The paper presents an approach to the construction of stabilizing feedback for strongly nonlinear systems. The class of systems of interest includes systems with drift which are affine in control and which cannot be stabilized by continuous state feedback. The approach is independent of the selection of a Lyapunov type function, but requires the solution of a nonlinear programming 'satisficing problem' stated in terms of the logarithmic coordinates of flows. As opposed to other approaches, point-to-point steering is not required to achieve asymptotic stability. Instead, the flow of the controlled system is required to intersect periodically a certain reachable set in the space of the logarithmic coordinates.
As sidewalk delivery robots become increasingly integrated into urban life, this paper begins with a critical provocation: Is robot labor labor? More than a rhetorical question, this inquiry invites closer attention to the social and political arrangements that robot labor entails. Drawing on ethnographic fieldwork across two smart-city districts in Seoul, we examine how delivery robot labor is collectively sustained. While robotic actions are often framed as autonomous and efficient, we show that each successful delivery is in fact a distributed sociotechnical achievement--reliant on human labor, regulatory coordination, and social accommodations. We argue that delivery robots do not replace labor but reconfigure it--rendering some forms more visible (robotic performance) while obscuring others (human and institutional support). Unlike industrial robots, delivery robots operate in shared public space, engage everyday passersby, and are embedded in policy and progress narratives. In these spaces, we identify "robot privilege"--humans routinely yielding to robots--and distinct perceptions between casual observers ("cute") and everyday coexisters ("admirable"). We contribute a conceptual reframing of robot labor as a collective assemblage, empirical insights into South Korea's smart-city automation, and a call for HRI to engage more deeply with labor and spatial politics to better theorize public-facing robots.
Abstract This study develops the first Vietnam-specific Environmental Dynamic Stochastic General Equilibrium (E-DSGE) model, explicitly incorporating household heterogeneity, revenue redistribution rules, and carbon-pricing mechanisms. Using this novel framework, the research examines the macroeconomic and distributional impacts of Vietnam’s carbon taxation and emissions trading systems. The analysis focuses on how alternative revenue redistribution strategies influence economic performance and income equity, thereby providing policy insights for designing effective and equitable climate policy at the national level. The framework is calibrated for the period 2010–2022 and simulates four revenue-recycling scenarios where carbon revenues are recycled through labor tax reductions and production incentives. Results indicate that using carbon revenues to reduce labor taxes and support production can enhance economic efficiency and employment while achieving emission reduction goals. Under the welfare-maximising scheme aggregate CO2 emissions fall 19.8%, real GDP contracts by only 0.95%, and aggregate welfare declines by about 1.1% relative to the no-policy baseline. However, these policies may worsen income inequality without targeted support for low-income households. The choice of redistribution mechanism plays a crucial role in policy effectiveness. The model abstracts from household-level heterogeneity and sectoral differences, which could be explored in future research for more detailed policy insights. This study contributes a novel E-DSGE modeling approach to evaluate carbon pricing and revenue use, offering insights particularly relevant for developing economies facing the dual challenge of economic development and climate mitigation.
Chavdar Z. Vezirov, Atanas Z. Atanasov, Plamena D. Nikolova
et al.
This study explores the potential of freely available tools for collecting, processing, and applying information in the management of mechanized fieldwork. A hierarchical approach was developed, integrating operational, logistical, and strategic levels of decision-making based on crop type, land conditions, machinery, labor, and time constraints. Various technological and technical solutions were evaluated through simulations and manual data processing. The proposed methodology was applied to a real-world case in Kalipetrovo, Bulgaria. The results include a 3.5-fold reduction in required tractors and a 50% decrease in tractor driver needs, achieved through extended working hours and shift scheduling. Additional benefits were identified from replacing conventional tillage with deep tillage, resulting in higher fuel consumption but improved soil preparation. Detailed resource schedules were created for machinery, labor, and fuel, highlighting seasonal peaks and optimization opportunities. The approach relies on spreadsheets and free AI-assisted platforms, proving to be a low-cost, accessible solution for mid-sized farms lacking advanced digital infrastructure. The findings demonstrate that structured information integration can support the effective renewal and utilization of tractor and machinery fleets while offering a scalable basis for decision support systems in agricultural engineering.
Josep Maria Argiles-Bosch, Josep Garcia-Blandon, Diego Ravenda
This study examines the relationship between Environmental, Social, and Governance (ESG) scores and labor costs per employee (LCE) in firms operating under different legal traditions, specifically comparing civil law (France) and common law (United Kingdom) countries. Utilizing data from the Orbis database for the period 2020–2022, the study employs random-effects estimations with robust standard errors. Results indicate that while the relationship between ESG and LCE is not significant in common law, it is positively significant in civil law. Results are robust to alternative ESG measures, such as the social pillar score (SOCP) estimations methods and samples. The findings suggest that the legal tradition moderates the ESG-LCE relationship, with stronger positive effects observed in civil law countries. The study highlights the importance of legal frameworks in shaping the economic impacts of ESG initiatives on labor costs. While ESG concerns may result in higher LCE, and thus increased employee compensation, implementing appropriate regulations to protect workers’ rights can foster a more effective ESG-LCE relationship than relying solely on market-based regulatory systems driven by stakeholder influence.
Diego Cornejo, Felipe Vera, Bastian Gamboa-Labbe
et al.
The classification of job postings into standardized occupational categories is a challenging task due to the unstructured, heterogeneous, and noisy nature of labor market data. This process is particularly relevant for labor market analysis conducted by government agencies and employment services, which require accurate and consistent occupational classifications to support public policy, workforce development, and training investment decisions. Manual classification is time-consuming and prone to inconsistencies, highlighting the need for scalable, intelligent systems. This study presents an applied artificial intelligence framework that integrates unstructured textual data from multiple online job boards with structured occupational taxonomies. The dataset comprises 4,605 manually labeled job postings covering 104 occupational classes, ensuring balanced representation across sectors and levels of specialization. We fine-tuned BETO, a Spanish-language variant of BERT (Bidirectional Encoder Representations from Transformers), to perform large-scale hierarchical classification of job descriptions mapped to the <italic>Clasificación Internacional Uniforme de Ocupaciones</italic> (CIUO 08.CL), the Chilean adaptation of ISCO-08 (International Standard Classification of Occupations). From an engineering standpoint, the model is integrated into a national labor market analytics platform to support real-time occupational demand monitoring and institutional decision-making. Our system outperforms traditional machine learning and deep learning baselines, achieving a weighted accuracy of 0.69 and an F1-score of 0.66 at the four-digit classification level. The proposed transformer-based architecture offers a robust and scalable solution for real-world labor market intelligence applications.
Electronic computers. Computer science, Information technology
Abstract Division of labor and specialization are common principles observed across all levels of biological organisms and societies, including humans that often rely on specialized roles to achieve a shared goal in complex coordination tasks. Understanding these principles in a quantitative fashion remains a challenge. In this study, we explore a novel experimental paradigm where two specialized groups of human players—a sensor group and an actor group—collaborate to accomplish a shared sensorimotor task of steering a cursor into a target. With all decision-makers initially unaware of their contribution and in the absence of verbal communication, the study explores how the group dynamics evolve over time, evaluating performance in terms of learning speed, group coherence and intergroup coordination. To gain quantitative insights, we simulate different computational models, including Bayesian learning and bounded rationality models, to describe human participants’ behavior. We also relate our findings to perceptual control theory, which emphasizes hierarchical control systems in which information flows bidirectionally between levels. Our results show that both human participants and model-based simulations (Bayesian and bounded rational agents) successfully complete the task. Over time, mutual information between actors and sensors increases, and cooperative behavior emerges within the groups. Interestingly, model-free hierarchical reinforcement learning fails to account for the observed data, being overwhelmed by task variability. In contrast, model-based approaches can be shown to generalize to larger groups and more complex network structures in evolutionary simulations. Our findings highlight the importance of internal models and concurrent co-optimization in facilitating adaptive coordination, offering insights into distributed information processing mechanisms.
Geemi P Wellawatte, Huixuan Guo, Magdalena Lederbauer
et al.
Retrieval-Augmented Generation (RAG) is a widely used strategy in Large-Language Models (LLMs) to extrapolate beyond the inherent pre-trained knowledge. Hence, RAG is crucial when working in data-sparse fields such as Chemistry. The evaluation of RAG systems is commonly conducted using specialized datasets. However, existing datasets, typically in the form of scientific Question-Answer-Context (QAC) triplets or QA pairs, are often limited in size due to the labor-intensive nature of manual curation or require further quality assessment when generated through automated processes. This highlights a critical need for large, high-quality datasets tailored to scientific applications. We introduce ChemLit-QA, a comprehensive, expert-validated, open-source dataset comprising over 1,000 entries specifically designed for chemistry. Our approach involves the initial generation and filtering of a QAC dataset using an automated framework based on GPT-4 Turbo, followed by rigorous evaluation by chemistry experts. Additionally, we provide two supplementary datasets: ChemLit-QA-neg focused on negative data, and ChemLit-QA-multi focused on multihop reasoning tasks for LLMs, which complement the main dataset on hallucination detection and more reasoning-intensive tasks.
As of 2022, the Republic of Korea accounted for 0.8% of global chicken meat production and 0.9% of global egg production. The country achieved self-sufficiency rates of 83.1% for chicken meat and 99.4% for eggs, demonstrating significant quantitative and qualitative growth to meet domestic demand. Although the industry is trending towards expansion and specialization, it faces several challenges in achieving sustainable poultry production. Key challenges in Korea include highly pathogenic avian influenza and pest issues, climate change and the push for carbon neutrality, reliance on imported breeding stock, insufficient preparedness for expanding cage space per laying hen, post-settlement payment systems for egg sales and an oversupply of chicken meat, and the aging poultry farming population and the closure of farms unable to secure successors. Following strategies are proposed to overcome or mitigate challenges mentioned above: (1) enhancing farm biosecurity and implementing vaccination policies for disease control, (2) modernizing facilities and promoting carbon-neutral practices to adapt to climate change, (3) diversifying breeding stocks across multiple locations and developing domestic strains, (4) implementing policies and supporting farms based on a comprehensive readiness assessment of all farms regarding expanded cage space requirements, (5) improving market transparency for the egg industry and regulating supply and demand in the broiler industry, and (6) offering incentives for farm succession, attracting labor, and promoting coexistence between corporations, rural communities, and small farms. In conclusion, the sustainable development of Korea's poultry industry is not a simple task. It requires a comprehensive approach considering economic efficiency, animal welfare, environmental protection, food security, and the symbiosis with rural communities. This approach necessitates efficient cooperation among all stakeholders, including the government, farmers, integrators, retailers, and research institutions, along with a comprehensive, phased strategy for both short- and long-term goals.
This paper presents Carleman-Fourier linearization for analyzing nonlinear real dynamical systems with periodic vector fields. Using Fourier basis functions, this novel framework transforms such dynamical systems into equivalent infinite-dimensional linear dynamical systems. In this paper, we establish the exponential convergence of the primary block in the finite-section approximation of this linearized system to the state vector of the original nonlinear system. To showcase the efficacy of our approach, we apply it to the Kuramoto model, a prominent model for coupled oscillators. The results demonstrate promising accuracy in approximating the original system's behavior.
This paper introduces a cost-effective robotic handwriting system designed to replicate human-like handwriting with high precision. Combining a Raspberry Pi Pico microcontroller, 3D-printed components, and a machine learning-based handwriting generation model implemented via TensorFlow, the system converts user-supplied text into realistic stroke trajectories. By leveraging lightweight 3D-printed materials and efficient mechanical designs, the system achieves a total hardware cost of approximately \$56, significantly undercutting commercial alternatives. Experimental evaluations demonstrate handwriting precision within $\pm$0.3 millimeters and a writing speed of approximately 200 mm/min, positioning the system as a viable solution for educational, research, and assistive applications. This study seeks to lower the barriers to personalized handwriting technologies, making them accessible to a broader audience.
Population migration continues to reshape the spatial pattern of China’s population and regional economic development. During this internal migration process, production and consumption patterns often change, ultimately leading to changes in green total factor productivity. This paper, based on the Chinese population census data and 1% sampling survey data from 2005 to 2015, utilizes social network analysis methods to measure the labor mobility network indicators of 284 prefecture-level cities. Further, this paper analyzes the impact and mechanisms of regional network status on green total factor productivity using a panel fixed effects model. We find that as network density increases, the interpersonal connections between regions become closer, and the network exhibits a clear pattern of “concentrated inflows” and “dispersed outflows”, with the trend of forming strong alliances becoming increasingly apparent. Regions positioned centrally either in terms of network in-degree or out-degree exhibit higher green total factor productivity. Among these, the labor mobility network plays a crucial role in enhancing green total factor productivity through the channel of technology diffusion effects, which improve investment efficiency via knowledge exchange and material capital accumulation. The promotive effect of labor network status on green total factor productivity is more pronounced in the eastern regions, where talent quality is higher, and in areas with fewer restrictions from the household registration system.
The existing global multi-crises have generated significant transformations in the architecture of financial systems, impacting local communities. Furthermore, the digital era has created a conducive environment for the development of financial innovations that can generate financial instruments supporting financial inclusion. Our research aims to identify and develop innovative financial instruments that foster closer collaboration within communities and promote a more equitable distribution of wealth and resources, directly impacting financial inclusion and well-being. The methodology used in our study is based on existing empirical research in the specialized scientific literature, as well as on identifying variables within existing models. Additionally, the use of bibliometric analyses and research tools based on artificial intelligence allows us to structure the innovative financial instruments found in the scientific databases. Building on the existence of innovative financial instruments, our paper specifically explores the concept of time banking as an innovative financial instrument, offering a new approach to economic exchange and the construction of financial mechanisms at the local community level. By using technology, especially in digital and ecological eras, time banks can be efficiently managed through online platforms where individuals can register their contributed hours and access the services they need. This study’s conclusions emphasize that time banks have the potential to serve as innovative financial instruments. Furthermore, through the analysis conducted in this study and the identified models, this study contributes to redefining the concept of time banking as an innovative financial instrument. Time banks focus on the productivity and efficiency of local community activities, with direct implications for reducing dependence on traditional currency and promoting an equitable distribution of labor. This innovative approach is promising, especially in an increasingly digitized financial landscape. Our paper seeks to capture this transformative potential and highlight our personal contributions to redefining the time bank as an innovative financial instrument.
The aim of the present study is to analyse the impact of the regulation of new technologies inspired by the risk-based approach, and in particular to examine the role of trade unions in the identification, assessment, management and mitigation of risks to workers’ rights. Through the analysis of the regulation inspired by the risk-based approach (GDPR, IA Act and the proposal for a directive on the improvement of working conditions in platform work), the essay identifies a new trend towards the proceduralisation of risk management, functional to preserve the effectiveness and efficacy of traditional labour regulation.
Relevance and goal. The relevance of this work is due to the need of finding effective approaches to determining the long-term structure of the regional economy for an alternative strategy for making management decisions in order to ensure balanced development of the internal territory. The research analyzes the capabilities of big data technology and demonstrates promising analytical tools for more effective use of the “smart specialization” approach in order to determine industry priorities for the structural transformation of regional economies. Materials and methods. The research is based on general scientific (induction, deduction, comparison, system-structural, etc.) and special research methods – big data analysis of the social network VKontakte, comparative analysis, analysis of the regulatory framework. This study was carried out using materials from two regions of the Russian Federation: Kaliningrad oblast and Penza oblast. Resources such as portal “RosNavyk”, social network VK, analytical platform PolyAnalyst were used. The data sources were the Spatial Development Strategy of the Russian Federation until 2025 and HeadHunter.ru, a website providing job search and recruitment services. Results. The authors obtained the following specific results: firstly, promising sectors of the regions were identified, taking into account the main parameters of the labor market; secondly, the authors conducted a comparative analysis of the results obtained with the data from the Spatial Development Strategy of the Russian Federation; thirdly, a relationship between promising regional specializations and the attitude of local residents towards popular professions in the region was identified based on social media data. Conclusions. The use of end-to-end big data technology to identify promising specializations in the region opens up new opportunities in this area and allows to operationalize the concept of “smart specialization” as a promising tool for implementing spatial development policies. The information about the attitude of local residents of the regions towards certain professions is of high value from the point of view of further connecting industry priorities identified as a result of the analysis of regional contexts, as well as the research and innovation potential that they possess, with the views and expectations of participants in regional economic systems. The practical use of this approach will allow to make effective management decisions and pursue a balanced industry policy that takes into account current patterns emerging in the labor market and the attitude of the region's population towards certain professions. Stakeholders of this information may be universities, employers, professional communities and associations, regional authorities, as well as relevant ministries and departments.
Pork production is a significant agricultural enterprise in the United States and Canada. The United States is the third-largest global producer of pork and Canada ranks seventh in pork production. The North American Free Trade Agreement and its successor, the U.S.-Mexico-Canada Agreement, have facilitated trade and integration between the two countries. The majority of production systems are modern and intensive, characterized by large vertically integrated farms using advanced technologies. Both nations benefit from their status as major producers of feed grains, with the United States leading in corn and soybeans, while Canada excels in canola and barley production. The regulatory frameworks for food safety, animal welfare, and environmental stewardship differ slightly, with the FDA and USDA overseeing these aspects in the United States, and Health Canada and the Canada Food Inspection Agency in Canada. The United States and Canada also have well-established distribution networks for pork products, relying on both domestic and international markets. Export markets play a crucial role, with the United States being a major importer of Canadian pigs, and both countries exploring opportunities in Asia. Despite a rise in global demand, domestic pork consumption trends differ, with per capita consumption remaining stable in the USA and declining in Canada. Changing consumer preferences, including a demand for ethically raised and locally sourced pork, may influence production practices. Future trends in pig production include a focus on consumer concerns, sustainability, disease prevention, reduction of antimicrobial use, and advancements in technology. The industry is adapting to challenges such as disease outbreaks and changing regulations, with a strong emphasis on animal welfare. Labor and workforce considerations, along with advancements in technology and automation, are expected to shape the efficiency of pork production in the future.
Many modern engineering structures exhibit nonlinear vibration. Characterizing such vibrations efficiently is critical to optimizing designs for reliability and performance. For linear systems, steady-state vibration occurs only at the forcing frequencies. However, nonlinearities (e.g., contact, friction, large deformation, etc.) can result in nonlinear vibration behavior including superharmonics - responses at integer multiples of the forcing frequency. When the forcing frequency is near an integer fraction of the natural frequency, superharmonic resonance occurs, and the magnitude of the superharmonics can exceed that of the fundamental harmonic that is externally forced. Characterizing such superharmonic resonances is critical to improving engineering designs. The present work extends the concept of phase resonance nonlinear modes (PRNM) to be applicable to general nonlinearities, and is demonstrated for eight different nonlinear forces. The considered forces include stiffening, softening, contact, damping, and frictional nonlinearities that have not been previously considered with PRNM. The proposed variable phase resonance nonlinear modes (VPRNM) method can accurately track superharmonic resonances for hysteretic nonlinearities that exhibit amplitude dependent phase resonance conditions that cannot be captured by PRNM. The proposed method allows for characterization of superharmonic resonances without constructing a full frequency response curve at every force level with the harmonic balance method. Thus, the present method allows for analysis of potential failures due to large amplitudes near the superharmonic resonance with reduced computational cost. The consideration of single degree of freedom systems in the present paper provides insights into superharmonic resonances and a basis for understanding internal resonances for multiple degree of freedom systems.
The prediction-based nonlinear reference governor (PRG) is an add-on algorithm to enforce constraints on pre-stabilized nonlinear systems by modifying, whenever necessary, the reference signal. The implementation of PRG carries a heavy computational burden, as it may require multiple numerical simulations of the plant model at each sample time. To this end, this paper proposes an alternative approach based on machine learning, where we first use a regression neural network (NN) to approximate the input-output map of the PRG from a set of training data. During the real-time operation, at each sample time, we use the trained NN to compute a nominal reference command, which may not be constraint admissible due to training errors and limited data. We adopt a novel sensitivity-based approach to minimally adjust the nominal reference while ensuring constraint enforcement. We thus refer to the resulting control strategy as the modified neural network reference governor (MNN-RG), which is significantly more computationally efficient than the PRG. The computational and theoretical properties of MNN-RG are presented. Finally, the effectiveness and limitations of the proposed method are studied by applying it as a load governor for constraint management in automotive fuel cell systems through simulation-based case studies.
This paper addresses the challenge of network synchronization under limited communication, involving heterogeneous agents with different dynamics and various network topologies, to achieve consensus. We investigate the distributed adaptive control for interconnected unknown linear subsystems with a leader and followers, in the presence of input-output disturbance. We enhance the communication within multi-agent systems to achieve consensus under the leadership's guidance. While the measured variable is similar among the followers, the incoming measurements are weighted and constructed based on their proximity to the leader. We also explore the convergence rates across various balanced topologies (Star-like, Cyclic-like, Path, Random), featuring different numbers of agents, using three distributed algorithms, ranging from first- to high-order tuners to effectively address time-varying regressors. The mathematical foundation is rigorously presented from the network designs of the unknown agents following a leader, to the distributed methods. Moreover, we conduct several numerical simulations across various networks, agents and tuners to evaluate the effects of sparsity in the interaction between subsystems using the $L_2-$norm and $L_\infty-$norm. Some networks exhibit a trend where an increasing number of agents results in smaller errors, although this is not universally the case. Additionally, patterns observed at initial times may not reliably predict overall performance across different networks. Finally, we demonstrate that the proposed modified high-order tuner outperforms its counterparts, and we provide related insights along with our conclusions.