This paper looks at the Bangladeshi apparel industry and critically draws on theories of labor control to examine how market and non-market actors control apparel workers and exploit their labor power at the bottom of a global value chain, reinforcing the capitalist system locally and globally. My research draws on a wide range of empirical evidence collected from 20 apparel factories in Dhaka City, Bangladesh and secondary evidence and identifies a new regime of labor control. I call this regime of labor control social despotism—a regime that deploys legal means, illegal coercion, informal power relations, and structural violence to govern and exploit workers. Social despotism is created by two reinforcing forms of oppression: instrumental oppression and structural oppression. Market actors organize instrumental oppression to normalize coercion in the factory, creating the forced consent of workers to their exploitation. Market and non-market actors organize structural oppression, limiting workers’ collective bargaining power within the factory and marginalizing their existence in social life. Both forms of oppression are present throughout four distinct phases of labor control: 1) searching for the cheapest labor forces and manufacturing sites; 2) recruiting workers; 3) organizing work; and 4) socializing, rewarding, and punishing workers.
Miguel A. Trujillo, Rodrigo Aldana-López, David Gomez Gutierrez
et al.
This paper addresses the problem of consensus tracking with fixed-time convergence, for leader-follower multi-agent systems with double-integrator dynamics, where only a subset of followers has access to the state of the leader. The control scheme is divided into two steps. The first one is dedicated to the estimation of the leader state by each follower in a distributed way and in a fixed-time. Then, based on the estimate of the leader state, each follower computes its control law to track the leader in a fixed-time. In this paper, two control strategies are investigated and compared to solve the two mentioned steps. The first one is an autonomous protocol which ensures a fixed-time convergence for the observer and for the controller parts where the Upper Bound of the Settling-Time (UBST) is set a priory by the user. Then, the previous strategy is redesigned using time-varying gains to obtain a non-autonomous protocol. This enables to obtain less conservative estimates of the UBST while guaranteeing that the time-varying gains remain bounded. Some numerical examples show the effectiveness of the proposed consensus protocols.
Context: To improve agricultural productivity and water sustainability in water-scarce regions, it is essential to understand the efficiency and diversity of farming practices Objective: This study aims to assess the diversity and efficiency of farming systems in Morocco’s Chtouka-Massa plain. It focuses on resource management, agricultural intensification, and water use, identifying inefficiencies and proposing sustainable solutions. Methods: Using Principal Component Analysis and Hierarchical Clustering, we classify 40 farm households into three distinct typologies: (i) extensive cereal-arboriculture systems, (ii) semi-intensive mixed cereal-vegetable systems, and (iii) intensive vegetable farming systems. A meta-frontier approach combined with Data Envelopment Analysis (DEA) is then applied to assess disparities in resource efficiency, technological performance, and environmental sustainability among these typologies. Results and conclusions: Our results show that extensive cereal-arboriculture systems exhibit the highest resource efficiency—particularly in water, nitrogen, and labor—but achieve the lowest gross margins due to limited agricultural intensification. Semi-intensive mixed systems demonstrate moderate efficiency but consume the largest amounts of water, largely sourced from subsidized private wells. Intensive vegetable farming systems, while generating the highest gross margins, are the least efficient due to high input costs, reliance on desalinated water, and labor-intensive practices. Targeted policy interventions are needed to optimize resource use and promote sustainable practices adapted to each farming typology. Significance: This study provides actionable insights for policymakers aiming to enhance the sustainability of agricultural systems and groundwater resources in arid and semi-arid regions. The findings support the need for targeted policies to enhance groundwater management.
Research objectives and hypothesis/research questions
The purpose of this article is to identify the role of resources and information systems operating in the IT environment as a component of strengthening the business potential of contemporary organisations for ensuring the quality of both products and projects and management processes. Not only aspects related to the functioning of a contemporary organisation are presented, but also the problems of creating quality and efficiency through the use of selected IT solutions. The entire study is profiled with the hypothesis that: the IT environment fosters the growth of the business potential of a contemporary organisation, which strengthens the quality and efficiency of the business processes implemented.
Research methods
In order to verify the formulated hypothesis, research methods related to critical analysis of scientific publications, industry reports and other sources related to the research topic, as well as in-house sub-studies such as diagnostic surveys according to the needs profile in question, were used.
Main results
The article exposes the criterion of quality as one of the most important attributes that determine the success of a company. In addition, it is noteworthy that the IT environment offers numerous advanced tools that support the processes of planning, organising, coordinating, supervising and monitoring and controlling at each stage of the implementation of business undertakings. The article also discusses the risks and opportunities arising from the use of the IT environment, taking into account the need for responsible management of modern technologies in business practice.
Implications for theory and practice
Adapting management models to the specifics of the sector or the individual needs of the organisation is key. Flexibility in the implementation of tools and methodologies allows companies to effectively face unique challenges and exploit opportunities offered by the market. Managers should be ready to adapt dynamically to change, enabling them not only to meet the quality criterion, but also to continuously adapt to the changing needs of customers and the business environment. In this way, organisations will be able to maintain their competitiveness and respond appropriately to rapid changes and market trends.
Management. Industrial management, Management information systems
While occupational health management (OHM) is critical for ensuring worker safety and productivity, its integration into local governance structures remains inconsistent in many developing regions. This qualitative case study examines the institutional challenges and opportunities in implementing OHM policies in the Rancaekek District, Bandung Regency, Indonesia, a rapidly industrializing area with a growing manufacturing sector. Data were collected from interviews with 23 industries and OHM stakeholders—which include policymakers, industry representatives, and workers—as well as from documents of regional regulations on OHM. The findings reveal that the local government has demonstrated commitment to integrating OHM through the establishment of the Regional General Hospital for Occupational Health (RSUD Kesehatan Kerja), although its effectiveness remains hindered by fragmented institutional coordination, limited resources, and varying levels of compliance among small and medium enterprises (SMEs). Key challenges include overlapping responsibilities between local health agencies and labor departments, inadequate training for occupational health inspectors, and a lack of worker awareness of safety procedures, particularly in informal businesses. However, this study has also identified opportunities for improvement, such as partnerships between occupational health hospitals and public and private institutions, digital reporting systems for workplace hazards, and greater involvement of labor unions in policy formulation. Addressing these matters can strengthen OHM frameworks and better protect workers in industrialized regions.
With rapid urbanization and increasing emphasis on sustainable mobility, slow-moving traffic systems, including pedestrian and cycling infrastructure, have become critical to urban transportation and quality of life. Conventional assessment methods are labor-intensive, time-consuming, and limited in coverage. Leveraging advances in deep learning and computer vision, this study develops a framework for bottleneck detection using street-level imagery and the You Only Look Once version 5 (YOLOv5) model. An evaluation system comprising 15 indicators across continuity, safety, and comfort is established. In a case study of Wuhan’s Third Ring Road, the YOLOv5 model achieved 98.9% mean Average Precision (mAP)@0.5, while spatial hotspot analysis (<i>p</i> < 0.05) identified severe demand–infrastructure mismatches in southeastern Wuhan, contrasted with fewer problems in the northern region due to stronger management. To ensure adaptability, a dynamic optimization mechanism integrating temporal imagery updates, transfer learning, and collaborative training is proposed. The findings demonstrate the effectiveness of street-level remote sensing for large-scale urban diagnostics, extend the application of deep learning in mobility research, and provide practical insights for data-driven planning and governance of slow-moving traffic systems in high-density cities.
Saba S Madae’en,1 Ahmed A Salem,2 Naila S Ararawi,3 Ezaldeen J Ramzi,3 Roa’a F Aloueedat,3 Abdullah M Saabenh,3 Diala A Allouzi,3 Reem H Abuoudeh,3 Osama E Hnaif,3 Leen M Musa,3 Salma H Alshdaifat,3 Ahmad J Al-Tanashat,3 Hala Y Almasa’afeh,3 Salma M Abuallaban3 1Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmaceutical Sciences, The Hashemite University, Zarqa, Jordan; 2Department of Anatomy, Physiology and Biochemistry, Faculty of Medicine, The Hashemite University, Zarqa, Jordan; 3Faculty of Medicine, Hashemite University, Zarqa, JordanCorrespondence: Saba S Madae’en, Department of Clinical Pharmacy and Pharmacy Practice, Faculty of Pharmaceutical Sciences, the Hashemite University, Zarqa, Jordan, Email saba@hu.edu.joBackground: Non-small cell lung cancer (NSCLC) treatment costs significantly impact healthcare systems. This study analyzes direct costs and cost drivers of perioperative and adjuvant systemic treatments for stage I–II NSCLC from Jordanian healthcare providers’ perspective using micro-costing methodology.Methods: We employed micro-costing to analyze direct medical expenses including drug acquisition, preparation, administration, pre/post-medications, diagnostics, labor, and wastage costs for perioperative regimens used in stage I–II NSCLC. International guidelines defined therapeutic regimens, while drug prices were extracted from Jordan Food and Drug Administration’s database. Published data and surveys quantified micro-costs.Results: Among 26 assessed regimens (2 targeted therapy, 10 chemotherapy, 10 chemo-immunotherapy, 4 immunotherapy), targeted/immunotherapy agents significantly increased costs. Chemotherapy regimen cost differences ranged from &dollar;633.68 (squamous) to &dollar;1,763.91 (non-squamous) per cycle. Antineoplastic agents were primary cost drivers, highest for Durvalumab (98.72% of cycle cost). Laboratory costs comprised up to 50.73% in chemotherapy and 7.24% in immunotherapy regimens. Wastage contributed up to 10.36% of total cycle costs. Average administration cost was &dollar;35 per cycle. Maximum cycle costs were: targeted therapy (Osimertinib) &dollar;7,206.44, immunotherapy (Durvalumab) &dollar;9,057.71, immune-chemotherapy (Durvalumab-Carboplatin-Pemetrexed) &dollar;11,358.43, and chemotherapy (Carboplatin-Pemetrexed) &dollar;2,300.72.Conclusion: Our results highlight the substantial economic impact and cost variability among treatment regimens. This variability presents opportunities for cost reduction through careful selection of therapeutically equivalent regimens based on pricing and toxicity profiles. The findings emphasize the need for comprehensive and precise cost analysis to inform healthcare policies and clinical practices. Future research should focus on cost-effectiveness analyses of these expensive agents to ensure value for money, support evidence-based decision-making, and strengthen price negotiations with suppliers.Keywords: chemotherapy, targeted therapy, immunotherapy, cost analysis, non-small cell lung cancer, sensitivity analysis, direct medical cost, administration cost, wastage cost
Air pollution poses significant threats to human health and the environment, necessitating accurate prediction models for effective management and mitigation strategies. This study presents a comprehensive analysis of hyperparameter optimization techniques for Long Short-Term Memory (LSTM) based deep learning models in urban air quality forecasting. We focus on predicting concentrations of four key pollutants: carbon monoxide (CO), nitrogen oxides (NOX), nitrogen dioxide (NO2), and particulate matter (PM10). The study employs and compares three prominent hyperparameter optimization methods: Random Search, Bayesian Optimization, and Hyperband. Using air quality data from Sakarya, Turkey, collected between January 2020 and September 2022, we first addressed missing data through comparative analysis of mean imputation and k-Nearest Neighbors (kNN) imputation methods. Our results demonstrate that kNN imputation generally outperforms mean imputation, except for NOX predictions. The hyperparameter-optimized LSTM models consistently outperformed baseline models across all pollutants. Notably, the Hyperband Search algorithm excelled in NOX prediction, while Bayesian Optimization showed superior performance for other pollutants. Our analysis also revealed temporal trends in pollutant concentrations during the COVID-19 pandemic, including significant reductions in PM10 and CO levels. This study contributes to AI-driven environmental monitoring by comparing hyperparameter optimization techniques in urban air quality modeling. The improved prediction accuracy offered by our optimized models has significant implications for public health protection, environmental policymaking, and smart city initiatives. Our findings underscore the importance of tailored optimization approaches for different pollutants and highlight the potential of advanced machine learning techniques in addressing environmental challenges.
Bhawana Charan , Swati Kochar , Asmita Nayak
et al.
Background: Hypertensive disorders in pregnancy account for 14% of global maternal mortality and are globally the second most common cause of maternal morbidity. Among the hypertensive disorders of pregnancy, pre-eclampsia and eclampsia lead to the most deaths. Pre-eclampsia can deteriorate quickly and affect multiple organ systems. Various biomarkers are increased in pre-eclamptic women, including N-terminal prohormone of brain natriuretic peptide (NT-proBNP). Hence, we studied the association of maternal serum levels of NT-proBNP with pre-eclampsia.
Aims and Objectives: To determine serum levels of NT-proBNP in patients with pre-eclampsia and to see the association of increased levels of NT-proBNP with the severity of pre-eclampsia.
Materials and Methods: A prospective observational study was conducted on 88 hypertensive pregnant women presenting to the gynecology outpatient department of PBM hospital, Bikaner. They were divided into two groups: group A consisting of 44 pregnant women with pre-eclampsia without severe features, and group B consisting of 44 pregnant women with pre-eclampsia with severe features. After getting proper written consent, 5 mL of fasting venous sample was collected at term or just before induction of labor or in early labor in both groups.
Results: Significantly elevated serum levels of NT-proBNP were seen in group B (pre-eclampsia with severe features) (mean systolic blood pressure [SBP]: 167.44±9.33, diastolic blood pressure [DBP]: 105.53±7.63) than in group A (pre-eclampsia without severe features) (mean SBP: 148.47±4.85, DBP: 98.36±4.88). The value of NT-proBNP was 489.2±259.2 in group B and 169.52±51.98 in group A (P<0.001). Thus, there is a significant association of NT-proBNP with increasing severity of pre-eclampsia.
Conclusion: NT-proBNP is a useful marker to predict the severity of pre-eclampsia. Higher levels of NT-proBNP are seen in more severe grades of pre-eclampsia.
As inverter-based resources (IBRs) penetrate power systems, the dynamics become more complex, exhibiting multiple timescales, including electromagnetic transient (EMT) dynamics of power electronic controllers and electromechanical dynamics of synchronous generators. Consequently, the power system model becomes highly stiff, posing a challenge for efficient simulation using existing methods that focus on dynamics within a single timescale. This paper proposes a Heterogeneous Multiscale Method for highly efficient multi-timescale simulation of a power system represented by its EMT model. The new method alternates between the microscopic EMT model of the system and an automatically reduced macroscopic model, varying the step size accordingly to achieve significant acceleration while maintaining accuracy in both fast and slow dynamics of interests. It also incorporates a semi-analytical solution method to enable a more adaptive variable-step mechanism. The new simulation method is illustrated using a two-area system and is then tested on a detailed EMT model of the IEEE 39-bus system.
This thesis studies the effectiveness of Long Short Term Memory model in forecasting future Job Openings and Labor Turnover Survey data in the United States. Drawing on multiple economic indicators from various sources, the data are fed directly into LSTM model to predict JOLT job openings in subsequent periods. The performance of the LSTM model is compared with conventional autoregressive approaches, including ARIMA, SARIMA, and Holt-Winters. Findings suggest that the LSTM model outperforms these traditional models in predicting JOLT job openings, as it not only captures the dependent variables trends but also harmonized with key economic factors. These results highlight the potential of deep learning techniques in capturing complex temporal dependencies in economic data, offering valuable insights for policymakers and stakeholders in developing data-driven labor market strategies
We develop delay-compensating feedback laws for linear switched systems with time-dependent switching. Because the future values of the switching signal, which are needed for constructing an exact predictor-feedback law, may be unavailable at current time, the key design challenge is how to construct a proper predictor state. We resolve this challenge constructing two alternative, average predictor-based feedback laws. The first is viewed as a predictor-feedback law for a particular average system, properly modified to provide exact state predictions over a horizon that depends on a minimum dwell time of the switching signal (when it is available). The second is, essentially, a modification of an average of predictor feedbacks, each one corresponding to the fixed-mode predictor-feedback law. We establish that under the control laws introduced, the closed-loop systems are (uniformly) exponentially stable, provided that the differences among system's matrices and among (nominal stabilizing) controller's gains are sufficiently small, with a size that is inversely proportional to the delay length. Since no restriction is imposed on the delay, such a limitation is inherent to the problem considered (in which the future switching signal values are unavailable), and thus, it cannot be removed. The stability proof relies on multiple Lyapunov functionals constructed via backstepping and derivation of solutions' estimates for quantifying the difference between average and exact predictor states. We present consistent numerical simulation results, which illustrate the necessity of employing the average predictor-based laws and demonstrate the performance improvement when the knowledge of a minimum dwell time is properly utilized for improving state prediction accuracy.
Even though the law of digital platforms has been a much-discussed topic in the past few years, academic studies have generally overlooked the gender approach to law and digital technologies. This is hardly surprising. Studies on gender, which have grown since the 60s, have remained a relatively separate field from law and the same can be said about many other disciplines. Legal scholars usually do not include gender in their studies, and the law of digital platforms and digital technologies makes no exception. Yet, a gender approach to the law of digital platforms is strongly needed, as it would help to unveil some of the most challenging aspects of the digital revolution. Against this backdrop, this Special Issue has gone the extra mile to propose studies on gendering platform law. The result is both international and multidisciplinary. The Issue gathers lawyers and scholars from a wide range of disciplines. Also it collects papers from scholars from different countries and organisation, such as the United Kingdom, Croatia, the European Union, Italy, Israel, the United States, Germany, India and China. Thanks to a very diverse background, each article touches upon different sectors of platform work and adopts a different perspective: from crowdwork to taxi rides and deliveries, from care work to reputational systems, from abortion to youtubers, among other subjects. By reading them together, it is remarkable to see that the issue of gendering platform work is significant and worth raising worldwide. The solutions proposed in the different articles are diverse and thought-provoking. They vary from suggesting amendments to specific laws to invoking policy or practical changes, emphasising the primary role of the state but also the importance of non-state actors.
Dina Idriss-Wheeler, Xaand Bancroft, Saredo Bouraleh
et al.
<h4>Background</h4>Survivors of intimate partner violence (IPV) often face increased incidents of violence during stressful life events (SLEs) such as economic recessions, environmental disasters, and pandemics. These events can diminish the effectiveness of both formal (e.g., health, social, justice, labor, community) and informal (e.g., friends, family, neighbors) support systems. Additionally, SLEs exacerbate existing health and social inequities, making it necessary to understand the accessibility of support services during these times. This scoping review investigates access to services by individuals experiencing IPV during SLEs in high-income countries.<h4>Approach</h4>A comprehensive search was conducted across several electronic databases including MEDLINE (OVID), Embase (OVID), PsychInfo (OVID), CINAHL (EBSCO), Global Health (EBSCO), Gender Watch (ProQuest), Web of Science, and Applied Social Sciences Index & Abstracts (ProQuest), along with the search engine Google Scholar. This search, which imposed no date restrictions, was extended through May 22nd, 2024. Key search terms were developed from prior literature and in consultation with an expert librarian, focusing on 'stressful life events,' 'intimate partner violence,' and 'access to services.'. Each study was screened and extracted by two reviewers and conflicts were resolved through discussion or a third reviewer.<h4>Results</h4>The search across eight databases and citation searching resulted in a total of 7396 potentially relevant articles. After removing 1968 duplicates and screening 5428 based on titles and abstracts, 200 articles underwent full abstract review. Ultimately, 74 articles satisfied the inclusion criteria and were selected for further analysis. The analysis focused on barriers and facilitators to access, identifying challenges within Survivors' support systems, redirected resources during crises, and complex control dynamics and marginalization. Over 90% of the literature included covered the recent COVID-19 pandemic. Addressing these challenges requires innovative strategies, sustained funding, and targeted interventions for high-risk subgroups.<h4>Conclusion</h4>This scoping review systematically outlined the challenges and enabling factors influencing the availability of support services for Survivors of IPV during SLEs. It underscores the need for robust, culturally sensitive health and social support mechanisms, and policies. Such measures are essential to better protect and assist IPV Survivors and their service providers during these critical times. Furthermore, it is imperative to integrate the insights and expertise of the violence against women (VAW) sector into emergency planning and policy-making to ensure comprehensive and effective responses that address the unique needs of Survivors in crises.
This work proposes an interval observer design for nonlinear discrete-time systems based on the Kazantzis-Kravaris/Luenberger (KKL) paradigm. Our design extends to generic nonlinear systems without any assumption on the structure of its dynamics and output maps. Relying on a transformation putting the system into a target form where an interval observer can be directly designed, we then propose a method to reconstruct the bounds in the original coordinates using the bounds in the target coordinates, thanks to the Lipschitz injectivity of this transformation achieved under Lipschitz distinguishability when the target dynamics have a high enough dimension and are pushed sufficiently fast. An academic example serves to illustrate our methods.
The rapid advancement of Generative AI (Gen AI) technologies, particularly tools like ChatGPT, is significantly impacting the labor market by reshaping job roles and skill requirements. This study examines the demand for ChatGPT-related skills in the U.S. labor market by analyzing job advertisements collected from major job platforms between May and December 2023. Using text mining and topic modeling techniques, we extracted and analyzed the Gen AI-related skills that employers are hiring for. Our analysis identified five distinct ChatGPT-related skill sets: general familiarity, creative content generation, marketing, advanced functionalities (such as prompt engineering), and product development. In addition, the study provides insights into job attributes such as occupation titles, degree requirements, salary ranges, and other relevant job characteristics. These findings highlight the increasing integration of Gen AI across various industries, emphasizing the growing need for both foundational knowledge and advanced technical skills. The study offers valuable insights into the evolving demands of the labor market, as employers seek candidates equipped to leverage generative AI tools to improve productivity, streamline processes, and drive innovation.
A System of Systems (SoS) comprises Constituent Systems (CSs) that interact to provide unique capabilities beyond any single CS. A key challenge in SoS is ad-hoc scalability, meaning the system size changes during operation by adding or removing CSs. This research focuses on an Unmanned Vehicle Fleet (UVF) as a practical SoS example, addressing uncertainties like mission changes, range extensions, and UV failures. The proposed solution involves a self-adaptive system that dynamically adjusts UVF architecture, allowing the Mission Control Center (MCC) to scale UVF size automatically based on performance criteria or manually by operator decision. A multi-agent environment and rule management engine were implemented to simulate and verify this approach.
Felix Maximilian Bauer, Lena Lärm, Shehan Morandage
et al.
Root systems of crops play a significant role in agroecosystems. The root system is essential for water and nutrient uptake, plant stability, symbiosis with microbes, and a good soil structure. Minirhizotrons have shown to be effective to noninvasively investigate the root system. Root traits, like root length, can therefore be obtained throughout the crop growing season. Analyzing datasets from minirhizotrons using common manual annotation methods, with conventional software tools, is time-consuming and labor-intensive. Therefore, an objective method for high-throughput image analysis that provides data for field root phenotyping is necessary. In this study, we developed a pipeline combining state-of-the-art software tools, using deep neural networks and automated feature extraction. This pipeline consists of two major components and was applied to large root image datasets from minirhizotrons. First, a segmentation by a neural network model, trained with a small image sample, is performed. Training and segmentation are done using “RootPainter.” Then, an automated feature extraction from the segments is carried out by “RhizoVision Explorer.” To validate the results of our automated analysis pipeline, a comparison of root length between manually annotated and automatically processed data was realized with more than 36,500 images. Mainly the results show a high correlation (r=0.9) between manually and automatically determined root lengths. With respect to the processing time, our new pipeline outperforms manual annotation by 98.1-99.6%. Our pipeline, combining state-of-the-art software tools, significantly reduces the processing time for minirhizotron images. Thus, image analysis is no longer the bottle-neck in high-throughput phenotyping approaches.