Search Results (446)

Rural revitalization has emerged as a core agenda in the global pursuit of sustainable development, with its success fundamentally hinging on enhancing the resilience of rural households to withstand shocks and restore their livelihoods. In contrast to mainstream research that primarily examines whether Medical Financial Assistance (MFA) reduces medical burden, this paper focuses on MFA as ex-post cash compensation and investigates whether and how it affects the sustainable livelihood recovery of low-income rural households following health shocks, thereby providing empirical evidence for understanding the foundational role of health security in rural revitalization. A quasi-natural experiment is constructed by leveraging the institutional feature that MFA eligibility is activated by exogenous health shocks. Using two-wave balanced panel data (2021–2022) from a nationally designated deep poverty-stricken county in Hebei Province, China, the Propensity Score Matching–Difference-in-Differences (PSM-DID) method and mediation models are employed for causal identification and mechanism testing. The findings indicate that (1) MFA significantly promotes household income recovery. It enables recipient households to recover per capita net income by an average of approximately 13.2% (p < 0.01), demonstrating a protective recovery effect, and simultaneously recovers per capita non-farm labor income by an average of approximately 13.8% (p < 0.05), revealing a developmental recovery effect. The latter is partially mediated by the non-farm labor participation rate (mediation ratio 51.7%, Sobel Z = 2.10). This finding validates the “time release effect,” demonstrating that MFA stimulates endogenous dynamics by restoring health capital and releasing labor previously constrained by family care responsibilities. It thereby extends the application of health capital theory from the individual to the household level. (2) Mechanism analysis shows that the protective recovery effect is fully mediated by the amount of MFA received (mediation ratio 326.7%, Sobel Z = 12.85), providing empirical evidence for precautionary saving theory in the context of targeted social assistance and revealing the potential productive attributes of the social safety net. (3) Heterogeneity analysis reveals clear group targeting and shock thresholds. The protective effect is concentrated among elderly households, while the developmental effect is primarily evident in middle-aged households. Both recovery effects manifest significantly only for households experiencing major disease shocks, confirming the theoretical expectation of “conditional effectiveness,” namely that policy effects are systematically moderated by household life-cycle characteristics and the severity of health shocks. This study demonstrates that MFA serves both as a safety net and an empowerment tool, but its effectiveness is highly contingent upon household characteristics and shock severity. By uncovering the foundational mechanisms through which health security contributes to rural household resilience, this study provides empirical evidence from China for building sustainable poverty prevention systems in the global process of rural revitalization. Full article

Against the backdrop of escalating global climate challenges, minimizing carbon emissions while enhancing energy efficiency in rice production has emerged as a core pathway toward achieving agricultural carbon neutrality. A two-year field study conducted in the Yangtze River Delta evaluated three rice cultivation practices: the farmers’ practice pattern (FPP), surface-applied controlled-release fertilizer with machine transplanting (S-CRF), and side-deep applied controlled-release fertilizer with machine transplanting (SD-CRF). Compared to FPP and S-CRF, SD-CRF increased grain yields by 11.3% and 9.2%, respectively, while reducing total energy input by 2.5% and 2.4%. It lowered the carbon intensity of production by 9.7% and 8.2% relative to FPP and S-CRF, primarily through reducing fertilizer/labor-associated carbon inputs and enhancing carbon-use efficiency via higher yield. Economically, SD-CRF outperformed traditional practices, achieving an 81.8% increase in net income and a 37.4% higher benefit-to-cost ratio compared with FPP, respectively, driven by labor cost savings and improved productivity. Notably, SD-CRF reduces labor input by 40.0% compared with FPP, simplifies fertilization operations, lowers farmers’ operational technical thresholds, and effectively boosts their economic income. Data envelopment analysis (DEA) further validated SD-CRF’s superior eco-efficiency, highlighting its dual advantage in balancing yield enhancement and environmental sustainability. Further clarification of SD-CRF application technical indicators, refinement of agronomic practices and machinery efficiency, and promotion of the integrated system’s synergistic benefits and scalable adoption are required to support global sustainable food systems and carbon neutrality goals. Full article

Based on panel data from 14 prefectures in Xinjiang from 2004 to 2022, this study employs the Super-SBM model and panel threshold regression to assess how urbanization and industrial growth influence industrial water resource utilization efficiency (IWRUE). Xinjiang exhibits a distinct “high-north–low-south” spatial pattern: Urumqi and other northern regions show continuous improvement and Tacheng maintains long-term superiority, while southern areas such as Kizilsu and Hotan remain persistently low. Although IWRUE increases overall, regional trajectories diverge considerably. Two significant thresholds are identified—industrial output value and urbanization rate. Below these thresholds, water consumption strongly suppresses IWRUE, industrial employment exerts a negative effect, and investment plays a positive role. Once the thresholds are exceeded, the negative effect of water consumption weakens, industrial employment turns positive, and investment becomes insignificant. Policy implications suggest that regions below the thresholds should strengthen investment in water-saving technologies and productive capital, whereas regions beyond the thresholds should focus on enhancing labor quality, promoting green innovation and improving refined management to stabilize IWRUE and foster coordinated regional development. Full article

Value investing remains grounded in intrinsic value estimation, margin-of-safety reasoning, and disciplined fundamental analysis, but its practical execution is increasingly constrained by the scale, heterogeneity, and velocity of modern financial information. Recent advances in artificial intelligence (AI), particularly large language models and automated information-extraction systems, create new opportunities to accelerate financial analysis; however, their outputs remain probabilistic, context-dependent, and potentially error-prone, making governance and verification essential. This article proposes an AI-assisted value investing framework that integrates automated extraction, valuation modeling, explainability, and human-in-the-loop (HITL) supervision into a unified decision-support architecture. The framework is organized into three layers: (i) a data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling layer for automated key performance indicator (KPI) computation, forecasting support, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer for traceability, verification, model-risk control, and analyst oversight. A central contribution of the paper is the operational characterization of prompt literacy as a determinant of analytical reliability, showing that structured, context-aware prompts materially affect extraction correctness, usability, and verification effort. The framework is evaluated through a case study using Rivanna AI on three large U.S. beverage firms—namely, The Coca-Cola Company, PepsiCo, and Keurig Dr Pepper—selected as a controlled, information-rich setting for comparative analysis. The results indicate that the proposed workflow can reduce end-to-end analysis time from approximately 25–40 h in a traditional manual process to approximately 8–12 h in an AI-assisted setting, including citation/source verification, unit and period reconciliation, and review of key valuation assumptions. Rather than eliminating analyst effort, AI shifts it from manual information processing toward verification, adjudication, and interpretation. Overall, the findings position AI not as an autonomous decision-maker, but as a governed reasoning accelerator whose effectiveness depends on structured human guidance, traceability, and disciplined validation. In value investing, a discipline traditionally grounded in labor-intensive fundamental analysis and disciplined intrinsic value estimation, AI introduces the potential to scale analytical coverage and accelerate evidence synthesis. However, AI systems in financial contexts are probabilistic, context-sensitive, and inherently dependent on human interaction, raising critical questions about reliability, governance, and operational integration. This article proposes a structured framework for AI-driven value investing that preserves the foundational principles of intrinsic value, margin of safety, and economic reasoning, while redesigning the analytical workflow through automation, explainability, and human-in-the-loop (HITL) supervision. The proposed architecture integrates three layers: (i) an AI-enabled data layer for traceable extraction and normalization of structured and unstructured financial information; (ii) a modeling and valuation layer combining automated KPI computation, machine learning forecasting, and discounted cash flow (DCF) valuation; and (iii) an explainability and governance layer ensuring traceability, verification, and model risk control. A central contribution of this work is the operational characterization of prompt literacy, namely the ability to formulate structured, context-aware requests to AI systems, as a critical determinant of system reliability and analytical correctness. Through a focused case study using an AI-assisted analysis platform (Rivanna AI) on three U.S. beverage firms, we provide evidence that structured prompt formulation can improve extraction consistency, reduce verification overhead, and increase workflow efficiency in a human-supervised setting. In this setting, analysis time decreased from a manual range of approximately 25–40 h to 8–12 h with AI assistance and HITL validation, while preserving traceability and decision accountability. The reported hour savings should be interpreted as conservative estimates from the initial deployment phase; additional efficiency gains are expected as operational maturity increases, driven by learning-economy effects. The findings position AI not as an autonomous decision-maker but as a probabilistic reasoning accelerator whose effectiveness depends on structured human guidance, verification discipline, and prompt-driven interaction. These results redefine the role of the financial analyst from manual data processor to reasoning architect, responsible for designing, guiding, and validating AI-assisted analytical workflows. Full article

This study examined associations between two strip-cropping innovations, cereal–legume (Mbili-Mbili) and legume–legume (doubled-up legume, DUL), and intra-household decision-making, labor allocation, and control over production benefits among smallholder farmers in Babati, northern Tanzania. Household survey data were collected from 157 households using a multi-stage cluster sampling approach, capturing variation by gender, age groups, and household characteristics. Across technologies, households were predominantly male-headed (91.7%), with men comprising 71.3% of respondents and managing 66.9% of trial plots. Decision-making on production, marketing, and income use was predominantly led by men, with joint decision-making accounting for approximately 24–32% of income-related decisions. Women contributed a larger share of field labor across both systems, providing 17.7% more labor than men under Mbili-Mbili and 29.7% more under DUL. Economically, Mbili-Mbili was associated with higher average net benefits (US$731 ha⁻¹) and benefit–cost ratios (2.5) than DUL (US$213 ha⁻¹; BCR = 0.7). More than half of Mbili-Mbili participants (53.3%) reported modifying the trial design, compared with 18.4% of DUL participants; Mbili-Mbili farmers trained more non-project farmers on average (4.0 vs. 0.9) and allocated larger areas for expansion (0.5 vs. 0.3 ha). Exploratory analysis suggested descriptive associations between productivity and economic outcomes and selected household characteristics, including labor availability and education. Overall, Mbili-Mbili exhibited stronger economic performance but higher labor requirements, with women contributing disproportionately to field operations under both technologies. These findings highlight the need for gender-responsive design, labor-saving options, and inclusive decision-making arrangements to support equitable and sustainable adoption of diversified strip-cropping innovations. Full article

This study aims to evaluate the impact of agricultural land property rights system reform on Agricultural Green Total Factor Productivity (AGTFP) and to uncover its underlying mechanisms. Treating the nationwide rollout of the Three Rights Separation Reform (TRSR) as a quasi-natural experiment, we employ provincial panel data from 2011 to 2023. The Super-SBM model is applied to measure AGTFP, followed by a multi-period Difference-in-Differences framework to identify the causal effects. The results indicate that the TRSR significantly enhances AGTFP, yielding an average improvement of 0.112 units. Mechanism analyses reveal that this gain is achieved through three distinct channels: promoting labor-saving technological progress, optimizing factor allocation efficiency, and facilitating agricultural green transformation. Heterogeneity analyses further demonstrate that the positive effects are more pronounced in plains regions, areas with lower rural per capita income, and jurisdictions with higher agricultural fiscal expenditure. These findings remain robust after a series of robustness and endogeneity tests. This study provides novel institutional evidence on the drivers of AGTFP and offers policy-relevant insights for advancing sustainable agricultural transformation in developing economies. Full article

The technological revolution and industrial transformation led by digital technologies are driving the shift from global value chains (GVCs) to digital global value chains (DGVCs). To address the challenge of global climate change while achieving economic growth, many countries are prioritizing practical energy-saving and emission reduction measures, while simultaneously seeking greater trade gains through participation in digital GVCs and the international division of labor. This study examines whether participation in DGVCs reduces carbon emissions. Using balanced panel data covering 62 countries from 2007 to 2021, we employ a Panel Smooth Transition Regression (PSTR) model to investigate the nonlinear relationship between DGVC participation and CO₂ emissions embodied in digital exports (EEDE). The empirical results reveal an inverted U-shaped relationship, indicating that DGVC participation increases emissions below a digitalization threshold but reduces emissions beyond this threshold. These findings provide new evidence for the dual role of digitalization in shaping trade-related emissions and highlight the importance of stage-specific strategies. Policy implications emphasize that less-digitized economies must prioritize breaking free from carbon lock-in by pursuing green transformation alongside digital expansion. The study deepens the understanding of the trade–environment nexus in the digital era and provides actionable insights for aligning digital economic development with global climate goals. Full article

Sweet cherry (Prunus avium L.) exhibits relatively low genetic diversity because of the self-compatibility of some varieties and repeated crossings of the same genotypes. High-quality markers are therefore needed for their reliable discrimination. However, the most currently used simple sequence repeat (SSR) markers offer only limited resolution for genotyping purposes. Here, thirty new highly polymorphic SSR markers were extracted from whole-genome sequences of 299 sweet cherry genotypes. Then, 16 highly polymorphic SSR markers were selected, multiplexed into one PCR, and successfully verified on a collection containing 294 unique genotypes. Compared with the set of 16 SSR markers recommended by the European Cooperative Programme for Plant Genetic Resources (ECPGR) for sweet cherry genotyping, our newly developed system has a seven orders of magnitude lower probability of the random identity of two genetically distinct samples than the ECPGR set (10⁻¹⁹ vs. 10⁻¹²). This higher resolution not only enables more precise genotyping but can also be successfully used for parentage or population analyses. This new and unique one-tube approach for sweet cherry genotyping will substantially simplify genotyping workflows, minimize errors, and save labor, time, and cost. Full article

Generative Artificial Intelligence (AI) has transitioned from a research milestone to a general-purpose technology with wide-ranging implications for organizations, labor markets, and information systems. Thanks to improvements in deep learning, generative adversarial networks (GANs), variational autoencoders (VAEs), diffusion models, transformer-based language models, and reinforcement learning from human feedback (RLHF), generative AI can now create high-quality text, images, audio, code, and other types of content. This review synthesizes the core technical foundations and best practices for training, evaluation, and governance, with an emphasis on scalability and human oversight. The paper examines applications across customer service, marketing, software development, healthcare, finance, law, logistics, and the creative industries, and assesses the labor implications of generative AI using a sociotechnical lens. This study also develops a disruption index that integrates task exposure, adoption rates, time savings, and skill complementarity. The paper concludes with actionable recommendations for policymakers, organizations, and workers, emphasizing the importance of reskilling, algorithmic transparency, and inclusive innovation. Taken together, these contributions situate generative AI within broader debates about automation, augmentation, and the future of work. Full article

Ex situ conservation in breading centers is one of the key strategies for saving giant pandas (Ailuropoda melanoleuca). Abnormal behaviors (e.g., inappetence) are key symptoms of potential health issues (e.g., Klebsiella pneumoniae) for the captives. Therefore, monitoring their normal activity patterns could set a baseline to detect these abnormalities for implementing timely interventions. However, traditional monitoring methods are labor-intensive, which often rely on manual observations. Here, we proposed a deep learning framework, termed as DeepPanda, for automatically recognizing four essential behaviors (i.e., eating, walking, resting and drinking) of giant pandas based on videos from common surveillance cameras. Experimental results demonstrated that the DeepPanda model achieved high performance on the self-established APanda dataset, with the testing mean average precision at an IoU threshold of 0.5 (mAP@0.5) of 98.8%. This methodology provides a powerful tool for monitoring the captive giant panda’s behaviors. Full article

In recent years, excessive tillering caused by high temperatures during early growth has contributed to rice quality deterioration in warm regions of Japan. Accurate determination of midseason drainage timing is essential but remains difficult due to year- and cultivar-dependent variability. In this study, we developed a smartphone-based web application that estimates rice tiller number from canopy images and diagnoses the optimal timing of midseason drainage by comparing estimated tiller numbers with cultivar-specific target values. The system operates entirely on a smartphone using HTML5 canvas-based pixel extraction, JavaScript computation, and Google Apps Script-based backend processing. Field experiments conducted in Chiba Prefecture using three rice cultivars showed a strong linear relationship between estimated and observed tiller numbers (R² = 0.9439). The root mean square error (RMSE) was 42.6 tillers m⁻², with a consistent negative bias (−34.6 tillers m⁻²), indicating systematic underestimation. Considering typical tiller increase rates near midseason drainage (12.0–24.3 tillers m⁻² day⁻¹), these errors correspond to approximately 1–3 days of growth progression, which is acceptable for timing-based decision-making. Although the system does not aim to provide precise absolute tiller counts, it reliably captures relative growth-stage dynamics and supports threshold-based diagnosis. The proposed approach enables rapid, on-site decision support using only a smartphone, contributing to labor-saving and improved water management in rice production. Full article

This paper explores the economic feasibility of Additive Manufacturing (AM) for producing prismatic battery cell housings, specifically targeting small production runs. A comprehensive cost analysis was conducted to compare AM with Tool-Based Manufacturing (TM) processes for battery cell caps and cans. This analysis takes various factors, including tooling, materials, machinery, labor, and part finishing costs, into account. The study demonstrates that AM offers significant economic advantages over TM for single-digit and low double-digit batch sizes, primarily due to the absence of expensive tooling costs associated with TM. AM-produced battery cell cans continue to be cost-effective even for medium-sized production runs. Additionally, AM allows for the integration of sensors directly within battery cell caps, providing enhanced real-time monitoring capabilities–an important benefit for development purposes. Further analysis, assuming a best-case scenario, indicated potential cost savings through the use of increased layer heights and faster recoating and scanning speeds, which enhances the economic appeal of AM. Overall, the findings suggest that AM is particularly beneficial for the production of battery cell housings in low- to mid-volume ranges, emphasizing its strategic importance for flexible manufacturing requirements and research-intensive applications. Full article

This study examines the regime-dependent threshold between fiscal and monetary policy interactions across the EU-27 states, utilizing quarterly data from 2000 to 2025. A fixed-effects panel threshold regression model has been adopted in this study, using endogenously determined debt thresholds, to assess how budget, debt, money supply, inflation, and fluctuations in interest rates interact under different debt regimes. This analysis also incorporates shock dummy variables following mild recessions and inflationary pressures, the global financial crisis, the sovereign debt crisis, the COVID-19 pandemic, and recent energy price and inflationary shocks. Consequently, three major findings emerge: firstly, fiscal deficits increase unemployment across both regimes, but their positive contribution is significantly reduced by 81% in high-debt regimes. Therefore, conventional Ricardian equivalence has been supported throughout this study in terms of precautionary savings and crowding-out impacts, which further contribute to intensifying with alternative debt regimes. Secondly, monetary variables, in this paper, have demonstrated limited direct effects on unemployment mitigation that highlight the transmission mechanisms under high-debt regimes. Thirdly, the effectiveness of crisis response critically depends on existing fiscal spaces, while the debt regime is interconnected with labor market outcomes. The main findings of the study provide empirical support for the Maastricht debt criterion of 60% as a structural threshold, which is a benchmark for a fundamental shift in the policy transmission mechanism. This study has identified rules and regulations for uniform fiscal consolidation as insufficient; rather, state-contingent governance frameworks have been highly recommended for managing asymmetrical fiscal–monetary policy interactions across different debt regimes. Furthermore, it contributes to the reformation of the more impactful fiscal and monetary policy interaction rule under a monetary union. Full article

A newly commercialized single-row multi-fan autonomous unmanned ground vehicle (UGV) sprayer, for use in trellised tree fruit crops, was tested to better understand air and spray patterns prior to wide-scale adoption in the modern apple orchard systems typical to Washington State. This sprayer was equipped with five brown and yellow Albuz ATR80 nozzles per fan (QM-420, Croplands Quantum). The fans were installed in a Q8 configuration, with eight fans (four on each side) staggered near the front and back as a stack to increase vertical span. Air velocity and spray delivery patterns of the commercialized sprayer unit were assessed in laboratory using a customized smart spray analytical system. Previous field trails of this sprayer unit revealed a hardware issue with electric proportional valve controls in fan-nozzle assembly, resulting in uneven spray deposition across V-trellised canopy. Post issue resolution, the sprayer characterization data showed an average Symmetry of 91%, and 84% for air velocity and spray volume delivery on either side. An average Uniformity of 57% and 48%, respectively was recorded for pertinent sprayer attributes across the spray height. Overall, after optimization, the UGV sprayer is suitable for efficient agrochemical application in modern orchard systems. Further evaluation of labor savings, biological efficacy gains from autonomous operation, and a full economic analysis would better inform grower adoption. Commercial viability of this UGV sprayer could also be improved by added features such as variable-rate application enabled by real-time crop sensing or task-map integration. Full article

Estimating the mass of Oudemansiella raphanipies quickly and accurately is indispensable in optimizing post-harvest packaging processes. Traditional methods typically involve manual grading followed by weighing with a balance, which is inefficient and labor-intensive. To address the challenges encountered in actual production scenarios, in this work, we developed a novel pipeline for estimating the mass of multiple Oudemansiella raphanipies. To achieve this goal, an enhanced deep learning (DL) algorithm for instance segmentation and a machine learning (ML) model for mass prediction were introduced. On one hand, to segment multiple samples in the same image, a novel instance segmentation network named FinePoint-ORSeg was applied to obtain the finer edges of samples, by integrating an edge attention module to improve the fineness of the edges. On the other hand, for individual samples, a novel cap–stem segmentation approach was applied and 18 phenotypic parameters were obtained. Furthermore, principal component analysis (PCA) was utilized to reduce the redundancy among features. Combining the two aspects mentioned above, the mass was computed by an exponential GPR model with seven principal components. In terms of segmentation performance, our model outperforms the original Mask R-CNN; the $\mathrm{A}\mathrm{P}$, ${\mathrm{A}\mathrm{P}}_{50}$, ${\mathrm{A}\mathrm{P}}_{75}$, and ${\mathrm{A}\mathrm{P}}_{\mathrm{s}}$ are improved by 2%, 0.7%, 1.9%, and 0.3%, respectively. Additionally, our model outperforms other networks such as YOLACT, SOLOV2, and Mask R-CNN with Swin. As for mass estimation, the results show that the average coefficient of variation (CV) of a single sample mass in different attitudes is 6.81%. Moreover, the average mean absolute percentage error (MAPE) for multiple samples is 8.53%. Overall, the experimental results indicate that the proposed method is time-saving, non-destructive, and accurate. This can provide a reference for research on post-harvest packaging technology for Oudemansiella raphanipies. Full article