Search Results (49,487)

Vegetables constitute an essential component of the daily diet in Albania; however, they also represent a major pathway of human exposure to pesticide residues. This study investigates the presence of pesticide residues in widely used vegetables, including leafy, fruity, root, and bulb types, and evaluates the potential dietary health risks associated with their consumption. Vegetable samples were analyzed using gas chromatography–tandem mass spectrometry (GC-MS/MS) and liquid chromatography–tandem mass spectrometry (LC-MS/MS), for the presence of 417 pesticide analytes, ensuring high analytical sensitivity and reliability. Pesticide residues were present, with 42 distinct compounds, including metabolites, found in all the analyzed samples. Notably, some of the detected substances are not currently authorized for use as plant protection products, suggesting either environmental persistence or regulatory non-compliance. Exceedances of European Union maximum residue limits (MRLs) were most frequently detected in leafy vegetables (42.31%), followed by fruity vegetables (18.75%), whereas no MRL exceedances were observed in root and bulb vegetables. According to the dietary exposure assessment conducted using European Food Safety Authority Pesticide Residue Intake Model (EFSA PRIMo model v.3.1), chronic dietary exposure to pesticide residues was below the acceptable daily intake (ADI). According to this assessment, the acute exposure exceeded the acute reference dose (ARfD) for several pesticide–vegetable combinations, particularly among children. This highlights the need for ongoing monitoring and better agricultural management techniques to reduce potential health risks related to pesticide residues in vegetables. The study results indicate the need to strengthen national monitoring programs, enforce pesticide regulations more strictly, and promote the wider adoption of integrated pest management strategies to reduce dietary pesticide exposure and protect public health in Albania. Full article

The attribution of systemic financial stress to specific market sectors requires metrics that are faithful to the model’s computations, statistically consistent, and connected to a physically meaningful measure of directed information flow. This paper addresses all three requirements through information geometry, contributing to SDGs 7, 8, 9, and 17 through an entropic causal chain linking energy infrastructure failure to financial market stress. We conjecture and empirically verify the Entropy–Saliency Equivalence: Metabolic Saliency is an asymptotically unbiased estimator of the local Kullback–Leibler divergence between stressed and resting sector return distributions, with bias decaying at a parametric rate under Gaussian regularity conditions. The finite-sample bias–variance decomposition of the Kraskov–Stögbauer–Grassberger transfer entropy estimator is derived, establishing a minimax-optimal convergence rate. A novel metric, the Spatio-Temporal Information Flux (STIF), quantifies directed inter-sector stress transmission in bits per trading day, providing a bootstrap-calibrated audit trail aligned with the South African Financial Sector Regulation Act and MiFID II. Empirical validation on the JSE canonical panel (87 securities, 2857 trading days, 2015–2026) with Eskom load-shedding stages as exogenous stress injectors confirms the equivalence ($R^2=0.810$, $\widehat{\rho }=0.90$), with walk-forward $R^2=0.789$ and placebo $R^2=0.081$ ruling out estimation artefacts. The energy sector is identified as the primary stress transmitter during Stage 4+ Eskom events (STIF rising from 0.14 to 0.43 bits/day, directional asymmetry ratio 4.7). Robustness checks confirm stability across non-Gaussian securities and rolling transfer entropy windows. Full article

The rapid expansion of e-commerce has transformed market access in developing economies, yet its impact on the spatial structure of market participation remains insufficiently understood. While existing studies primarily examine welfare outcomes such as income growth and consumption smoothing, few investigate how digital platforms reshape the balance of market entry between urban and rural areas. Drawing on New Economic Geography and platform economics theory, this study proposes that e-commerce development rebalances urban–rural market vitality through three associative pathways: alleviating rural capital constraints, improving rural innovation environments, and promoting agricultural-industry agglomeration. Using county-level panel data covering 2725 Chinese counties from 2011 to 2022, we employ a Double Machine Learning (DML) framework to examine the association between designation as an “E-commerce into Rural Comprehensive Demonstration County” and changes in the urban–rural market vitality balance (URMAR). The results indicate that demonstration county designation is associated with a statistically significant reduction in urban–rural market disparity, as measured by both the Theil index and the absolute difference in new enterprise registrations. The directional URMAR indicator further reveals that this convergence is driven primarily by accelerated rural enterprise formation. Subsample analysis confirms that the rebalancing interpretation holds across counties with different baseline market structures. Mechanism analysis provides suggestive evidence consistent with all three proposed associative pathways. Heterogeneity analysis further reveals that these effects are stronger in economically developed eastern regions, in counties linked to higher-tier cities, and in secondary and tertiary industries. These findings advance a market-structure perspective on digital development that complements existing welfare-based approaches and offer policy insights for fostering balanced regional development through targeted digital and complementary investments. Full article

The present study seeks to synthesize the scientific evidence from the last decade (2015–2025) regarding the transition from inequality in technological access toward social stratification mediated by automated decision-making systems. Following PRISMA 2020 guidelines and the SPIDER model, a corpus of 74 high-impact records from Scopus, Web of Science, ProQuest, and PsycINFO was examined. The results reveal an exponential growth in scientific production since 2018, marking a shift from infrastructure-based inequality toward a systemic stratification mediated by algorithmic opacity. Three critical sectors of exclusion are categorized: the socio-health nexus, the labor market, and the educational ecosystem. Methodologically, quantitative algorithmic auditing predominates (58%), although mixed sociotechnical approaches have increased by 25% since 2021 to capture experiences of intersectional vulnerability. The study concludes that AI acts as an active agent of social reproduction, necessitating a transition toward “Algorithmic Justice” and “Human-Centric Governance.” Finally, a “Reinstating AI” framework is proposed to democratize technological development and mitigate systemic biases, offering a roadmap for researchers and policymakers in the pursuit of technological sovereignty. Full article

As a core component of power system reform, the transmission and distribution price reform plays a critical role in optimizing the grid regulation model and promoting efficient allocation of power resources by establishing an independent pricing mechanism based on “permitted cost plus reasonable return”. This study evaluates the provincial transmission and distribution price reform effect in China. First, an evaluation index system is constructed from four dimensions, namely, economic efficiency, security guarantee, market mechanism and social welfare. Second, a comprehensive evaluation model is developed using a multi-attribute decision-making model consist of the Best–Worst Method (BWM), entropy weight method (EWM) and cloud model. Of these, the BWM and EWM are employed to determine the indicator weights, and the cloud model is utilized to rank the transmission and distribution price reform effect. Third, an empirical assessment and analysis are conducted on three typical provinces in China. Empirical analysis reveals significant regional heterogeneity in reform effectiveness. Based on the comprehensive cloud expectation (Ex) values, Province B (eastern coastal) ranks first with an Ex of 82.10 (on a 0–100 scale), falling into the “good” grade; Province C (northern) ranks second with an Ex of 81.05, also “good”; and Province A (central-western) ranks third with an Ex of 78.70, likewise “good”. Province B’s leading position is attributed to synergistic outcomes in cost control, market vitality, and social welfare. The study can provide references for the sustainable development of electric power companies and the electricity industry. Full article

Abrupt maritime-energy disruption can generate system-wide production losses before firms and policymakers can adjust. Existing assessments usually emphasize direct exposure or long-run equilibrium responses, which makes them less suitable for short-run risk assessment in energy-dependent production systems. We develop a threshold-cascade framework that combines dual-track dependence topology, edge-level inventories, smooth operability bands, and a separate price-validation step to identify the blockade intensity at which a localized chokepoint shock becomes systemic production loss. The framework is evaluated against the March 2021 Suez blockage and the 2022 Russia–Ukraine producer-price episode, and then applied to a 2026 Strait of Hormuz stress scenario using the Organisation for Economic Co-operation and Development (OECD) Inter-Country Input-Output (ICIO) tables, 2025 edition, with the 2022 benchmark year. Under the baseline 150-day horizon, terminal loss first reaches 50% at about 32% blockade intensity, with a broader calibrated threshold band of 32–46%. Losses spread beyond the point of origin and become concentrated in East and Southeast Asian manufacturing supply chains and in downstream consumer markets after inventories at connected hubs are depleted. Policy experiments show that single-channel interventions shift the threshold only modestly, whereas an integrated package that relaxes logistics, inventories, and upstream scarcity moves the threshold to about 46% in this calibration. The analysis targets the weeks-to-months interval before substitution, contract renegotiation, and broader market adjustments dominate. Within that interval, the model identifies when buffers fail, how production losses spread, and which intervention packages delay systemic disruption. Full article

To address the challenges of difficult value quantification, lack of market benchmarks, and scarcity of historical data for embedded software amidst the intelligent transformation of equipment systems, this study develops a scientific price estimation method based on functional capability contribution. A nonlinear pricing model is constructed to accurately characterize the two-stage evolution of software price: diminishing marginal utility during the mature technology accumulation stage and exponential growth during the technical bottleneck breakthrough stage. To ensure the consistency of pricing logic between hardware and software, a penalty function is innovatively designed to modify the standard likelihood function, effectively transforming practical business logic into a model regularization term. Parameter estimation is achieved by employing a Bayesian inference framework integrated with operational constraints, utilizing Markov Chain Monte Carlo (MCMC) sampling to realize robust posterior inference under small-sample constraints. Empirical analysis demonstrates that the proposed method achieves superior cross-domain data transfer performance compared to traditional baseline models, with a Leave-One-Out Cross-Validation (LOOCV) Mean Absolute Percentage Error (MAPE) of 21.2%. This research provides a practical value-oriented price estimation method for embedded equipment software pricing. Full article

Background/Objectives: Dairy yogurts are a source of protein and micronutrients in the Canadian diet. However, Canada’s Food Guide emphasizes the consumption of plant-based foods, which is facilitated by a greater availability of dairy alternatives on the market. The nutritional composition of these products varies and can differ from dairy foods such as yogurt, which contain high-quality protein and micronutrients. The objective of this study was to examine the effect of dairy yogurt consumption as part of a diet on any given day on nutrient intakes in Canadians across ages. Methods: The 2015 Canadian Community Health Survey (CCHS)—Nutrition first day 24 h recalls of males and females > 1 years of age (n = 17,308) and of yogurt consumers (n = 3788) were examined to estimate nutrient intakes arising from yogurt consumption. Respondents were allocated into four groups defined by their daily yogurt intake in grams (i.e., Group I/non-yogurt consumers: <1 g; Group II: 1–90 g; Group III: 90–115 g; Group IV: >115 g). Results/Conclusions: The results of this study provide timely data on Canadian yogurt consumption across the ages and show that those consuming yogurt have higher intakes of essential nutrients, such as protein, calcium, potassium, vitamin D, and dietary fibre. The data from this study emphasize the importance of yogurt in the context of a healthy eating pattern and emphasize the need to encourage consumption of yogurt within Canada’s Healthy Eating Strategy. Full article

Construction firms in emerging markets operate in highly volatile environments that threaten business continuity and sector-wide resilience. This study provides a novel, integrated framework that links multiple strategic survival models to construction business resilience and development in Ghana’s construction industry, with particular emphasis on the evolving role of digitalisation. Four survival models are conceptualised as strategic portfolios: Innovation and Digital Transformation, Diversification and Growth, Lean and Resilience, and Strategic Risk and Partnerships. A quantitative research design was employed, using structured questionnaires administered to 128 construction industry stakeholders. Data were analysed using Partial Least Squares Structural Equation Modelling to assess direct, indirect, and mediating effects among survival models, construction business resilience, and construction business development. All four survival models have significant positive effects on construction business resilience, with Diversification and Growth (β = 0.404) and Innovation and Digital Transformation (β = 0.377) exerting the strongest influence, followed by Strategic Risk and Partnerships (β = 0.265) and Lean and Resilience (β = 0.207). The structural model explains 55.7% of the variance in construction business resilience, while construction business resilience is positively and strongly related to construction business development (β = 0.439), accounting for 19.3% of its variance. The findings show, for the first time in this context, that construction business resilience systematically mediates the relationship between distinct strategic survival portfolios and business growth in an emerging-market construction sector. This study advances the resilience and construction management literature by empirically demonstrating the hierarchical effectiveness of different survival models and by positioning construction business resilience as both a defensive capability and a strategic engine of sustainable development for construction firms in volatile markets. This paper recommends that firms develop composite resilience portfolios that integrate these strategies, while policymakers foster enabling regulations, digitalisation incentives, and joint risk-sharing arrangements that amplify sector-wide resilience. It offers a portfolio-based perspective on how to combine diversification, digital transformation, lean management, and strategic partnerships to build resilient, growth-oriented construction businesses. Convenience sampling and a cross-sectional design in a single national context highlight the need for longitudinal and cross-country research to validate and extend the proposed framework. Full article

Housing inequalities remain a major challenge for contemporary urban governance, as they combine economic, social, spatial, and demographic dynamics that are difficult to capture through single indicators. This paper develops a data-driven assessment of housing inequalities in Portugal between 2015 and 2025, drawing on official national and European statistics and applying a Business Intelligence (BI) and urban analytics framework oriented towards policy monitoring. Official data from Statistics Portugal and Eurostat are integrated through an analytical pipeline including automated extraction via public APIs, data enrichment, and visual analytics. The workflow follows a CRISP-DM-inspired structure, creating a set of normalized indicators to capture different dimensions of housing conditions. The results point to a structurally polarized housing market. Housing valuations increased across all regions, but at uneven rates, reinforcing territorial disparities rather than convergence. Metropolitan and tourism-oriented regions experienced faster appreciation and indirect effects, while year-over-year growth in completed dwellings slowed after 2021–2022, indicating an uneven supply response. Beyond its empirical findings, the primary contribution of this study lies in demonstrating how BI and data science methodologies can be operationalized to monitor housing inequalities using official statistics. The proposed framework is replicable and can be adapted to other territorial and policy contexts. Full article

The intersection of financial innovation and environmental sustainability offers important opportunities for low- and middle-income (LMI) countries. This study examines the association between FinTech investment, geopolitical-economic uncertainty, urbanization, economic development, and carbon dioxide (CO₂) emissions in LMI countries. CO₂ emissions per capita are used as an environmental outcome indicator rather than as a direct measure of green finance. Using a panel dataset covering 2010–2021, the study applies fixed-effects panel regressions as the main empirical approach and reports one-step difference the Generalized Method of Moments (GMM) estimates as exploratory dynamic evidence. The fixed-effects results indicate that GDP per capita is positively and significantly associated with CO₂ emissions, while FinTech investment and urbanization do not show consistent significant associations. Geopolitical risk is positively associated with CO₂ emissions in some static specifications, but this association becomes insignificant once gross domestic product (GDP) per capita is included. The exploratory GMM results, estimated with collapsed instruments and restricted lag depth, do not provide statistically significant evidence that FinTech investment is associated with lower CO₂ emissions. Overall, the findings suggest that FinTech investment may be relevant for environmental outcomes in LMI countries, but its role is neither automatic nor uniform and remains sensitive to model specification. Policy implications emphasize the need to strengthen digital financial infrastructure, regulatory transparency, institutional stability, urban planning, and climate-oriented investment channels to support FinTech-driven environmental performance. Full article

Given the urgent need for corporate green transformation in the context of global climate governance, the sustainable development goals, and China’s dual carbon goals, this study examines the spillover effects of venture capital networks formed through common shareholder ties on green technology innovation from a complex network perspective. Based on regression analysis of panel data from Chinese A-share STAR and ChiNext Market listed companies between 2015 and 2023, we find the following: (1) Within venture capital networks, enterprises with higher centrality and structural hole positions exhibit more significant green technology innovation performance. (2) This facilitation effect varies across firm types. Private enterprises, foreign-invested enterprises and enterprises with weaker ESG performance rely more heavily on network advantage for innovation. (3) The mechanism analysis shows that occupying advantageous positions in venture capital networks enables firms to increase R&D personnel and R&D expenditure, thereby strengthening their ability to absorb external knowledge and transform innovation resources, which further enhances green technology innovation output. Full article

Occlusion is a critical challenge in person re-identification (ReID), as partial visibility severely degrades feature discriminability and matching reliability. To address this issue, we propose a novel framework termed Visibility-Guided and Occlusion-Simulated Learning (VGOSL) for robust person ReID. The framework consists of two key components: a part-aware visibility modeling (PVM) module and an occlusion box simulation (OBS) module. The PVM module explicitly estimates part-level visibility reliability and adaptively reweights local features to guide global representation learning, enabling the model to emphasize informative regions while suppressing occluded ones. Meanwhile, the OBS module introduces structured occlusion box simulation during training to enhance robustness against realistic obstruction patterns through multi-branch supervision. Extensive experiments on Occluded-DukeMTMC, DukeMTMC-reID, Market-1501, Partial-ReID, and MSMT17 demonstrate that the proposed framework achieves competitive performance under both occluded and holistic settings. The source code has been publicly released on GitHub. Full article

Extended-spectrum β-lactamase (ESBL)-producing bacteria are a growing public health concern within the One Health framework. This study aimed to characterize ESBL-producing Enterobacterales in industrial and artisanal organic fertilizers marketed in southwestern Colombia. Five commercial fertilizer brands were analyzed using a selective culture on ceftriaxone supplemented media (4 µg/mL), antimicrobial susceptibility testing by broth microdilution to determine minimum inhibitory concentrations (MICs), phenotypic synergy testing for ESBL confirmation, and polymerase chain reaction (PCR) to detect bla_TEM, bla_SHV, and bla_CTX-M genes. Overall, 18.6% of the samples showed growth of ceftriaxone-resistant Enterobacterales, predominantly Escherichia coli and Klebsiella pneumoniae. ESBL producers accounted for 84% of the isolates, all of which carried at least one bla gene, predominantly bla_CTX-M. Statistically significant differences in bacterial growth frequency were observed among fertilizer types, with higher positivity rates observed in manure-based artisanal formulations (p < 0.05). Whole-genome sequencing of selected isolates identified Klebsiella pneumoniae ST37 and Escherichia coli ST224, both harboring bla_CTX-M-55 and additional resistance and virulence determinants. These findings demonstrate that organic fertilizers, particularly manure-derived products, may act as reservoirs and potential dissemination routes for clinically relevant antimicrobial-resistant bacteria. This is the first study in Colombia documenting the presence of ESBL-producing bacteria in organic fertilizers. These results underscore the need to incorporate surveillance of these products into national policies under a One Health perspective. Full article

In high-inflation economies, stablecoins are increasingly becoming infrastructural channels through which households and firms access U.S.-dollar value outside traditional financial arrangements. We study Argentina as a fragmented USD access system composed of a regulated official channel, an informal parallel channel (the Blue Dollar), and platform-based USDT channels on Binance and Bitso. Using a quantile time-frequency connectedness framework, we estimate reduced-form dynamic dependence and spillover patterns across these interdependent subsystems under normal and extreme market states and across short- and long-term horizons. Four main findings emerge. First, system-wide connectedness is dominated by short-term transmission and rises sharply during policy regime transitions, particularly around the relaxation of capital controls. Second, under normal conditions, stablecoin markets behave as early-moving net spillover transmitters, whereas the Blue Dollar and the official rate primarily absorb shocks. Third, connectedness exhibits a symmetric U-shaped pattern across quantiles, indicating that tail events intensify cross-channel dependence regardless of shock direction. Fourth, under upper-tail extreme market states, the official rate becomes a net transmitter in the long-term frequency band, implying that major devaluation episodes can temporarily reconfigure the system’s transmission architecture, even though stablecoin channels remain important in overall connectedness. These findings should be interpreted as evidence of dynamic dependence rather than structural causality. They suggest that digital dollarization does not simply add another trading venue; it increases boundary permeability, reshapes information hierarchy, and changes the monitoring problem faced by authorities in fragmented financial systems. Full article