Recent advances in large language models (LLMs) have led to substantial progress in domain-specific applications, particularly within the legal domain. However, general-purpose models such as GPT-4 often struggle with specialized subdomains that require precise legal knowledge, complex reasoning, and contextual sensitivity. To address these limitations, we present LabourLawLLM, a legal large language model tailored to Chinese labor law. We also introduce LabourLawBench, a comprehensive benchmark covering diverse labor-law tasks, including legal provision citation, knowledge-based question answering, case classification, compensation computation, named entity recognition, and legal case analysis. Our evaluation framework combines objective metrics (e.g., ROUGE-L, accuracy, F1, and soft-F1) with subjective assessment based on GPT-4 scoring. Experiments show that LabourLawLLM consistently outperforms general-purpose and existing legal-specific LLMs across task categories. Beyond labor law, our methodology provides a scalable approach for building specialized LLMs in other legal subfields, improving accuracy, reliability, and societal value of legal AI applications.
We consider the problem of optimal annuitization with labour income, where an agent aims to maximize utility from consumption and labour income under age-dependent force of mortality. Using a dynamic programming approach, we derive closed-form solutions for the value function and the optimal consumption, portfolio, and labor supply strategies. Our results show that before retirement, investment behavior increases with wealth until a threshold set by labor supply. After retirement, agents tend to consume a larger portion of their wealth. Two main factors influence optimal annuitization decisions as people get older. First, the agent's perspective (demand side); the agent's personal discount rate rises with age, reducing their desire to annuitize. Second, the insurer's perspective (supply side); insurers offer higher payout rates (mortality credits). Our model demonstrates that beyond a certain age, sharply declining survival probabilities make annuitization substantially optimal, as the powerful incentive of mortality credits outweighs the agent's high personal discount rate. Finally, post-retirement labor income serves as a direct substitute for annuitization by providing an alternative stable income source. It enhances the financial security of retirees.
Although Digital Twin is actively deployed in manufacturing, its human-centric counterpart - Human Digital Twin (HDT) is understudied, especially in job-shop production with high task variability and manual labor. HDT applications like ergonomic posture monitoring, fatigue prediction and health-based task assignment offer benefits to industries in emerging economies. However, poor digital maturity, lack of awareness and doubts about use-case applicability hinder adoption. This study provides a strategic prioritization framework to aid human-centric digital evolution in labor-intensive industries for guiding the selection of HDT applications delivering the highest value with the lowest implementation threshold. An integrated Fuzzy AHP-TOPSIS approach evaluates the use-cases based on criteria like implementation cost, technological maturity, scalability. These criteria and use-cases were identified based on input from a five-member expert panel and verified for consistency (CR < 0.1). Analysis shows posture monitoring and fatigue prediction as most influential and practicable, especially in semi-digital environments. Strengths include compliance with Industry 5.0 principles incorporating technology and human factors. Lack of field validation and subjective knowledge pose drawbacks. Future work should include simulation-based validation and pilot tests on real job-shop settings. Ultimately, the research offers a decision-support system helping industries balance innovativeness and practicability in early stage of HDT adoption.
The farming–pastoral ecotone in northern China is an ecologically vulnerable area with low-quality arable land, and cash crops are an important economic source for local farmers. Although local governments have introduced supportive policies, there are still several factors that hinder the implementation of the policies: there is a lack of sufficient research on the distribution of specialty crops, and the driving factors for agricultural planting structure adjustment are not yet clear. In this study, the specialty cash crop of the daylily planting industry in Yunzhou District, in the Farming–Pastoral Ecotone in northern China, was selected as the research object. Field surveys were conducted to collect sample points and village-level survey data, which were further combined with Sentinel-1 and Sentinel-2 data, and vegetation indices. Support vector machine (SVM) and random forest (RF) classifiers were utilized to identify daylilies and compare the accuracy using different combinations of input data. Furthermore, the classification results were counted by village, and spatial autocorrelation was used to analyze the spatial distribution pattern of daylilies. Finally, in conjunction with the village-level survey data, Spearman correlation analysis, multiple regression trees (MRT), and random forests were employed to explore the driving factors of daylily cultivation. The results indicate that using an RF classification tree of 300 resulted in the optimal method, as it achieved the highest accuracy for crop classification. The overall accuracy and daylily classification accuracy were 94.6% and 94.75%, respectively. Daylily distributions were mainly concentrated near the Sanggan River, urban areas, and the tourism industry. The distribution area of daylilies in each village was concentrated in 13.4–38.8 hm2. Spatial clustering showed more aggregation of low–low and high–high types. Labor force and daylily yield were identified as the most significant influencing factors. Further analysis of the different regions revealed the importance of industry support policies and technical training. This study provides data to support the distribution of specialty crops in Yunzhou District and a technical basis for adjusting agricultural planting structures.
Introduction: Limited information is available regarding effect of vaccination on protection against Covid-19 infections and their severity as well. Objectives: In the present study, we assessed the effect of Covid-19 vaccination on incidences and severity of break through Covid-19 infections. Method: This retrospective study was conducted at a tertiary care center in Northern India during one calendar year, 1st August 2021 to 31st July 2022. The study population included Health-care workers (HCWs) who were treated for Covid 19 infection and had already received at least 1 dose of Covishield TM (AZD1222) Covid-19 vaccine. Results: Out of 1868 health care workers enrolled for the study, 513 contracted Covid-19 infections. Amongst infected HCWs, number of single and double doses of CovishieldTM (AZD1222) recipients were 112 and 401 respectively. Out of the 513 covid positive HCWs, 459 (89.4%) had mild disease, whereas 54 (10.6%) had moderate disease. None of the HCWs developed severe disease and no mortality was noted in either group. Conclusion: In this study, we found that immunization with two doses of CovishieldTM (AZD1222) vaccine was associated with decline in number of cases with moderate or severe Covid-19. Moreover, immunization with even single dose of CovishieldTM (AZD1222) vaccine prevented development of severe disease. Henceforth, it is concluded that although, immunization with CovishieldTM (AZD1222) could not protect all recipients from SARS-Cov-2 infection, it did prevent the progress of disease to severe grades.
Social media analytics is the backbone of successful marketing. It allows you to accurately find and understand the target audience of the company. However, this requires analyzing a huge amount of publicly available data. This is where software tools come to the rescue, allowing such analysis to be carried out in real time. Nevertheless, there are a large number of software products on the market, each of which has a different set of functions - from complex products to highly specialized ones. A wide or vice versa narrow range of functions makes it difficult to choose a specific software solution for a specific task. For this reason, the main purpose of the article is to make a comparative analysis of the functions of the most popular social media analytics software tools designed to identify and analyze the target audience.
Land cover classification faces persistent challenges with inter-investigator variability and salt-and-pepper noise. Although cloud platforms such as Google Earth Engine have made land cover classification more accessible, these issues persist, particularly when multiple investigators contribute to the process. This study developed a robust classification approach that integrates unsupervised clustering of investigator maps with a Bayesian inference framework using Dirichlet distributions. In this study, 44 investigators collected stratified reference samples across four land cover classes using point-based visual interpretation in Saitama City, Japan. We trained three different classifiers, Random Forests (RF), Support Vector Machines (SVM), and Single hidden layer Feed-forward Neural Networks (SFNN), and enhanced the system by implementing unsupervised clustering (k-Means or k-Medoids) to group reliable maps based on entropy characteristics. The Bayesian framework, employing Dirichlet distributions for both likelihood and prior distributions, enables sequential probability updates while preserving probabilistic class assignments. The Bayesian inference from the SVM classification maps achieved the highest mean overall accuracy of 0.857 for Monte Carlo sampling from the referenced JAXA land use land cover map, improving upon the non-Bayesian SVM map (0.855, p < 0.001). Analysis revealed a strong correlation (r=0.710) between investigators' labeling quality and classification accuracy, suggesting that selecting high-quality investigator maps improves the robustness of fusion. The Interspersion and Juxtaposition Index (IJI) showed that fused maps from SVM-based maps selected by k-Means reduced salt-and-pepper noise (IJI: 56.652) compared to baseline maps (IJI: 69.867). Our approach demonstrates an effective approach for combining multiple land cover classifications.
This paper presents quasi-experimental research examining the effect of both local and state anti-discrimination laws on sexual orientation on the labor supply and wages of lesbian, gay, and bisexual (LGB) workers. To do so, we use the American Community Survey data on household composition to infer sexual orientation and combine this with a unique panel dataset on state and local anti-discrimination laws. Leveraging variation in law implementation across localities over time and between same-sex and different-sex couples, we find that anti-discrimination laws significantly narrow gaps in labor force participation and employment for men in same-sex couples relative to men in different-sex couples, and also increase their percentile rank in the wage distribution. Our analysis reveals mostly null effects for female same-sex couples; however, in metropolitan areas these laws significantly reduce their employment compared to women in different-sex couples. One explanation for the reduced labor supply is that female same-sex couples begin to have more children in response to the laws. Finally, we present evidence that state anti-discrimination laws significantly and persistently increased support for same-sex marriage. This research shows that anti-discrimination laws can be an effective policy tool for reducing labor market inequalities across sexual orientation and improving sentiment toward LGB Americans.
Abstract Making apiaries more effective is only one aspect of reviving the beekeeping industry. Additionally, the beekeeping industry is generating employment both in rural and urban locations. This study’s goal was to identify the technical levels of honey production in Ethiopia’s Horo Guduru Wollega zone and their contributing factors. To accomplish the aforementioned objective, structured questionnaire data collected from 396 households were used. Stochastic production frontier estimator shows that the number of hives, the amount of work put into producing honey, and the area of the land all significantly influenced the amount of honey produced. In the mean technical of both traditional and modern hives, there were 56.68% and 73.93%, respectively. This demonstrates how technically more efficient farmers who use contemporary hives to make honey are. According to mean technical efficiency, both beekeepers were, however, operating below the production frontier. Household sex, credit utilization, extension services, training, beekeeping experience, and family size were significant technical efficiency variables for honey producers. The study suggests policies to address technical inefficiencies by increasing the number of hives, extending the best performers’ experience by increasing the frequency of extension contacts on honey production, facilitating and expanding credit service in the study area, making bee forage access simple, and increasing forest coverage on land area in line with current policy of Ethiopia. Additionally, since farmers in the study area spend their time guarding the honey from damage by ants, labor that utilizes technology must be made available.
Akifumi Kira, Nobuo Terajima, Yasuhiko Watanabe
et al.
The logistics industry in Japan is facing a severe shortage of labor. Therefore, there is an increasing need for joint transportation allowing large amounts of cargo to be transported using fewer trucks. In recent years, the use of artificial intelligence and other new technologies has gained wide attention for improving matching efficiency. However, it is difficult to develop a system that can instantly respond to requests because browsing through enormous combinations of two transport lanes is time consuming. In this study, we focus on a form of joint transportation called triangular transportation and enumerate the combinations with high cooperation effects. The proposed algorithm makes good use of hidden inequalities, such as the distance axiom, to narrow down the search range without sacrificing accuracy. Numerical experiments show that the proposed algorithm is thousands of times faster than simple brute force. With this technology as the core engine, we developed a joint transportation matching system. The system has already been in use by over 150 companies as of October 2022, and was featured in a collection of logistics digital transformation cases published by Japan's Ministry of Land, Infrastructure, Transport and Tourism.
Objectives: This study aimed to detect the relationship between body mass index (BMI), selective voluntary motor control (SVMC), and functional independence in children with spastic diplegic cerebral palsy (CP) with levels II and III of gross motor function classification system.
Methods: A total of eighty-four children with spastic diplegic CP, aged 6-9 years with a mean age of 7.09±0.68 participated in this correlational study. BMI, selective control assessment of the lower extremity (SCALE), and pediatric functional independence measure (WEE FIM) were used for the assessment of BMI, SVMC, and functional independence, respectively.
Results: The results demonstrated the presence of a positive strong significant correlation between SCALE and WEE FIM, a negative strong significant correlation between WEE FIM and BMI, and a negative moderate significant correlation between BMI and SCALE.
Discussion: Functional independence is significantly correlated with BMI and SVMC in children with spastic diplegic CP. This study provides original evidence that BMI and SVMC are major factors that influence functional independence in these children. So, they are highly recommended to be part of the evaluation of their functional independence in clinical settings and research.
Medicine, Vocational rehabilitation. Employment of people with disabilities
Umul Hidayati, Sumarni Sumarni, Suprapto Suprapto
et al.
The purpose of this study was to investigate how public sector workers in Indonesia use e-learning systems and how they can benefit from them. The researchers analyzed five variables that contribute to the effectiveness of e-learning: system reliability, information sharing, service quality, user satisfaction, and net benefit. Structural Equation Model analysis was used to analyze the data collected from 203 respondents who were public sector employees in Indonesia. The findings of this study revealed that information sharing, and service quality significantly impact user satisfaction, which in turn has a significant effect on net benefits. Additionally, system reliability was found to significantly impact user satisfaction. This theoretical implication suggests that there is a direct relationship between the level of information sharing and service quality provided by a public sector organization and the level of user satisfaction experienced by its usage of e-learning. The practical implication of the finding is that public sector organizations must prioritize the reliability of their e-learning systems. This includes investing in regular maintenance and updates, ensuring proper testing and quality control procedures, and addressing any issues or downtime quickly and effectively.
Social Sciences, Management. Industrial management
Cris Kuntadi, Winda Widyanty, R. Nurhidajat
et al.
AbstractThis study aims to analyze the influence of transformational leadership on the employee performance of National Transportation Safety Committee (NTSC) with the mediating role of work engagement and work motivation. The study employed a census approach utilizing structural equation modeling (SEM) to analyze the data collected from all employees of the NTSC in Indonesia. Primary data were gathered through a comprehensive questionnaire, which was administered to the entire population of 107 NTSC employees, ensuring complete coverage and representation. The results of this study indicate that transformational leadership has a positive and significant effect on work engagement, work motivation, and employee performance. The results show that work engagement and work motivation have a positive and significant effect on employee performance. While the mediation test results show that work engagement and work motivation have an indirect effect on employee performance. The current study provides fresh insights and validates extant knowledge on transformational leadership, work engagement, work motivation and employee performance within the NTSC. This study suggests NTSC management to communicate the company’s vision and mission to employees with openness and realize improvements in operational standards so that the performance created in the organization provides value that can be understood appropriately. In addition, management also needs to create policies that are in line with the NTSC vision and mission.
Shengjie Hu, Zhenlei Yang, Sergio Andres Galindo Torres
et al.
Urban land growth presents a major sustainability challenge, yet its growth patterns and dynamics remain unclear. We quantified urban land evolution by analyzing its statistical distribution in 14 regions and countries over 29 years. The results show a converging temporal trend in urban land expansion from sub-country to global scales, characterized by a coherent shift of urban area distributions from initial power law to exponential distributions, with the consequences of reduced system stability and resilience, and increased exposure of urban populations to extreme heat and air pollution. These changes are attributed to the increased influence from external economies of scale associated with globalization and are predicted to intensify in the future. The findings will advance urban science and direct current land urbanization practices toward sustainable development, especially in developing regions and medium-size cities.
By understanding the economics of agribusiness, an important economic sector for developing countries, this article explores possibilities for a new development paradigm based on areas of opportunities created for local entrepreneurs. Based on a detailed study of the soybean market chain in Brazil, this paper illustrates that the current neoliberal economic approach has resulted in a business which is dependent on foreign multinationals. While foreign companies hold 60% of the soybean market share, Brazilian groups hold only 40% of the entire business, with the domestic market share concentrated in land (13.3%) and labor (14.3%). But the expansion of foreign investments in agribusiness in the country offers opportunities occupied by Brazilian companies, characterizing a situation of associated dependent development. Currently, 12.4% of the share held by Brazilian companies belongs to capital and technology intensive segments such as seed production (2.4%), fertilizers (4.8%), agrochemicals (0.6%), machinery (0.3%), and agro-industry trade (4.3%). The increase in the participation of Brazilian groups in agribusiness requires agricultural policies that can be inspired by a new development paradigm. Opportunities created by foreign investments can be used by domestic groups to increase their share in agro-industrial sectors. Lessons from the Brazilian case can help other developing countries to explore areas of opportunities for domestic investments in dynamic economic sectors such as agribusiness.
Pratyay Chattopadhyay, Homak P. Patel, Viral Parmar
The Internet of Things (IoT) offers enormous potential to carry out tasks efficiently in the modern era of technology. IoT devices, as their name suggests, include sensors and other tools that are directly connected to the internet. Such a machine can be taught to perform a wide range of tasks in the fields of agriculture, science, and military, among many others. The proposed approach in this study addresses how the agriculture industry could benefit from the Internet of Things. An IoT can be used for a variety of purposes, including farm security, ensuring that agricultural products are produced, transported, and sold properly, and much more. The proposed model, which is covered in more detail below, discusses the use of drones, autonomous tractors, and defense systems that are essential for creating intelligent agricultural land. Through adequate AI monitoring and control, these gadgets can be made to operate well and serve a variety of purposes. After the labor in the fields is finished, a farmer may practically unwind thanks to the installation of adequate sensors in the aerial machine, ground machine, and defensive mechanism.
With the availability of various economical sensors and the implementation of Internet-of-Things (IoT), agriculture industry is moving towards more precise, data centric and smarter than ever. Almost every industrial domain has been resigned, including smart agriculture, with the rapid emergence of IoT based technologies with the aid of economical sensor technology. The use of agricultural robots in agriculture field is increasing to cover the necessity ever increasing population with static piece of land. In this work, a multi-utility agricultural IoT based robot has been proposed for performing various agricultural activities. It provides control of the agricultural activities remotely through VLAN and also through cloud network (be it Server based or Serverless), i.e. a hybrid control model. The proposed method requires minimal human labor to perform various farming activities. The end user can control the machine in the field and all agricultural activities can be performed remotely from anywhere with the help of Android application. Three modes of operation are supported, manual, semi-automatic and automated. The proposed system is to be controlled and trained for few days manually so that it gains accuracy in the path being followed to perform the task. Proposed system has solar powered system as the primary source of energy and Lithium-ion polymer battery for battery backup support. Anti-theft mechanism is provided with the help of GSM module.