Hasil untuk "Labor systems"

M. Silasi, I. Cardenas, J. Kwon et al.

D. S. Tayal

The aim of the article is to present an experiment carried out by some female students and the results of a study that was conducted to identify and describe some of the relationships between access to STEM education, which includes focusing on educational techniques, academic achievement, and the ability to possess the necessary skills among primary school students to prepare them for the labor market. Among the most important of these skills, which are known as the skills of the 21st century, are the skills of problem-solving and critical thinking, while identifying the challenges facing traditional education due to the acceleration of technical developments and the digital revolution that is imposing itself in the labor market . The article also intended to present the beliefs of female teachers in these schools towards this type of study. The experimental and descriptive approaches were relied upon, as the experimental approach was used to detect the impact of the study in these schools by identifying two groups (experimental and control), an experimental sample from a STEM school, and a control sample from other schools. They have to test critical thinking and problem-solving skills, and the results showed that there are statistically significant differences in the arithmetic mean of the students' scores, and the differences came in favor of the experimental sample, which uses educational techniques according to the STEM approach in its education> The study was also intended to reveal the impact of the use of educational technologies on the academic achievement of female students. The results of post-tests showed that there were differences in favor of the group that used educational techniques The descriptive approach was applied through a questionnaire measuring the beliefs of teachers according to the STEM system about 21st century skills and the need to use educational techniques that prepare students for the labor market, and compared them with the beliefs of teachers in traditional schools. It was found that there were differences between the two groups in favor of the group of STEM teachers. The presentation ended with some recommendations, including: the necessity of integrating educational techniques according to the STEM approach in the basic education stages of students to work on preparing them for a successful experience in the labor market, in their professional future, as today's primary school students are the ones who will go to the labor market in 2030. That is why we recommend the need for professional development of teachers with regard to the skills needed for the labor market, especially with regard to educational technologies, in order to create a digital generation that lives the experience of education for the future and keeps pace with the rapid technical development that we are witnessing in our reality.

Rany Aprilyanta, Fajar Sidiq Adi Prabowo

The growth of Micro, Small, and Medium Enterprises (MSMEs) in Indonesia plays a crucial role in supporting national economic development. Nevertheless, between 2017 and 2023, the progress of MSMEs has experienced fluctuations, even among those that are officially registered and supported by the 99% Usahaku platform. These inconsistencies are primarily due to ongoing challenges, particularly limited access to funding and technology. Given the definition and unique characteristics of MSMEs, this study seeks to examine whether there is a correlation between specific key factors and the growth of MSMEs listed on the 99% Usahaku platform. Furthermore, the research aims to identify which of these factors most significantly influence their growth. To achieve this, the study utilizes primary data collected via questionnaires distributed to 390 MSMEs, determined through the Slovin formula. The responses are analyzed using the SPSS statistical software, with Principal Component Analysis (PCA) serving as the primary method of analysis.The results, indicated by a KMO Measure of Sampling Adequacy (MSA) value of 0.586 (greater than 0.50) and a significant Bartlett’s Test of Sphericity (p-value < 0.05), suggest a meaningful correlation between the identified key factors and the growth of MSMEs on the 99% Usahaku platform, thus supporting the alternative hypothesis (H1). The analysis identifies thirteen key factors contributing to MSME growth on the platform: labor ecosystem, MSME ecosystem, competitive capacity, market operational ability, skilled human capital, business ecosystem, business information systems, digital adoption, organizational structure, market orientation, financial management, marketing competence, and operational support.

Sokratis Sokratous, Athanasios Ragkos, Georgios Arsenos et al.

In this study, an empirical analysis was applied to measure the efficiency level of dairy farms in Cyprus and estimate the capacity of sheep farmers to support the increasing demand for halloumi cheese. Data Envelopment Analysis was used on data from 50 dairy sheep farms in Cyprus, which operate under extensive, semi-intensive, and intensive systems. The main features of the most efficient farms are presented, and a comparative financial analysis is implemented between the efficient and less efficient farms. The results indicate room for improvement in extensive and semi-intensive dairy sheep farming and verify that the transition that takes place in sheep farming towards more intensive systems constitutes the optimal approach. The most efficient farms operate under semi-intensive and intensive dairy sheep farming and achieve higher milk yields than the farms operating under extensive systems. Feeding constitutes the main cost driver, exceeding 60% in both efficient and inefficient farms, while labor wages and fixed capital cost varies between 25% and 30% of the total production cost for both efficiency groups. The findings indicate that the farms should utilize economies of scale to reduce production costs and utilize fixed capital endowments at full capacity.

Bojan Baškot, Ognjen Erić, Dragan Gligorić et al.

This study explores the drivers and evolution of the shadow economy in Bosnia and Herzegovina—a transitional, post-conflict country facing persistent institutional fragility. Using the Multiple Indicators and Multiple Causes (MIMIC) model, an extension of Structural Equation Modeling, the paper estimates the size and dynamics of the shadow economy from 1996 to 2022. The model integrates macroeconomic indicators (employment rate, GDP per capita, tax revenues) and institutional variables (rule of law, control of corruption), with data primarily sourced from the World Bank. The results show that institutional quality, tax burden, and labor market conditions are significant determinants of the informal sector. The model demonstrates strong statistical validity (CFI = 0.986, RMSEA = 0.05), supported by robustness checks including unit root tests, structural break analysis, and the exclusion of controversial benchmarking methods. The shadow economy responds markedly to major shocks such as the 2008 global financial crisis and the 2014 floods. Findings provide valuable policy insights: strengthening institutions, simplifying tax systems, and encouraging formal labor market participation can significantly reduce informality. The study supports evidence-based reforms to enhance transparency, resilience, and sustainable development in Bosnia and Herzegovina.

Dina Retno Wulandari, Yosafat Hermawan Trinugraha

This study aims to examine the transformation of Royongan Omah, a communal house-building tradition in Ngasinan Village, amid modernization and socio-economic changes. Traditionally, community participation in this practice was rooted in voluntary collective labor, driven by social solidarity and mutual aid. However, over time, participation has become increasingly selective and economically motivated. Employing a qualitative case study approach, this research collected data through passive participant observation, semi-structured interviews, and document analysis, which were analyzed using Miles and Huberman’s framework within Max Weber’s social action theory. The findings indicate that rationalization and shifting economic perspectives have significantly reshaped community participation in Royongan Omah. While participation was previously dominated by traditional, affective, and value-rational actions, it has now transitioned towards value-rational and instrumental-rational actions. Full community engagement—including labor, cognitive involvement, and material contributions—has declined, giving way to a more pragmatic approach that prioritizes skilled, paid labor for complex construction tasks. Despite the growing dominance of instrumental rationality, elements of traditional and affective rationality persist, demonstrating an ongoing negotiation between modern efficiency and cultural heritage. This study contributes to sociological discourse on modernization and cultural adaptation, highlighting how traditional cooperative labor systems evolve in response to socio-economic transformations.

Luca Ballotta, Nicola Bastianello, Riccardo M. G. Ferrari et al.

In this paper, we address two practical challenges of distributed learning in multi-agent network systems, namely personalization and resilience. Personalization is the need of heterogeneous agents to learn local models tailored to their own data and tasks, while still generalizing well; on the other hand, the learning process must be resilient to cyberattacks or anomalous training data to avoid disruption. Motivated by a conceptual affinity between these two requirements, we devise a distributed learning algorithm that combines distributed gradient descent and the Friedkin-Johnsen model of opinion dynamics to fulfill both of them. We quantify its convergence speed and the neighborhood that contains the final learned models, which can be easily controlled by tuning the algorithm parameters to enforce a more personalized/resilient behavior. We numerically showcase the effectiveness of our algorithm on synthetic and real-world distributed learning tasks, where it achieves high global accuracy both for personalized models and with malicious agents compared to standard strategies.

Mantas Mazeika, Alice Gatti, Cristina Menghini et al.

AIs have made rapid progress on research-oriented benchmarks of knowledge and reasoning, but it remains unclear how these gains translate into economic value and automation. To measure this, we introduce the Remote Labor Index (RLI), a broadly multi-sector benchmark comprising real-world, economically valuable projects designed to evaluate end-to-end agent performance in practical settings. AI agents perform near the floor on RLI, with the highest-performing agent achieving an automation rate of 2.5%. These results help ground discussions of AI automation in empirical evidence, setting a common basis for tracking AI impacts and enabling stakeholders to proactively navigate AI-driven labor automation.

Jinwei Hu, Zezhi Tang, Xin Jin et al.

This paper presents HERO (Hierarchical Testing with Rabbit Optimization), a novel black-box adversarial testing framework for evaluating the robustness of deep learning-based Prognostics and Health Management systems in Industrial Cyber-Physical Systems. Leveraging Artificial Rabbit Optimization, HERO generates physically constrained adversarial examples that align with real-world data distributions via global and local perspective. Its generalizability ensures applicability across diverse ICPS scenarios. This study specifically focuses on the Proton Exchange Membrane Fuel Cell system, chosen for its highly dynamic operational conditions, complex degradation mechanisms, and increasing integration into ICPS as a sustainable and efficient energy solution. Experimental results highlight HERO's ability to uncover vulnerabilities in even state-of-the-art PHM models, underscoring the critical need for enhanced robustness in real-world applications. By addressing these challenges, HERO demonstrates its potential to advance more resilient PHM systems across a wide range of ICPS domains.

Jorge Figueroa, David Rivas-Villar, José Rouco et al.

Water quality can be negatively affected by the presence of some toxic phytoplankton species, whose toxins are difficult to remove by conventional purification systems. This creates the need for periodic analyses, which are nowadays manually performed by experts. These labor-intensive processes are affected by subjectivity and expertise, causing unreliability. Some automatic systems have been proposed to address these limitations. However, most of them are based on classical image processing pipelines with not easily scalable designs. In this context, deep learning techniques are more adequate for the detection and recognition of phytoplankton specimens in multi-specimen microscopy images, as they integrate both tasks in a single end-to-end trainable module that is able to automatize the adaption to such a complex domain. In this work, we explore the use of two different object detectors: Faster R-CNN and RetinaNet, from the one-stage and two-stage paradigms respectively. We use a dataset composed of multi-specimen microscopy images captured using a systematic protocol. This allows the use of widely available optical microscopes, also avoiding manual adjustments on a per-specimen basis, which would require expert knowledge. We have made our dataset publicly available to improve the reproducibility and to foment the development of new alternatives in the field. The selected Faster R-CNN methodology reaches maximum recall levels of 95.35%, 84.69%, and 79.81%, and precisions of 94.68%, 89.30% and 82.61%, for W. naegeliana, A. spiroides, and D. sociale, respectively. The system is able to adapt to the dataset problems and improves the results overall with respect to the reference state-of-the-art work. In addition, the proposed system improves the automation and abstraction from the domain and simplifies the workflow and adjustment.

Li Yu, Baiyang Chen, Ziying Li et al.

Synthetic chemistry has played a vital role in miscellaneous fields of human civilization over the past century. The synthetic stage yet remains time-consuming and labor-intensive. To overcome these limitations, automation has been introduced to transform synthetic chemistry, leading to the development of high-throughput methods for molecular discovery. Automated flow chemical synthesis (AFCS) has recently emerged as a promising candidate, offering improved efficiency, scalability, and sustainability over the well-known automated solid-phase peptide synthesis. To further advance AFCS, elements like artificial intelligence-based computer-aided structure design and synthesis planning, autonomously assembled compatible synthesis with enhanced automated process control, and autonomous optimization can be considered. This review focuses on recent advances in computer-aided automated flow chemical synthesis (CAAFCS) of polymers in living polymerization and iterative synthesis strategy. The current challenges and outlook are finally discussed for developing more powerful CAAFCS systems and expanding their applicability across numerous fields, potentially providing brand-new perspectives and guidelines for future developments in this field.

Jiakun Zheng, Junjie Wang, Jing Shen et al.

BackgroundAccurate measurement of food and nutrient intake is crucial for nutrition research, dietary surveillance, and disease management, but traditional methods such as 24-hour dietary recalls, food diaries, and food frequency questionnaires are often prone to recall error and social desirability bias, limiting their reliability. With the advancement of artificial intelligence (AI), there is potential to overcome these limitations through automated, objective, and scalable dietary assessment techniques. However, the effectiveness and challenges of AI applications in this domain remain inadequately explored. ObjectiveThis study aimed to conduct a scoping review to synthesize existing literature on the efficacy, accuracy, and challenges of using AI tools in assessing food and nutrient intakes, offering insights into their current advantages and areas of improvement. MethodsThis review followed the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) guidelines. A comprehensive literature search was conducted in 4 databases—PubMed, Web of Science, Cochrane Library, and EBSCO—covering publications from the databases’ inception to June 30, 2023. Studies were included if they used modern AI approaches to assess food and nutrient intakes in human subjects. ResultsThe 25 included studies, published between 2010 and 2023, involved sample sizes ranging from 10 to 38,415 participants. These studies used a variety of input data types, including food images (n=10), sound and jaw motion data from wearable devices (n=9), and text data (n=4), with 2 studies combining multiple input types. AI models applied included deep learning (eg, convolutional neural networks), machine learning (eg, support vector machines), and hybrid approaches. Applications were categorized into dietary intake assessment, food detection, nutrient estimation, and food intake prediction. Food detection accuracies ranged from 74% to 99.85%, and nutrient estimation errors varied between 10% and 15%. For instance, the RGB-D (Red, Green, Blue-Depth) fusion network achieved a mean absolute error of 15% in calorie estimation, and a sound-based classification model reached up to 94% accuracy in detecting food intake based on jaw motion and chewing patterns. In addition, AI-based systems provided real-time monitoring capabilities, improving the precision of dietary assessments and demonstrating the potential to reduce recall bias typically associated with traditional self-report methods. ConclusionsWhile AI demonstrated significant advantages in improving accuracy, reducing labor, and enabling real-time monitoring, challenges remain in adapting to diverse food types, ensuring algorithmic fairness, and addressing data privacy concerns. The findings suggest that AI has transformative potential for dietary assessment at both individual and population levels, supporting precision nutrition and chronic disease management. Future research should focus on enhancing the robustness of AI models across diverse dietary contexts and integrating biological sensors for a holistic dietary assessment approach.

Anukesh Krishnankutty Ambika, Akshay Rajeev, Matthew Huber

Abstract Because of the climatological prevalence of hot, humid conditions, moist heat extremes are a significant challenge to the health and wellbeing of the people in India. While research has demonstrated the importance of summer monsoon to moist heat in India, impact of monsoon‐break and warm spells in modulating extreme moist heat regionally has not been fully investigated. Here we investigate moist heat extremes, as measured by the Wet‐Bulb Globe Temperature (WBGT) metric, specifically during monsoon and monsoon‐break periods and find that they pose a major threat to physical labor and health relative to other seasons. During the 1951–2020 break period, an increase in area exposed (∼42.76 million km2), representing at least 670 million people, to extreme and detrimental WBGT values >31°C occur. Our results imply that future studies on extreme moist heat must pay close attention to the variation of weather systems on synoptic to subseasonal time scales that are superimposed on the seasonal monsoon migration.

Robayet Ferdous Syed

Zhonglin Tang, Min Fu, Yuting Wang et al.

As the core carrier and organizational bodies of the regional industrial space, the study of the location of industrial enterprises and the formation of their economic location is related to the rational development of regional industries, the coordination of humans and the environment, and the effective allocation of resources. Taking Chongqing, one of the six old industrial bases in China, as an example, this study analyzed the spatial distribution characteristics, economic location characteristics, and formation laws of industrial enterprises based on the Points of interest data (POI), and investigation data. The results showed that industrial enterprises in Chongqing show obvious spatial clustering characteristics. About 93.50%, 60.34%, 96.67%, 97.57%, 73.57%, 64.83% of industrial enterprises were distributed within the spatial range of 10 Km from the motorways, national highways, provincial highways, county highways, main streams of rivers and central towns, and 93.48% of industrial enterprises were distributed at an altitude of 800 m or less. In order to further reveale the economic location characteristics of industrial enterprises in Chongqing, this study further quantified the spatial differentiation law of industrial economic location based on Geographically weighted regression (GWR). The results showed that factors such as the Distance to National Highways (DNH), Distance to County Highways (DCH), Distance to Central Towns (DCC), Distance to River systems (DR), and Population Density (POP) had significant positive impacts on the formation of economic locations of industrial enterprises, while the Distance to Motorway (DMW) exerted a certain negative influence, but the effectiveness sees strong spatial heterogeneity according to the type of industry and the actual regional industrial development, with factors such as transportation accessibility, environment, and labor force playing a moderating role.

Arthur N. Montanari, Chao Duan, Adilson E. Motter

The duality between controllability and observability enables methods developed for full-state control to be applied to full-state estimation, and vice versa. In applications in which control or estimation of all state variables is unfeasible, the generalized notions of output controllability and functional observability establish the minimal conditions for the control and estimation of a target subset of state variables, respectively. Given the seemly unrelated nature of these properties, thus far methods for target control and target estimation have been developed independently in the literature. Here, we characterize the graph-theoretic conditions for target controllability and target observability (which are, respectively, special cases of output controllability and functional observability for structured systems). This allow us to rigorously establish a weak and strong duality between these generalized properties. When both properties are equivalent (strongly dual), we show that efficient algorithms developed for target controllability can be used for target observability, and vice versa, for the optimal placement of sensors and drivers. These results are applicable to large-scale networks, in which control and monitoring are often sought for small subsets of nodes.

Guglielmo Frigo, Paolo Attilio Pegoraro, Sergio Toscani

Fast dynamics and transient events are becoming more and more frequent in power systems, due to the high penetration of renewable energy sources and the consequent lack of inertia. In this scenario, Phasor Measurement Units (PMUs) are expected to track the monitored quantities. Such functionality is related not only to the PMU accuracy (as per the IEC/IEEE 60255-118-1 standard) but also to the PMU reporting rate (RR). High RRs allow tracking fast dynamics, but produce many redundant measurement data in normal conditions. In view of an effective tradeoff, the present paper proposes an adaptive RR mechanism based on a real-time selection of the measurements, with the target of preserving the information content while reducing the data rate. The proposed method has been tested considering real-world datasets and applied to four different PMU algorithms. The results prove the method effectiveness in reducing the average data throughput as well as its scalability at PMU concentrator or storage level.

Ji Jiehan, Liu Yi, Mei Murong et al.

The current research on regional collaboration lacks the research perspective of global production network, which makes it difficult for the research on the collaboration of the Guangdong-Hong Kong-Macao Greater Bay Area to recognize the differences in the development of strategic coupling modes of various cities in the Greater Bay Area, and to clarify the differences in the status and division of labor of these cities in the global production network. These differences are the key to the coordinated development of the Greater Bay Area. Therefore, based on the theoretical framework of the global production network, this study uses the variable of strategic coupling to analyze the development of the strategic coupling mode of the cities in the Greater Bay Area and the differences in their status and division of labor in the global production network, so as to further understand the coordinated development of the Greater Bay Area. There are three main findings in this study. First, the strategic coupling mode and evolution process of Guangdong, Hong Kong and Macao are essentially different, belonging to different global production networks. Hong Kong has experienced two processes of dependency coupling to decoupling in the development of local manufacturing and the later "having stores in front and factories behind" mode, forming a mutually beneficial coupling mode in the financial industry. Macao has experienced two processes from dependency coupling to decoupling in the manufacturing industry, forming an absorption coupling mode in the gambling industry. Second, there are also significant differences in the strategic coupling modes of cities in the Pearl River Delta. Each city is embedded into different global production networks through different leading industries. Third, under the influence of the strategic coupling differences of the above two scales, the regional economy of the Greater Bay Area does not gradually move toward coordinated development, but presents the characteristics of less connection-collaboration-collaborative difficulties. The core argument of this paper is that the differences of strategic coupling mode lead to the embedding of cities into global production networks with significant differences in economic cycles, production systems and technological structures, making it difficult to achieve the overall regional collaboration. And the contribution of this study is that with the help of the global production network theory, it provides a new interpretation perspective for regional collaboration research, reveals the difficulties of regional collaboration in the Greater Bay Area, enriches the study of the long-term dynamic evolution of relational economic geography, and provides some suggestions for the formulation of collaborative policy.

Norbert Kawęcki

“The social economy” has played an important role in addressing and mitigating the short- and long-term economic and social impacts of the COVID-19 crisis. In the short term, social economy actors helped emerge from the crisis by providing innovative solutions to strengthen public services to complement government action. In the longer term, social economy organizations can help transform the post-crisis economy by promoting inclusive and sustainable economic models. Relying on decades of experience, its peculiarities and fundamental principles, the social economy can inspire models for social innovation and a sense of business operating in a market economy. The aim of this article is to show how the social economy is mitigating the effects of the COVID-19 crisis and how governments are responding to these problems, as well as to show how the pandemic has caused numerous changes in economic activity and the way companies operate. The main conclusions of the article are: the social economy helps recover from COVID-19, the social economy helps to transform societies in crisis, the social economy inspires the mainstream economy to achieve a social goal, Policy makers support the political economy in its transformation.

Armin Nurkanović, Moritz Diehl

This letter introduces the NOnSmooth Numerical Optimal Control (NOSNOC) open-source software package. It is a modular MATLAB tool based on CasADi and IPOPT for numerically solving Optimal Control Problems (OCP) with piecewise smooth systems (PSS). The tool supports: 1) automatic reformulation of systems with state jumps into PSS (via the time-freezing reformulation [Nurkanović et al., 2021]) and of PSS into computationally more convenient forms, 2) automatic discretization of the OCP via, e.g., the recently introduced Finite Elements with Switch Detection [Nurkanović et al., 2022] which enables high accuracy optimal control and simulation of PSS, 3) solution methods for the resulting discrete-time OCP. The nonsmooth discrete-time OCP are solved with techniques of continuous optimization in a homotopy procedure, without the use of integer variables. This enables the treatment of a broad class of nonsmooth systems in a unified way. Two tutorial examples are given. A benchmark shows that NOSNOC provides both faster and more accurate solutions than conventional approaches, including mixed-integer formulations.