Search Results (1,534)

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

Degraded industrial sites in arid and semi-arid regions often suffer from loose surface substrates, weak water-retention capacity, high wind-erosion risk, and poor early vegetation establishment. Combining photovoltaic (PV) deployment with ecological utilization may improve near-surface habitats by shading, reducing wind speed, and regulating soil heat and moisture. This study investigated an ash storage yard of a coal-fired power plant in Ordos, Inner Mongolia, China, by comparing soil temperature, soil moisture, and near-surface wind-speed responses under three representative fixed PV tilt angles of 36°, 43°, and 50°, together with the corresponding early plant-growth suitability. A multi-physics model coupling near-surface airflow, water-vapor transport, and porous-media hydrothermal migration was established. A Gaussian suitability function combined with AHP-CRITIC weighting was used to construct a model-based comprehensive growth index (CGI) from soil temperature and moisture, while short-term field monitoring was used to validate afternoon soil hydrothermal trends. Among the three scenarios, the 36° configuration produced the widest horizontal heat–moisture-affected zone and the highest CGI values for alfalfa and Elymus nutans, reaching 0.7741 and 0.6875, respectively. Relative to the outside reference area, the rear PV zone reduced the near-surface wind speed by 33–40% and increased the plant heights of alfalfa and Elymus nutans by 49.4% and 37.8%, respectively. A first-order PVsyst assessment showed that the 43° configuration achieved the highest specific energy yield of 1814 kWh kWp⁻¹ year⁻¹, whereas the annual grid-connected output at 36° was only 0.59% lower. These findings indicate that the 36° configuration may provide a favorable compromise between early vegetation establishment and photovoltaic electricity generation among the tested scenarios. By linking renewable-energy production with microenvironment regulation and early vegetation establishment, the proposed framework provides a decision basis for the multifunctional and sustainable reuse of degraded industrial land. Nevertheless, the results represent a site-specific, single-season assessment and should not be interpreted as a universal optimum. Full article

Signal interpretation in embedded ultrasonic monitoring depends strongly on the internal transmission path of the sensing element, yet this design factor has received limited systematic attention. This work compares two triangular quartz sensors that differ only in the configuration of their epoxy region: an epoxy-notched triangular quartz (ENTQ) device and an epoxy-solid triangular quartz (ESTQ) device. Their responses were examined during 24 h mortar hydration, controlled heating and cooling, and loading-induced debonding. The notch in ENTQ exposes part of the direct-wave route to changing external media, whereas the continuous epoxy region in ESTQ provides a more uniform internal route. Across the investigated conditions, ESTQ retained a more consistent direct-wave waveform and dominant-frequency pattern, while its later-arriving components remained responsive to changes in the surrounding mortar. Energy and time-frequency features also tracked the evolution of mortars prepared at two mix proportions. The comparison indicates that separation of a stable internal reference path from an environmentally sensitive reflected-wave path can improve the interpretability of embedded ultrasonic measurements. The results support structural-path design as a useful strategy for sensor development. Full article

With social media as the main channel for college students to express emotions, sentiment public opinion analysis in big data environments poses three core challenges to campus sentiment monitoring and psychological counseling: severe data noise interference, insufficient multi-attribute feature extraction, and the trade-off between recognition accuracy and inference efficiency. This paper proposes a university student public opinion prediction model integrating multi-attribute decision-making and BERT–Mamba. First, an anti-interference matching filter cleans raw data by filtering out advertisements and irrelevant comments to improve data quality. Second, a multi-attribute decision object model extracts quantifiable attributes covering media sources, themes, and temporal dimensions. Third, BERT generates textual sentiment representations, and a three-stage deep feature extraction architecture with Mamba balances accuracy and efficiency. Finally, multi-attribute features and sentiment representations are fused for dynamic public opinion prediction. Validated using the ChnSentiCorp Chinese sentiment analysis benchmark dataset and university student Weibo public opinion corpus, the model achieves 97.44% average sentiment recognition accuracy. It provides technical support for universities to understand student sentiment trends and address negative public opinions, with practical value for enhancing campus public opinion monitoring and assisting mental health counseling. Full article

Background and Objectives: Lipoprotein(a) [Lp(a)] is an established cardiovascular risk factor associated with atherosclerosis, vascular inflammation, and endothelial dysfunction. However, data regarding its relationship with hypertension-mediated organ damage and circadian blood pressure abnormalities remain limited. We aimed to investigate the association of elevated Lp(a) levels with carotid atherosclerosis and nocturnal blood pressure dipping patterns in patients with hypertension. Materials and Methods: This retrospective cross-sectional study included 80 nondiabetic patients with essential hypertension. Patients were categorized according to serum Lp(a) levels into elevated Lp(a) (≥50 mg/dL, n = 20) and lower Lp(a) (<50 mg/dL, n = 60) groups. All participants underwent carotid ultrasonography, transthoracic echocardiography, ambulatory blood pressure monitoring, and ophthalmologic evaluation. Multivariable logistic and linear regression analyses were performed to determine independent associations between Lp(a), carotid plaque presence, and nocturnal dipping percentage. Results: Patients with elevated Lp(a) levels had a significantly higher prevalence of carotid plaque (55.0% vs. 23.3%, p = 0.008) and non-dipper hypertension (75.0% vs. 46.7%, p = 0.028) compared with patients with lower Lp(a) levels. Serum Lp(a) concentrations were inversely correlated with nocturnal dipping percentage (r = −0.362, p = 0.001). In multivariable logistic regression analysis adjusted for age, sex, LDL cholesterol, and active smoking, elevated Lp(a) remained independently associated with carotid plaque presence (OR 3.24, 95% CI 1.06–9.80, p = 0.039). In multiple linear regression analysis adjusted for age, sex, LDL cholesterol, and office systolic blood pressure, higher Lp(a) levels remained independently associated with lower nocturnal dipping percentage (β = −0.294, p = 0.008). No significant associations were observed between Lp(a) and carotid intima–media thickness, left ventricular hypertrophy, hypertensive retinopathy, or renal functional parameters. Conclusions: Elevated Lp(a) levels were independently associated with carotid plaque presence and impaired nocturnal blood pressure decline in hypertensive patients. These findings suggest that Lp(a) may be more closely linked to focal macrovascular atherosclerosis and abnormal circadian blood pressure regulation rather than diffuse hypertensive structural remodeling. Full article

Background: The prevalence of overweight and obesity in Chinese school-aged children has continued to rise, posing significant health risks. Rational nutrition knowledge and dietary behaviors are important for obesity prevention and control. The ‘Know Your Food’ campaign aimed to promote nutritious diets and empower informed food choices among Chinese children and young people. This study aims to assess both the implementation and effectiveness of the campaign in primary school settings. Methods: The monitoring framework was designed using the Reach, Effectiveness, Adoption, Implementation, and Maintenance (RE-AIM) framework and consisted of a series of social media advocacy and school-based nutrition sessions. A monitoring system based on the RE-AIM model was developed to assess the impact of the campaign. Observational data on reach, effectiveness, adoption, implementation, and maintenance were collected through media tracking, self-reported questionnaires, and focus group discussions. Results: Between 13 May and 15 June 2022, the campaign gained 77.13 million views and 291,029 interactions from social media. The campaign garnered positive feedback as it reached a wide audience through traditional media, social media and school sessions. 94.7% of the respondents were willing to share campaign information. After the session, more than two thirds of the students reported behavior change, with 70.0% reporting eating five vegetables a day, 67.7% reporting drinking fewer sugary drinks, 74.0% reporting replacing snacks with fruits/nuts, 76.9% reporting doing exercises, and 78.0% reporting getting sufficient sleep. Conclusions: The ‘Know Your Food’ campaign was able to reach and engage the target audience, increasing nutrition knowledge, and influencing behavior change within the short observation window. The findings exemplify the implementation and effectiveness of this campaign and highlight the importance of using a comprehensive assessment framework to assess such nutrition campaigns. Full article

Very large-scale biogas (biomethane) projects are important infrastructure systems for integrating organic waste treatment, renewable energy substitution, and carbon mitigation under China’s carbon peaking and carbon neutrality goals. However, their long process chains, concentrated hazardous media, and frequent confined-space operations create coupled safety risks that may undermine sustainable operation. To identify the dominant risk drivers and safety governance priorities, this study investigated five operating very large-scale biogas projects in Shanxi Province, China. On-site inspections, semi-structured interviews, and document reviews were used to identify ten safety-risk causative factors. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method was combined with Interpretive Structural Modeling (ISM) to quantify causal relationships and reveal the hierarchical transmission structure among the factors. The results show a structural imbalance between document-based compliance and operational implementation. Although basic safety documents were generally established, only 20% of the projects had scenario-specific emergency response plans for major accident scenarios; the compliance rate of explosion-proof electrical equipment, the configuration rate of fixed monitoring and alarm systems for combustible and toxic gases, and the effective operation rate of forced ventilation facilities were 40%, 60%, and 40%, respectively. Insufficient enterprise safety investment (M1) and unclear external regulatory responsibilities (M2) were the dominant root causes of system-level risk propagation, while inadequate control of high-risk operations (M9) and unsafe worker behavior (M10) were the final manifestations. A four-pillar governance pathway is proposed, including policy and standard improvement, technological support and equipment upgrading, personnel capacity enhancement, and sustainable funding mechanisms. The findings provide empirical evidence for risk-based supervision and indicate how operational safety governance can serve as an enabling condition for the long-term sustainability of the biomethane industry, rather than as a direct measurement of carbon-mitigation or energy-performance outcomes. Full article

Alternaria leaf spot caused by Alternaria alternata affects Ocimum basilicum L. worldwide, yet its occurrence and impact in Serbia have remained unknown. This study identified the causal agent of leaf spot on O. basilicum cv. Genovese using molecular, morphological, and pathogenic analyses, and assessed the effects of disease and meteorological conditions on herb yield and morphometric traits. From 109 sampled plants in the preliminary survey (2021 and 2022) and field experiment (2023–2025), 59 isolates were obtained from symptomatic leaves, stems, branches, and seeds. Isolates were preliminarily identified based on the ITS rDNA region. Pathogenicity tests were done on detached leaves, stems, and seedlings. For selected A. alternata isolates, identification was confirmed based on additional regions (Alt a1, ATP, and CAL) and morphology (PDA, PCA, and V8 media), while pathogenicity was confirmed on whole plants. Disease incidence varied from 0% (2023) to 34.87% (2024) under warm and favourable moisture conditions. Significant reductions in plant height, plant weight, number of branches, and leaf size indicated the combined effects of disease and unfavourable meteorological conditions, while dry herb yield was reduced by up to 59.8%. Temperature and precipitation between harvests played a key role in disease development and yield loss. This study provides the first comprehensive molecular, morphological, and pathogenic characterization of A. alternata causing basil leaf spot in Serbia and demonstrates the importance of climate–disease interactions for basil production. These findings provide a basis for targeted disease monitoring, early detection, and climate-informed disease management strategies. Future studies should validate these findings in additional basil cultivars and production environments. Full article

Magnesium alloys are promising candidates for biomedical implants, but their rapid corrosion limits clinical use. Simulated body fluid (SBF) is commonly used to evaluate corrosion behaviour; however, Ca-P and carbonate deposits can mask the intrinsic performance of protective coatings. Tris(hydroxymethyl)aminomethane (Tris) has been proposed as an SBF modifier, although its effect on coated magnesium remains poorly understood. While Tris modifies the buffering characteristics of the solution, it also alters the stability of Mg(OH)₂ and the precipitation equilibria of corrosion products, resulting in more aggressive corrosion conditions than standard SBF. Here, the corrosion behaviour of AZ31 alloy, a plasma electrolytic oxidation (PEO) coating, and a sol-gel sealed PEO coating was investigated in SBF with and without Tris. Electrochemical impedance spectroscopy, immersion tests, pH monitoring, and post-immersion SEM/EDS analyses were used to evaluate coating performance under physiological and aggressive conditions. The results show that AZ31 and PEO coatings exhibit higher apparent corrosion resistance in SBF without Tris due to corrosion-product stabilization and Ca-P-rich deposits that partially block electrolyte access. In contrast, SBF with Tris accelerates degradation, causing uniform corrosion of AZ31 and premature PEO failure through electrolyte penetration and coating cracking. The PEO-AR/ZTP system maintains the highest electrochemical resistance and the best protective performance in both media. Full article

Microalgal bioprocesses increasingly rely on predictive modelling to support automated control and reduce the cost of laboratory experimentation. Yet, the scarcity of high-quality datasets and the nonlinear nature of microalgal growth severely limit the accuracy and robustness of conventional machine-learning approaches. This study introduces an ensemble-learning framework for forecasting biomass accumulation in Limnospira platensis cultures supplemented with Effective Microorganism (EM) consortia. The method combines temporally consistent, strictly causal feature engineering with tree-based ensemble learning under a prospective validation protocol designed to mirror real deployment. Growth measurements are transformed into temporal descriptors that encode phase transitions, short-term growth dynamics, and treatment effects, enabling tree-based ensemble algorithms to capture nonlinear patterns inaccessible to traditional models. When features are restricted to information available strictly before each prediction, and models are evaluated only on real held-out measurements, one-step nowcasting does not surpass a trivial persistence baseline. For genuine multi-horizon forecasting of a monitored culture, the regime with practical value, a horizon-aware gradient-boosting model trained jointly on two cultivation media (Jordan and Zarrouk), attains R² ≈ 0.72 on the held-out Jordan observations (0.72 ± 0.02, mean ± SD over 30 seeds; RMSE ≈ 0.20 g L⁻¹, Pearson r ≈ 0.85) and remains stable across forecast horizons of 1–28 days, outperforming persistence roughly fourfold in pooled R² (0.72 vs. 0.16) at medium-to-long horizons where the naive baseline collapses. Permutation-based feature-importance analysis identifies current biomass and the EM dilution level as the leading predictors, whereas EM treatment identity contributes only modestly. Including an independent second-medium experiment (Zarrouk) in joint training did not materially change held-out Jordan performance, indicating that, under these data-limited conditions, neither additional model complexity nor a second training medium substantially improved forecasting once the causal, horizon-aware framework was in place. Among treatments, EM3 most strongly suppressed biomass accumulation. Overall, the study provides an honest, fully reproducible, two-medium benchmark for microalgal biomass forecasting under data-limited conditions, and identifies the forecast horizon as the regime in which ensemble learning adds genuine value over trivial baselines. Full article

Low-field nuclear magnetic resonance (NMR) is widely used for nondestructive characterization of petroleum-related porous media, including pore-structure evaluation, fluid identification, relaxation analysis, wettability assessment, and displacement monitoring. Conventional NMR signal models usually rely on the quasi-static approximation, in which the detected magnetic field is assumed to respond instantaneously to Bloch-governed nuclear magnetization. However, classical electrodynamics requires electromagnetic fields generated by time-dependent magnetization sources to depend on the source state at a retarded time. In this study, a retarded magnetic-dipole formulation is developed to evaluate finite-propagation-time effects in low-field NMR signal modeling. The analysis shows that the correction appears mainly as a phase shift governed by the dimensionless parameter $\mathsf{ϵ}={\mathsf{\omega }}_0\mathrm{L}/\mathrm{v}$, where ${\mathsf{\omega }}_0$ is the Larmor angular frequency, $\mathrm{L}$ is the characteristic source–receiver distance, and $\mathrm{v}$ is the effective electromagnetic propagation velocity, with $\mathrm{v}=\mathrm{c}$ in free space. Relaxation-induced amplitude corrections are generally smaller. Numerical examples demonstrate that the quasi-static approximation is well justified when $\mathsf{ϵ}\ll 1$, as typically satisfied in laboratory core NMR. For extended-scale configurations, including unilateral, borehole, underground, and surface NMR, larger propagation paths and medium-dependent electromagnetic properties may increase \epsilon and produce systematic phase deviations. This work provides a theoretical criterion for assessing the validity range of the quasi-static approximation in low-field NMR applications for petroleum-related porous media. Full article

Stray cats may act as reservoirs of antimicrobial-resistant and potentially pathogenic bacteria, representing a potential public health concern within a One Health framework. This study investigated the occurrence of antimicrobial resistance (AMR), multidrug resistance (MDR), and virulence-associated traits in commensal Escherichia coli isolated from free-living cat colonies in southern Spain. A total of 169 rectal swabs were collected from cats belonging to feline colonies and shelters in Benalmádena (Málaga, Spain). Bacterial isolation and identification were performed using selective culture media, conventional biochemical tests, and MALDI-TOF mass spectrometry. Antimicrobial susceptibility was determined by minimum inhibitory concentration (MIC) testing against 15 antimicrobial agents, and isolates were screened for selected virulence-associated genes associated with major diarrheagenic E. coli pathotypes. A total of 68 E. coli isolates (40.2%) were recovered. The highest resistance frequencies were observed for sulfamethoxazole (25.0%) and ampicillin (20.6%), whereas all isolates showed high susceptibility rates to most of the antimicrobials tested, including azithromycin (100%), as well as gentamicin, amikacin, cefotaxime, ceftazidime, meropenem, colistin, chloramphenicol and tigecycline (98.5%). Six isolates (8.8%) were classified as multidrug-resistant. In addition, six isolates (8.8%) were identified as atypical enteropathogenic E. coli (aEPEC), although none exhibited a multidrug resistance phenotype. These findings demonstrate the presence of both AMR and virulence-associated traits among commensal E. coli circulating in free-living cats. The inclusion of feline colonies or shelters in AMR surveillance programmes may contribute valuable information for integrated One Health monitoring strategies. Full article

Regenerative agriculture is increasingly promoted as a pathway towards sustainable food production, climate resilience, and the restoration of soil ecosystem functions. However, evidence on the actual uptake of regenerative practices and the factors influencing their adoption remains limited. This study compared the self-reported prevalence of 17 soil-health-oriented regenerative practices among farmers in Poland, Germany, and Belarus using a questionnaire survey conducted in 2025 (N = 150). The survey also examined farmers’ motivations, perceived barriers, knowledge sources, and definitions of regenerative agriculture. Adoption frequencies were assessed using a five-point Likert scale and analysed using non-parametric statistical methods. Several practices, including crop rotation and soil pH management, were widely implemented across all three countries and showed only slight variation. In contrast, more complex, system-based practices, such as agroforestry, biological soil monitoring, and crop–livestock integration, showed lower and more variable levels of adoption. Additional subgroup analyses were conducted to assess the robustness of the observed cross-country patterns. Although some associations weakened after stratification, many significant differences persisted. Across all countries, improving soil health was the primary motivation for adopting regenerative agriculture, whereas financial constraints and limited equipment access were the main barriers. Digital media served as the primary source of knowledge about regenerative agriculture across the surveyed countries, although in Belarus, peers and neighbours also represented a highly important source of information. Farmers in all three countries expressed a preference for online communication channels for further learning about regenerative agriculture; however, Polish and Belarusian farmers prefer social media, whereas German farmers preferred webinars and dedicated websites. In-person training sessions also attracted considerable interest among Polish and Belarusian farmers, but were the least preferred information source among German respondents. On the basis of these results, targeted investment support and direct financial incentives appear to be key priorities for promoting the further uptake of regenerative agriculture across all surveyed countries. However, communication and knowledge-transfer strategies are likely to require greater adaptation to country-specific preferences, although digital media are likely to represent the most effective primary channel for disseminating information on regenerative agriculture. Full article

Hydrogel-based three-dimensional culture systems are widely used in tissue engineering; however, their maturation is often limited under static conditions. This study investigates the combined effects of dynamic perfusion and initial cell seeding density on remodeling behavior and mechanical properties of bioprinted collagen hydrogel constructs, with additional assessment of smooth muscle cell-like (SMC) differentiation. Rectangular constructs (30 × 15 × 1.5 mm) were fabricated using high-concentration collagen (30 mg/mL) with cell densities of 10 and 20 million cells/mL. MCDB- and DMEM-based media were first compared using growth curves, leading to the selection of MCDB differentiation medium for subsequent experiments. Constructs were then cultured statically or under dynamic perfusion (20 mL/min) for up to 7 days. Remodeling was evaluated by monitoring changes in construct dimensions over time. Static constructs exhibited non-uniform deformation and rolling, whereas dynamically perfused samples retained their geometry and underwent homogeneous contraction. Mechanical testing revealed a transition from stiff and brittle to more compliant and ductile behavior, with preserved load-bearing capacity at large strains. Remodeling and mechanical outcomes were strongly influenced by cell density and culture medium, with differentiation conditions promoting more stable constructs. These changes were accompanied by increased expression of smooth muscle–related markers under dynamic culture. Overall, dynamic perfusion and cell seeding density jointly govern remodeling and mechanical maturation of bioprinted collagen constructs, highlighting their importance for functional hydrogel-based tissue development. Full article

Project-Based Learning (PBL) has become an important pedagogical strategy for developing technical and professional competencies in engineering education through authentic, multidisciplinary experiences. This paper presents a PBL case study conducted in an undergraduate Computer Engineering course in which students designed and implemented Smart Fisio Borregos, an Internet of Things (IoT) and Artificial Intelligence (AI) system designed as both a learning vehicle for students and a prototype tool intended to support rehabilitation training through real-time exercise guidance and monitoring; the educational effectiveness of the rehabilitation-support function has not yet been formally validated. The project followed a prototype-driven methodology that integrated low-cost sensors, embedded systems, cloud databases, computer vision, and AI services into a unified platform. The resulting prototype incorporated equipment occupancy monitoring, environmental control, RFID-based access management, a web dashboard for data visualization, and a computer-vision module based on MediaPipe Pose Landmarker for exercise analysis and feedback. The project provided students with opportunities to apply knowledge from programming, embedded systems, databases, networking, and AI while developing collaboration, problem-solving, and project-management skills. The paper describes the pedagogical framework, system architecture, implementation process, and project outcomes, illustrating how multidisciplinary engineering projects can be used to create authentic learning experiences connected to real-world challenges. The proposed approach offers a replicable model for integrating IoT and AI technologies into engineering curricula while contributing to educational innovation related to health and well-being. The study aligns with Sustainable Development Goal 3 (Good Health and Well-Being) and Sustainable Development Goal 9 (Industry, Innovation, and Infrastructure). Full article