Hasil untuk "Labor systems"

P. Qiu, Erin F. Simonds, S. Bendall et al.

The ability to analyze multiple single-cell parameters is critical for understanding cellular heterogeneity. Despite recent advances in measurement technology, methods for analyzing high-dimensional single-cell data are often subjective, labor intensive and require prior knowledge of the biological system. To objectively uncover cellular heterogeneity from single-cell measurements, we present a versatile computational approach, spanning-tree progression analysis of density-normalized events (SPADE). We applied SPADE to flow cytometry data of mouse bone marrow and to mass cytometry data of human bone marrow. In both cases, SPADE organized cells in a hierarchy of related phenotypes that partially recapitulated well-described patterns of hematopoiesis. We demonstrate that SPADE is robust to measurement noise and to the choice of cellular markers. SPADE facilitates the analysis of cellular heterogeneity, the identification of cell types and comparison of functional markers in response to perturbations.

F. Gotsch, R. Romero, J. Kusanovic et al.

A. Lithourgidis, C. Dordas, C. Damalas et al.

M. Mogstad, C. Pronzato

There is a heated debate in many European countries about a move towards a welfare system that increases the incentives for lone mothers to move off welfare and into work. We analyze the consequences of a major Norwegian workfare reform of the generous welfare system for lone mothers. Our difference-in-differences estimates show that the policy changes were successful in improving labor market attachment and increasing disposable income of new lone mothers. By contrast, the reform led to a substantial decrease in disposable income and a significant increase in poverty among persistent lone mothers, because a sizeable group was unable to offset the loss of out-of-work welfare benefits with gains in earnings. This suggests that the desired effects of the workfare reform were associated with the side-effects of income loss and increased poverty among a substantial number of lone mothers with insurmountable employment barriers. This finding stands in stark contrast to evidence from similar policy changes in Canada, the UK, and the US, and underscores that policymakers from other developed countries should be cautious when drawing lessons from the successful welfare reforms implemented in Anglo-Saxon countries.

Treb Allen, Costas Arkolakis, Costas Arkolakis et al.

R. Walton, R. B. Mckersie

J. Heathcote, Kjetil Storesletten, Giovanni L. Violante

V. Venkatesh, Kai Kang, Bill Wang et al.

Abstract Social sustainability is a major concern in global supply chains for protecting workers from exploitation and for providing a safe working environment. Although there are stipulated standards to govern supply chain social sustainability, it is not uncommon to hear of businesses being reported for noncompliance issues. Even reputable firms such as Unilever have been criticized for production labor exploitation. Consumers now increasingly expect sellers to disclose information on social sustainability, but sellers are confronted with the challenge of traceability in their multi-tier global supply chains. Blockchain offers a promising future to achieve instant traceability in supply chain social sustainability. This study develops a system architecture that integrates the use of blockchain, internet-of-things (IoT) and big data analytics to allow sellers to monitor their supply chain social sustainability efficiently and effectively. System implementation cost and potential challenges are analyzed before the research is concluded.

Sasi Iamsiraroj

Frank Yeboah Adusei, Williams Agyemang-Duah, Yaw Asamoah Akowuah et al.

BackgroundSmallholder farmers bear disproportionate climate-related risks due to heavy reliance on natural resources and limited access to adaptive technologies. Male and female farmers experience climate change impacts differently, yet there remains limited localized understanding of gender-specific adaptation strategies in Ghana. This study explores gendered perspectives on climate adaptation in the Ejura-Sekyedumase Municipality. It addresses essential knowledge gaps in how structural inequalities, indigenous knowledge systems, and institutional access intersect to shape differentiated adaptive pathways among smallholder farming communities.MethodsGrounded in Feminist Political Ecology and Adaptive Capacity theory, we employed an exploratory qualitative design from June–August 2023. Using purposive sampling, we conducted semi-structured interviews (n = 40), focus group discussions (n = 3), and field observations with smallholder farmers across three sub-districts: Ejura, Hiawoanwu, and Sekyedumase. Data were thematically analyzed following Braun and Clarke's approach, with data integration enhancing validity and contextual depth. Verbatim quotations were employed to preserve research participants' voices and validate emergent themes.ResultsFindings revealed profound gender disparities in access to land, credit, and extension services that systematically constrain women's adaptive capacity. Male farmers engaged in larger-scale, capital-intensive practices such as mechanized ridging, enabled by institutional connections and cooperative membership. Conversely, women developed innovative, smaller-scale strategies including mulching, seed preservation, food processing, and water conservation that required minimal external inputs yet demonstrated ecological sustainability and community focus. Women's adaptation operated through informal knowledge-sharing networks, collective labor arrangements, and indigenous forecasting methods. These practices reveal adaptive agency despite structural marginalization. Spatial inequalities across sub-districts and intergenerational tensions between traditional knowledge and technological innovation further shaped adaptation dynamics.ConclusionThis study demonstrates that effective climate adaptation requires gender-transformative policies addressing structural inequalities in land tenure, credit access, and institutional support. It also emphasizes recognizing and scaling women's ecologically grounded innovations. The research advances Sustainable Development Goals 2, 5, and 13 by revealing their fundamental interdependence. It emphasizes that climate-resilient food systems cannot be achieved without dismantling gender inequalities that constrain adaptive capacity among smallholder farmers.

V. Ham, K.E. Kliem, L.A. Crompton et al.

ABSTRACT: The United Kingdom's climate and topography enable multiple different calving patterns to operate within the same market, facilitated by industry infrastructure that allows for a variety of milk purchasing arrangements. All-year-round (AYR) calving is most common, and with current labor challenges, spring block (SB), autumn block (AB), and twin block (TB) calving systems could potentially become more popular, but research comparing the efficiency of AYR and block-calving systems operating within the same market conditions is limited. This study compared the costs of production of AYR against 3 block-calving systems on a pence per liter (PPL) of ECM basis (1 pence = ₤0.01, US$1 = £1.28 at the time of the study), to assist benchmarking activities, costs, and management decisions. Farm accounts data (from 2017 to 2020), from 604 farms broadly representing the national split of calving patterns in the United Kingdom were included in a linear mixed effects (LME) model used for inference with maximum likelihood estimation. Random effects included year and farm, with fixed effects including herd size (cows), farm size (hectares), and average annual milk yield per cow, which were each standardized to enable all calving patterns to be compared at the same scale (i.e., same herd size, farm size, and milk yield). Calving pattern was self-determined by the farmer under guidance from a trained data collector. Cost of production variables investigated included milk price, stock sales (calves, cull cows, breeding animals), total income (all dairy farm revenues), total purchased feed, purchased forage, variable costs, gross margin, labor and overhead costs, and net profit. The AB herds had lower total income, lower forage purchases, higher labor costs and lower net profit compared with AYR. The SB herds had higher total income, higher forage purchases, and lower labor and overhead costs compared with AYR. No differences were found between TB and AYR herds. Using the LME model, we estimated the impact of changing the fixed effects on costs of production based on a 1-SD change. Increasing herd size (1 SD, 345 cows) was associated with a reduction in net profit of AB herds by 3.34 PPL but an increase in net profit for SB herds by 5.57 PPL compared with AYR. For increasing farm size (1 SD, 164 ha), all 3 block-calving herds had different associations compared with AYR; net profits would be increased for AB and TB herds (by 1.33 and 2.12 PPL, respectively), whereas SB herds would have reduced net profit by 4.26 PPL. Increasing energy corrected milk yield (1 SD, 4,038 L) would only benefit the net profit of SB herds over AYR by 6.04 PPL, as SB herds had the lowest milk yield per cow. The results demonstrated that increasing land, cows, or milk yield per cow was associated with different responses in cost of production depending on calving pattern. Findings from this study could be used by extension services, farm advisors, and farmers for benchmarking purposes and when considering farm-scale decisions or switching from an AYR to a block-calving pattern.

Ray-Jade Chen, Mai-Szu Wu, Lung-Wen Tsai et al.

BackgroundThe global nursing shortage, exacerbated by heavy workloads and high turnover rates associated with the COVID-19 pandemic, continues to undermine care quality and nurse well-being. Although digital health technologies have enhanced coordination, improved communication, and reduced clinical errors in nursing practice, they have also increased nurses’ documentation burden. Advances in large language models (LLMs) and other generative artificial intelligence (GenAI) tools facilitate the generation of accurate reports from electronic medical records (EMRs), thereby streamlining documentation workflows, saving time, and reducing nurses’ workloads. Accordingly, integrating LLMs into electronic nursing documentation systems warrants further exploration. ObjectiveThis study examines the integration of an LLM into an in-house nursing information system (NIS) implemented across 3 hospitals in Taiwan to reduce the time and effort required for nursing handover documentation and to preliminarily assess the operational and economic implications of GenAI-assisted workflows. MethodsA multidisciplinary team of nursing specialists and information technology experts at Taipei Medical University (TMU) restructured the organization’s existing nursing handover documentation process to facilitate interaction with the LLM. The team also developed prompt-based interfaces to automatically generate section-specific content for the nursing handover document. The LLM-integrated NIS was subsequently deployed across 3 hospitals in Taiwan: Taipei Medical University Hospital (TMUH), Wan Fang Hospital (WFH), and Shuang Ho Hospital (SHH). We then extracted and analyzed NIS log data to compare documentation times before and after LLM implementation, thereby quantifying time savings. ResultsIntegration of the LLM into nursing handover documentation was associated with shorter per-patient documentation time in routine clinical use across TMUH, WFH, and SHH. Based on preintegration NIS logs (September 2024), the average handover document completion time per patient ranged from 3.45 (SD 3.82) to 4.32 (SD 4.48) minutes across hospitals and shifts, providing a preliminary baseline for subsequent comparisons. In postintegration NIS logs (October-December 2024), the overall handover document completion time per patient (mean) was substantially lower, ranging from 1.17 (SD 1.86) to 2.54 (SD 2.82) minutes across hospitals and shifts. Using monthly patient volume to estimate time savings, 113-273, 160-314, and 198-391 hours were saved per month at TMUH, WFH, and SHH, respectively, corresponding to aggregate savings of 474-981 hours per month across hospitals during the study period. ConclusionsWe integrated an LLM into an NIS to generate nursing handover documents without altering existing workflows. Across 3 hospitals within TMU’s health system, GenAI assistance was associated with shorter documentation time and a positive net labor value from October to December 2024. Prompts were constrained, and nurse verification was required to mitigate hallucinations. Future work will enhance logging to capture reliability and editing metrics, compare LLM-generated drafts with nurse-finalized notes to inform prompt refinement, and assess generalizability to other documentation workflows.

Abdullah N Alhawsawi, Sultan Daud Khan, Faizan Ur Rehman

Abstract With the rapid growth of urbanization, large public gatherings in the form of religious festivals, marathons, political rallies are commonly observed in modern cities. Ensuring public safety and security is one the main responsibilities of law enforcement agencies during such events. Despite safety measures, crowd disaster often occurs during such events which leads to the fatalities and injuries. Therefore, effective crowd management is important to ensure public safety and support resilient urban life in smart cities. Traditional crowd management systems rely on manual monitoring and analysis of video streams by human analyst. These methods are slow and labor-intensive and are not suitable for real-time decision making. To address this challenge, we propose a framework based on advanced deep learning approach that detects and localizes congested areas in crowded scenes. Our framework utilizes recurrence plots (RP) images to encode irregularities in crowd motion. These RP images are then classified using a specialized convolutional neural network tailored for congestion detection. We use both synthetic and real-world datasets to evaluate the performance of proposed framework. From the extensive experimental results, we conclude that proposed framework achieves better performance in detecting congested areas compared to state-of-the-art methods.

Yuriy Vasilev, Irina Raznitsyna, Anastasia Pamova et al.

<b>Background:</b> Electronic health records (EHRs) remain a vital source of clinical information, yet processing these heterogeneous data is extremely labor-intensive. Summarization of these data using Large Language Models (LLMs) is considered a promising tool to support practicing physicians. Unbiased, automated quality control is crucial for integrating the tools into routine practice, saving time and labor. This pilot study aimed to assess the potential and constraints of self-contained evaluation of summarization quality (without expert involvement) based on automatic evaluation metrics and LLM-as-a-judge. <b>Methods:</b> The summaries of text data from 30 EHRs were generated by six open-source low-parameter LLMs. The medical summaries were evaluated using standard automatic metrics (BLEU, ROUGE, METEOR, BERTScore) as well as the LLM-as-a-judge approach using the following criteria: relevance, completeness, redundancy, coherence and structure, grammar and terminology, and hallucinations. Expert evaluation was conducted using the same criteria. <b>Results:</b> The results showed that LLMs hold great promise for summarizing medical data. Nevertheless, neither the evaluation metrics nor LLM judges are reliable in detecting factual errors and semantic distortions (hallucinations). In terms of relevance, the Pearson correlation between the summary quality score and the expert opinions was 0.688. <b>Conclusions:</b> Completely automating the evaluation of medical summaries remains challenging. Further research should focus on dedicated methods for detecting hallucinations, along with investigating larger or specialized models trained on medical texts. Additionally, the potential integration of retrieval-augmented generation (RAG) within the LLM-as-a-judge architecture deserves attention. Nevertheless, even now, the combination of LLMs and the automatic evaluation metrics can underpin medical decision support systems by performing initial evaluations and highlighting potential shortcomings for expert review.

Qinlang Liang, Gang Liu, Yazhi Luan et al.

Feeding frequency is crucial for the growth and development of white shrimp (<i>Litopenaeus vannamei</i>) at various life stages. Although higher feeding frequencies can enhance growth, manual feeding methods significantly increase labor costs. Automatic feeding systems offer a cost-effective and efficient alternative, yet their application in intensive shrimp culture remains under-researched. This study evaluates different feeding frequencies for <i>L. vannamei</i> in intensive aquaculture tanks, focusing on growth performance, survivability, feed utilization, digestive and antioxidant capacities, and economic viability. Juvenile shrimp (3.85 ± 0.3 g) were cultured for 63 days with feeding frequencies of 6, 8, 10, and 12 times/day (A6, A8, A10, and A12 groups, respectively) using automatic feeders, with a control group fed manually 6 times/day (M6). Results indicated that automatic feeding significantly improved final body weight, specific growth rate, and feed conversion ratio compared to manual feeding. Among automatic feeding groups, A6 and A8 showed optimal performance, with a quadratic regression identifying 7.83 times/day as the optimal frequency. While digestive enzyme activity remained consistent across groups, A6 and A8 demonstrated significantly higher antioxidant enzyme levels (superoxide dismutase (SOD) and glutathione peroxidase (GPx)) and lower lipid peroxidation (MDA), suggesting superior digestive and antioxidant capacities. Pearson’s correlation confirmed a positive relationship between SOD and GPx. Economically, the A8 group achieved the highest profitability. Consequently, a feeding frequency of 6–8 times/day using automatic feeders is recommended as an optimal and feasible strategy for intensive white shrimp culture in this life stage.

Venkat Ram Reddy Ganuthula, Krishna Kumar Balaraman

This paper examines labor market polarization through a comparative analysis of skill-based employment and wage distributions in India and the United States during 2018-2023, with particular attention to differential automation risks and AI preparedness. Using detailed occupation-level data, automation risk metrics, and a series of statistical tests including wage premium analysis, employment share tests, and wage-employment regressions, we document significant structural differences in labor markets between developing and developed economies. Our analysis yields four key findings. First, we find statistically significant differences in employment distribution patterns, with India showing disproportionate concentration in low-skill employment compared to the US, particularly in occupations with high automation risk. Second, regression analysis reveals that wage premiums differ systematically between the two countries, with significantly larger skill-based wage gaps in India. Third, we find robust evidence of a negative relationship between employment size and wages, suggesting stronger labor supply effects in developing economies. Fourth, analysis of occupation-specific automation risk reveals that developing economies face a "double vulnerability" - concentration of employment in both low-skill occupations and jobs with higher automation potential, complicated by lower AI preparedness scores. These findings provide novel empirical evidence on how development stages influence labor market polarization patterns and carry important implications for skill development and technology adoption policies in developing economies. Our results suggest that traditional approaches to labor market development may need significant modification to account for the differential impacts of AI across development stages.

Shakya Jayakody, Youpeng Zhao, Jun Wang

Graph convolutional networks (GCNs) are fundamental in various scientific applications, ranging from biomedical protein-protein interactions (PPI) to large-scale recommendation systems. An essential component for modeling graph structures in GCNs is sparse general matrix-matrix multiplication (SpGEMM). As the size of graph data continues to scale up, SpGEMMs are often conducted in an out-of-core fashion due to limited GPU memory space in resource-constrained systems. Albeit recent efforts that aim to alleviate the memory constraints of out-of-core SpGEMM through either GPU feature caching, hybrid CPU-GPU memory layout, or performing the computation in sparse format, current systems suffer from both high I/O latency and GPU under-utilization issues. In this paper, we first identify the problems of existing systems, where sparse format data alignment and memory allocation are the main performance bottlenecks, and propose AIRES, a novel algorithm-system co-design solution to accelerate out-of-core SpGEMM computation for GCNs. Specifically, from the algorithm angle, AIRES proposes to alleviate the data alignment issues on the block level for matrices in sparse formats and develops a tiling algorithm to facilitate row block-wise alignment. On the system level, AIRES employs a three-phase dynamic scheduling that features a dual-way data transfer strategy utilizing a tiered memory system: integrating GPU memory, GPU Direct Storage (GDS), and host memory to reduce I/O latency and improve throughput. Evaluations show that AIRES significantly outperforms the state-of-the-art methods, achieving up to 1.8x lower latency in real-world graph processing benchmarks.

Mohamed Yassine Arkhis, Denis Efimov

In this paper, first, it is shown that if a nonlinear time-varying system is contractive, then it is incrementally exponentially stable. Second, leveraging this result, under mild restrictions, an approach is proposed to design feedforward inputs for affine in control systems providing contraction/incremental exponential stability. Unlike standard stability notions, which have well-established control design techniques, this note can be considered among the first ones to provide such a tool for a kind of incremental stability. The theoretical findings are illustrated by examples.

Sana Khalil, Angela Warner

We highlight the barriers shaping women's economic opportunities in Pakistan, where female labor force participation remains among the lowest globally. Labor force surveys (2020-21) show a stark rural-urban divide: 28 percent for rural women versus 69 percent for rural men, and 10 percent for urban women versus 66 percent for urban men. Unemployment is higher for women (7 percent in rural areas; 16 percent in urban areas) than for men (5 and 6 percent, respectively). Women are concentrated in agriculture (68 percent), with limited presence in services (17 percent) and industry (15 percent), and mostly in rural (51 percent) or home-based (30 percent) work; only 14 percent are in formal business settings. Employment status reflects vulnerability: 63 percent of rural women are unpaid contributing family workers versus 17 percent of urban women. Interviews with married women in Karachi underscore childcare constraints, harassment and safety concerns, transport barriers, and family opposition. Together, the evidence points to structural and cultural constraints that restrict access to paid work; easing them will require labor market reforms, better transport and childcare, stronger protections against harassment and discrimination, and a gradual change in gender norms and household decision-making.

Eri Katsumata, Abhishek Kumar Ranjan, Yoshihiko Tashima et al.

ABSTRACT Purpose: Amid rising health awareness, natural products which has milder effects than medical drugs are becoming popular. However, only few systems can quantitatively assess their impact on living organisms. Therefore, we developed a deep-learning system to automate the counting of cells in a gerbil model, aiming to assess a natural product’s effectiveness against ischemia. Methods: The image acquired from paraffin blocks containing gerbil brains was analyzed by a deep-learning model (fine-tuned Detectron2). Results: The counting system achieved a 79%-positive predictive value and 85%-sensitivity when visual judgment by an expert was used as ground truth. Conclusions: Our system evaluated hydrogen water’s potential against ischemia and found it potentially useful, which is consistent with expert assessment. Due to natural product’s milder effects, large data sets are needed for evaluation, making manual measurement labor-intensive. Hence, our system offers a promising new approach for evaluating natural products.