logistics supply scale on NLC/GDP using time series data from 1991 to 2022 in China. The findings suggest that the industrial structure is the primary factor influencing logistics demand and a significant determinant of the value of NLC/GDP. Whether assessing long-term or short-term effects, the industrial structure has a substantial impact on NLC/GDP compared to logistics supply scale and logistics commodity value. The research offers two policy implications: firstly, the goals of reducing NLC/GDP and boosting the logistics industry’s GDP are inherently incompatible; it is not feasible to simultaneously enhance the logistics industry’s GDP and decrease the macro logistics cost. Secondly, if China aims to lower its macro-level logistics costs, it must make corresponding adjustments to its industrial structure.

Heat recovery is one of the measures proposed for the appropriate use of ammonia in tropical countries. This article analyzes a heat recovery system installed in an industrial refrigeration plant. Based on comparative readings of operating parameters of the installation, determined the effectiveness of the heat exchange, the increase in the efficiency of the refrigeration system, as well as the fuel saved by heating water in the industry. The results obtained reported that the thermal design based on heat exchange in annular spaces allows a significant saving of resources and a high rate of thermal utilization.