Hasil untuk "Labor systems"

E. Bonabeau, M. Dorigo, G. Theraulaz

Martinek Milan, Zapletal Jan, Drochytek Vit et al.

Abstract The Vojta’s method (VRL) is a neurophysiological rehabilitation method used to support and induce reflex responses of locomotor and vegetative systems. It uses involuntary motor reaction of the body during pressure stimulation of so-called trigger zones. Pregnancy is currently considered a contraindication for the use of VRL due to the potential risk of inducing regular uterine activity and, consequently, triggering labor. The aim of the study is to evaluate changes in uterine activity during Vojta’s reflex locomotion and to determine whether the method is associated with the induction of labor. Secondary goal is to assess its safety in term pregnancy and its potential use for rehabilitative purposes. This single-center, single-blinded, parallel-group randomized controlled pilot trial included 40 pregnant patients between 40 + 0 to 41 + 0 weeks of gestational age. Participants were randomized (sealed envelope method) to receive either stimulation of trigger zones according to VRL (n = 20) or sham stimulation (n = 20). Cardiotocographic (CTG) recordings were conducted right before and immediately after stimulation and evaluated by two independent obstetricians. Additionally, participants completed a questionnaire evaluating subjective responses to stimulation. Primary outcomes were CTG-detected uterine changes and time from stimulation to delivery. Secondary outcomes included self-reported sensations and pain intensity (VAS). None of the patients delivered as a result of VRL stimulation. CTG showed increased uterine activity in 45% of VRL stimulated participants vs. 10% in the control group. Median time to labor onset in the VRL group was 5 days (1–8), compared to 4 days (1–9) in the control group. VRL was well tolerated (mean VAS 1.85), but rated significantly less pleasant than sham (p = 0.043). Our findings suggest that the use of VRL in term pregnancy is likely safe and well tolerated. While uterine activity was observed in 45% of cases, this activity was not sufficient to induce labor. Our findings, however, do not support pregnancy as a blanket contraindication. Trial registration: This clinical trial was registered at http://clinicaltrials.gov (ID: NCT06339528).

Ravish Gupta, Saket Kumar

This paper extends the Acemoglu-Restrepo task exposure framework to address the labor market effects of agentic artificial intelligence systems: autonomous AI agents capable of completing entire occupational workflows rather than discrete tasks. Unlike prior automation technologies that substitute for individual subtasks, agentic AI systems execute end-to-end workflows involving multi-step reasoning, tool invocation, and autonomous decision-making, substantially expanding occupational displacement risk beyond what existing task-level analyses capture. We introduce the Agentic Task Exposure (ATE) score, a composite measure computed algorithmically from O*NET task data using calibrated adoption parameters--not a regression estimate--incorporating AI capability scores, workflow coverage factors, and logistic adoption velocity. Applying the ATE framework across five major US technology regions (Seattle-Tacoma, San Francisco Bay Area, Austin, New York, and Boston) over a 2025-2030 horizon, we find that 93.2% of the 236 analyzed occupations across six information-intensive SOC groups (financial, legal, healthcare, healthcare support, sales, and administrative/clerical) cross the moderate-risk threshold (ATE >= 0.35) in Tier 1 regions by 2030, with credit analysts, judges, and sustainability specialists reaching ATE scores of 0.43-0.47. We simultaneously identify seventeen emerging occupational categories benefiting from reinstatement effects, concentrated in human-AI collaboration, AI governance, and domain-specific AI operations roles. Our findings carry implications for workforce transition policy, regional economic planning, and the temporal dynamics of labor market adjustment

Marios Impraimakis, Feiyu Zhou, Andrew Plummer

The system identification capabilities of a novel information-theoretic method are examined here. Specifically, this work uses information-theoretic metrics and vibration-based measurements to enhance damping estimation accuracy in mechanical systems. The method refers to a key limitation in system identification, signal processing, monitoring, and alert systems. These systems integrate various components, including sensors, data acquisition devices, and alert mechanisms. They are designed to operate in an environment to calculate key parameters such as peak accelerations and duration of high acceleration values. The current operational modal identification methods, though, suffer from limitations related to obtaining poor damping estimates due to their empirical nature. This has a significant impact on alert warning systems. This occurs when their duration is misestimated; specifically, when using the vibration amplitudes as an indicator of danger alerts for monitoring systems in damage or anomaly detection scenarios. To this end, approaches based on the Shannon entropy and the Kullback-Leibler divergence concept are proposed. The primary objective is to monitor the vibration levels in near real-time and provide immediate alerts when predefined thresholds are exceeded. In considering the proposed approach, both new real-world data from the multi-axis simulation table at the University of Bath, as well as the benchmark International Association for Structural Control-American Society of Civil Engineers (IASC-ASCE) structural health monitoring problem are considered. Importantly, the approach is shown to select the optimal model, which accurately captures the correct alert duration, providing a powerful tool for system identification and monitoring.

Julia Kokina, Shay Blanchette

Abstract Robotic Process Automation (RPA) is an emerging technology that enables the automation of rules-based business processes and tasks through the use of software bots. Drawing upon the theory of Task-Technology Fit (TTF) and Technology-to-Performance Chain (TPC) (Goodhue and Thompson 1995) and research on expert systems (Messier and Hansen 1987; Sutton 1990), this study explores emerging themes surrounding bot implementation for accounting and finance tasks. We collect and analyze interview data from adopters of RPA and document task suitability, task-technology fit, implementation issues, and resulting performance outcomes. We find that securing technical capability is only a part of RPA implementation process. Organizations engage in standardization and optimization of processes, develop scorecard-like tools to rank tasks, adjust governance structures to include digital employees, and redefine internal controls. Organizations benefit from automating only certain processes, those that are structured, repeated, rules-based, and with digital inputs. Along with cost savings, organizations experience improved process documentation, lower error rates, more accurate measurement of process performance, and better report quality.

Ryan Tsoi, Feilun Wu, Carolyn Zhang et al.

Emmanouil E. Malandrakis

Recirculating Aquaculture Systems (RAS) represent a high-density, controlled-environment fish farming method that requires constant monitoring of critical water quality parameters to ensure high water quality and fish stock health. Manual monitoring is labor-intensive and prone to error, creating a significant risk of catastrophic loss. This work presents the design and implementation of an automated monitoring system built on a Raspberry Pi platform that integrates multiple sensors (temperature, pH, conductivity, water level, and pumps’ functionality) to provide continuous, real-time data acquisition. A key feature is a software-based outlier rejection algorithm that enhances data integrity, and the code is freely available on the GitHub platform for further development. The collected data has been published on the ThingsBoard IoT platform for visualization and historical analysis via the HTTPS protocol. Furthermore, the system implements a proactive alerting mechanism using the Pushover notification service to deliver instant mobile alerts when parameters deviate from predefined thresholds. Commercial solutions cost in the order of thousands of euros, have high maintenance and operational costs, and pose integration and compatibility challenges. This solution provides a reliable, scalable, and cost-effective method for maintaining optimal conditions in a RAS, with hardware costs of less than EUR 150.

Ramón Sanguino, Nilgün Çağlarırmak Uslu, Pınar Karahan-Dursun et al.

Education–employment mismatch represents a persistent structural issue across Europe, especially among young people. In line with the digital transformation, green transformation and population aging, new jobs are emerging every day, and some of the older jobs are disappearing. However, existing skills of job seekers may not fit these new jobs. This article presents results from the EMLT + AI project, which aimed to explore how artificial intelligence (AI) tools could contribute to reducing such mismatches and supporting inclusive labor market integration. Based on a sample of 1039 participants across European countries, we analyzed the alignment between individuals’ educational background and their current employment, as well as their willingness to reskill. Using binary logistic regression models, the study identifies key factors influencing mismatch and reskilling motivation, including educational level, type of occupation, the presence of meaningful career guidance, and AI-based job search practices. The results indicate that individuals who hold a master’s degree and work in positions requiring at least bachelor’s level degrees are more likely to be matched with jobs that align with their field of study. However, access to mentoring remains limited. The paper concludes by proposing an AI-supported training model integrating career recommendation systems, flexible learning modules, and structured mentoring. These findings provide empirical evidence on how emerging technologies can foster more responsive and adaptive education-to-employment transitions, contributing to policy innovation and the development of inclusive digital labor ecosystems in Europe.

Alex Farach, Alexia Cambon, Jared Spataro

As the digital economy grows increasingly intangible, traditional productivity measures struggle to capture the true economic impact of artificial intelligence (AI). AI systems capable of cognitive work significantly enhance productivity, yet their contributions remain obscured within the residual category of Total Factor Productivity (TFP). This paper explores whether it is time for a conceptual shift to explicitly recognize "digital labor," the autonomous cognitive capability of AI, as a distinct factor of production alongside capital and human labor. We outline the unique economic properties of digital labor, including scalability, intangibility, self-improvement, rapid obsolescence, and elastic substitutability. By integrating digital labor into growth models (such as those by Solow and Romer), we demonstrate strategic implications for business leaders, including new approaches to productivity tracking, resource allocation, investment strategy, and organizational design. Ultimately, treating digital labor as an independent factor offers a clearer view of economic growth and helps organizations manage AI's transformative potential.

Janneke Pieters, Ana Kujundzic, Rulof Burger et al.

Technological change can have profound impacts on the labor market. Decades of research have made it clear that technological change produces winners and losers. Machines can replace some types of work that humans do, while new technologies increase human's productivity in other types of work. For a long time, highly educated workers benefitted from increased demand for their labor due to skill-biased technological change, while the losers were concentrated at the bottom of the wage distribution (Katz and Autor, 1999; Goldin and Katz, 2007, 2010; Kijima, 2006). Currently, however, labor markets seem to be affected by a different type of technological change, the so-called routine-biased technological change (RBTC). This chapter studies the risk of automation in developing country labor markets, with a particular focus on differences between men and women. Given the pervasiveness of gender occupational segregation, there may be important gender differences in the risk of automation. Understanding these differences is important to ensure progress towards equitable development and gender inclusion in the face of new technological advances. Our objective is to describe the gender gap in the routine task intensity of jobs in developing countries and to explore the role of occupational segregation and several worker characteristics in accounting for the gender gap.

Sungjun Seo, Kooktae Lee

This paper addresses the fundamental problem of non-uniform area coverage in multi-agent systems, where different regions require varying levels of attention due to mission-dependent priorities. Existing uniform coverage strategies are insufficient for realistic applications, and many non-uniform approaches either lack optimality guarantees or fail to incorporate crucial real-world constraints such as agent dynamics, limited operation time, the number of agents, and decentralized execution. To resolve these limitations, we propose a novel framework called Density-Driven Optimal Control (D2OC). The central idea of D2OC is the integration of optimal transport theory with multi-agent coverage control, enabling each agent to continuously adjust its trajectory to match a mission-specific reference density map. The proposed formulation establishes optimality by solving a constrained optimization problem that explicitly incorporates physical and operational constraints. The resulting control input is analytically derived from the Lagrangian of the objective function, yielding closed-form optimal solutions for linear systems and a generalizable structure for nonlinear systems. Furthermore, a decentralized data-sharing mechanism is developed to coordinate agents without reliance on global information. Comprehensive simulation studies demonstrate that D2OC achieves significantly improved non-uniform area coverage performance compared to existing methods, while maintaining scalability and decentralized implementability.

Ni Huang, Gordon Burtch, Y. Hong et al.

The gig economy comprises a large portion of the workforce in today’s economy. The gig economy has low barriers to entry, enabling flexible work arrangements and allowing workers to engage in contingent employment, whenever, and in some cases, such as online labor markets, wherever, workers desire. And many of the workers seek and complete work via digital platforms. However, there is a lack of understanding into the participation in such platforms. The growth of the gig economy has been partly attributed to technological advancements that enable flexible work environments. In this study, we consider the role of an alternative driver, economic downturns, and associated financial stressors in the offline economy, for example, unemployment. Our analysis combines data from a leading online labor market and various archival sources such as the Bureau of Labor Statistics. We find local economic conditions significantly impact the intensive and extensive margins of labor supply in online labor markets. And such impacts are heterogeneous across different county characteristics. Given the prominence of the gig economy, we believe more research is needed to understand gig-economy participation. It is notable that policy makers recently started to look at related issues, proposing laws to protect the gig workers, such as the recent California Assembly Bill 5.

M. Carolan

ABSTRACT This paper draws from interviews with (1) US farmers who have adopted automated systems; (2) individuals employed by North American firms that engineer, manufacture, and/or repair these technologies; and (3) US farm laborers (immigrant and domestic) and representatives from farm labor organizations. The argument draws from the literature interrogating the fictional expectations that underlie capitalist reproduction, reading it through a distributed (ontological) lens. The framework questions whether concepts like ‘automation’ and ‘skill’ provide sufficient analytic and conceptual clarity to critically engage these platforms and suggests that we think about what these technologies do rather than fixate on what each is.

Mei Xue, Xing Cao, Xu Feng et al.

ABSTRACT As a general-purpose technology, artificial intelligence (AI) is expected to transform almost all industries and aspects of our society. Thus, it is important to understand the potential changes within the firms related to how AI applications change their labor force. Using a panel dataset with over 1,300 publicly-traded companies in China from 2007 to 2018, we examine the relationship between AI applications and firm labor structure with workers with or without formal college education. The study indicates that AI applications were positively associated with the overall employment as well as the employment of nonacademically- trained workers with no college degrees at the firm level. These associations were more significant in the service sector than in the manufacturing sector. Further causal analysis shows increasing AI applications have a positive effect on a firm’s employment of nonacademically-trained workers and its overall employment but a negative effect on academically-trained workers. We attribute the findings to the technology deskilling effect of AI. The findings suggest that, in response to the potential labor force transformation with increasing AI applications, information-systems research needs to focus on structural changes of labor forces and the implications for preparing human employees to work with AI side by side.

Omran Gheisar, Sima Eskandari Sabzi, ali salmanpour et al.

The trend of population growth in the last three decades will cause extensive changes in the age structure of Iran's population. So that it can be one of the most important challenges of the country in the coming decades. This development will have different effects and consequences in the process of social, economic and political development. In this research, with the aim of dynamic analysis of the economic effects of the structural changes of the age groups (the age group of the workforce) of Iran's population in the coming decades until 1455, and then the role of women's labor force in the process of gross domestic product is studied and review puts. Therefore, this research aims to understand more about the structural changes of the population in four age groups (under 15 years, between 15 to 44 years, 45 to 64 years and over 65 years) in the past decades, the present and its future forecast; Using the global model "World3" modeling of dynamic systems to simulate the country's population trend from 1355 to 1455, with "Vensim" software, it has predicted the structural changes of the population. Forecasts show that based on the probable fertility rate of 1.6 (announcement of the researches of the Statistics Center), the growth trend of the entire country's population will be increasing until 1425, and the trend will decrease from this year onwards. Also, until 1455, the growth trend of the population in the age group below 15 years will be decreasing, and the growth trend in the age group of the workforce (between 15-44 years, 45-64 years) will increase until 1415, and from this year onwards, the trend will decrease. According to the forecast, the growth trend in the age group above 65 years will increase. The findings show that the demographic trend of working age will happen about 10 years earlier than the decreasing trend of the total population. Therefore, to compensate for the deficit of economically active labor and improve the production process and increase per capita; Considering the existing capacity in the country, increasing the employment of women will be one of the most effective solutions in this crisis. In the following, a dynamic economic model is presented using Solow's growth model. To show how the effects of changes in the labor force pattern will be on the growth process of gross domestic production. Then the operational scenarios related to increasing the employment of women in the growth of production and the growth and development of the country; Provided. Also, practical and operational suggestions have been presented regarding how to reduce the side effects of population structural changes and its negative effects on the growth of domestic production (GDP) by establishing women's employment in the country's economic cycle.

Chuan Shen, Xia Li, Jianfeng Qin

Abstract Intercropping systems have garnered attention as a sustainable agricultural approach for efficient land use, increased ecological diversity in farmland, and enhanced crop yields. This study examined the effect of intercropping on the kiwifruit rhizosphere to gain a deeper understanding of the relationships between cover plants and kiwifruit in this sustainable agricultural system. Soil physicochemical properties and bacterial communities were analyzed using the Kiwifruit-Agaricus blazei intercropping System. Moreover, a combined analysis of 16S rRNA gene sequencing and metabolomic sequencing was used to identify differential microbes and metabolites in the rhizosphere. Intercropping led to an increase in soil physicochemical and enzyme activity, as well as re-shaping the bacterial community and increasing microbial diversity. Proteobacteria, Bacteroidota, Myxococcota, and Patescibacteria were the most abundant and diverse phyla in the intercropping system. Expression analysis further revealed that the bacterial genera BIrii41, Acidibacter, and Altererythrobacter were significantly upregulated in the intercropping system. Moreover, 358 differential metabolites (DMs) were identified between the monocropping and intercropping cultivation patterns, with fatty acyls, carboxylic acids and derivatives, and organooxygen compounds being significantly upregulated in the intercropping system. The KEGG metabolic pathways further revealed considerable enrichment of DMs in ABC transporters, histidine metabolism, and pyrimidine metabolism. This study identified a significant correlation between 95 bacterial genera and 79 soil metabolites, and an interactive network was constructed to explore the relationships between these differential microbes and metabolites in the rhizosphere. This study demonstrated that Kiwifruit-Agaricus blazei intercropping can be an effective, labor-saving, economic, and sustainable practice for reshaping bacterial communities and promoting the accumulation and metabolism of beneficial microorganisms in the rhizosphere.

Yi-Sin Tan, Ching-Chang Tsai, Hsin-Hsin Cheng et al.

Background: The COVID-19 pandemic has substantially impacted healthcare systems and obstetric practices worldwide. Labor induction is a common procedure for preventing obstetric complications in high-risk populations. This study evaluated perinatal outcomes of labor induction using a modified management protocol in a tertiary care center during the COVID-19 pandemic. Methods: We conducted a retrospective study by reviewing electronic structured delivery records of women who underwent elective labor induction between June 2020 and October 2022. We analyzed maternal characteristics, maternal outcomes, and neonatal outcomes during the pre-pandemic (June 2020 to May 2021) and pandemic periods (May 2021 to October 2022). Results: The study included 976 cases: 325 pregnancies in the pre-pandemic group and 651 in the pandemic group. The pandemic group showed earlier gestational age at delivery (39 vs. 40 weeks, <i>p</i> < 0.01) and lower body mass index (27.1 vs. 27.5 kg/m², <i>p</i> = 0.03). During the pandemic period, we observed a significant increase in labor induction cases and a decrease in cesarean sections. Neonatal outcomes, including Apgar scores and intensive care admissions, showed no significant differences between groups. Subgroup analysis identified advanced maternal age (OR = 1.08; 95% CI = 1.03–1.14; <i>p</i> < 0.01) and primiparity (OR = 5.24; 95% CI = 2.75–9.99; <i>p</i> < 0.01) as independent risk factors for cesarean delivery. Conclusions: Even under modified protocols for labor induction during the COVID-19 pandemic, more pregnancies underwent labor induction while achieving a significant reduction in cesarean sections. Advanced maternal age and primiparity were identified as independent risk factors associated with cesarean delivery.

Somia F. E. Fahmi, Zeinab A. A. Baraia, Inaam H. Abdelati

Context: Infection prevention remains a significant public health challenge for healthcare systems, especially in maternity and delivery units. Good understanding and compliance of nurses with infection control measures during delivery are essential factors that improve maternal and neonatal outcomes and decrease morbidity and mortality. Aim: This study aimed to assess nurses' practice regarding infection control measures during the second stage of labor in multiple centers. Methods: Cross-sectional descriptive observational study was adopted in this study. The study population included all nurses working in labor rooms of four hospitals (100 nurses), namely Suez Canal University Hospital, Zagazig University Hospital, Ismailia General Hospital, And Zagazig General Hospital. Data collection tool encompassed a structured interviewing questionnaire to assess nurses' general characteristics, physical and organizational barriers that prevent nurses from complying with infection control measures, infection control practice checklist to assess nurses` compliance with infection control measures during the second stage of labor. Results: The highest percentage of the studied nurses' age was between 19-<29 (56.6%, 63.8%). Near half were technical nurses (43.4%, 44.7%). The majority of the studied nurses had not had periodic checks. Also, most of them were vaccinated against viral hepatitis B (86.8%, 91.5%). There was a statistically significant difference between Ismalia and Zagazig hospitals in physical barriers. The highest mean percent for infection control practice was for perineal care 100%, using the invasive device during labor 92%, preparing birthing room and its equipment 75.9%. A satisfactory infection control practice was revealed among 88% of the studied nurses. The satisfactory practice of nurses was 100%, 92.1%, 86.9%, 44.1% in Zagazig General Hospital, Ismalia University Hospital, Zagazig University Hospital, Ismalia General Hospital, respectively. Conclusion: The result of the study concluded that most nurses' practice regarding infection control in the delivery room was satisfactory. The study recommended upgrading and qualifying nurses in the labor room to improve their practical skills in Obstetric Nursing.

Pegah Moradi, Karen Levy, Cristobal Cheyre

Self-service machines are a form of pseudo-automation; rather than actually automate tasks, they offset them to unpaid customers. Typically implemented for customer convenience and to reduce labor costs, self-service is often criticized for worsening customer service and increasing loss and theft for retailers. Though millions of frontline service workers continue to interact with these technologies on a day-to-day basis, little is known about how these machines change the nature of frontline labor. Through interviews with current and former cashiers who work with self-checkout technologies, we investigate how technology that offsets labor from an employee to a customer can reconfigure frontline work. We find three changes to cashiering tasks as a result of self-checkout: (1) Working at self-checkout involved parallel demands from multiple customers, (2) self-checkout work was more problem-oriented (including monitoring and policing customers), and (3) traditional checkout began to become more demanding as easier transactions were filtered to self-checkout. As their interactions with customers became more focused on problem solving and rule enforcement, cashiers were often positioned as adversaries to customers at self-checkout. To cope with perceived adversarialism, cashiers engaged in a form of relational patchwork, using techniques like scapegoating the self-checkout machine and providing excessive customer service in order to maintain positive customer interactions in the face of potential conflict. Our findings highlight how even under pseudo-automation, workers must engage in relational work to manage and mend negative human-to-human interactions so that machines can be properly implemented in context.

Max H. Cohen, Ryan K. Cosner, Aaron D. Ames

Certifying the safety of nonlinear systems, through the lens of set invariance and control barrier functions (CBFs), offers a powerful method for controller synthesis, provided a CBF can be constructed. This paper draws connections between partial feedback linearization and CBF synthesis. We illustrate that when a control affine system is input-output linearizable with respect to a smooth output function, then, under mild regularity conditions, one may extend any safety constraint defined on the output to a CBF for the full-order dynamics. These more general results are specialized to robotic systems where the conditions required to synthesize CBFs simplify. The CBFs constructed from our approach are applied and verified in simulation and hardware experiments on a quadrotor.