Hasil untuk "Labor systems"

Zixun Lan, Maochun Xu, Yifan Ren et al.

Recent advances in large language models (LLMs) have led to substantial progress in domain-specific applications, particularly within the legal domain. However, general-purpose models such as GPT-4 often struggle with specialized subdomains that require precise legal knowledge, complex reasoning, and contextual sensitivity. To address these limitations, we present LabourLawLLM, a legal large language model tailored to Chinese labor law. We also introduce LabourLawBench, a comprehensive benchmark covering diverse labor-law tasks, including legal provision citation, knowledge-based question answering, case classification, compensation computation, named entity recognition, and legal case analysis. Our evaluation framework combines objective metrics (e.g., ROUGE-L, accuracy, F1, and soft-F1) with subjective assessment based on GPT-4 scoring. Experiments show that LabourLawLLM consistently outperforms general-purpose and existing legal-specific LLMs across task categories. Beyond labor law, our methodology provides a scalable approach for building specialized LLMs in other legal subfields, improving accuracy, reliability, and societal value of legal AI applications.

K. Uvnäs-Moberg

Oxytocin is a reproductive hormone implicated in the process of parturition and widely used during labor. Oxytocin is produced within the supraoptic nucleus and paraventricular nucleus of the hypothalamus and released from the posterior pituitary lobe into the circulation. Oxytocin is released in pulses with increasing frequency and amplitude in the first and second stages of labor, with a few pulses released in the third stage of labor. During labor, the fetus exerts pressure on the cervix of the uterus, which activates a feedforward reflex-the Ferguson reflex-which releases oxytocin. When myometrial contractions activate sympathetic nerves, it decreases oxytocin release. When oxytocin binds to specific myometrial oxytocin receptors, it induces myometrial contractions. High levels of circulating estrogen at term make the receptors more sensitive. In addition, oxytocin stimulates prostaglandin synthesis and release in the decidua and chorioamniotic membranes by activating a specific type of oxytocin receptor. Prostaglandins contribute to cervical ripening and uterine contractility in labor. The oxytocin system in the brain has been implicated in decreasing maternal levels of fear, pain, and stress, and oxytocin release and function during labor are stimulated by a social support. Moreover, studies suggest, but have not yet proven, that labor may be associated with long-term, behavioral and physiological adaptations in the mother and infant, possibly involving epigenetic modulation of oxytocin production and release and the oxytocin receptor. In addition, infusions of synthetic oxytocin are used to induce and augment labor. Oxytocin may be administered according to different dose regimens at increasing rates from 1 to 3 mIU/min to a maximal rate of 36 mIU/min at 15- to 40-minute intervals. The total amount of synthetic oxytocin given during labor can be 5 to 10 IU, but lower and higher amounts of oxytocin may also be given. High-dose infusions of oxytocin may shorten the duration of labor by up to 2 hours compared with no infusion of oxytocin; however, it does not lower the frequency of cesarean delivery. When synthetic oxytocin is administered, the plasma concentration of oxytocin increases in a dose-dependent way: at infusion rates of 20 to 30 mIU/min, plasma oxytocin concentration increases approximately 2- to 3-fold above the basal level. Synthetic oxytocin administered at recommended dose levels is not likely to cross the placenta or maternal blood-brain barrier. Synthetic oxytocin should be administered with caution as high levels may induce tachystole and uterine overstimulation, with potentially negative consequences for the fetus and possibly the mother. Of note, 5 to 10 IU of synthetic oxytocin is often routinely given as an intravenous or intramuscular bolus administration after delivery to induce uterine contractility, which, in turn, induces uterine separation of the placenta and prevents postpartum hemorrhage. Furthermore, it promotes the expulsion of the placenta.

Gonçalo Granjal Cruz, Balazs Renczes, Mark C Runacres et al.

While accurate, black-box system identification models lack interpretability of the underlying system dynamics. This paper proposes State-Space Kolmogorov-Arnold Networks (SS-KAN) to address this challenge by integrating Kolmogorov-Arnold Networks within a state-space framework. The proposed model is validated on two benchmark systems: the Silverbox and the Wiener-Hammerstein benchmarks. Results show that SS-KAN provides enhanced interpretability due to sparsity-promoting regularization and the direct visualization of its learned univariate functions, which reveal system nonlinearities at the cost of accuracy when compared to state-of-the-art black-box models, highlighting SS-KAN as a promising approach for interpretable nonlinear system identification, balancing accuracy and interpretability of nonlinear system dynamics.

Adilet Yerkin, Pakizar Shamoi

Group Decision-Making (GDM) plays a crucial role in various real-life scenarios where individuals express their opinions in natural language rather than structured numerical values. Traditional GDM approaches often overlook the subjectivity and ambiguity present in human discussions, making it challenging to achieve a fair and consensus-driven decision. This paper proposes a fuzzy consensus-based group decision-making system that integrates sentiment and emotion analysis to extract preference values from textual inputs. The proposed framework combines explicit voting preferences with sentiment scores derived from chat discussions, which are then processed using a Fuzzy Inference System (FIS) to compute a total preference score for each alternative and determine the top-ranked option. To ensure fairness in group decision-making, we introduce a fuzzy logic-based consensus measurement model that evaluates participants' agreement and confidence levels to assess overall feedback. To illustrate the effectiveness of our approach, we apply the methodology to a restaurant selection scenario, where a group of individuals must decide on a dining option based on brief chat discussions. The results demonstrate that the fuzzy consensus mechanism successfully aggregates individual preferences and ensures a balanced outcome that accurately reflects group sentiment.

Qing Xiao, Rongyi Chen, Jingjia Xiao et al.

This study examines the discursive construction of algorithms and its role in labor management in Chinese live-streaming industry by focusing on how intermediary organizations (Multi-Channel Networks, MCNs) actively construct, stabilize, and deploy particular interpretations of platform algorithms as instruments of labor management. Drawing on a nine-month ethnographic fieldwork and 44 interviews with live-streamers, former live-streamers, and MCN staff, we examine how MCNs produce and circulate structured interpretations of platform algorithms across organizational settings. We show that MCNs articulate two asymmetric yet interconnected forms of algorithmic interpretations. Internally, MCNs managers approach algorithms as volatile and uncertain systems and adopt probabilistic strategies to manage performance and risk. Externally, in interactions with streamers, MCNs circulate simplified and prescriptive algorithmic narratives that frame platform systems as transparent, fair, and responsive to individual effort. These organizationally produced algorithmic interpretations are embedded into training materials, live-streaming performance metrics, and everyday management practices. Through these mechanisms, streamers internalize responsibility for outcomes, intensify self-discipline, and increase investments in equipment, performing skills, and routines to maintain streamer-audience relationship, while accountability for unpredictable outcomes is increasingly shifted away from managers and platforms. This study contributes to CSCW and platform labor research by demonstrating how discursively constructed algorithmic knowledge can function as an intermediary infrastructure of soft control, shaping how platform labor is regulated, moralized, and governed in practice.

Simpson Zhang, Tennison Liu, Mihaela van der Schaar

Current labor markets are strongly affected by the economic forces of adverse selection, moral hazard, and reputation, each of which arises due to $\textit{incomplete information}$. These economic forces will still be influential after AI agents are introduced, and thus, agents must use metacognitive and strategic reasoning to perform effectively. Metacognition is a form of $\textit{internal reasoning}$ that includes the capabilities for self-assessment, task understanding, and evaluation of strategies. Strategic reasoning is $\textit{external reasoning}$ that covers holding beliefs about other participants in the labor market (e.g., competitors, colleagues), making strategic decisions, and learning about others over time. Both types of reasoning are required by agents as they decide among the many $\textit{actions}$ they can take in labor markets, both within and outside their jobs. We discuss current research into metacognitive and strategic reasoning and the areas requiring further development.

Gianna Williams, Maya De Los Santos, Alexandra To et al.

The global AI surge demands crowdworkers from diverse languages and cultures. They are pivotal in labeling data for enabling global AI systems. Despite global significance, research has primarily focused on understanding the perspectives and experiences of US and India crowdworkers, leaving a notable gap. To bridge this, we conducted a survey with 100 crowdworkers across 16 Latin American and Caribbean countries. We discovered that these workers exhibited pride and respect for their digital labor, with strong support and admiration from their families. Notably, crowd work was also seen as a stepping stone to financial and professional independence. Surprisingly, despite wanting more connection, these workers also felt isolated from peers and doubtful of others' labor quality. They resisted collaboration and gender-based tools, valuing gender-neutrality. Our work advances HCI understanding of Latin American and Caribbean crowdwork, offering insights for digital resistance tools for the region.

Edson Serván-Mori, Sergio Meneses-Navarro, Rocío García-Díaz et al.

Abstract Background Ethnic and racial discrimination in maternal health care has been overlooked in academic literature and yet it is critical for achieving universal health coverage (UHC). There is a lack of empirical evidence on its impact on the effective coverage of maternal health interventions (ECMH) for Indigenous women in Mexico. Documenting progress in reducing maternal health inequities, particularly given the disproportionate impact of the Covid-19 pandemic on ethnic minorities, is essential to improving equity in health systems. Methods We conducted a population-based, pooled cross-sectional, and retrospective analysis for 2009–2023, using data from the last three waves (2014, 2018, and 2023) of a nationally representative demographic survey (ENADID). Our study included n = 72,873 (N = 23,245,468) Mexican women aged 12–54 with recent live births. We defined ECMH as adequate antenatal care (ANC), skilled and/or institutional delivery care, timely postpartum care, and complication-free postpartum/puerperium. After describing sociodemographic characteristics and maternal health coverage by Indigenous status, we estimated a pooled fixed-effects multivariable regression model to adjust ECMH for relevant covariates. We used the Blinder-Oaxaca decomposition for nonlinear regression models to quantify inequities in ECMH due to ethnic-racial discrimination, defined as differences in outcomes attributable to differential treatment. Findings Indigenous women had lower education, labor market participation, and socioeconomic position, higher parity, and more rural, poorer state residence than non-Indigenous women. They faced significant health coverage loss due to the dismantling of Seguro Popular, a public health insurance mechanism in place until the end of 2019, right before the start of the Covid pandemic. Adjusted ECMH was 25.3% for non-Indigenous women and 18.3% for Indigenous women, peaking at 28.8% and 21.2% in 2013–2018, declining to 25.7% and 18.7% pre-Covid (January 2019 to March 2020), and further declining to 24.0% and 17.4% during Covid, with an increase to 26.6% for non-Indigenous women post-Covid, while remaining similar for Indigenous women. Decomposition analyses revealed that during the analyzed period, 30.8% of the gap in ECMH was due to individual characteristics, 51.7% to ethnic-racial discrimination, and 17.5% to their interaction. From 2009 to 2012, 42.2% of the gap stemmed from observable differences, while 40.4% was due to discrimination. In the pre-Covid-19 phase, less than 1% was from observable characteristics, with 75.3% attributed to discrimination, which remained in the post-Covid-19 stage (78.7%). Conclusions Despite modest health policy successes, the ethnic gap in ECMH remains unchanged, indicating insufficient action against inequity-producing structures. Ethnic and racial discrimination persists, exacerbated during the pandemic and coinciding with the government’s cancellation of targeted social programs and public health insurance focused on the poorest populations, including Indigenous peoples. Thus, prioritizing maternal and child health underscores the need for comprehensive policies, including specific anti-racist interventions. Addressing these inequities requires the recognition of both observable and unobservable factors driven by discriminatory ideologies and the implementation of targeted measures to confront the complex interactions driving discrimination in maternal health care services for Indigenous women.

Memoona Farooq, Chih-Yuan Chen, Cheng-Pin Wang

Watermelon is a popular fruit, predominantly cultivated in Asian countries. However, the production and harvesting processes present several challenges. Due to its size and weight, manually harvesting watermelons is labor-intensive and costly. In the future, technology is expected to enable robots to harvest watermelons. Therefore, it becomes essential to introduce intelligent systems to effectively identify and locate watermelons in harvesting. This research aims to develop an advanced methodology for watermelon identification and location using You Look Only Once (YOLO)v8 and YOLOv8-oriented bounding box (OBB) algorithms. Furthermore, the simple online and real-time tracking (SORT) algorithm was employed to track and count watermelons and estimate yield. The performance of YOLOv8-OBB was better than that of YOLOv8 and the highest precision (0.938) was achieved by YOLOv8s-OBB. Additionally, the size of each watermelon was measured with both models. The models help farmers find the optimal watermelons for harvest.

Samuel Folorunsho, Maggie Ni, William Norris

This paper presents the development of a comprehensive dynamics and stabilizing control architecture for Tethered Unmanned Aerial Vehicle (TUAV) systems. The proposed architecture integrates both onboard and ground-based controllers, employing nonlinear backstepping control techniques to achieve asymptotic stability of the TUAV's equilibrium. The onboard controllers are responsible for the position and attitude control of the TUAV, while the ground controllers regulate the winder mechanism to maintain the desired tether length, ensuring it retains its catenary form. Simulation results demonstrate the ability of the TUAV system to accurately track linear and circular trajectories, ensuring robust performance under various operational scenarios. The code and movies demonstrating the performance of the system can be found at https://github.com/sof-danny/TUAV\_system\_control.

Ying Zhang, Gang Xiao

Abstract Knowledge graphs are now widely used in various domains, including Question-and-answer systems, intelligent search and recommendation systems, and intelligent decision-making systems. However, knowledge graphs inevitably contain inaccurate and incomplete knowledge during the creation process, which leads to a reduction in the usefulness of knowledge graphs. Therefore, to assess the usefulness of knowledge graphs based on specific application requirements, quality assessment is particularly important. Among them, accuracy assessment, as a necessary dimension, reflects the degree of correctness of the triples. However, in the actual assessment process, the existing assessment methods do not consider the user’s needs and do not implement the concept of “Fitness for Use”. Meanwhile, it takes a lot of labor cost to evaluate the accuracy of large-scale knowledge graphs. Therefore, to ensure the accuracy of the assessment in a cost-saving way while meeting the needs of users, we propose and implement a novel accuracy assessment method that focuses on the requirements of users by designing an effective sampling method to obtain accurate assessment results that are more practical and instructive for users. Finally, the performance of our proposed method is evaluated by comparing it with the real accuracy rate, and the experimental results show that the accuracy rate obtained by the proposed method is very close to the real accuracy rate, and the sample size is minimized.

James Zimmer-Dauphinee, Steven A. Wernke

The monumental scale agricultural infrastructure systems built by Andean peoples during pre-Hispanic times have enabled intensive agriculture in the high-relief, arid/semi-arid landscape of the Southern Peruvian Andes. Large tracts of these labor-intensive systems have been abandoned, however, owing in large measure to a range of demographic, economic, and political crises precipitated by the Spanish invasion of the 16th century CE. This research seeks to better understand the dynamics of agricultural intensification and deintensification in the Andes by inventorying through the semantic segmentation of active and abandoned agricultural fields in satellite imagery across approximately 77,000 km² of the Southern Peruvian Highlands. While manual digitization of agricultural fields in satellite imagery is time-consuming and labor-intensive, deep learning-based semantic segmentation makes it possible to map and classify en masse Andean agricultural infrastructure. Using high resolution satellite imagery, training and validation data were manually produced in distributed sample areas and were used to transfer-train a convolutional neural network for semantic segmentation. The resulting dataset was compared to manual surveys of the region and results suggest that deep learning can generate larger and more accurate datasets than those generated by hand.

Jingyu Wang, Jinfu Chen, Dongyuan Shi et al.

Topology diagrams are widely seen in power system applications, but their automatic generation is often easier said than done. When facing power transmission systems with strongly-meshed structures, existing approaches can hardly produce topology diagrams catering to the aesthetics of readers. This paper proposes an integrated framework for generating aesthetically-pleasing topology diagrams for power transmission systems. Input with a rough layout, the framework first conducts visibility region analysis to reduce line crossings and then solves a mixed-integer linear programming problem to optimize the arrangement of nodes. Given that the complexity of both modules is pretty high, simplification heuristics are also proposed to enhance the efficiency of the framework. Case studies on several power transmission systems containing up to 2,046 nodes demonstrate the capability of the proposed framework in generating topology diagrams conforming to aesthetic criteria in the power system community. Compared with the widespread force-directed algorithm, the proposed framework can preserve the relative positions of nodes in the original layout to a great extent, which significantly contributes to the identification of electrical elements on the diagrams. Meanwhile, the time consumption is acceptable for practical applications.

Robert A. Moffitt, Matthew V. Zahn

Existing research on the static effects of the manipulation of welfare program benefit parameters on labor supply has allowed only restrictive forms of heterogeneity in preferences. Yet preference heterogeneity implies that the marginal effects on labor supply of welfare expansions and contractions may differ in different time periods with different populations and which sweep out different portions of the distribution of preferences. A new examination of the heavily studied AFDC program uses variation in state-level administrative barriers to entering the program in the late 1980s and early 1990s to estimate the marginal labor supply effects of changes in program participation induced by that variation. The estimates are obtained from a theory-consistent reduced form model which allows for a nonparametric specification of how changes in welfare program participation affect labor supply on the margin. Estimates using a form of local instrumental variables show that the marginal treatment effects are quadratic, rising and then falling as participation rates rise (i.e., becoming more negative then less negative on hours of work). The average work disincentive is not large but that masks some margins where effects are close to zero and some which are sizable. Traditional IV which estimates a weighted average of marginal effects gives a misleading picture of marginal responses. A counterfactual exercise which applies the estimates to three historical reform periods in 1967, 1981, and 1996 when the program tax rate was significantly altered shows that marginal labor supply responses differed in each period because of differences in the level of participation in the period and the composition of who was on the program.

V. A. Myznikova, V. V. Ustimenko, A. V. Chubar et al.

In today's world robotic devices are more and more often used to help people. They solve both domestic and industrial problems. When designing any object inevitably have to deal with testing under different conditions. To do this can build a test model, but if the object is quite complex and several models need to be built at once, to save labor and material resources can help mathematical modeling. This article presents mathematical modeling of processes based on typical functional blocks in the form of systems of differential-algebraic equations. Mathematical modeling and control algorithm as a set of interrelated structures are considered. The robot motion is simulated for the forward direction of rotation of wheels, the reverse direction of rotation, and the opposite directions of rotation. A model of the control device, which forms the control actions on the wheel motor according to the value of the deviation of the current orientation of the wheeled robot from the preset one, is constructed. These influences allow you to bring the orientation of the robot to the desired orientation. Quality metrics are obtained for various values of the rotational speed of the work. Although this model neglects the action of many forces that arise during the motion, it allows us to identify the influence on the motion and trajectory of the robot of such factors as the radius of the wheels, the distance between them, the magnitude of the voltage applied to the motors during the turn.

Mohamed A. El-Aghoury, Ahmed M. Ebid, Ibrahim Mahmoud Mahdi

Portal frame systems are widely used as coverage system in industrial projects. Selecting the proper portal frame system for a certain project depends on many technical, financial and logistical factors such as estimated cost, construction duration, availability of materials, equipment and skilled labor, besides environmental factors such as recycling and durability. The aim of this research is to create a Decision Support System (DSS) to decide the optimal portal frame system considering all these factors. The proposed (DSS) depends on integrating the Value Engineering (VE) concept with the Analytical Hierarchy Process (AHP) technique to identify the optimum system. The considered systems in this research are conventional portal made of hot rolled section, pre-engineered built-up portal frame, trussed frames and portal Frame truss. The developed (DSS) was tuned for the current Egyptian market conditions in 2019 and successfully verified using four selected projects with different height to span ratio.

mehdi zahedi, Shirin Sharifzadeh Tadi

Originality is an essential requirement for the copyrightability of any artistic and literary work. Creative works are afforded copyright protection only if they are original. Originality has yet to be defined by international or national laws including Iranian Laws. The judicial interpretation of national laws also differ from one another and there is no consensus on the concept of Originality. Under traditional approach, Originality is often referred as to "labor and effort" or "self expression " of the author, whereas the more approach is that of "creativity " and exercise of "skill and judgment ". However, all legal systems recognize that the work must be independent and not copied from another work.The main question of this Article is which approach can strike a balance between author’s rights and public interest to cheap and easy access to artistic works. This article will examine originality under different jurisdictions and concludes the Canadian definition is the more appropriate approach to the said question. Finally, it suggests that the Iranian legislature shall replace the term “creativity” with “skill and judgment” in 14 of article 1 of the copyright bill.

Mehmet Akif Ozdemir, Ali Tangel

Positron emission tomography (PET) is a medical imaging method based on the measurement of concentrations of positron-emitting radionuclides in a living body. In the PET imaging system, glucose is labeled with a positron-emitting radionuclide and injected intravenously. Then, the positrons move through the tissue and collide with the electrons of the cells in which they interact. As a result of this interaction, two gamma rays are emitted in the opposite direction. Gama rays emitted from cancerous tissue that has retained radioactive glucose are detected through ring-shaped detectors. And the detected signals are converted into an electrical response. Subsequently, these responses are sampled with electronic circuits and recorded as histogram matrix to generate the image set. The gamma rays may not reach the detectors located in the opposite position in equal time. In PETs having TOF characteristics, it is aimed to obtain better positioning information by a method based on the principle of measuring the difference between the reach time of the two photons to detectors. The measurement of the flight time is carried out with TDC structures. The measurement of this time difference at the ps level is directly related to the spatial resolution of the PET system. In this study, 45 nm CMOS VLSI simulations of TDC structures that have various architectural approaches were performed for use in PET systems. With the designed TDC architectures, two gamma photons time reach to detectors have been simulated and the time difference has been successfully digitized. In addition, various performance metrics such as input and output voltages, time resolutions, measurement ranges, and power analysis of TDC architectures have been determined. Proposed Vernier oscillator-based TDC architecture has been reached 25 ps time resolution with a low power consumption of 1.62681 mW at 1V supply voltage.

Iasson Karafyllis

This paper presents a fundamental relation between Output Asymptotic Gains (OAG) and Input-to-Output Stability (IOS) gains for linear systems. For any Input-to-State Stable, strictly causal linear system the minimum OAG is equal to the minimum IOS-gain. Moreover, both quantities can be computed by solving a specific optimal control problem and by considering only periodic inputs. The result is valid for wide classes of linear systems (involving delay systems or systems described by PDEs). The characterization of the minimum IOS-gain is important because it allows the non-conservative computation of the IOS-gains, which can be used in a small-gain analysis. The paper also presents a number of cases for finite-dimensional linear systems, where exact computation of the minimum IOS gain can be performed.

Hongyinping Feng, Xiao-Hui Wu, Bao-Zhu Guo

This is the second part of four series papers, aiming at the problem of sensor dynamics compensation for abstract linear systems. Two major issues are addressed. The first one is about the sensor dynamics compensation in system observation and the second one is on the disturbance dynamics compensation in output regulation for linear system. Both of them can be described by the problem of state observation for an abstract cascade system. We consider these two apparently different problems from the same abstract linear system point of view. A new scheme of the observer design for the abstract cascade system is developed and the exponential convergence of the observation error is established. It is shown that the error based observer design in the problem of output regulation can be converted into a sensor dynamics compensation problem by the well known regulator equations. As a result, a tracking error based observer for output regulation problem is designed by exploiting the developed method. As applications, the ordinary differential equations (ODEs) with output time-delay and an unstable heat equation with ODE sensor dynamics are fully investigated to validate the theoretical results. The numerical simulations for the unstable heat system are carried out to validate the proposed method visually.