Search Results (523)

Rapid urbanization in the Global South has intensified the formation of mega-urban regions, where conventional urban–rural classifications often fail to capture the complexity of peri-urban systems. In the Jakarta–Bandung Mega-Urban Region (JBMUR), rapid land-use change and socio-economic transformation have produced hybrid landscapes that challenge binary zoning approaches. This study aims to delineate urban, peri-urban, and rural spatial structures using a spatially constrained clustering framework and to evaluate the performance of the Rustiadi Quantitative Zoning Method-2 (RQZM-2) compared with conventional non-spatial clustering (Non-RQZM). Built-environment, accessibility, environmental, and socio-economic indicators derived from remote sensing and spatially disaggregated statistical data were analyzed using grid-based K-Means clustering. Comparative validation using internal metrics, stability analysis, spatial coherence diagnostics, and statistical differentiation tests indicates that RQZM-2 produces more stable, spatially coherent, and interpretable clusters than conventional clustering. The validated four-cluster solution identifies compact urban cores, extensive peri-urban transition belts, and two distinct rural sub-types, revealing a functionally differentiated regional structure across the JBMUR. These findings demonstrate that incorporating spatial contextualization into clustering improves the empirical representation of peri-urban spatial continuity and provides a robust analytical basis for spatial zoning and regional planning in rapidly urbanizing mega-urban regions. Full article

Promoting resilience, increasing productivity and sustainability, and profit maximization remain key challenges facing farmers globally. These are exacerbated by factors such as climate change, low to no access to technological advancement, financial constraints, poor technical and management skills, inadequate government support, and limited access to resources. However, there are diverse strategies that abound, including on-farm diversification, that farmers could leverage on to address these numerous and complex challenges. This study investigated the determinants of on-farm diversification strategies among smallholders in Mpumalanga Province. The study employed a quantitative approach using closed-ended survey questionnaires to elicit information from a total of 465 farmers who were randomly sampled from a total population of 14,411. The data gathered were analysed using descriptive statistics to determine the on-farm diversification strategies employed by farmers and the factors influencing the use of these strategies. A binary logistic regression model was employed to establish the relationship between on-farm diversification strategies and the determining factors. More than half of the farmers were female (51.8%), with only 48.2% male. The majority (59.1%) of the farmers were between the ages of 36 and 60, with only 20.2% youth participation in farming. Slightly more than half (50.8%) of the farmers practise mixed farming as their on-farm diversification strategy, while only 4.3% of the farmers practise mono-cropping. The study identified significant variables such as level of education (p = 0.001), secondary source of income (p = 0.057), farmland size (p = 0.022), number of farm assistants (p = 0.016), and on-farm diversification awareness as key determinants of on-farm diversification among smallholder farmers in Mpumalanga Province. Therefore, it is recommended that policies within the agricultural sector be revised to encourage on-farm diversification in order to motivate farmers to transition to agripreneurship for poverty alleviation, food security and rural economic development (RED). Full article

In the context of China’s transition from “dual control of energy consumption” to “dual control of carbon emissions,” understanding the synergistic mechanisms among carbon emission trading (CET), energy use rights trading (EURT), and electricity markets is critical for achieving the nation’s dual carbon goals. This study develops a system dynamics (SD) model to examine the coupled interactions within this “carbon–electricity–energy” ternary market system, focusing on thermal power enterprises as the primary analytical subject. The model reveals that the ternary market framework drives energy conservation and emission reduction through three key mechanisms: price signal transmission, dual regulatory constraints, and mutual quota recognition. These mechanisms propagate low-carbon incentives throughout the industrial chain by transmitting cost signals to end-users via electricity prices. Compared to binary market structures, the ternary framework achieves superior outcomes, it facilitates higher renewable energy consumption, maintains more stable price levels, enhances market liquidity for both carbon and energy rights, and improves resource allocation efficiency alongside environmental–economic performance. However, the simulation also exposes critical inefficiencies under the current “dual control of energy consumption” regime. The parallel operation of EURT and CET markets creates functional overlap and duplicated compliance burdens. This redundancy increases enterprise costs without commensurate environmental gains, validating the necessity of transitioning to carbon-focused dual control. Further analysis demonstrates that a mutual recognition mechanism between carbon and energy rights effectively alleviates dual compliance pressures and improves enterprise profitability. Optimal market performance emerges when the recognition ratio is appropriately calibrated. Additionally, gradually increasing the share of auctioned quotas while maintaining appropriate levels of free allowances can drive emission reductions without compromising enterprise profitability. This research provides both theoretical foundations and practical policy recommendations for building an efficient multi-market coordination mechanism, facilitating the policy transition, and advancing low-carbon transformation in China’s power sector. Full article

Optimizing energy-intensive manufacturing under time-varying electricity tariffs requires scheduling strategies that reduce cost without compromising operational feasibility. This study is grounded in readily available industrial sensing: we exclusively use time-series measurements of aggregated active power and energy at the main distribution board of a quicklime production plant. We propose a tariff-aware load-shifting framework in which a Proximal Policy Optimization (PPO) reinforcement learning agent is trained in a custom Gymnasium environment to apply discrete consumption scaling actions constrained to 80–125% of a baseline profile during the operating shift (08:00–16:00), explicitly accounting for demand-charge exposure in the TOU peak window (13:00–15:00). The reward design combines instantaneous electricity cost with cumulative energy-tracking penalties and terms associated with operational constraints. Multi-day validation over $N=30$ working days shows consistent economic benefits, with a median total cost reduction on the order of 10% (narrow IQR) driven by reduced peak-window energy and demand peaks. However, the script-based binary compliance indicators (viol_energy, viol_prod_min) reveal deviations from the energy-balance criterion and occasional minimum-production shortfalls under the tolerances used, highlighting the cost–production trade-off and the need for stricter constraint handling for industrial deployment. In addition, we benchmark against dynamic programming (DP), an alternative RL policy (DQN), and a greedy heuristic (GREEDY), comparing cost; operational performance; and, when applicable, computational efficiency, which positions PPO as a competitive alternative among the considered methods. Overall, this work demonstrates how learning-based decision making can be coupled with real-world industrial sensing infrastructures, providing a data-driven tariff-aware scheduling layer for industrial energy management under practical constraints. Full article

This study proposes a novel theoretical synthesis, bridging the sociology of lived religion with economic club good theory to explore the high-commitment dynamics in domestic spheres in the analysis of “Amen meals”, a rapidly spreading ritual among Jewish women. Using a qualitative–ethnographic methodology based on 23 participant observations and 53 in-depth interviews with a diverse spectrum of Jewish women in Israel, the research examines the ways this ritual functions as a gendered religious economy. The findings identify emotional stringency as a key mechanism for communal cohesion: unlike traditional religious clubs that filter out free riders through external prohibitions, this economy demands a tariff of emotional exposure and vulnerability, where public tears serve as costly signals of commitment. These enable the participants to gain access to exclusive club goods such as social insurance and spiritual agency. The study concludes that Amen meals challenge the binary between institutional–rational and private–emotional spheres, positioning women’s ritual creativity as a mutual insurance system for risks that formal institutions fail to cover. It reveals the powerful economies operating within the lived religion of women. Full article

The jujube (Ziziphus jujuba Mill.) is a significant economic fruit tree, valued for its nutritional and medicinal properties. However, advances in functional genomics are hindered by the lack of an efficient transformation system. To overcome the limitations of conventional explant, we established a fruit-pulp-derived, callus-based Agrobacterium-mediated transformation system using fruit-pulp harvested 50 days after pollination. Through orthogonal experimental design, 6-benzylaminopurine and 2,4-dichlorophenoxyacetic acid were identified as key regulators for inducing high-quality, friable callus in two jujube genotypes, ‘JZ60’ and ‘LWCZ’. This system revealed significant genotype-specific variation in auxin requirements for callus proliferation and in differential antibiotic sensitivity. Transformation efficiency, as evaluated by fluorescence screening, was primarily determined by acetosyringone concentration and the binary vector architecture. The results revealed that the compact pCY (kanamycin resistance) vector achieved higher transformation efficiency (up to 77.8%) than pCAMBIA1301, whereas the pCAMBIA1301 (hygromycin resistance) vector enabled more uniform transgene expression. Integration and expression of the ZjCBF3 transgene were confirmed by polymerase chain reaction (PCR), reverse transcription quantitative PCR, and green fluorescent protein fluorescence assays. This study established a fruit-pulp-based callus transformation system for jujube, providing a rapid platform for its functional genomic studies. Full article

Background and Objectives: Depression has emerged in recent years as a significant global health issue, drawing considerable research interest and attention. The development of depression could be impacted by a range of environmental factors. Aim: To investigate the relationship between depressive symptoms and various indoor environmental factors, such as microclimate, odors, mold, and room ventilation, in association with some sociodemographic and lifestyle factors. Materials and Methods: This epidemiological health survey of the study “Chronic diseases and their risk factors in the adult population” was performed during 2023–2024 in Kaunas city (Lithuania) following the methodology of the WHO MONICA study. A random sample of Kaunas inhabitants aged 25–69 years, stratified by sex and age, was randomly selected from the Lithuanian population register. The 3426 individuals were screened. The associations of various indoor environmental factors with depressive symptoms were investigated using binary logistic regression analysis. Results: Depressive symptoms were associated with sociodemographic, lifestyle, and indoor environmental factors. Poor microclimate conditions, unpleasant household odors, mold exposure, and insufficient room ventilation were associated with increased odds of depressive symptoms. The significance of these associations varied across sex, age, marital status, socioeconomic status, and physical activity of responders. Additional multivariable logistic regression analyses, including interaction terms between each indoor environmental factor and the stratification variables (sex, age groups, marital status, family economic situation, and physical activity), were performed. Significant interaction was found only between family status and room ventilation (p = 0.007). This indicates that the association between ventilation and depressive symptoms differed by family status. Conclusions: This study contributes to the cross-disciplinary understanding of the role of indoor environmental quality, sociodemographic, and lifestyle factors in the development of depression, adding to the evidence on the role of other factors in depression inequalities. Full article

The Polish energy sector is undergoing a profound transformation driven by decarbonization targets and the implementation of the European Union’s sustainability governance framework, including the Corporate Sustainability Reporting Directive, the European Sustainability Reporting Standards and the EU Taxonomy Regulation. These policy instruments aim to align corporate behavior, capital allocation, and risk management with long-term sustainability and climate objectives, particularly in energy systems characterized by high carbon intensity. This study examines how ESG reporting requirements are perceived by professionals involved in ESG reporting in Poland’s energy sector and how they are expected to influence economic performance and investment decisions. The analysis is based on survey data from 43 entities. Although the sample size is limited, it covers the key energy-sector entities in Poland, providing a comprehensive sector-level perspective. Non-parametric statistical tests, binary and ordinal logit models, principal component analysis, Kendall’s tau correlations, and cluster analysis are used to assess perceived economic benefits, compliance capacity, and cost-related challenges associated with ESG reporting. The results indicate that ESG reporting is perceived as an economically relevant instrument improving transparency and supporting the integration of environmental performance into investment and strategic decision-making. At the same time, respondents identify significant economic barriers, including high administrative costs, regulatory complexity, and legal uncertainty, particularly affecting carbon-intensive entities. Full article

The acceleration of economic, technological, geopolitical and environmental processes has significantly increased the exposure of national economies to interconnected financial and non-financial risks. While global financial risks and corporate risks have been extensively analyzed, significant national risks—such as fiscal sustainability, debt vulnerability, systemic inefficiency and investment uncertainty—are often treated fragmentarily or descriptively within conventional sovereign risk frameworks. This article offers a comparative analytical approach to identifying national financial and non-financial risks by examining the degree of convergence and divergence between risks identified in four different sources: national expert scientific studies, World Economic Forum global risk assessments, strategic development documents of Bulgaria and national media coverage. Using expert data, structured content analysis, a modified media visibility index, and nonparametric statistical tests for linked binary data, the study identifies risks that are consistently recognized across sources and therefore pose an increased threat to financial stability, as well as risks that remain systematically underestimated despite their potential fiscal and macroeconomic consequences. The results show that cross-source comparison significantly improves the detection of national risks and reveals blind spots in fiscal planning, investment, and social policy. This article contributes to the literature on the management of risks with a direct negative financial effect or with an indirect financial impact on the national economy by positioning national risk identification within a governance-oriented, multi-source analytical framework. Full article

This study examines the application of ensemble machine learning methods for identifying and flagging potentially risky transactions in military public procurement in Ukraine, a sector characterized by elevated financial and security sensitivity and limited capacity for comprehensive ex post control. Using an integrated dataset of procurement procedures conducted between 2021 and 2025, enriched with 56 financial, economic, and behavioral indicators of suppliers, the study develops and compares standard logistic and LASSO-penalized regression as econometric benchmarks, Random Forest, XGBoost, XGBoost with SMOTE balancing, and CatBoost classification models. The target variable is defined on the basis of officially detected violations identified through state monitoring. Model performance is evaluated using standard binary classification metrics, with particular emphasis on recall. Model uncertainty and predictive robustness are addressed through partial dependence analysis, temporal stability assessment, and out-of-sample residual diagnostics. The results indicate that the CatBoost model demonstrates the most balanced performance across evaluation measures. Feature importance analysis identifies expected contract value, procurement method, CPV code, and suppliers’ financial capacity as significant determinants of procurement-related risk. The findings provide empirical evidence on the usefulness of risk-oriented machine learning tools in supporting earlier detection and monitoring of irregularities in military procurement. Full article

Environmental pollutants are persistent chemicals that pose substantial risks to human health, contributing to global mortality and economic burden. In real-world situations, exposure rarely occurs to single compounds; instead, people are chronically exposed to complex mixtures at low concentrations. However, most regulatory frameworks still rely on single-substance risk assessments, potentially underestimating the hazards associated with combined exposures. This study investigated the cytotoxic interactions of binary mixtures of five environmentally relevant pollutants: bisphenol A (BPA), bisphenol A diglycidyl ether (BADGE), dibutyl phthalate (DBP), di(2-ethylhexyl) phthalate (DEHP), and perfluorooctanoic acid (PFOA), using the human lymphoblast cell line NALM-6. Cells were exposed for 72 h to each compound individually and to all possible binary combinations, reflecting concentrations reported in human plasma or serum. Cell viability was assessed using the 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) assay, and interactions were analyzed using the Bliss model of independence and two-way analysis of variance (ANOVA). Intracellular reactive oxygen species were measured using the 2′,7′-dichlorodihydrofluorescein diacetate (DCFH-DA) probe to explore the involvement of oxidative stress. Synergistic interactions were observed under specific conditions, although not all statistically identified interactions corresponded to biologically significant effects. The BPA-DBP combination produced the highest cytotoxicity when both pollutants were present at 100 nM (31%), consistent with a strong synergistic effect. A similar pattern was observed for BADGE-BPA. ROS production was partially associated with cytotoxicity in these selected mixtures. Overall, these findings highlight the importance of distinguishing statistical synergy from toxicological relevance. Full article

Background: Accurate diagnosis of periprosthetic joint infection (PJI) remains challenging, particularly in culture-negative and borderline cases where current practices lead to high diagnostic uncertainty. SynTuition™, a machine-learning-based probability score integrating preoperative biomarkers, was developed to support clinical decision-making. This study compared its diagnostic performance and economic impact with standard physician practice. Methods: A total of 12 physicians provided diagnoses of 274 clinical vignettes representing suspected PJI cases. SynTuition probabilities were converted to binary diagnostic classifications using a validated threshold. Diagnostic accuracy, agreement, indecision rates, decision curve analysis, and misdiagnosis-related costs were evaluated. Results: SynTuition achieved an overall percent agreement of 96.0% when compared against the expert adjudicated clinical reference, outperforming the pooled physician group at 90.8%. Physicians showed high indecision (38–48%) in inconclusive 2018 ICM cases, whereas SynTuition generated a definitive diagnosis with an 86.7% agreement against expert adjudication. Decision curve analysis demonstrated a higher net benefit for SynTuition across a broad range of thresholds, reducing projected unnecessary revision by up to 5.8%. Economic modeling showed a reduction in misdiagnosis-related costs from $6.9 million to $2.9 million per 1000 suspected PJI cases, yielding estimated savings of $4000 per suspected case. Conclusions: SynTuition demonstrated high diagnostic accuracy, lower uncertainty, and significant clinical and economic advantages over routine physician practice, supporting its integration into clinical decision-making for suspected PJI, particularly in diagnostically ambiguous cases. Full article

Morocco is increasingly vulnerable to climate change, as reflected by recurrent droughts and rising soil and groundwater salinization, which threaten staple crops and rural livelihoods. In this context, the introduction of drought- and salinity-tolerant crops such as quinoa represents a strategic option for enhancing agricultural resilience and supporting sustainable rural development. This study analyzes quinoa adoption in two contrasting Moroccan regions, Rehamna and the Oriental, with the aim of determining key socio-economic, institutional, and environmental drivers. Field surveys were conducted to collect data on farmers’ personal characteristics, farm attributes, and access to resources related to quinoa cultivation, including water, information, and credit. Data analysis combined descriptive statistics, a binary logistic regression model (Logit), Factorial Analysis for Mixed Data (FAMD), and Hierarchical Cluster Analysis (HCPC) to identify adoption determinants and explore heterogeneity among farmers. The results reveal both common factors and region-specific dynamics shaping quinoa adoption. Cooperative membership emerges as a central determinant in both regions, facilitating access to information, collective learning, and market integration, with a stronger effect observed in the Oriental region. Water scarcity appears as a critical constraint, particularly in Rehamna. Adoption pathways also differ across regions, with a higher prevalence of direct adoption among farmers in the Oriental. Interpreted through the lens of innovation diffusion and multidimensional sustainability, the findings show that quinoa adoption is not merely a technical choice but a socio-economic adaptation strategy. Quinoa should therefore be considered a complementary crop within diversified farming systems, contributing to environmental resilience, income diversification, and social inclusion. These results provide relevant insights for the design of policies aimed at promoting sustainable agricultural innovation in marginal environments. Full article

Cross-border e-commerce, as a vital form of digital trade, is emerging as a new engine for corporate internationalization. This study employs China’s cross-border e-commerce pilot zones (established since 2015) as a quasi-natural experiment to investigate their causal effects on Chinese cities’ outward foreign direct investment (OFDI) and the underlying mechanisms. Distinct from previous trade-focused studies, this paper innovatively adopts a greenfield investment perspective. By integrating the Global Greenfield Investment Database (2010–2022) with the China City Statistical Yearbook, we constructed a greenfield OFDI dataset spanning the city–destination–target industry dimensions. Based on this dataset, this study employs a time-varying DID approach combined with PSM-DID, parallel trend tests, and placebo tests to empirically analyze how cross-border e-commerce development influences OFDI and its underlying mechanisms. The findings reveal that establishing cross-border e-commerce pilot zones boosts local outward investment by approximately 18.8%. A binary marginal decomposition analysis indicates that this effect primarily manifests through the extensive margin—significantly driving investment into new destination markets. Additionally, the mechanism operates by reducing information search costs and enhancing factor allocation efficiency. Furthermore, the outward investment promotion effect of cross-border e-commerce pilot zones is more pronounced in samples where the destination is a developed country, the target industry is high-tech, and the origin is eastern China. This study not only expands the dimensions for assessing the economic effects of cross-border e-commerce but also provides concrete empirical evidence for governments to optimize digital trade policy arrangements and for enterprises to leverage digital tools to overcome the “Liability of Foreignness” and achieve internationalization. Full article

This study examines the association between the formal (de jure) adoption of renewable energy source (RES) support instruments and observed RES deployment outcomes across 36 European countries. We assess whether broader legislative adoption—measured by a transparent breadth/coverage index (SIC/OIL) based on binary coding and equal sector weights—correlates with higher RES shares. The empirical design comprises three complementary steps: (i) hierarchical clustering (Ward’s method; Euclidean distance on standardised indicators) to classify countries by legislative adoption profiles; (ii) parallel clustering of countries by RES utilisation profiles using 10 z-score-standardised outcome indicators (total and sectoral RES shares and per capita RES use by source); and (iii) an integrated comparison of both typologies, followed by a cross-sectional regression test of the OIL–RES association. Legislative and utilisation clusters do not systematically coincide, and the baseline regression shows a weak, statistically insignificant association with very low explanatory power (R² = ≈ 0.015), supporting heterogeneity (H1) rather than a universal positive average relationship (H₂). Interpretation is conservative because SIC/OIL captures policy-mix coverage (not budgets, enforcement, or design stringency) and because some low/zero policy entries may reflect limited source coverage. Overall, the findings suggest that observed RES performance is primarily shaped by country-specific structural conditions (resource endowments, economic capacity, and sustained long-term investment), implying that context-sensitive instruments and stronger implementation capacities should complement formal policy adoption. Full article