Hasil untuk "Labor systems"

O. O. Zhurba

This article examines the legal mechanisms for regulating the migration of third-country nationals within the European Union, focusing on international legal norms and European standards. Globalization, changes in the composition of the EU Member States and the growth of migration flows create new challenges that require flexible and balanced approaches to migration management. The social, economic and humanitarian aspects of migration are considered, including its impact on the standard of living of the population, access to employment and education, security and respect for human rights and freedoms. Particular attention is paid to how a balance can be maintained between the effectiveness of migration control and compliance with the principles of humanity, proportionality and non-discrimination. These principles are particularly important in the context of the digitalization of border control and the automation of decision-making processes. The article emphasizes the legal status of third-country nationals as subjects of international law with the right to social integration and the protection of fundamental rights. Modern technologies for border control are analyzed, including the automated ETIAS and EES systems. While these systems improve the accuracy of forecasting migration flows, they also call into question compliance with international standards, in particular the principles of non-discrimination and proportionality. The importance of transparent complaint mechanisms and codes of ethics for the use of algorithms in migration management is emphasized. Particular attention is paid to the social rights of migrants, their integration into the labor market and humane return procedures. The reform of the Single Work Permit Directive, which harmonizes national legislation with international employment standards, and proposals for a common return system that guarantees respect for human rights and prohibits collective expulsions are discussed. The practical significance of this article lies in the development of recommendations for improving EU migration policy, developing transparent and humane control and integration procedures that comply with international legal obligations and guarantee the protection of the rights and freedoms of each migrant.

Lucian Cristian Iacob, Roland Tóth, Maarten Schoukens

The challenge of finding exact and finite-dimensional Koopman embeddings of nonlinear systems has been largely circumvented by employing data-driven techniques to learn models of different complexities (e.g., linear, bilinear, input affine). Although these models may provide good accuracy, selecting the model structure and dimension is still ad-hoc and it is difficult to quantify the error that is introduced. In contrast to the general trend of data-driven learning, in this paper, we develop a systematic technique for nonlinear systems that produces a finite-dimensional and exact embedding. If the nonlinear system is represented as a network of series and parallel linear and nonlinear (polynomial) blocks, one can derive an associated Koopman model that has constant state and output matrices and the input influence is polynomial. Furthermore, if the linear blocks do not have feedthrough, the Koopman representation simplifies to a bilinear model.

Rintaro Ando

This paper studies the impact of the 2022 Dobbs decision and subsequent state level abortion bans on the labor supply of young women (ages 18-24). Using monthly CPS micro data from January 2021 to December 2023, I exploit cross state variation in post Dobbs abortion policy and estimate Difference-in-Differences (DiD) and Triple-Difference (DDD) models. In a simple DiD comparing young women in ban versus protected states, labor force participation in ban states rises by 3.6 percentage points, while participation among young men in the same states falls by 2.9 percentage points, suggesting that the female response is unlikely to be driven by stronger local labor demand. The preferred DDD specification with state-by-month and gender interacted fixed effects implies a 6.6 percentage point increase in labor force participation for young women in ban states relative to young men. School enrollment does not change significantly, whereas employment increases by about 3 percentage points. These results suggest that abortion bans are associated with an immediate increase in young women's labor market attachment, potentially shifting their short run focus toward current earnings rather than human capital accumulation.

Guang An Ooi, Otavio Bertozzi, Mohd Asim Aftab et al.

Modern power systems with high penetration of inverter-based resources exhibit complex dynamic behaviors that challenge the scalability and generalizability of traditional stability assessment methods. This paper presents a dynamic recurrent adjacency memory network (DRAMN) that combines physics-informed analysis with deep learning for real-time power system stability forecasting. The framework employs sliding-window dynamic mode decomposition to construct time-varying, multi-layer adjacency matrices from phasor measurement unit and sensor data to capture system dynamics such as modal participation factors, coupling strengths, phase relationships, and spectral energy distributions. As opposed to processing spatial and temporal dependencies separately, DRAMN integrates graph convolution operations directly within recurrent gating mechanisms, enabling simultaneous modeling of evolving dynamics and temporal dependencies. Extensive validations on modified IEEE 9-bus, 39-bus, and a multi-terminal HVDC network demonstrate high performance, achieving 99.85%, 99.90%, and 99.69% average accuracies, respectively, surpassing all tested benchmarks, including classical machine learning algorithms and recent graph-based models. The framework identifies optimal combinations of measurements that reduce feature dimensionality by 82% without performance degradation. Correlation analysis between dominant measurements for small-signal and transient stability events validates generalizability across different stability phenomena. DRAMN achieves state-of-the-art accuracy while providing enhanced interpretability for power system operators, making it suitable for real-time deployment in modern control centers.

Abhishek Singh, Roland Bol, Viktoriia Lovynska et al.

Salinity stress is one of the most challenging constraints affecting wheat production, limiting both yield and nutritional quality. Wheat is one of the most important staple cereals as well as a major source of carbohydrates for a considerable portion of the world population, yet wheat has suffered from significant productivity constraints due to salt stress. Such stress adversely affects germination, vegetative growth, reproductive organ development, enzymatic activity, photosynthesis photostability, and hormonal equilibrium, eventually causing oxidative stress and drastic loss of crop yield. Furthermore, the reducing nutritional quality of wheat further aggravates the issues regarding malnutrition and food security, highlighting the need for effective mitigation strategies. Although various methods have been investigated, including plant breeding, genetic engineering, and agronomic management, they are labor, cost, and time-intensive. Nanotechnology is a novel, eco-friendly and efficient approach for controlling salinity stress and improving crop biofortification. Some common methods of applications of nanotechnology-based products like nanoparticles (NPs) are foliar spraying, soil amendments and seed priming, which have shown considerable promise in improving salinity stress resistance, nutrient absorption, and wheat yield. This review outlines the extent of contribution of NPs in alleviating salinity stress, as well as the enhancement of the nutritional qualities of wheat. This work uniquely combines both salinity stress adaptation and nanofortification strategies under one framework that filling crucial information gaps. Investigating the mechanisms underlying NPs interaction with plant systems is essential for designing effective, green, and cost-efficient nanotechnology tools for sustainable wheat production. In the long run, this knowledge will aid sustainable agricultural practices and food security worldwide.

Christos Theodoropoulos, Andrei Catalin Coman, James Henderson et al.

Abstract Background Knowledge discovery in scientific literature is hindered by the increasing volume of publications and the scarcity of extensive annotated data. To tackle the challenge of information overload, it is essential to employ automated methods for knowledge extraction and processing. Finding the right balance between the level of supervision and the effectiveness of models poses a significant challenge. While supervised techniques generally result in better performance, they have the major drawback of demanding labeled data. This requirement is labor-intensive, time-consuming, and hinders scalability when exploring new domains. Methods and Results In this context, our study addresses the challenge of identifying semantic relationships between biomedical entities (e.g., diseases, proteins, medications) in unstructured text while minimizing dependency on supervision. We introduce a suite of unsupervised algorithms based on dependency trees and attention mechanisms and employ a range of pointwise binary classification methods. Transitioning from weakly supervised to fully unsupervised settings, we assess the methods’ ability to learn from data with noisy labels. The evaluation on four biomedical benchmark datasets explores the effectiveness of the methods, demonstrating their potential to enable scalable knowledge discovery systems less reliant on annotated datasets. Conclusion Our approach tackles a central issue in knowledge discovery: balancing performance with minimal supervision which is crucial to adapting models to varied and changing domains. This study also investigates the use of pointwise binary classification techniques within a weakly supervised framework for knowledge discovery. By gradually decreasing supervision, we assess the robustness of these techniques in handling noisy labels, revealing their capability to shift from weakly supervised to entirely unsupervised scenarios. Comprehensive benchmarking offers insights into the effectiveness of these techniques, examining how unsupervised methods can reliably capture complex relationships in biomedical texts. These results suggest an encouraging direction toward scalable, adaptable knowledge discovery systems, representing progress in creating data-efficient methodologies for extracting useful insights when annotated data is limited.

JOSEPH OLORUNFEMI AKANDE

Despite decades of intervention, women across Sub-Saharan Africa continue to face systemic exclusion from formal financial systems, limiting their ability to participate fully in economic, social, and political domains. This paper examines how microfinance interventions between 2014 and 2024 have influenced various dimensions of women’s empowerment across the region. Using a conceptual framework grounded in context-mechanism-outcome (CMO) logic, the study analyzes access to credit, savings behavior, digital financial inclusion, and gendered economic participation. It draws on national survey data, institutional databases, and regional trends presented in Tables 1 through 8. Findings show that while female labor force participation rose modestly from 62.1 percent in 2014 to 66.3 percent in 2024, formal credit access remains disproportionately low, with only 23 percent of women having access to microloans in 2024 compared to 41 percent for men. However, mobile money adoption among women increased from 11 percent to 47 percent over the same period, signaling a shift toward digital-led inclusion. Savings mobilization improved in countries with well-structured microfinance institutions, with Rwanda and Ghana outperforming regional averages by over 18 percentage points. The study recommends that regional financial inclusion policies prioritize digital microfinance platforms tailored for women, enforce gender-balanced governance in lending institutions, and integrate financial literacy into women-focused rural programs. By expanding access to adaptive and non-extractive microfinance models, Sub-Saharan Africa can unlock broader empowerment outcomes that extend beyond income generation to include voice, agency, and collective well-being.

Ishaya Usman Gadzama, Homa Asadi, Qazal Hina et al.

Virtual fencing (VF) technology represents an innovative approach to livestock management, utilizing GPS-enabled collars to establish invisible boundaries through auditory and mild electrical stimuli. While VF offers potential benefits such as enhanced pasture management flexibility and reduced labor costs, its widespread adoption faces challenges including high initial investment costs, connectivity issues, GPS accuracy limitations, potential device durability concerns, and individual animal variability in learning and response. Furthermore, despite studies showing rapid learning and generally minimal long-term welfare impacts, questions remain regarding optimizing training protocols, addressing occasional short-term behavioral disruptions and collar abrasions, assessing long-term welfare effects across diverse systems (especially intensive and dairy), and improving scalability. To comprehensively assess the potential and limitations of this technology and guide its future development and implementation, a review integrating existing knowledge on the efficacy, welfare implications, and practical applications of VF in cattle production systems is essential. This review examines the efficacy, welfare implications, and practical applications of VF in cattle production systems. Studies demonstrate that cattle rapidly learn to associate auditory cues with electrical pulses, achieving high containment rates (≥90%) within days, with minimal long-term welfare impacts as indicated by stable cortisol levels. However, short-term behavioral disruptions and occasional collar-related abrasions have been reported, particularly in dairy cattle. While VF enhances pasture management flexibility and reduces labor costs, challenges such as connectivity issues, individual animal variability, and high initial investment costs limit its widespread adoption. The findings suggest that VF is a promising tool for precision livestock farming, though further research is needed to optimize training protocols, assess long-term welfare effects, and improve scalability across diverse farming systems.

Francesco Del Prato, Marc Fleurbaey

What happens when employers value worker welfare in frictional labor markets? We show this "responsibility" creates an endogenous wedge in the marginal labor cost -- akin to a hiring subsidy -- altering wage and vacancy incentives rather than only changing the surplus split. The wedge is strongest when outside options are weak and separations rare, implying larger wage premia in slack, low-mobility markets. In a wage-posting model with on-the-job search, responsible firms may occupy the high-wage segment even when less productive. In a DMP model, responsible firms commit to higher worker bargaining power, raising the value of unemployment and thereby wages at regular firms.

Changwu Liu, Yuan Shen

Linear observed systems on manifolds are a special class of nonlinear systems whose state spaces are smooth manifolds but possess properties similar to linear systems. Such properties can be characterized by preintegration and exact linearization with Jacobians independent of the linearization point. Non-biased IMU dynamics in navigation can be constructed into linear observed settings, leading to invariant filters with guaranteed behaviors such as local convergence and consistency. In this letter, we establish linear observed property for systems evolving on a smooth manifold through the connection structure endowed upon this space. Our key findings are the existence of linear observed systems on manifolds poses constraints on the curvature of the state space, beyond requiring the dynamics to be compatible with some connection-preserving transformations. Specifically, the flat connection case reproduces the characterization of linear observed systems on Lie groups, showing our theory is a true generalization.

Arnold William Tazon, Fatima Awwad, Fatma Meddeb-Mouelhi et al.

Vanillin, an aromatic aldehyde, is one of the most popular flavors worldwide, extensively used in the food, cosmetics, pharmaceutical, and agrochemical industries. Despite its widespread use, less than 1% of the total vanillin production is natural, with the majority being synthesized chemically. While chemical synthesis can help to meet the growing demand for vanillin, a strong market trend has rapidly developed for products created from natural ingredients, including natural vanillin. Given the labor-intensive process of extracting vanillin from vanilla pods, there is a critical need for new metabolic engineering platforms to support the biotechnological production of nature-identical vanillin. This review highlights the significance of vanillin in various markets, its diverse applications, and the current state of bio-engineered production using both prokaryotic and eukaryotic biological systems. Although recent advancements have demonstrated successful vanillin production through biocatalytic approaches, our focus was to provide a current and innovative overview of vanillin bioengineering across various host systems with special consideration placed on microalgae, which are emerging as promising platforms for vanillin production through metabolic engineering. The use of these systems to support the biotechnological production of vanillin, while leveraging the photosynthetic capabilities of microalgae to capture CO₂ and convert it into biomass, can significantly reduce the overall carbon footprint.

Kaito Ito, Kenji Kashima

This letter investigates dynamical optimal transport of underactuated linear systems over an infinite time horizon. In our previous work, we proposed to integrate model predictive control and the celebrated Sinkhorn algorithm to perform efficient dynamical transport of agents. However, the proposed method requires the invertibility of input matrices, which severely limits its applicability. To resolve this issue, we extend the method to (possibly underactuated) controllable linear systems. In addition, we ensure the convergence properties of the method for general controllable linear systems. The effectiveness of the proposed method is demonstrated by a numerical example.

Ke, Ma, Sophie Yanying Sheng et al.

How does employer reputation affect the labor market? We investigate this question using a novel dataset combining reviews from Glassdoor.com and job applications data from Dice.com. Labor market institutions such as Glassdoor.com crowd-sources information about employers to alleviate information problems faced by workers when choosing an employer. Raw crowd-sourced employer ratings are rounded when displayed to job seekers. By exploiting the rounding threshold, we identify the causal impact of Glassdoor ratings using a regression discontinuity framework. We document the effects of such ratings on both the demand and supply sides of the labor market. We find that displayed employer reputation affects an employer's ability to attract workers, especially when the displayed rating is "sticky." Employers respond to having a rating above the rounding threshold by posting more new positions and re-activating more job postings. The effects are the strongest for private, smaller, and less established firms, suggesting that online reputation is a substitute for other types of reputation.

Jerome Rousseau, Mike Todd

Given a dynamical system, we prove that the shortest distance between two $n$-orbits scales like $n$ to a power even when the system has slow mixing properties, thus building and improving on results of Barros, Liao and the first author. We also extend these results to flows. Finally, we give an example for which the shortest distance between two orbits has no scaling limit.

Matthew Deakin, Phil C. Taylor, Janusz Bialek et al.

Distribution systems will require new cost-effective solutions to provide network capacity and increased flexibility to accommodate Low Carbon Technologies. To address this need, we propose the Hybrid Multi-Terminal Soft Open Point (Hybrid MT-SOP) to efficiently provide distribution system interconnection capacity. Each leg of the Hybrid MT-SOP has an AC/DC converter connected in series with a bank of AC switches (Feeder Selector Switches) to allow the converter to connect to any of the feeders at a node. Asymmetric converter sizing is shown to increase feasible power transfers by up to 50% in the three-terminal case, whilst a conic mixed-integer program is formulated to optimally select the device configuration and power transfers. A case study shows the Hybrid MT-SOP increasing utilization of the converters by more than one third, with a 13% increase in system loss reduction as compared to an equally-sized MT-SOP.

Yuqing Zhang, Ya Gao, Chengcheng Zhan et al.

The education level and social participation of contemporary Chinese women have reached their historical peak; work is fast becoming the dominant theme of their lives. However, influenced by traditional attitudes, women are still expected to undertake the main family care tasks, thus, facing dual constraints of family and work, which seriously affect their life happiness. Based on the theory of subjective well-being and feminist geography, this study used the questionnaire survey and in-depth interview results of professional females in Dalian High-tech Industrial Zone as basic data to explore the life satisfaction and emotional cognition in intra- and extra-household life of professional females (Professional females: In this study, they are the women who have received formal education and currently have full-time and steady job (including regular employees in the national systems and those who have signed labor contracts with labor units).). The following results were obtained: (1) Most professional females reported higher life satisfaction in intra- rather than extra-household life, and it varied with individual attributes, reflecting the internal differences among them. (2) The positive emotions of professional females came from the company of family and friends in intra-household life, and satisfaction with the working environment and treatment in extra-household life. (3) The negative emotions came from the pressure of “marriage,” “birth,” and other traditional concepts in intra-household life. In extra-household life, it came from the health problems caused by working stress, interpersonal problems and gender inequality in the workplace, and the anxiety of age and future career development. Therefore, this study committed to revealing the living status and subjective feelings of contemporary professional females in China, hoping to improve women’s life quality and enhance their life happiness from a theoretical and realistic perspective.

Muhammad Rehan Hakro, Aneel Kumar, Mujahid Ali et al.

The load–deformation relationship under the footing is essential for foundation design. Shallow foundations are subjected to changes in hydrological conditions such as rainfall and drought, affecting their saturation level and conditions. The actual load–settlement response for design and reconstructions is determined experimentally, numerically, or utilizing both approaches. Ssettlement computation is performed through large-scale physical modeling or extensive laboratory testing. It is expensive, labor intensive, and time consuming. This study is carried out to determine the effect of different saturation degrees and loading conditions on settlement shallow foundations using numerical modeling in Plaxis 2D, Bentley Systems, Exton, Pennsylvania, US. Plastic was used for dry soil calculation, while fully coupled flow deformation was used for partially saturated soil. Pore pressure and deformation changes were computed in fully coupled deformation. The Mohr–Columb model was used in the simulation, and model parameters were calculated from experimental results. The study results show that the degree of saturation is more critical to soil settlement than loading conditions. When a 200 KPa load was applied at the center of the footing, settlement was recored as 28.81 mm, which was less than 42.96 mm in the case of the full-depth shale layer; therefore, settlement was reduced by 30% in the underlying limestone rock layer. Regarding settlement under various degrees of saturation (DOS), settlment is increased by an increased degree of saturation, which increases pore pressure and decreases the shear strength of the soil. Settlement was observed as 0.69 mm at 0% saturation, 1.93 mm at 40% saturation, 2.21 mm at 50% saturation, 2.77 mm at 70% saturation, and 2.84 mm at 90% saturation of soil.

Anne Gilbert

This article addresses streaming television platforms’ reliance on algorithms, serial narratives, and user interfaces and the effect of these strategies on the television viewing audience. The datafication of television means that opaque, proprietary computational processes play a significant role in the television that gets made, in steering viewing to particular series, and in reshaping the identity of the viewing public. User interfaces, data tracking, seriality, and algorithms have thus far proven inadequate for navigating the vast databases offered by streaming platforms. In practice, these systems off-load work to viewers and actively create obstacles to the formation of a collective viewing experience. Algorithmic audiences are trained to be sequential, isolated viewers, whose work of watching occurs in labor-intensive ways.

Yuki Soma, Yoshihiro Izumi, Takehiko Shimohira et al.

Pre-column fluorescent derivatization has been used for the fast quantification of amino acids using high-performance liquid chromatography (HPLC) systems. However, it generally requires an offline in-vial derivatization process with multiple derivatization reagents. The offline derivatization requires the same number of reaction vials as the number of sample vials for use as a reaction chamber for the derivatization reaction in an autosampler. Therefore, the number of samples analyzed per batch using the pre-column derivatization method is halved. To benefit from the pre-column derivatization method, we transformed the derivatization process from an offline chamber process to an online in-needle process (in-needle Pre-column Derivatization for Amino acids Quantification; iPDAQ). Fluorescent derivatization in the injection needle obviated the need for vacant vials as reaction chambers. Consequently, the throughput per batch improved up to two times, and the consumption of derivatization reagents was reduced to less than one-tenth of that in the conventional vial method. We demonstrated to separate and quantify the amino acids in various biological samples. Herein, we presented a novel HPLC-based amino acid quantification method that enables the continuous analysis of a large number of samples. The iPDAQ facilitates accurate amino acid quantification due to the automation of derivatization and achieves improvement in the throughput and reduction of analysis labor.

Patrick J. W. Koelewijn, Roland Tóth

The dissipativity framework is widely used to analyze stability and performance of nonlinear systems. By embedding nonlinear systems in an LPV representation, the convex tools of the LPV framework can be applied to nonlinear systems for convex dissipativity based analysis and controller synthesis. However, as has been shown recently in literature, naive application of these tools to nonlinear systems for analysis and controller synthesis can fail to provide the desired guarantees. Namely, only performance and stability with respect to the origin is guaranteed. In this paper, inspired by the results for continuous-time nonlinear systems, the notion of incremental dissipativity for discrete-time nonlinear systems is proposed, whereby stability and performance analysis is done between trajectories. Furthermore, it is shown how, through the use of the LPV framework, convex conditions can be obtained for incremental dissipativity analysis of discrete-time nonlinear systems. The developed concepts and tools are demonstrated by analyzing incremental dissipativity of a controlled unbalanced disk system.