Search Results (90)

E-commerce is reshaping rural economies in developing countries, yet micro-level evidence on its early effects on rural labor supply and retention remains limited. This study examines these effects in China, distinguishing between platform e-commerce (e.g., Taobao) and social e-commerce (e.g., WeChat) to uncover heterogeneous effects across different models and demographic subgroups. We employ a propensity score matching difference-in-differences (PSM-DID) method using two-period panel data from 3234 rural residents, covering the critical period of rural e-commerce emergence and expansion in China from 2013 and 2017. Results show that different e-commerce models shape rural labor allocation along both temporal and spatial dimensions. Platform e-commerce significantly promotes localized labor participation, while social e-commerce offers flexible entry points for vulnerable populations such as child-bearing, left-behind women. These findings offer lessons for inclusive digital development in developing countries facing rural labor outflow and digital divides. Full article

Digital transformation has profoundly reshaped caregiving practices, yet its influence on family cohesion within disability contexts remains underexplored, particularly in Arab societies. This qualitative phenomenological study examines how digital technologies shape family cohesion among Jordanian caregivers of children with disabilities. In-depth, semi-structured interviews were conducted with 22 primary caregivers, and data were analyzed using reflexive thematic analysis. The findings reveal a central tension of being “between connectivity and care,” articulated through four interrelated themes: (1) a digital double-bind in which online support networks function as a vital “virtual village” while simultaneously contributing to intra-familial fragmentation; (2) the reconfiguration of care labor, whereby digital management emerges as an invisible and gendered form of caregiving work, often positioning mothers as primary digital coordinators; (3) the translation of traditional social capital (wasta) into digital spaces to navigate systemic resource constraints, producing new moral and emotional burdens; and (4) the strategic use of digital platforms to preserve cultural, religious, and familial identity in the face of stigma, thereby reinforcing internal cohesion. These findings suggest that digital technologies do not merely facilitate connection but actively reconfigure family dynamics through ongoing negotiation between support and strain. The study underscores the need for family-centered digital inclusion policies and support interventions that mitigate digital burdens while harnessing technology’s potential to strengthen culturally grounded resilience among families of children with disabilities. Full article

Production greenhouse complexes increasingly require automation and digitalization to address rising labor costs, improve productivity, and support sustainable resource use. However, most existing solutions target isolated tasks and lack a unified framework for continuous monitoring and production-oriented accounting at facility scale. This paper proposes a system-level architecture that integrates robotic monitoring platforms, AI-based perception, and cloud-based data management into a coherent operational framework. The robotic monitoring platforms operate on rails and concrete surfaces and are capable of elevating cameras and sensors up to 5 m to support plant-health assessment, environmental monitoring, and production accounting. Aggregated data are incorporated into a digital twin that supports spatial traceability, historical analysis, and decision support. The proposed approach enables continuous inspection, improves early detection of crop stress, reduces repetitive manual scouting, and supports targeted interventions. The framework provides a scalable foundation for sustainable, data-driven greenhouse management and practical deployment of robotic monitoring systems in industrial production environments. Full article

Determining the optimal harvest time is among the most critical economic decisions for peanut (Arachis hypogaea L.) growers, directly influencing yield, quality, and market value. Unlike many other crops, peanuts are indeterminate, continuing to flower and produce pods throughout their life cycle. As a result, pod development and maturation are asynchronous, making harvest timing particularly challenging. Conventional maturity estimation techniques, including the hull scrape method, pod blasting, and visual maturity profiling, are invasive, labor-intensive, time-consuming, and spatially limited. Moreover, differences in cultivar maturity rates and agroclimatic conditions exacerbate inconsistencies in maturity prediction. These challenges highlight the urgent need for scalable, objective, and data-driven methods to support growers in achieving optimal harvest outcomes. This review synthesizes the current understanding of peanut pod maturity and evaluates existing traditional and non-invasive approaches for maturity estimation. It aims to identify the limitations of conventional techniques and explore the integration of advanced sensing technologies, artificial intelligence (AI), and geospatial analytics to enhance precision and scalability in peanut maturity assessment and harvest decision-making. This review examines traditional destructive techniques such as the hull scrape method and pod blasting, followed by emerging non-invasive methods employing proximal and remote sensing platforms. Applications of vegetation indices, multispectral and hyperspectral imaging, and AI-based data analytics are discussed in the context of maturity prediction. Additionally, the potential of multimodal remote sensing data fusion and digital frameworks integrating spatial big data analytics, centralized data management, and cloud-based graphical interfaces is explored as a pathway toward end-to-end decision-support systems. Recent advances in non-invasive sensing and AI-assisted modeling have demonstrated significant improvements in scalability, precision, and automation compared with traditional manual approaches. However, their effectiveness remains constrained by the limited inclusion of agroclimatic, phenological, and cultivar-specific variables. Furthermore, the translation of model outputs into actionable, field-level harvest decisions is still underdeveloped, underscoring the need for integrated, user-centric digital infrastructure. Achieving a robust and transferable digital peanut maturity estimation system will require comprehensive ground-truth data across cultivars, regions, and growing seasons. Multidisciplinary collaborations among agronomists, data scientists, growers, and technology providers will be essential for developing practical, field-ready solutions. Integrating AI, multimodal sensing, and geospatial analytics holds immense potential to transform peanut maturity estimation. Such innovations promise to enhance harvest precision, economic returns, and sustainability while reducing manual effort and uncertainty, ultimately improving the efficiency and quality of life for peanut producers worldwide. Full article

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 (S²SA) 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. Full article

Rubber trees are crucial to the global industrial economy, but they are facing the threat of powdery mildew caused by Erysiphe quercicola. Effective management of this disease depends on early detection. However, traditional monitoring methods are labor-intensive and often inaccurate. This limitation underscores the need for more precise and efficient techniques. This study developed and validated an integrated molecular detection platform that combines quantitative PCR (qPCR), droplet digital PCR (ddPCR), and propidium monoazide (PMA) treatments. The platform demonstrated a robust detection range, accurately quantifying E. quercicola at concentrations as low as 10 spores/mL spore DNA and 10⁻⁵ ng/μL mycelial DNA. Additionally, the system distinguished viable from non-viable spores and detected E. quercicola mycelia in both asymptomatic leaves and aged lesions, significantly enhancing early-stage detection and disease monitoring. This technology also helps assess the efficacy of fungicides against powdery mildew, potentially reducing the use of chemicals and their environmental impact. By improving early diagnosis and disease management, this approach promises to reduce dependence on fungicides and mitigate economic and environmental impacts, highlighting the enormous potential of advanced molecular technologies in sustainable agricultural practices in rubber plantations. Full article

Public cultural services are traditionally viewed as welfare provisions. However, this perspective overlooks their productive externalities as critical social infrastructure. This study treats China’s National Public Cultural Service System Demonstration Zone program as a quasi-natural experiment to examine its economic performance. The analysis utilizes panel data from 280 prefecture-level cities between 2008 and 2021 and employs a multi-period difference-in-differences model. Results show that the policy successfully increased employment in the cultural sector. This was achieved by enabling flexible labor opportunities through digital platforms and government procurement, rather than through significant growth in formal enterprises. We term this structural divergence De-organized Growth. Mechanism analysis confirms that Fiscal-Digital Synergy drives this phenomenon. Effective collaboration between government funding and digital technology activates cultural consumption on the demand side and facilitates disintermediation on the supply side. Crucially, we identify a nonlinear Digital Exclusion Trap. In this trap, fiscal support is ineffective or even counterproductive in regions falling below a critical digital infrastructure threshold. The findings suggest that the equalized provision of public culture serves as a productive input for achieving UN Sustainable Development Goal 8 regarding decent work. We advocate for a shift in governance paradigms from traditional administration to a strategic purchaser role. This role leverages digital platforms to foster a more inclusive labor market. Full article

AgriRover was developed to address key operational constraints faced by smallholder vineyards in Peru, including sandy and saline soils, labor shortages, and limited access to advanced agricultural machinery. The platform features an articulated, all-wheel-drive chassis designed to ensure mobility and stability on loose terrain while minimizing soil compaction. This study presents the simulation-driven development of a digital pre-twin, conceived as a virtual prototype prepared for future sensor integration but currently operating without real-time data feedback. The pre-twin was implemented in MATLAB/Simulink (vers. 2024b) using a multibody dynamics model and evaluated through eight scenario-based simulations, varying field geometry, soil type, and slope conditions. The results show stable operation on slopes up to 10°, wheel sinkage values ranging between approximately 20 and 45 mm depending on terrain conditions, and a moderate battery state-of-charge reduction across most scenarios, with higher power demand observed on sandy soils. A scenario-based comparison indicates a potential reduction of approximately 50% in total monitoring time relative to manual field scouting, while advanced sensing, autonomous navigation, and AI-based analytics remain part of future developments. The current pre-twin provides a validated, low-cost foundation for context-specific phenological monitoring and early-stage precision agriculture applications in developing regions. Full article

Platform-mediated work (PMW) represents a highly unregulated and individualized segment of the labor market, with significant implications for psychosocial work environment and limited occupational health and safety (OHS) management efforts. The use of algorithmic management (AM) by digital platforms extensively directs and disciplines remote workers in PMW, and may exacerbate risks. This study employs the affordance concept initially introduced into safety science by Vicente and Rasmussen in 1992 and later applied in social media studies. Adopting a platform-sensitive approach, this study examines how digital mediation facilitates encounters between platform workers and customers across three types of PMW, and in turn affects harassment among platform workers. The analysis draws on 22 qualitative interviews with young platform workers supplemented by three workshops involving 13 stakeholder participants, informed by the Canadian Knowledge Transfer–Exchange approach. The findings identify three high-level affordances that significantly shape risks of harassment: (1) platforms’ ability to transcend physical space; (2) a digital blurring of private–professional boundaries; and (3) the amplification of asymmetric power relations among platform workers’ customers and platforms, relations that are gendered, classed, and racialized. The type and severity of harassment differ across the three types of platforms explored. Full article

This article examines labor relations in China’s entertainment live-streaming, where the state and private companies jointly regulate desire to secure political control and economic profit. Using Hao Wu’s documentary People’s Republic of Desire as a case study, I analyze how physical and affective labor are converted into emotional commodities circulated across platforms. Drawing on Jean-François Lyotard’s concept of the “libidinal economy,” I argue that while desire carries the potential to disrupt economic structures, it is ultimately absorbed into sustaining the political-economic status quo in contemporary China. Moreover, engaging Thomas Lamarre’s notion of “platformativity,” I further show how video platforms interweave the political, economic, and psychic to sustain a “tittytainment” economy that masks ongoing labor exploitation. The rise of live-streaming thus offers a critical lens for understanding the shifting relations among capital, labor, technology, and state governance in the digital age. Full article

The integrated platform provides a safe operating environment for high-rise residential construction and enables the simultaneous advancement of main structural works and facade operations. However, the construction workflow based on an integrated platform is highly complex, with tightly interlinked processes, making construction duration optimization an urgent issue. Focusing on the construction characteristics of the integrated platform for facade operations and the coordinated execution of structural and facade works, this study investigates the problem of construction duration optimization. With the objective of minimizing the overall construction period, the logical relationships among various processes are systematically sorted out, and a mathematical optimization model is established that considers precedence constraints, overlapping relationships, and labor resource conditions. By introducing a genetic algorithm, the optimal construction scheme under the shortest possible duration is obtained. An empirical analysis based on an actual engineering project demonstrates that the construction cycle of a standard floor was shortened from the original 6 days to 5 days, effectively reducing technical interruptions on site and lowering labor resource demand by 10–15%. This improvement enhances lean construction performance at the project level. The research results provide theoretical support and methodological reference for construction duration optimization using integrated construction equipment and hold significant engineering value and practical significance for promoting the digitalization, systematization, and efficiency of building construction. Full article

Optimizing coagulant dosages in Drinking Water Treatment Plants (DWTPs) is critical for reducing operational costs, minimizing chemical waste, mitigating environmental impacts, and ensuring consistent water quality, particularly in resource-constrained settings where conventional jar tests are labor-intensive and poorly suited to real-time demands. This study develops and validates a Random Forest (RF) machine learning model to predict optimal dosages of aluminum sulfate, polyaluminum chloride, and a polymer flocculant at the Miguel de la Cuba Ibarra DWTP in Peru, addressing the need for an efficient, real-time decision support system. Using a historical dataset of 2556 jar tests, a univariate RF model was developed to predict settled water turbidity, tailored to the plant’s typical operational range. The model demonstrated robust predictive performance, achieving a coefficient of determination (R²) of 0.92 during training and 0.76 during validation with unseen data, alongside a Root Mean Square Error (RMSE) of 0.11 NTU and a Mean Absolute Percentage Error (MAPE) of 0.11 in the training phase. Integrated into a digital platform, the model generates real-time NTU ppm dosing curves, providing a practical and responsive tool to enhance operational efficiency for DWTP operators. This work offers a scalable, data-driven solution to improve water treatment processes in resource-limited contexts. Full article

Agricultural mechanization and advanced technology have developed significantly in the coffee industry. However, there are still requirements for human laborers to operate, monitor crop health care, and manage production. The integration of advanced technology can significantly enhance the production efficiency and management practices of agricultural enterprises. This paper aims to address these gaps by proposing and implementing a computer vision-based attendance tracking system on mobile platforms that are suitable for the requirements and limitations of agricultural enterprises. First, the face detection process involves interpreting and locating facial structure. Next, the model transforms a photographic image of a human face into digital data based on the unique features and facial structure. We utilize the InsightFace model with the buffalo_l variant, as well as ArcFace with a ResNet backbone, as a facial recognition algorithm. After capturing a facial image, the system conducts a matching process against the existing database to verify identity. Finally, we implement a mobile application prototype on both iOS and Android platforms, ensuring accessibility for farm workers. As a result, our system achieved 95.2% accuracy on the query set, with an average processing time of <200 ms per image (including face detection, embedding extraction, and database matching). The system performs real-time attendance monitoring, automatically recording the entry and exit times of farm workers using facial recognition technology, and enables quick registration of new workers. Our work is expected to enhance transparency and fairness in the human management process, focusing on the coffee farm use case. Full article

This study examines how organizational models are evolving beyond post-Fordism in the context of digitalization, platformization, and new forms of labor governance. Using a bibliometric analysis of 1573 Web of Science publications, the article maps the intellectual genealogy, disciplinary foundations, and global collaborative patterns of research on the platform economy. The field has consolidated around three core concepts—platform economy, gig economy, and sharing economy—anchored in clusters focused on business models, labor precarity, and regulatory and governance debates. The analysis reveals a temporal shift from early narratives centered on sharing and collaborative consumption to contemporary concerns with algorithmic management, precarious work, and worker resistance. Parallel discussions of Industry 4.0 and 5.0 expose tensions between human-centered aspirations and the continued expansion of platform capitalism. The global landscape shows both vitality and asymmetry: China leads in empirical output, while the USA and England dominate theoretical agenda-setting and international collaboration. Overall, the findings demonstrate that platform research constitutes a mature, interdisciplinary field bridging labor sociology and management studies. The study calls for stronger integration of Global South perspectives and further examination of whether human-centered organizational visions can meaningfully counteract the structural inequalities embedded in platform-mediated work. Full article

Fabric drape characterization is vital for textile performance and aesthetics, but the conventional Cusick method is labor-intensive and incompatible with digital workflows. This study assesses a smartphone-enabled digital image processing (DIP) method for fabric drape coefficient (DC) measurement, providing an accessible, low-cost alternative to the Cusick method. Draped specimens of light, medium, and heavy fabrics were imaged at three diameters (24, 30, and 36 cm) using a smartphone positioned at three vertical distances (22, 32, and 42 cm). DCs were determined through pixel-based analysis in Adobe Photoshop^®, ImageJ^®, and MATLAB^®. Statistical comparison against the Cusick method employed F-tests for variance, two-sample t-tests for mean differences, and skewness analysis. No statistically significant differences were found between smartphone DIP (with either the iPhone or Samsung device) and Cusick measurements (p > 0.05). Neither imaging height nor software platform significantly influenced outcomes, though a 22 cm height consistently provided the most stable conditions. ImageJ^® was identified as an effective open-source option for reliable analysis. The findings establish a reliable, cost-effective, and portable method for drape evaluation, reducing technical and economic barriers while aligning with Industry 4.0 digitalization. This approach enables broader adoption of reliable textile characterization across research, industry, and domains. Full article