The digital nomad economy—the ecosystem in which professional skills are traded through online platforms independent of geographic co-location—dynamically recombines skills into project-based portfolios with absent firm-level hierarchy. Yet it remains shaped by platform taxonomies, interfaces, and ranking/recommendation incentives. This study examines the emergent structure within this setting using the Semantic-Structural Systems Analysis (S<sup>2</sup>SA) framework, which integrates LLM-assisted skill extraction, transformer-based semantic embeddings, and multi-layer network analysis. We analyze a dual-source dataset comprising approximately 50,000 public Upwork profiles from a top-rated/high-earning segment (January–March 2023) and 2.0 million Reddit posts and comments (2018–2023) from remote-work and digital-nomad communities. The resulting skill network exhibits a pronounced core–periphery organization and modular “skill ecotopes” corresponding to coherent functional specializations. In predictive models of skill-level effective hourly rates, semantic brokerage and semantic diversity function as robust predictors of higher rates, significantly outperforming popularity-only baselines. Longitudinal discourse analyses surrounding the COVID-19 pandemic and the generative AI shock reveal rapid attentional shifts followed by the emergence and recombination of new skill clusters. We interpret these results as evidence consistent with constrained self-organization in platform-mediated labor markets. To support replication, prompts, parameters, and robustness checks are fully reported.
Declining fertility and population aging intensify labor shortages, making women’s reemployment after caregiving a policy priority. Using Taiwan as a case study, this study develops a real-time public opinion analysis system to complement delayed surveys and capture emerging barriers in labor-market reintegration. Drawing on 2022–2024 social media posts, the system applies sentiment co.mputing, clustering, and algorithmic attention to map four phases: withdrawal, intention, search, and reintegration. Findings show that younger women stress flexibility and childcare, while older returnees prioritize skill renewal and confidence rebuilding; sectoral variation supports life-cycle and clockspeed theories. Policy recommendations emphasize subsidies, training, quotas, and street-level implementation. Beyond technical contributions, the study embeds digital transformation (DT) into labor governance, showing a shift from as-is retrospective surveys to to-be-real-time monitoring. This transformation enhances policy agility, inclusiveness, and alignment with citizens’ lived experiences. The system thus functions as both a tool for rapid intervention and a DT-driven theoretical lens extending reemployment scholarship, offering transferable insights for aging societies.
As garment factories face increasing pressure to meet the demands of a rapidly growing global market for efficient, sustainable, and high-quality apparel production, transitioning from manual processes to technology-driven systems offers both promising opportunities and competitive challenges. This study explores the gradual adoption of advanced textile manufacturing technologies, focusing on key innovations such as automated pattern cutting, computerized embroidery, RFID tracking, and AI-driven quality control.
As the world’s population continues to rise while the agricultural workforce declines, farmers are increasingly challenged to meet the growing food demand. Strawberries grown in the U.S. are especially threatened by such stipulations, as the cost of labor for such a delicate crop remains the bulk of the total production costs. Autonomous systems within the agricultural sector have enormous potential to catalyze the labor and land expansions required to meet the demands of feeding an increasing population, as well as heavily reducing the amount of food waste experienced in open fields. Our team is working to enhance robotic solutions for strawberry production, aiming to improve field processes and better replicate the efficiency of human workers. We propose a modular configuration that includes a Delta X parallel robot and a pneumatically powered end effector designed for precise strawberry harvesting. Our primary focus is on optimizing the design of the end effector and validating its high-speed actuation capabilities. The prototype of the presented end effector achieved high success rates of 94.74% in simulated environments and 100% in strawberry fields at Farias Farms, even when tasked to harvest in the densely covered conditions of the late growing season. Using an off-the-shelf robotic configuration, the system’s workspace has been validated as adequate for harvesting in a typical two-plant-per-row strawberry field, with the hardware itself being evaluated to harvest each strawberry in 2.8–3.8 s. This capability sets the stage for future enhancements, including the integration of the machine vision processes such that the system will identify and pick each strawberry within 5 s.
Materials of engineering and construction. Mechanics of materials, Production of electric energy or power. Powerplants. Central stations
Abstract With the rapid development of new energy vehicle technology, electric drive systems play a crucial role in the modern automotive industry. Ensuring the efficient and stable operation as well as reliability of electric drive systems has become a critical task. In order to prevent serious faults in the short-term leading to potential accidents, this paper proposes an innovative approach for embedding the Token Merging (ToMe) algorithm into the Vision Transformer (ViT), called the VToMe algorithm and combining it with the Bidirectional Gated Recurrent Unit (BiGRU) network to form the VToMe-BiGRU architecture for electric drive system fault prediction. Specifically, the VToMe algorithm achieves stable detection of medium to long term system faults, while the BiGRU network achieves rapid fault prediction in the short term. The VToMe-BiGRU is an intelligent analysis method applied to automobile workshops, which is closer to the data source for data processing and analysis, alleviates the strong dependence on real-time network transmission, reduces the time consuming and labor-intensive process of manually extracting and analyzing the features, and improves the accuracy and reliability of the fault prediction. The optimized VToMe-BiGRU algorithm combines the Transformer model and the BiGRU network, which effectively captures the critical features in the electric drive system data, thus improving the fault prediction performance. Experimental validation on real-world electric vehicle (EV) maintenance datasets demonstrates outstanding performance of the proposed method. The multi-class fault classification achieves an average accuracy of 93.49% with a 32 $$\times$$ 32 patch size, outperforming state-of-the-art ViT++ by 0.12% while enhancing inference speed by 28% (32 FPS vs. 25 FPS for ViT++) to balance high precision and real-time efficiency. The short-term prediction yields a root-mean-square error (RMSE) as low as 6.33 and an accuracy (ACC) of 74.7% for complex fault modes such as bearing inner ring fault, surpassing traditional GRU/RNN models by over 20% in prediction accuracy. Moreover, the VToMe algorithm reduces computational complexity by 25% through hierarchical token merging, enabling efficient processing of high-dimensional sensor data without performance degradation. This research establishes a robust framework for real-time diagnosis of EV drive systems, effectively detecting critical faults like battery over-discharge and motor encoder errors with minimized false positives (FP < 5%), enhancing system reliability, reducing maintenance costs, and supporting proactive safety measures in EV applications.
Kiwifruit harvesting is labor-intensive, and social issues like an aging population and a declining agricultural workforce have significantly increased costs, presenting unprecedented challenges to the industry. Automatic harvesting systems utilizing multi-sensor fusion, AI, and automation technologies show great potential for replacing manual labor in kiwi harvesting. This paper reviews over 140 research articles related to kiwi fruit harvesting robots, summarizing existing progress in two key areas: target fruit recognition and positioning systems, and fruit picking and collection systems. We compare the pros and cons of various methods, including traditional image recognition and deep learning, active and passive localization techniques, diverse end-effector design structure and driving mechanisms, robotic arm path planning, and harvesting systems. The results show that challenges remain in the commercialization of kiwi harvesting robots. The absence of a unified evaluation standard for robot performance makes the latest research achievements hard to be inherited, leading to slow advancements. Current algorithms are often not lightweight enough for low-cost embedded systems. Additionally, the reliance on manual labeling of dense targets and the accumulation of system error compromise the robustness of target recognition and spatial positioning in open environments. The existing studies tend to focus on local improvements rather than the entire harvesting system. So addressing these issues should be a priority for future research. This paper can provide a reference for researchers and assist industry professionals in understanding the trends in harvesting robot development.
Griffin Carpenter, Myriam Vanderzwalmen, Helen Lambert
Stunning of farmed fish prior to slaughter is increasingly recognized as a key animal welfare priority, yet uptake remains limited in the EU aquaculture sector. While the effects of different stunning methods on fish welfare are the subject of significant recent research, the effect on aquaculture businesses remains unclear. Therefore, this study assesses the economic feasibility of implementing electrical stunning for four species where it is not currently routine: carp, trout, seabass, and seabream. Using a granular cost model across 17 country–species–system combinations, and cost data from 2018 to 2020, the impact of introducing in-water and dry electrical stunning systems under various cost pass-through and sensitivity scenarios is evaluated. Results show that while stunning increases the production costs, under realistic assumptions, 16 out of 17 segments remain profitable, with the one unprofitable segment already being unprofitable under business-as-usual conditions. Three trout systems even experience cost savings due to reduced labor requirements. Sensitivity analyses confirm the robustness of these findings across plausible increases in operating costs and financing assumptions. Even under a 0% cost pass-through, 16 segments still remain profitable. These results provide timely, policy-relevant evidence to support species-specific welfare legislation, while identifying segments that may require targeted support for compliance.
In the first part, the essay analyses the timing and modalities of digitalisation in the last-mile logistics sector, describing the forms that algorithmic management takes in the management of drivers' and porters' activities. These observations are then applied to the corporate structure typically found in the examined sector in Italy, i.e. the subcontracting chain, to identify the legal issues posed by this intersection to the interpreter. In this way, the authors seek to answer two questions: can the management of labour by means of algorithms by client companies be considered an instance of illicit labour brokerage, and what information tools are available to employees of contracting companies to “unveil” the algorithm and its user?
Flying multiple quadrotors in close proximity presents a significant challenge due to complex aerodynamic interactions, particularly downwash effects that are known to destabilize vehicles and degrade performance. Traditionally, multi-quadrotor systems rely on conservative strategies, such as collision avoidance zones around the robot volume, to circumvent this effect. This restricts their capabilities by requiring a large volume for the operation of a multi-quadrotor system, limiting their applicability in dense environments. This work provides a comprehensive, data-driven analysis of the downwash effect, with a focus on characterizing, analyzing, and understanding forces, moments, and velocities in both single and multi-quadrotor configurations. We use measurements of forces and torques to characterize vehicle interactions, and particle image velocimetry (PIV) to quantify the spatial features of the downwash wake for a single quadrotor and an interacting pair of quadrotors. This data can be used to inform physics-based strategies for coordination, leverage downwash for optimized formations, expand the envelope of operation, and improve the robustness of multi-quadrotor control.
PurposeThis study investigates how organizational control systems induce emotional labor in frontline service employees (FLEs). Drawing on the stimulus–organism–response (S-O-R) theory, we hypothesized that two control systems, an outcome-based control system (OBCS) and a behavior-based control system (BBCS), trigger work engagement rather than organizational dehumanization in FLEs, leading them to choose deep acting rather than surface acting as an emotional labor strategy.Design/methodology/approachThis study employed three-wave online surveys conducted 3–4 months apart to assess the time-lagged effects of S-O-R. We measured OBCS, BBCS (stimuli) and control variables at Time 1 (T1); work engagement and organizational dehumanization (organisms) at Time 2 (T2) and emotional labor strategies (responses) at Time 3 (T3). A total of 218 employees completed the T1, T2 and T3 surveys.FindingsOBCS increased work engagement, leading to increased deep acting. BBCS enhanced organizational dehumanization, leading to increased surface acting. Post-hoc analysis confirmed that the indirect effect of OBCS on deep acting through work engagement and the mediation effect of BBCS on surface acting through organizational dehumanization were statistically significant.Originality/valueThis study collected three-wave data to reveal how organizational control systems affect FLEs’ emotional labor in the S-O-R framework. It illustrated how organizations induce FLEs to perform effective emotional strategies by investigating the effects of organizational control systems on their internal states.
Unmanned aerial vehicle (UAV) systems have recently become essential for mapping, surveying, and three-dimensional (3D) modeling applications. These systems are capable of providing highly accurate products through integrated advanced technologies, including a digital camera, inertial measurement unit (IMU), and Global Navigation Satellite System (GNSS). UAVs are a cost-effective alternative to traditional aerial photogrammetry, and recent advancements demonstrate their effectiveness in many applications. In UAV-based photogrammetry, ground control points (GCPs) are utilized for georeferencing to enhance positioning precision. The distribution, number, and location of GCPs in the study area play a crucial role in determining the accuracy of photogrammetric products. This research evaluates the accuracy of positioning techniques for image acquisition for photogrammetric production and the effect of GCP distribution models. The camera position was determined using real-time kinematic (RTK), post-processed kinematic (PPK), and precise point positioning-ambiguity resolution (PPP-AR) techniques. In the criteria for determining the GCPs, six models were established within the İstanbul Technical University, Ayazaga Campus. To assess the accuracy of the points in these models, the horizontal, vertical, and 3D root mean square error (RMSE) values were calculated, holding the test points stationary in place. In the study, 2.5 cm horizontal RMSE and 3.0 cm vertical RMSE were obtained with the model containing five homogeneous GCPs by the indirect georeferencing method. The highest RMSE values of all three components in RTK, PPK, and PPP-AR methods were obtained without GCPs. For all six models, all techniques have an error value of sub-decimeter. The PPP-AR technique yields error values that are comparable to those of the other techniques. The PPP-AR appears to be an alternative to RTK and PPK, which usually require infrastructure, labor, and higher costs.
Resumen: Prácticamente todos los países desarrollados hacen ejercicios de planificación de médicos. Podemos aprender de las experiencias exitosas. La modelización y proyección de la oferta es técnicamente compleja, pero es solo una cuestión técnica, mientras que la evaluación de la demanda o necesidad, y por tanto el resultado en términos de déficit o superávit, requiere estándares, generalmente en ratios poblacionales, que se basan en juicios de expertos y pertenecen al universo normativo. Un tipo de problema técnico no del todo resuelto es el de convertir «cabezas» en equivalentes a tiempo completo. Afortunadamente, se está avanzando en la dirección correcta. Necesitamos más y mejor información, en particular el Registro Estatal de Profesionales Sanitarios, pero aun con las limitaciones y servidumbres de los datos, hay que planificar. El Ministerio de Sanidad, las comunidades autónomas y otras organizaciones profesionales y sindicales realizan regularmente ejercicios de planificación. Tenemos altas tasas de médicos y de graduados, y bajas de enfermeras, un pluriempleo creciente simultaneando la práctica pública y la privada, y déficits a corto plazo en algunas especialidades, en particular en medicina de familia, que necesita urgentemente incentivos específicos para estimular vocaciones. Los números cuentan solo una parte de la historia. Los desequilibrios en los mercados educativo y laboral no se resuelven convocando plazas, sino reformando el marco regulatorio, los sistemas de incentivos y la holgura de la gestión pública para competir con la privada por la atracción y la retención de talento. Abstract: Virtually all developed countries conduct physician planning exercises. We can learn from successful experiences. The modeling and projection of supply is technically complex, but it is a technical matter, whereas the assessment of demand or need, and therefore the outcome in terms of deficit or surplus, requires standards, usually in population ratios, which are based on expert judgments and belong to the normative universe. One type of technical problem insufficiently solved is that of converting “heads” into full time equivalents. Fortunately, progress is being made in the right direction. We need more and better information, in particular the State Register of Health Professionals, but even with the limitations of the data, it is necessary to plan. The Ministry of Health, the Autonomous Regions and other professional and union organizations regularly carry out planning exercises. We have high rates of physicians and graduates, and low rates of nurses, a growing number of physicians in both public and private practice, and short-term deficits in some specialties, particularly family medicine, which urgently needs specific incentives to stimulate vocations. The numbers tell only part of the story. The imbalances in the educational and labor markets are not resolved by creating vacancies, but rather by reforming the regulatory framework, incentive systems and public management slack to compete with the private sector in attracting and retaining talent.
This study examines the influence of lag fertilization techniques on Pakistani wheat production, highlighting the need to understand and mitigate the environmental impacts of farming methods. The basic purpose of this study is to investigate the impact of CO2 emission from fertilization and other factors on wheat production in Pakistan, using a time series of data from 1990 to 2020. CO2 emission from fertilization (CO2EF) is estimated using the default values provided by the IPCC guidelines. The ARDL approach analyses the short-run and long-run effects of CO2EF, technology level, energy use, agricultural land, and agricultural labor on wheat production. The results show that all factors have significantly impacted wheat production in Pakistan at levels of 1% and 5% significance, both in the short and long run. These findings suggest that reducing CO2EF, technology level, energy use, agricultural land, and agricultural labor on wheat production can help to increase wheat production in Pakistan. The study also highlights the importance of adopting sustainable and efficient fertilization practices, exploring alternative fertilizers, and using crop rotation systems to mitigate the adverse effects of carbon emissions from nitrogen fertilization, energy use, and the use of technology. These measures can contribute to a more sustainable and climate-resilient agriculture sector in Pakistan.
Ahmad Bin Afzal, Nabil Mohammed, Shehab Ahmed
et al.
Climate change has led to an increase in the frequency and severity of extreme weather events, posing significant challenges for power distribution systems. In response, this work presents a planning approach in order to enhance the resilience of distribution systems against climatic hazards. The framework systematically addresses uncertainties during extreme events, including weather variability and line damage. Key strategies include line hardening, backup diesel generators, and sectionalizers to strengthen resilience. We model spatio-temporal dynamics and costs through a hybrid model integrating stochastic processes with deterministic elements. A two-stage stochastic mixed-integer linear approach is developed to optimize resilience investments against load loss, generator operations, and repairs. Case studies on the IEEE 15-bus benchmark system and a realistic distribution grid model in Riyadh, Saudi Arabia demonstrate enhanced system robustness as well as cost efficiency of 10% and 15%, respectively.
Doris Allhutter, Florian Cech, Fabian Fischer
et al.
As of 2020, the Public Employment Service Austria (AMS) makes use of algorithmic profiling of job seekers to increase the efficiency of its counseling process and the effectiveness of active labor market programs. Based on a statistical model of job seekers' prospects on the labor market, the system—that has become known as the AMS algorithm—is designed to classify clients of the AMS into three categories: those with high chances to find a job within half a year, those with mediocre prospects on the job market, and those clients with a bad outlook of employment in the next 2 years. Depending on the category a particular job seeker is classified under, they will be offered differing support in (re)entering the labor market. Based in science and technology studies, critical data studies and research on fairness, accountability and transparency of algorithmic systems, this paper examines the inherent politics of the AMS algorithm. An in-depth analysis of relevant technical documentation and policy documents investigates crucial conceptual, technical, and social implications of the system. The analysis shows how the design of the algorithm is influenced by technical affordances, but also by social values, norms, and goals. A discussion of the tensions, challenges and possible biases that the system entails calls into question the objectivity and neutrality of data claims and of high hopes pinned on evidence-based decision-making. In this way, the paper sheds light on the coproduction of (semi)automated managerial practices in employment agencies and the framing of unemployment under austerity politics.
Raghad Alqobali, Maha Alshmrani, Reem Alnasser
et al.
Robot autonomous navigation has become a vital area in the industrial development of minimizing labor-intensive tasks. Most of the recently developed robot navigation systems are based on perceiving geometrical features of the environment, utilizing sensory devices such as laser scanners, range-finders, and microwave radars to construct an environment map. However, in robot navigation, scene understanding has become essential for comprehending the area of interest and achieving improved navigation results. The semantic model of the indoor environment provides the robot with a representation that is closer to human perception, thereby enhancing the navigation task and human–robot interaction. However, semantic navigation systems require the utilization of multiple components, including geometry-based and vision-based systems. This paper presents a comprehensive review and critical analysis of recently developed robot semantic navigation systems in the context of their applications for semantic robot navigation in indoor environments. Additionally, we propose a set of evaluation metrics that can be considered to assess the efficiency of any robot semantic navigation system.
The ratoon rice cropping system (RR) is developing rapidly in China due to its comparable annual yield and lower agricultural and labor inputs than the double rice cropping system (DR). Here, to further compare the greenhouse effects of RR and DR, a two-year field experiment was carried out in Hubei Province, central China. The ratoon season showed significantly lower cumulative CH<sub>4</sub> emissions than the main season of RR, the early season and late season of DR. RR led to significantly lower annual cumulative CH<sub>4</sub> emissions, but no significant difference in cumulative annual N<sub>2</sub>O emissions compared with DR. In RR, the main and ratoon seasons had significantly higher and lower grain yields than the early and late seasons of DR, respectively, resulting in comparable annual grain yields between the two systems. In addition, the ratoon season had significantly lower global warming potential (GWP) and greenhouse gas intensity-based grain yield (GHGI) than the main and late seasons. The annual GWP and GHGI of RR were significantly lower than those of DR. In general, the differences in annual CH<sub>4</sub> emissions, GWP, and GHGI could be primarily attributed to the differences between the ratoon season and the late season. Moreover, GWP and GHGI exhibited significant positive correlations with cumulative emissions of CH<sub>4</sub> rather than N<sub>2</sub>O. The leaf area index (LAI) and biomass accumulation in the ratoon season were significantly lower than those in the main season and late season, and CH<sub>4</sub> emissions, GWP, and GHGI showed significant positive correlations with LAI, biomass accumulation and grain yield in the ratoon and late season. Finally, RR had significantly higher net ecosystem economic benefits (NEEB) than DR. Overall, this study indicates that RR is a green cropping system with lower annual CH<sub>4</sub> emissions, GWP, and GHGI as well as higher NEEB.
Underlying relationships among Multi-Agent Systems (MAS) in hazardous scenarios can be represented as Game-theoretic models. This paper proposes a new hierarchical network-based model called Game-theoretic Utility Tree (GUT), which decomposes high-level strategies into executable low-level actions for cooperative MAS decisions. It combines with a new payoff measure based on agent needs for real-time strategy games. We present an Explore game domain, where we measure the performance of MAS achieving tasks from the perspective of balancing the success probability and system costs. We evaluate the GUT approach against state-of-the-art methods that greedily rely on rewards of the composite actions. Conclusive results on extensive numerical simulations indicate that GUT can organize more complex relationships among MAS cooperation, helping the group achieve challenging tasks with lower costs and higher winning rates. Furthermore, we demonstrated the applicability of the GUT using the simulator-hardware testbed - Robotarium. The performances verified the effectiveness of the GUT in the real robot application and validated that the GUT could effectively organize MAS cooperation strategies, helping the group with fewer advantages achieve higher performance.