Hasil untuk "Labor systems"

Anil Rupasingha, S. Goetz, D. Freshwater

Sid Terason, Chaithanaskorn Phawitpiriyakliti

This study investigates how industrial executives in Thailand's manufacturing sector navigate artificial intelligence integration into leadership practices. As AI systems increasingly participate in organizational decision-making, questions arise about how leaders experience shared authority with algorithmic agents and how cultural context shapes that experience. Drawing on semi-structured interviews with 25 senior executives from medium- to large-scale manufacturing firms, this study employed thematic analysis within a constructivist paradigm to explore perceptions, strategies, and ethical considerations surrounding AI adoption. Four themes emerged: technological augmentation of human labor, transformed executive roles in AI-mediated decision-making, ethical and workforce challenges, and uneven readiness for technological transformation. The findings reveal a fundamental paradox: executives increasingly depend on AI for operational decisions while simultaneously resisting delegation of authority to machines. This tension reflects Thai cultural values including bun khun (reciprocal obligation) and kreng jai (reluctance to impose or cause discomfort). We propose pragmatic posthumanism as a concept capturing this liminal condition—where leaders functionally share agency with AI systems yet remain institutionally and culturally positioned as sole decision-makers. The hybrid-intelligence leadership framework emerges from these findings, integrating cognitive integration, ethical governance, and workforce adaptation as culturally embedded domains. This study contributes to leadership theory by demonstrating how AI-driven transformation unfolds differently across cultural contexts, challenges Western-centric assumptions about human-machine collaboration, and offers practical guidance for executives, policymakers, and educators navigating Industry 4.0 in Southeast Asia.

T. K. Hopkins, I. Wallerstein

I. R. Begishev, K. L. Tomashevski

Three books on the legal regulation of artificial intelligence and the digital transformation of labor were analyzed. The contributions of Russian and Belarussian scholars working in an emerging interdisciplinary field at the intersection of jurisprudence, social sciences, and technology were summarized and assessed. The first publication, I.A. Filipova’s textbook, includes lectures, seminar plans, recommended readings, practical assignments, etc. The lecture content using a problem-based approach to cover the legal regulation of artificial intelligence was examined. The author not only discusses and briefly describes the seven topics of the lecture course but also raises some controversial issues associated with the development and legal regulation of artificial intelligence systems and technologies. The second publication, I.A. Filipova’s monograph, is dedicated to the challenges of digitalization and its impact on labor relations and labor law. The third publication, written by a team of Belarussian researchers, explores the global experience in artificial intelligence regulation and outlines the strategies for artificial intelligence governance in the Republic of Belarus. Collectively, all three books considered here hold significant value for comparative legal studies and the coordinated development of legal regulation of artificial intelligence within the Union State of Russia and Belarus.

Constantinos Challoumis

Economic systems face critical challenges, including widening income inequality, unemployment driven by automation, mounting public debt, and environmental degradation. This study introduces Economocracy as a transformative framework aimed at addressing these systemic issues by integrating democratic principles into economic decision-making to achieve social equity, economic efficiency, and environmental sustainability. The research focuses on two core mechanisms: Economic Productive Resets (EPRs) and Economic Periodic Injections (EPIs). EPRs facilitate proportional redistribution of resources to reduce income disparities, while EPIs target investments to stimulate job creation, mitigate automion-related job displacement, and support sustainable development. The study employs a theoretical and analytical methodology, developing mathematical models to quantify the impact of EPRs and EPIs on key economic indicators, including the Gini coefficient for inequality, unemployment rates, average wages, and job displacement due to automation. Hypothetical scenarios simulate baseline conditions, EPR implementation, and the combined application of EPRs and EPIs. The methodology is threefold: (1) a mathematical–theoretical validation of the Cycle of Money framework, establishing internal consistency; (2) an econometric analysis using global historical data (2000–2023) to evaluate the correlation between GNI per capita, Gini coefficient, and average wages; and (3) scenario simulations and Difference-in-Differences (DiD) estimates to test the systemic impact of implementing EPR/EPI policies on inequality and labor outcomes. The models are further strengthened through tools such as OLS regression, and Impulse results to assess causality and dynamic interactions. Empirical results confirm that EPR/EPI can substantially reduce income inequality and unemployment, while increasing wage levels, findings supported by both the theoretical architecture and data-driven outcomes. Results demonstrate that Economocracy can significantly lower income inequality, reduce unemployment, increase wages, and mitigate automation’s effects on the labor market. These findings highlight Economocracy’s potential as a viable alternative to traditional economic systems, offering a sustainable pathway that harmonizes growth, social justice, and environmental stewardship in the global economy. Economocracy demonstrates potential to reduce debt per capita by increasing the efficiency of public resource allocation and enhancing average income levels. As EPIs stimulate employment and productivity while EPRs moderate inequality, the resulting economic growth expands the tax base and alleviates fiscal pressures. These dynamics lead to lower per capita debt burdens over time. The analysis is situated within the broader discourse of institutional economics to demonstrate that Economocracy is not merely a policy correction but a new economic system akin to democracy in political life.

Zeynep Aca, Arzu Kırcal-Şahin, Akın Özdemir et al.

IntroductionGender roles and stereotypes play a significant role in shaping the nursing profession, perpetuating systemic inequities that negatively impact professional experiences and healthcare system efficiency. In Türkiye, patriarchal norms and systemic disparities exacerbate these workplace challenges, particularly for female nurses.MethodsThis qualitative study utilized semi-structured interviews with 13 female nurses working in intensive care units to examine the influence of societal expectations, workplace discrimination, and institutional policies on gender inequities in nursing.ResultsThe findings reveal that cultural norms, family influence, and constrained career planning often channel women into nursing, reinforcing perceptions of the profession as an extension of caregiving roles. While participants rejected the notion of nursing as a “women’s profession,” their narratives highlighted the pervasive impact of gendered expectations. Additionally, political favoritism and nepotism were identified as factors exacerbating workplace challenges, reflecting broader systemic issues in Türkiye’s labor market. The normalization of gender norms and their internalization by female nurses further complicate efforts to combat discrimination.DiscussionThe study underscores the necessity for policy interventions to address systemic gender inequities in nursing. Recommendations include implementing mandatory gender equality education within healthcare institutions, stricter enforcement of anti-violence laws, and the establishment of psychological and legal support systems for workplace violence victims. Additional measures, such as childcare support and regulations against marital status-based discrimination, are essential to mitigate inequities. By addressing societal, cultural, and institutional factors, this research provides actionable strategies for healthcare organizations and policymakers to promote equity and improve sector efficiency.

Domenico Trezza, Giuseppe Luca De Luca Picione, Carmine Sergianni

This study investigated how generative Artificial Intelligence (AI) systems—now increasingly integrated into public services—respond to different technical configurations, and how these configurations affect the <i>perceived quality</i> of the outputs. Drawing on an experimental evaluation of <i>Govern-AI</i>, a chatbot designed for professionals in the social, educational, and labor sectors, we analyzed the impact of the <i>temperature</i> parameter—which controls the degree of creativity and variability in the responses—on two key dimensions: <i>accuracy</i> and <i>comprehensibility</i>. This analysis was based on 8880 individual evaluations collected from five professional profiles. The findings revealed the following: (1) the high-temperature responses were generally more comprehensible and appreciated, yet less accurate in strategically sensitive contexts; (2) professional groups differed significantly in their assessments, where trade union representatives and regional policy staff expressed more critical views than the others; (3) the <i>type of question</i>—whether operational or informational—significantly influenced the perceived output quality. This study demonstrated that the AI performance was far from neutral: it depended on technical settings, usage contexts, and the profiles of the end users. Investigating these “behind-the-scenes” dynamics is essential for fostering the <i>informed governance</i> of AI in public services, and for avoiding the risk of technology functioning as an opaque <i>black box</i> within decision-making processes.

Katharina Danhauser, Yingding Wang, Christoph Klein et al.

Most medical documentation, including clinical reports, exists in unstructured formats, which hinder efficient data analysis and integration into decision-making systems for patient care and research. Both fields could profit significantly from a reliable automatic analysis of these documents. Current methods for data extraction from these documents are labor-intensive and inflexible. Large Language Models (LLMs) offer a promising alternative for transforming unstructured medical documents into structured data in a flexible manner. This study assesses the performance of large language models (LLMs) in extracting structured data from pediatric clinical reports. Nine different LLMs were assessed. The results demonstrate that both commercial and open-source LLMs can achieve high accuracy in identifying patient-specific information, with top-performing models achieving over 90% accuracy in key tasks.

Viktoriia Mykytas

The purpose of this paper is to explore the transformative role of artificial intelligence (AI) in shaping the structure and dynamics of modern economies. Particular attention is given to AI’s impact on labor markets, the evolution of new professions, and the development of human capital. As AI technologies continue to permeate production, services, and public administration, they are reshaping not only economic processes but also the very nature of work and professional identity. The primary objective of this research is to identify both the strategic opportunities enabled by AI integration and the key risks it poses, especially in terms of employment disruption, technological inequality, and ethical challenges. In doing so, the paper proposes frameworks for sustainable and inclusive adaptation to the AI-driven economy. Methodology. The study is based on a comprehensive literature review combined with the analysis of recent statistical data and forecasts from leading international organizations, such as the World Economic Forum, the World Bank, and the International Labor Organization. To capture the practical implications of AI transformation, comparative and sectoral case studies are employed, focusing on industries including manufacturing, logistics, finance, healthcare, and agriculture. These are supplemented by empirical indicators related to education, employment, and technological adoption. Results. The study finds that AI significantly accelerates automation, transforming both manual and cognitive labor by replacing repetitive tasks and supporting decision-making processes. This transition reduces the demand for low-skilled labor while increasing the demand for highly specialized professionals in AI development, data analysis, AI ethics, and cybersecurity. However, it also raises profound concerns related to job displacement, unequal access to retraining, data privacy, and algorithmic transparency. The development of human capital through lifelong learning, digital upskilling, and educational reform is identified as a core component of economic resilience in the AI era. Additionally, the emergence of remote and hybrid work models is reshaping employment practices and necessitating new regulatory frameworks. The paper emphasizes that successful integration of AI into economic systems requires a balanced approach that promotes innovation while safeguarding social cohesion. Governments, businesses, and educational institutions must collaborate to ensure that the benefits of AI are broadly shared and that societies remain adaptable in the face of accelerating technological change.

Alice Qian Zhang, Jina Suh, Mary L. Gray et al.

As artificial intelligence (AI) systems become increasingly embedded in critical societal functions, the need for robust red teaming methodologies continues to grow. In this forum piece, we examine emerging approaches to automating AI red teaming, with a particular focus on how the application of automated methods affects human-driven efforts. We discuss the role of labor in automated red teaming processes, the benefits and limitations of automation, and its broader implications for AI safety and labor practices. Drawing on existing frameworks and case studies, we argue for a balanced approach that combines human expertise with automated tools to strengthen AI risk assessment. Finally, we highlight key challenges in scaling automated red teaming, including considerations around worker proficiency, agency, and context-awareness.

Davor Dragičević, Juho Leppänen

We study random dynamical systems composed of LSV maps with varying parameters, without any mixing assumptions on the base space of random dynamics. We establish a quenched central limit theorem and identify conditions under which the associated limit variance varies continuously and differentiably with respect to perturbations of the random dynamics. Our arguments rely on recent results on statistical stability and linear response for random intermittent maps established in Dragicevic et al. (J. Lond. Math. Soc. 111 (2025), e70150).

Alice Nabatanzi, Abdul Walusansa, Joanita Nangobi et al.

Abstract Pregnant women in rural Uganda largely rely on medicinal plants for inducing labor, treating postpartum hemorrhage (PPH), and inducing abortion. 90% of the women in both rural and urban Uganda use plants to manage pregnancy symptoms like constipation, heartburn, morning sickness, body aches, nausea, and vomiting. After delivery women continue using plants to manage postpartum complications and for infant care especially herbal baths. This study documented how ethnomedical folklore has been used to aid childbirth, manage postpartum hemorrhage, and induce abortion. Methods A cross-sectional ethnobotanical survey was conducted from May – December 2023 in Najjemebe sub-county, Buikwe district. 206 respondents from 12 villages were selected using snowball sampling. Key informants included Traditional Birth Attendants (TBAs) and herbalists. Data was collected using semi-structured questionnaires and focus group discussions. Voucher specimens of the plants were identified and authenticated at Makerere University Herbarium. Data were analyzed using descriptive statistics, Informant Consensus factor (ICF), Use Reports (URs), paired comparisons, and GraphPad Prism® version 9.0.0 software. Results All respondents (N = 206, 100%), used plants to induce labour, treat PPH, and induce abortion. One hundred four plant species were documented: most cited or preferred were: Hoslundia opposita (N = 109, 53%), Phytolacca dodecandra (N = 72, 35%), and Commelina erecta (N = 47, 23%). The plants belonged to 49 families, Lamiaceae (16.3%) and Fabaceae (14.3%) having the majority of the species. Herbs were 42 (40%) and trees 23 (22%). Oral administration 95(72%) was the commonest, then topical 19 (14.4%) and vaginal 14(10.6%). Conclusion Health surveys revealed that about 27% of deliveries in Uganda take place outside a health facility. Due to the oxytocic effects of plant species reported in this study, they play a triple role of being uterotonics, abortifacients, and treating postpartum haemmorhage. The dilemma lies in the unknown dosages and toxicity levels that could endanger both the mother’s and the unborn child’s lives. Due to Uganda’s high rates of population growth, overall fertility, maternal mortality, and morbidity, policies, and programmes on gendered health provision need to be reevaluated. Integrating herbal medicine into health care systems appears to be a feasible solution.

Kamil Bader El Dine, Noujoud Nader, Mohamad Khalil et al.

Preterm labor (PL) has globally become the leading cause of death in children under the age of 5 years. To address this problem, this paper will provide a new approach by analyzing the EHG signals, which are recorded on the abdomen of the mother during labor and pregnancy. The EHG signal reflects the electrical activity that induces the mechanical contraction of the myometrium. Because EHGs are known to be non-stationary signals, and because we anticipate connectivity to alter during contraction, we applied the windowing approach on real signals to help us identify the best windows and the best nodes with the most significant data to be used for classification. The suggested pipeline includes i) divide the 16 EHG signals that are recorded from the abdomen of pregnant women in N windows; ii) apply the connectivity matrices on each window; iii) apply the Graph theory-based measures on the connectivity matrices on each window; iv) apply the consensus Matrix on each window in order to retrieve the best windows and the best nodes. Following that, several neural network and machine learning methods are applied to the best windows and best nodes to categorize pregnancy and labor contractions, based on the different input parameters (connectivity method alone, connectivity method plus graph parameters, best nodes, all nodes, best windows, all windows). Results showed that the best nodes are nodes 8, 9, 10, 11, and 12; while the best windows are 2, 4, and 5. The classification results obtained by using only these best nodes are better than when using the whole nodes. The results are always better when using the full burst, whatever the chosen nodes. Thus, the windowing approach proved to be an innovative technique that can improve the differentiation between labor and pregnancy EHG signals.

Jun Dai, Guanqing Shi, Xiaoke Xie et al.

Chinese-style modernization involves the modernization of a large population, requiring top-level design in terms of scale and structure. The population perspective in Xi Jinping's Thought on Socialism with Chinese Characteristics for a New Era serves as the fundamental guide for population policies. The three-child policy and delayed retirement will affect the supply of labor in China and challenge the previous assessments of China's Lewis Turning Point. This study examines the rural surplus labor transfer from 2013 to 2022 based on urban and rural data. The results indicate that China's overall wage levels have continuously increased, the urban-rural income gap has narrowed, and the transfer of surplus rural labor has slowed. China has passed the first turning point and entered a transitional phase. Factors such as the level of agricultural mechanization, urbanization rate, and urban-rural income gap are more significant in influencing the transfer of surplus labor than the normal working-age population ratio. The delayed retirement policy has a more immediate impact on the supply and transfer of rural surplus labor than the three-child policy. Additionally, delayed retirement can offset the negative impact of the reduced relative surplus labor supply caused by the three-child policy, although the three-child policy could increase the future absolute surplus labor supply.

Aleksandr V. Portnov

Increasing labor productivity has been on the agenda of science for many years. The search continues for optimal ways to improve with a parallel search for measurement methods both at the country level in the form of national projects, and at the enterprise level in the form of initiatives to optimize the production process, including the introduction of integrated work systems. As part of the application of the diagnostic approach, a synthesis of two functions is assumed: management and analytics. The basis of this approach is the idea of continuous monitoring of the state of production systems to diagnose and make preventive management decisions to prevent a decrease in production efficiency, including labor productivity. One of the advantages of the diagnostic approach is the applicability of methods and tools in almost any field. The article discusses benchmarking as one of the possible tools for a diagnostic approach to the analysis and management of labor productivity. The possibility of adapting this tool is revealed. The practical applicability of benchmarking to an application is demonstrated using the example of mass production. An example of benchmarking between two production lines within the same production unit is given. The main goal of adaptation and application is to identify best practices for the effective management of production equipment to increase labor productivity. Purpose is to formalize benchmarking as a tool for analyzing labor productivity. Methodology: the article used the methods of analysis and synthesis of quantitative and qualitative data using benchmarking. Results: approbation of benchmarking as a tool for measuring labor productivity based on real data from production lines. Practical implications: application of the method in the framework of assessing the efficiency of production equipment at industrial enterprises of the in-line type.

Kai-Chieh Hsu, Haimin Hu, Jaime Fernández Fisac

Recent years have seen significant progress in the realm of robot autonomy, accompanied by the expanding reach of robotic technologies. However, the emergence of new deployment domains brings unprecedented challenges in ensuring safe operation of these systems, which remains as crucial as ever. While traditional model-based safe control methods struggle with generalizability and scalability, emerging data-driven approaches tend to lack well-understood guarantees, which can result in unpredictable catastrophic failures. Successful deployment of the next generation of autonomous robots will require integrating the strengths of both paradigms. This article provides a review of safety filter approaches, highlighting important connections between existing techniques and proposing a unified technical framework to understand, compare, and combine them. The new unified view exposes a shared modular structure across a range of seemingly disparate safety filter classes and naturally suggests directions for future progress towards more scalable synthesis, robust monitoring, and efficient intervention.

Divyanshu Saxena, Nihal Sharma, Donghyun Kim et al.

This paper lays down the research agenda for a domain-specific foundation model for operating systems (OSes). Our case for a foundation model revolves around the observations that several OS components such as CPU, memory, and network subsystems are interrelated and that OS traces offer the ideal dataset for a foundation model to grasp the intricacies of diverse OS components and their behavior in varying environments and workloads. We discuss a wide range of possibilities that then arise, from employing foundation models as policy agents to utilizing them as generators and predictors to assist traditional OS control algorithms. Our hope is that this paper spurs further research into OS foundation models and creating the next generation of operating systems for the evolving computing landscape.

Kenichiro Motomura, Roberto Romero, Jose Galaz et al.

Preterm birth is the leading cause of neonatal morbidity and mortality worldwide. One of every 4 preterm neonates is born to a mother with intra-amniotic inflammation driven by invading bacteria. However, the molecular mechanisms underlying this hostile immune response remain unclear. Here, we used a translationally relevant model of preterm birth in Nlrp3-deficient and -sufficient pregnant mice to identify what we believe is a previously unknown dual role for the NLRP3 pathway in the fetal and maternal signaling required for the premature onset of the labor cascade leading to fetal injury and neonatal death. Specifically, the NLRP3 sensor molecule and/or inflammasome is essential for triggering intra-amniotic and decidual inflammation, fetal membrane activation, uterine contractility, and cervical dilation. NLRP3 also regulates the functional status of neutrophils and macrophages in the uterus and decidua, without altering their influx, as well as maternal systemic inflammation. Finally, both embryo transfer experimentation and heterozygous mating systems provided mechanistic evidence showing that NLRP3 signaling in both the fetus and the mother is required for the premature activation of the labor cascade. These data provide insights into the mechanisms of fetal-maternal dialog in the syndrome of preterm labor and indicate that targeting the NLRP3 pathway could prevent adverse perinatal outcomes.

Francesca Ghiani

The essay aims at analyzing the Italian and comparative case law on digital platform workers’ classification in order to reflect upon the changing functions of working time in digital work widely considered. The Author, taking into account the difficulties in overcoming the dichotomy subordination-autonomy, believes that it is important to introduce a new notion of working time, in line with the new ways of carrying out work.

Manuel S. Vidal, Manuel S. Vidal, Ryan C. V. Lintao et al.

Survivors of preterm birth struggle with multitudes of disabilities due to improper in utero programming of various tissues and organ systems contributing to adult-onset diseases at a very early stage of their lives. Therefore, the persistent rates of low birth weight (birth weight &lt; 2,500 grams), as well as rates of neonatal and maternal morbidities and mortalities, need to be addressed. Active research throughout the years has provided us with multiple theories regarding the risk factors, initiators, biomarkers, and clinical manifestations of spontaneous preterm birth. Fetal organs, like the placenta and fetal membranes, and maternal tissues and organs, like the decidua, myometrium, and cervix, have all been shown to uniquely respond to specific exogenous or endogenous risk factors. These uniquely contribute to dynamic changes at the molecular and cellular levels to effect preterm labor pathways leading to delivery. Multiple intervention targets in these different tissues and organs have been successfully tested in preclinical trials to reduce the individual impacts on promoting preterm birth. However, these preclinical trial data have not been effectively translated into developing biomarkers of high-risk individuals for an early diagnosis of the disease. This becomes more evident when examining the current global rate of preterm birth, which remains staggeringly high despite years of research. We postulate that studying each tissue and organ in silos, as how the majority of research has been conducted in the past years, is unlikely to address the network interaction between various systems leading to a synchronized activity during either term or preterm labor and delivery. To address current limitations, this review proposes an integrated approach to studying various tissues and organs involved in the maintenance of normal pregnancy, promotion of normal parturition, and more importantly, contributions towards preterm birth. We also stress the need for biological models that allows for concomitant observation and analysis of interactions, rather than focusing on these tissues and organ in silos.