Search Results (13,162)

This study analyzes the relationship between economic inequality, social capital, and crime across Mexican municipalities in 2020, adopting a mediation approach and an exploratory territorial analysis. Using data from the National Council for the Evaluation of Social Development Policy (CONEVAL), the National Institute of Statistics and Geography (INEGI), the Executive Secretariat of the National Public Security System (SESNSP), the National Statistical Directory of Economic Units (DENUE), and the National Electoral Institute (INE), a structural social capital index was constructed through Principal Component Analysis (PCA), a structural social capital index was constructed through Principal Component Analysis (PCA), incorporating indicators of civic participation, associational density, and local social infrastructure. The empirical strategy combines ordinary least squares (OLS) regression models, Exploratory Spatial Data Analysis (ESDA), bootstrap mediation models with 5000 resamples, and spatial regression robustness checks. The results show that inequality is positively and significantly associated with municipal crime rates; the Gini index is associated with vehicle theft (β = 0.115, p < 0.001) and homicide (β = 0.073, p < 0.001). After structural covariates are included, inequality loses statistical significance for property crime but remains associated with homicide (β = 0.098, p < 0.001). Social capital shows a significant negative association with both crime outcomes. The mediation analysis indicates a statistically significant indirect pathway linking inequality and crime through structural social capital; however, the form of mediation differs by crime type. For homicide, the evidence is consistent with complementary or partial mediation, whereas the covariate-adjusted vehicle theft model shows a competitive mediation or suppression pattern. The spatial analysis reveals territorial autocorrelation in all variables. Spatial regression robustness checks further show that structural social capital remains negatively associated with both crime outcomes after accounting for spatial dependence. The findings suggest that the inequality–crime relationship depends on contextual factors and local social structures and should be interpreted as statistical associations rather than causal effects. Full article

Background: Health security among structurally vulnerable populations depends not only on healthcare access but also on livelihood stability, caregiving capacity, and social participation. Evidence on how community-engaged participatory interventions address social determinants of health in low- and middle-income settings remains limited, particularly regarding how such interventions are implemented, documented, and evaluated in practice. Methods: This qualitative implementation study examined a participatory livelihood and caregiving intervention in a rural Thai setting. Over a nine-month period, 50 core participants from structurally vulnerable households engaged in integrated livelihood and caregiving activities. Data were collected through field observations, participatory reflection meetings, implementation records, LINE-group communication archives, and voluntary household financial logs, and were analysed using inductive thematic analysis. Results: Findings revealed perceived improvements across four interconnected dimensions of well-being: caregiving capacity, social connectedness, livelihood flexibility, and household economic buffering. Participants more consistently described household stabilization achieved through reduced expenditure, resource sharing, and livelihood diversification than rapid income growth. Sustained group participation was associated with strengthened peer support networks and greater self-reported confidence in managing caregiving and household challenges; however, these findings rest on self-reported and predominantly qualitative evidence, with financial documentation available for only a small, non-random subset of participants. Conclusions: These exploratory findings suggest that community-engaged livelihood and caregiving interventions may strengthen multidimensional health security by supporting stabilization processes rather than delivering immediate economic gains. Relational support, adaptive implementation, and community participation emerged as candidate mechanisms linking policy intentions to community health responsiveness. Health policy frameworks in comparable low- and middle-income settings may benefit from incorporating structural and relational social determinants alongside income-based metrics, although confirmatory research with comparison groups is needed before these implementation-level insights can inform generalizable policy guidance. Full article

Cross-domain data flows are commonplace in regulated inter-organizational environments, where durable audit evidence must be retained without publicly exposing sensitive flow metadata. This paper presents a privacy-preserving on-chain attestation framework for recorded cross-domain data transfers in a permissioned setting. Its core data structure, termed GBFPlus, extends the Garbled Bloom Filter (GBF) with explicit occupancy indicators, constrained payloads that encode a consistency prefix and an adjacent-domain identifier, and distinct pairing-derived positions. Each domain administrator records observed inbound and outbound transfers in directional GBFPlus instances and periodically commits signed filter attestations to an append-only ledger. An authorized regulator can reconstruct candidate transfer edges from available bilateral attestations, while light clients verify ledger inclusion through Merkle proofs. A traceable anonymous attestation signature conceals the uploader’s cryptographic identity from ordinary ledger observers while retaining regulator-assisted accountability. The security analysis establishes integrity, conditional anonymity, traceability, and metadata-privacy properties for committed attestations under the stated trust assumptions, and the prototype evaluation reports the measured costs of GBFPlus and the signature operations. Full article

Agricultural production trusteeship (APT) represents an innovative model of agricultural socialized services in which specialized service organizations provide scaled operations to smallholders without transferring land rights. It can effectively address rural labor shortages while contributing to China’s grain security and agricultural modernization by improving production efficiency and promoting more sustainable resource allocation. Using survey data from farm households across six Shandong cities, this study applies an Ordered Probit model to examine how off-farm employment influences farmers’ adoption of APT, together with its underlying mechanisms and heterogeneous patterns. The results show that a higher share of household labor engaged in off-farm employment is associated with a higher probability of APT adoption. This association is stronger under flexible employment model and weaker as the location of off-farm work becomes more distant. Mechanism analysis shows capital endowment mediates the association between off-farm employment and APT adoption, with physical capital having the strongest effect and economic capital the weakest. Higher risk perception weakens the positive effect of off-farm employment on farmers’ adoption of APT. These findings provide novel evidence and practical policy implications for improving APT implementation and facilitating smallholders’ effective participation in modern agricultural production systems. Full article

This work provides a structured synthesis of visual cryptography, a secret sharing technique that enables image reconstruction only when a specific number of shares are combined, with decryption performed visually by overlaying the shares. Although conceptually simple and distinctive in its reliance on the human visual system rather than complex computation, this method has been predominantly studied from theoretical and construction-oriented perspectives. The work consolidates fundamental concepts, mathematical foundations, operational principles, and security considerations of selected schemes, providing a common context for analyzing their characteristics. Particular attention is given to known attack vectors, including information leakage from individual shares and integrity violations caused by forged shares, together with corresponding mitigation approaches reported in the literature. In addition to this synthesis, the work presents an experimental investigation of the $\varphi$ correlation coefficient and its behavior for genuine and forged shares. Experiments conducted on a dataset of 100 images show that genuine–genuine share pairs consistently exhibit higher mean $\varphi$ correlation values than genuine–forged pairs, although the observed values depend on the underlying scheme. While these results suggest that $\varphi$ correlation may provide useful information for share authenticity analysis, the experiment used the same forged target image throughout the dataset, limiting the variation of the forged samples and potentially making the observed differences partly dependent on the selected target image. Finally, three Proof-of-Concept application scenarios demonstrate possible integrations of visual cryptography into QR code security, physical document verification, and IoT access control, illustrating its potential use in practical security systems. Full article

Background/Objectives: Enteral nutrition (EN), delivered either exclusively (EEN) or partially (PEN) in combination with the Crohn’s Disease Exclusion Diet (CDED), represents an evidence-based dietary therapy for active Crohn’s Disease (CD), recommended in clinical guidelines. However, adherence to this therapy remains suboptimal among adult patients. This study aimed to explore the intention to repeat CDED & PEN alongside perceived factors affecting adherence to CDED & PEN in CD adult patients. Methods: A single-center, cross-sectional qualitative study was conducted between October 2025 and March 2026 at a private dietetic practice in Larissa, Greece. Semi-structured interviews were undertaken and analyzed using thematic analysis. Results: Out of the 88 patients screened, 15 adults with CD participated in semi-structured interviews. Four overarching themes emerged: (1) intention to repeat CDED & PEN, with all participants reporting willingness to repeat CDED & PEN in the event of future relapse; (2) barriers to CDED & PEN adherence, including challenges in social situations involving food; (3) facilitators of CDED & PEN adherence, such as improved symptom control and increased sense of security; and (4) dual factors affecting CDED & PEN adherence, whereby meal preparation demands, taste and variety, and social and environmental support acted as either facilitators or barriers against adherence depending on individual circumstances. Conclusions: Greek adults with CD showed strong willingness to reinitiate CDED & PEN during relapse. Addressing modifiable barriers, particularly dietary monotony and financial burden, alongside strengthening structured dietitian support and personalized dietary guidance, may help optimize adherence in clinical practice. Full article

Higher education institutions operate complex digital environments that combine administrative services, research infrastructures, learning platforms, identity systems, and heterogeneous departmental IT. In the European Union, the NIS2 Directive increases the need for structured cybersecurity governance, while ISO/IEC 27001:2022 provides a mature information security management system standard that can support implementation. This paper proposes a design science artefact for aligning NIS2 obligations with ISO/IEC 27001:2022 clauses and Annex A controls in the context of higher education institutions. The framework organizes cybersecurity governance, asset and service scoping, risk management, incident handling, business continuity, supplier and cloud dependencies, access control, awareness, monitoring, and continual improvement into a staged maturity model. The artefact is instantiated for a Romanian public university context and assessed through internal traceability analysis, including mappings between NIS2 Articles 20, 21, and 23, Romanian NIS2 transposition requirements, and ISO/IEC 27001:2022 control areas. The institutional illustration identifies candidate assessment domains and evidence requirements but does not assign maturity levels because the internal records required by the scoring protocol were unavailable; it therefore does not constitute an audit, verified institutional measurement, or empirical validation. The contribution is therefore a structured and reusable compliance design artefact, together with a transparent mapping method that can support future expert validation, institutional pilots, and audit-oriented refinement. Full article

Sweetpotato (Ipomoea batatas [L.] Lam.) is a vital crop for food security, yet yields in Cuba remain below potential (2.5–10.0 t ha⁻¹) due to suboptimal propagation practices. This study investigated the impact of nodal position (1–22) on root development in stem cuttings of cv. ‘INIVIT B-240’ under controlled conditions. Root primordia (24 h), fibrous roots (7 days), and storage roots (60 days) were evaluated. Intermediate nodes (8–12) exhibited superior rhizogenic capacity, with node 10 showing the highest root primordia density (3.0 ± 3) and nodes 10–12 producing the most fibrous roots (24.6–26.7 roots/cutting). Storage root formation was optimal at nodes 5–13, particularly nodes 8–11 (1.30+ roots/node; highest biomass). Multivariate analysis (Integrated Performance Index) confirmed nodes 8–12 as the most productive cluster. These findings challenge traditional apical-cutting practices, showing that mid-stem nodes optimize both rooting and tuberization. We propose inverted planting of pre-apical sections to bury high-performance nodes, potentially increasing yields in resource-limited systems. Full article

Spatial clustering does not show that specific built-form characteristics lead to the concentration of terrorist incidents, but it is common for incidents to be clustered in urban areas. This is an exploratory study that examines 7960 incidents of ISIS terrorism recorded in the GTD from 2012 to 19. Geocoded incident coordinates and Haversine great-circle distance were used to implement the Density-Based Spatial Clustering of Applications with Noise (DBSCAN). An epsilon radius of 50 km and MinPts = 15 were chosen for the primary model, and 15 parameter combinations were analysed to investigate the robustness of the results. The main cluster found was 18 clusters with 7403 incidents (93.00%), and noise was the other cluster (557 incidents, 7.00%). The bulk of incidents (6118) were in Iraq, while the second largest cluster was in Syria with 675 incidents. The results show high geographical concentration, which is mainly due to the operational geography of ISIS, the intensity of the conflicts, the levels of exposure, and reporting. The analysis does not directly measure architectural morphology, sight lines, surveillance, permeability, crowding, and emergency egress. Based on this, the study suggests an initial multi-scalar urban design assessment framework that includes hotspot analysis as a first step, followed by site-specific assessment using the urban security, CPTED and crowd safety, and evacuation principles. The contribution is methodological and involves showing how the large-scale incident data can help inform, but not supplant, detailed architectural evaluation. Full article

Smart meter anomaly detection is critical for ensuring the security and stability of smart grids. However, existing detection methods face the following limitations: insufficient feature extraction, severe class imbalance, inefficient manual hyperparameter tuning, and poor model interpretability. To overcome these limitations, we propose an accurate and interpretable anomaly detection method based on Bayesian-optimized XGBoost and SHAP. Our method integrates multi-dimensional feature extraction to enrich feature information, Borderline-SMOTE to mitigate class imbalance, Bayesian optimization to tune XGBoost hyperparameters, and SHAP to quantify feature contributions and provide model interpretability. Experimental results on the public MAD dataset demonstrate that our method consistently outperforms both classical machine learning models, including decision tree, Random Forest, XGBoost, and LightGBM, as well as representative deep learning models such as CNN, TCN, LSTM, and CNN-LSTM in binary and multi-class classification tasks, achieving superior accuracy, precision, recall, and F1 scores. SHAP analysis further reveals that three-phase unbalance features are the dominant indicators of abnormal samples, a finding highly consistent with the physical mechanisms of power systems. Our method achieves both competitive detection performance and transparent decision-making, providing an actionable solution for smart meter anomaly detection in practical engineering applications. Full article

Reliable sustainability analysis requires more than the presence of ESG disclosure; it requires information that is specific, traceable and analytically usable. This exploratory study assesses a purposive corpus of 36 organisations in Bulgaria and Moldova (18 per country) using a Data Gap Matrix covering 15 ESG indicators. The revised framework explicitly separates three observed disclosure-quality dimensions—availability, granularity and auditability—from two framework-assigned analytical overlays—LCSA relevance and environmental-risk relevance. Environmental security is retained only as a framework-based interpretive lens for the environmental-risk overlay and is not treated as an observed outcome. The matrix generated 540 organisation–indicator coding units. Medians, interquartile ranges and score frequencies are the primary descriptive summaries; country and sector tests are retained only as secondary, sample-specific checks and are restricted to the three observed dimensions. Auditability is the principal weakness (median = 1.00), with only 7 of 540 coding units receiving the maximum score, while Scope 3 greenhouse gas emissions constitute the weakest observed indicator. No Holm-adjusted country or sector difference is established for the observed disclosure-quality dimensions, consistent with limited statistical power. The findings diagnose limited disaggregation, source traceability and assurance readiness, but do not measure organisational ESG performance, conduct an LCSA, or establish environmental-security outcomes. Interpretation is constrained by purposive sampling, heterogeneous report types, common-indicator applicability and the absence of independent double coding. Full article

Book 1 of Valerius Maximus’ Facta et Dicta Memorabilia places cult, ritual failure, religious performance, and divine signs at the opening of the collection. This article examines how the book’s received organization guides the evaluation of human action. It argues that Book 1 presents a graded field of religious judgement. Religio and neglecta religio provide the clearest opposition; simulata religio and superstitio are more ambiguous; and auspices, omens, prodigies, dreams, and miracles acquire force through human response, narrative outcome, and public confirmation. The analysis combines structural reading with close examination of key cases. Because 1.2–1.4 survive only in late epitomes, it distinguishes between the explicit evaluation of 1.1, the less secure boundary cases of 1.2–1.4, and the fuller sign narratives of 1.5–1.8. Book 1 thus provides an opening framework in which ritual practice and responses to divine signs become objects of ethical and historical interpretation. Full article

Background: Diabetes mellitus is a major public health concern in Saudi Arabia, affecting approximately 18.3% of adults. Although over 95% of people in the Qassim region own smartphones, limited information exists regarding how and why patients with diabetes utilize digital health technologies. Objective: This study employed an extended Technology Acceptance Model (TAM) incorporating trust, privacy concerns, and self-efficacy to investigate the factors influencing digital health technology adoption among patients with diabetes in Qassim Unaizah, Saudi Arabia. Methods: A cross-sectional study was conducted from November 2025 to January 2026 following institutional review board approval. The quantitative phase utilized a structured survey (n = 203) measuring TAM constructs via validated 5-point Likert scales. Descriptive statistics, Pearson correlations, and one-way ANOVA were performed. Qualitative themes were derived from open ended responses to contextualize quantitative findings. Results: Most participants were male (62.6%), with a mean age of 47.2 years (SD ± 13.1). Smartphone ownership was nearly universal (99.5%), and 82.8% used blood glucose tracking applications. All TAM constructs exhibited significant positive correlations with behavioural intention (p < 0.001): attitude (r = 0.450), perceived usefulness (r = 0.438), self-efficacy (r = 0.394), trust (r = 0.379), ease of use (r = 0.340), and privacy concern (r = 0.309). Mean scores indicated strong acceptance: perceived usefulness (4.31, SD ± 0.39), behavioural intention (4.21, SD ± 0.44), and attitude (4.17, SD ± 0.42). Daily usage was reported by 63.1% of participants, 81.8% expressed satisfaction, and 63.1% reported that digital tools greatly improved their diabetes management. Privacy concerns were notably low (mean 1.70, SD ± 0.89, reverse-coded; Cronbach’s α = 0.921). Supplementary qualitative content analysis of optional open-ended comments (n = 40 respondents) identified six recurring topics: clinical utility, digital literacy, trust, data security awareness, patient empowerment, and social support. Conclusions: Patients with diabetes in Qassim Unaizah demonstrate substantial digital health adoption, predominantly driven by perceived usefulness, positive attitudes, and self-efficacy. The extended TAM effectively explains adoption within this Saudi Arabian context. Findings support interventions emphasizing clinical benefits, user friendly design, and trust building to optimize digital health utilization in diabetes care. Full article

Freshwater scarcity can weaken water governance when hydrological pressure interacts with intensive agricultural demand, regulatory weakness, and political conflict. This research evaluates whether Irrigation District 005 (IR 005) in Chihuahua, northern Mexico, demonstrates local water-governance fragility across three domains: public security, the rule of law, and the ability to sustain water access and food production. A mixed-methods approach integrates legal and human rights documentation, institutional records, published studies, and a structured media review with hydrological, agricultural, climatic, and reservoir data. Water balances were analysed for 1998–2023, precipitation trends for 1980–2020, and crop water requirements were estimated using the Food and Agriculture Organization’s Irrigation and Drainage Paper No. 56 (FAO-56) Penman–Monteith framework, the crop coefficient (Kc), the water-stress coefficient (Ks), the United States Soil Conservation Service (SCS) Curve Number method, and application-efficiency assumptions. The 2020 water conflict resulted in fatalities, injuries, arrests, and documented human rights violations. Rule-of-law capacity was further diminished by unauthorised withdrawals, cultivation beyond authorised irrigation plans, and limited enforcement. The annual water balance shifted to persistent deficits after 2016, reaching an estimated deficit of 2268 cubic hectometres (hm³) in 2020. Annual precipitation did not exhibit a statistically significant monotonic decline during 1980–2020 (Mann–Kendall Z = −0.79, τ = −0.0878, p = 0.4251; Sen’s slope = −1.1628 mm yr⁻¹; Mann–Whitney p = 0.5313), indicating that recent stress is more closely linked to production scale, crop mix, governance conditions, and irrigation efficiency than to a long-term reduction in rainfall. Sensitivity analysis revealed that ±15% changes in Kc and Ks altered gross water requirements by approximately ±16–17%, while equivalent changes in effective precipitation produced changes of only 1–3%. These results demonstrate heightened water-governance fragility resulting from mutually reinforcing hydrological, institutional, and conflict-related pressures. Future research should refine locally calibrated water-demand parameters and develop reproducible monitoring systems that combine hydrological, institutional, satellite, and participatory data to support anticipatory, transparent, and rights-based water governance. Full article

The decline in groundwater storage (GWS) poses a critical threat to water security in semi-arid regions where increasing agricultural water demand and climate variability are increasing pressure on aquifers. This study presents a novel hybrid modeling framework integrating multi-source satellite and climate data (GRACE, GLDAS, TerraClimate, and MODIS) with machine learning and explanatory artificial intelligence techniques for the long-term assessment and interpretation of GWS anomalies in the data-poor Iğdır Basin. Three different modeling approaches were developed: XGBoost, Long Short-Term Memory (LSTM) networks, and their combined model, and interpreted using the Shapley Additive Explanations (SHAP) method. The results showed a significant long-term decreasing trend in groundwater storage anomalies at a rate of −0.87 mm per month during the 2002–2016 period, indicating continuous depletion. The LSTM model demonstrated the best performance with R² of 0.59, RMSE of 19.5 mm, and MAE of 15.1 mm, revealing the dominant role of temporal dependencies in groundwater systems. SHAP analysis identified lagged groundwater anomalies (especially GWS_lag3) as the most effective predictors; this may reflect the memory effect and lagged response specific to semi-arid aquifer systems, but this interpretation needs to be validated in different study areas. Snow water equivalent and total water storage anomalies also emerged as significant determinants, while the direct effect of instantaneous precipitation was found to be limited. This study addresses significant gaps in the literature by combining sequence-based modeling with model interpretability in a semi-arid closed basin. The findings highlight the necessity of using system memory and explainable artificial intelligence together for reliable groundwater prediction. While the proposed hybrid approach has the potential for application in other semi-arid regions, its broader usability needs to be supported by independent validation studies under different hydrogeological and climatic conditions. Full article