Search Results (56)

Freshwater scarcity in arid regions is driving increased use of saline irrigation, yet salinity severely degrades soil structure and suppresses enzymatic function. To address this critical challenge for sustainable soil management, this study systematically evaluated magnetized saline water (MSW) across three salinity levels (1, 3, and 6 g L⁻¹) and four magnetic field strengths (0, 0.2, 0.4, and 0.6 T), confirming the magnetic field intensity (C) × salinity (S) interaction. The comprehensive analysis integrated data on aggregate stability, key ion concentrations (Ca²⁺, Mg²⁺, Cl⁻), and major enzyme activities. Structural Equation Modeling (SEM) was utilized to quantify the underlying mechanisms, demonstrating that structural improvement is primarily driven by strong indirect pathways, mediated by optimized ion dynamics and increased enzyme-mediated organic matter turnover. The moderate-salinity (3 g L⁻¹), moderate-magnetic-field (0.4 T) regime emerged as the optimal balanced strategy for overall soil health. These findings offer a scalable approach, guiding future field-scale research toward long-term agricultural sustainability. Full article

Background: Digital indirect bonding (IB) has emerged as a reliable approach to improving the precision and efficiency of orthodontic bracket placement. Methods: This in vitro study evaluated and compared the positional accuracy and efficiency of two digitally driven indirect bonding (IB) techniques—a rigid single-tooth transfer jig (Leone Jig System) and a flexible three-part transfer tray (IBT Flex Resin)—as well as conventional direct bonding. Ten sets of 3D-printed resin dental models were randomly allocated to the three bonding protocols. Bracket positions were virtually planned and analyzed by superimposing pre- and post-bonding STL models using landmark- and surface-based registration. Linear discrepancies were measured along the axial, sagittal, and vertical planes, and data were analyzed using repeated-measures ANOVA and Friedman tests (α = 0.05). Results: Both indirect bonding techniques showed significantly smaller deviations from the ideal virtual setup compared with direct bonding across all spatial planes (p < 0.001). Mean discrepancies were consistently below 0.3 mm for the indirect protocols, compared with values exceeding 0.4 mm for direct bonding. The rigid jig demonstrated the highest precision, particularly in the sagittal (0.18 ± 0.06 mm) and vertical (0.21 ± 0.07 mm) planes, while the flexible tray showed slightly higher deviations (approximately 0.25–0.30 ± 0.08–0.09 mm across planes). Chairside bonding time per full arch was reduced by more than 50% with both IB techniques, with the jig-based system being the most time-efficient. No significant interaction between bonding method and spatial plane was observed. Conclusions: Within the limitations of this in vitro study, digital indirect bonding—especially rigid, patient-specific jigs—demonstrated superior bracket placement accuracy and procedural efficiency compared with direct bonding. Full article

Hypertension (HTN) and intestinal permeability (IP) are increasingly recognized as interrelated processes driven by shared oxidative and inflammatory mechanisms. This review synthesizes evidence linking HTN-induced vascular dysfunction to alterations in intestinal barrier integrity and explores the potential of dietary flavonoids as modulators of these pathologies. A narrative approach was used to synthesize findings from cellular, animal, and human studies that specifically address how flavonoids influence the molecular pathway connecting HTN and IP. Emerging evidence suggests that HTN-driven vascular injury, which is characterized by reduced nitric oxide bioavailability, increased reactive oxygen species, and pro-inflammatory signaling, contributes to tight junction disruption and increased IP. Mechanistic evidence indicates that flavonoids exert both direct antioxidant effects and indirect actions via the modulation of key cellular pathways. Preclinical and clinical data demonstrate that flavonoid-rich foods and isolated compounds can lower blood pressure, enhance endothelial function, and preserve intestinal barrier integrity by stabilizing tight junction proteins and attenuating pro-inflammatory signaling. Together, these findings highlight flavonoids as cross-system modulators that may mitigate HTN-associated increases in IP. Further research addressing sex, race, and age differences, as well as flavonoid bioavailability and dose optimization, is needed to clarify their translational potential. Full article

The global aquaculture sector faces mounting pressure to transition towards sustainable production, with innovation being a critical lever for change, especially among smallholder farmers who dominate the industry. This study examines the drivers of innovation in China’s freshwater aquaculture sector by constructing a multi-dimensional innovation index—encompassing infrastructure, machinery, inputs, environmental management, and production models—and analyzing survey data from 336 farmers. Our findings reveal that direct government funding is significantly associated with innovation, but its effect is narrow, primarily linked to machinery upgrades, and effective only in the developed eastern region. In contrast, indirect support through technical training shows a broader, stronger, and more consistent association with innovation across all types, with effects lagging by 1–2 years and yielding the highest returns in less-developed western China. Notably, farmers’ ex post evaluations of training are a stronger predictor of innovation than training frequency itself, underscoring the importance of quality and relevance. We further find that production scale and industrial organization are positively associated with innovation, with no evidence of an inverted U-shaped relationship, reflecting the sector’s small-scale structure. These results highlight the need for a differentiated policy approach: prioritizing high-quality, demand-driven training nationwide; targeting direct funding to where complementary capacities exist; and fostering cooperatives and scale-enhancing institutions to systematically strengthen the sector’s innovative capacity. Full article

Background/Objectives: Diet and physical activity are key lifestyle behaviours associated with sleep quality, yet their combined and sex-specific associations remain insufficiently understood. This study examined the associations between dietary behaviours and sleep quality among university students and assessed whether physical activity formed part of an indirect statistical association between these variables, with sex considered as a moderator. Methods: A cross-sectional study was conducted among 418 students (199 males, 219 females) from the Wroclaw University of Health and Sport Sciences. Body height and body mass were measured using standard anthropometric procedures. Sleep quality (SQ) was registered with the Pittsburgh Sleep Quality Index (PSQI), dietary habits were assessed with the Questionnaire of Eating Behaviours (QEB) and physical activity (PA) was assessed with the International Physical Activity Questionnaire (IPAQ). Data-driven feature-selection methods were applied to identify dietary behaviours associated with sleep quality, which were combined into a Synthetic Dietary Behaviour Index (SDBI). A moderated mediation model, adjusted for body mass index (BMI), was then used to examine the statistical associations between dietary behaviours, physical activity, sleep quality, and sex. Sleep quality was modelled as a continuous PSQI score in mediation analyses, while the dichotomised PSQI category was used only for feature selection. Results: Machine-learning feature selection identified nine dietary behaviours statistically associated with sleep quality. Unfavourable behaviours—fast food, fried meals, sweetened beverages, energy drinks and alcohol—were linked to poorer sleep, whereas vegetables, curd cheese and wholegrain bread were associated with better sleep. Poor sleep was more prevalent among females (45.2% vs. 14.6%, χ² (1) = 65.4, p < 0.001). The mediation model indicated that physical activity formed part of a statistically significant but modest indirect association between dietary behaviour and sleep quality, with sex moderating the IPAQ → PSQI path (β = −0.45, p = 0.006). Indirect associations were significant for both sexes but stronger among females (males: β = 0.032, p = 0.021; females: β = 0.102, p = 0.004). Conclusions: Unfavourable dietary patterns and lower physical activity were statistically associated with poorer sleep quality, with a stronger indirect statistical effect observed among females. These findings support the relevance of integrated, sex-sensitive lifestyle approaches addressing both dietary behaviours and physical activity, while acknowledging the cross-sectional nature of the data. Full article

Data incompleteness is a common problem in real-life datasets. This is caused by acquisition problems, sensor failures, human errors, and so on. Missing values and their subsequent imputation can significantly affect the performance of data-driven models and can also distort the interpretability of explainable artificial intelligence (XAI) models, such as fuzzy models. This paper presents a novel imputation algorithm based on granular computing. This method benefits from the local structure of the dataset, explored using the granular approach. The method elaborates a set of granules that are then used to impute missing values in the dataset. The method is evaluated on several datasets and compared with several state-of-the-art imputation methods, both directly and indirectly. The direct evaluation compares the imputed values with the original data. The indirect evaluation compares the performance of fuzzy models built with TSK and ANNBFIS neuro-fuzzy systems. This enables not only the evaluation of the quality of numerically imputed values but also their impact on the interpretability of the constructed fuzzy models. This paper is accompanied by numerical experiments. The implementation of the method is available in a public GitHub repository. Full article

In Saudi Arabia, coffee (Coffea arabica L.) has been grown for centuries on the mountain terraces of the southwestern regions. Jazan region accounts for about 80% of the total production. The acreage allocated to coffee is comparatively small but it is expanding rapidly thanks to a strong government-supported drive to increase local coffee production. Despite the initial success, the effort is hampered by the limited water supply available for irrigating the new plantings and the increased incidence of pests and diseases. The magnitude of these natural handicaps appears to have increased as of late, apparently due to climate change (CC). This review examines strategies to mitigate the consequences of CC on the coffee sector through the implementation of precision agriculture (PA) techniques, with the focus on addressing the challenges posed by biotic and abiotic stresses. The impact of CC is both direct by rendering present growing regions unsuitable and indirect by amplifying the severity of biotic and abiotic tree stressors. Precision agriculture (PA) techniques can play a key role in tackling these challenges through data-driven tools like sensors, GIS, remote sensing, machine learning and smart equipment. By monitoring soil, climate, and crop conditions, PA enables targeted irrigation, fertilization, and pest control thus improving efficiency and sustainability. This approach reduces costs, conserves resources, and minimizes environmental impact, making PA essential for building climate-resilient and sustainable coffee production systems. The review synthesizes insights from case studies, research papers, and other scientific literature concerned with precision farming practices and their effectiveness in alleviating biotic and abiotic pressures on coffee trees. Additionally, it evaluates technological advances, identifies existing knowledge gaps, and suggests areas for future research. Ultimately, this study seeks to contribute to enhancing the resilience of coffee farming in Saudi Arabia amidst ongoing CC challenges by educating farmers about the potential of PA technologies. Full article

Fishery resources of tuna serve as a vital source of global protein. This study investigates the key environmental drivers influencing the spatial distribution of yellowfin tuna (Thunnus albacares) in the western tropical Pacific Ocean. A comprehensive dataset was constructed by linking the catch per unit effort (CPUE) from 43 Chinese longline fishing vessels (2008–2019) with 24 multi-source environmental variables. To accurately model this complex relationship, a total of 16 machine learning regression models, including advanced ensemble methods like Light Gradient Boosting Machine (LightGBM), Random Forest, and Categorical Boosting Regressor (CatBoost), were evaluated and compared using multiple performance metrics (e.g., Coefficient of Determination [R²], Root Mean Squared Error [RMSE]). The results indicated that the Light Gradient Boosting Machine (LightGBM) model achieved superior performance, demonstrating excellent nonlinear fitting capabilities and generalization ability. For robust feature interpretation, the study employed both the model’s internal feature importance metrics and the SHapley Additive exPlanations (SHAP) method. Both approaches yielded highly consistent results, identifying temporal (month), spatial (longitude, latitude), and key seawater temperature indicators at intermediate depths (T450, T300, T150) as the most critical predictors. This highlights significant spatiotemporal heterogeneity in the distribution of Thunnus albacares. The analysis suggests that mid-layer ocean temperatures directly influence catch rates by governing the species’ vertical and horizontal movements. In contrast, large-scale climate indices such as the Oceanic Niño Index (ONI) exert indirect effects by modulating ocean thermal structures. This research confirms the dominance of spatiotemporal and thermal variables in predicting yellowfin tuna distribution and provides a reliable, data-driven framework for supporting sustainable fishery management, resource assessment, and operational forecasting. Full article

Maintaining an effective facial seal is critical for the performance of tight-fitting industrial respirators used in high-risk sectors such as mining, manufacturing, and construction. Traditional fit verification methods—Qualitative Fit Testing (QLFT) and Quantitative Fit Testing (QNFT)—are limited to periodic assessments and cannot detect fit degradation during active use. This study presents a real-time fit detection system based on embedded breath sensors and machine learning algorithms. A compact sensor module inside the respirator continuously measures pressure, temperature, and humidity, transmitting data via Bluetooth Low Energy (BLE) to a smartphone for on-device inference. This system functions as a multimodal biosensor: intra-mask pressure tracks flow-driven mechanical dynamics, while temperature and humidity capture the thermal–hygrometric signature of exhaled breath. Their cycle-synchronous patterns provide an indirect yet reliable readout of respirator–face sealing in real time. Data were collected from 20 healthy volunteers under fit and misfit conditions using OSHA-standardized procedures, generating over 10,000 labeled breathing cycles. Statistical features extracted from segmented signals were used to train Random Forest, Support Vector Machine (SVM), and XGBoost classifiers. Model development and validation were conducted using variable-size sliding windows depending on the person’s breathing cycles, k-fold cross-validation, and leave-one-subject-out (LOSO) evaluation. The best-performing models achieved F1 scores approaching or exceeding 95%. This approach enables continuous, non-invasive fit monitoring and real-time alerts during work shifts. Unlike conventional techniques, the system relies on internal physiological signals rather than external particle measurements, providing a scalable, cost-effective, and field-deployable solution to enhance occupational safety and regulatory compliance. Full article

Maternal health care during labor requires the continuous and reliable monitoring of analgesic procedures, yet conventional systems are often subjective, indirect, and operator-dependent. Infrared thermography (IRT) offers a promising non-invasive approach for labor epidural analgesia (LEA) monitoring, but its practical implementation is hindered by clinical and hardware limitations. This work presents a novel artificial intelligence-driven mobile platform to overcome these hurdles. The proposed solution integrates a lightweight deep learning model for semantic segmentation, a B-spline-based free-form deformation (FFD) approach for non-rigid dermatome registration, and efficient on-device inference. Our analysis identified a U-Net with a MobileNetV3 backbone as the optimal architecture, achieving a high Dice score of 0.97 and a 4.5% intersection over union (IoU) gain over heavier backbones while being 73% more parameter-efficient. The entire AI pipeline is deployed on a commercial smartphone via TensorFlow Lite, achieving an on-device inference time of approximately two seconds per image. Deployed within a user-friendly interface, our approach provides straightforward feedback to support decision making in labor management. By integrating thermal imaging with deep learning and mobile deployment, the proposed system provides a practical solution to enhance maternal care. By offering a quantitative, automated tool, this work demonstrates a viable pathway to augment or replace subjective clinical assessments with objective, data-driven monitoring, bridging the gap between advanced AI research and point-of-care practice in obstetric anesthesia. Full article

Purpose—This study explores the determinants of entrepreneurial intention (EI) among university students, analyzing entrepreneurial motivation (EM) as a mediator and perceived institutional support (PIS) as a moderator within the Theory of Planned Behavior (TPB) framework. Design/Methodology/Approach—Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 128 students at the Polytechnic Institute of Cávado and Ave, Portugal, were analyzed to assess direct, indirect, and moderating effects of entrepreneurial attitudes, education, and social norms. Findings—EM significantly mediates the relationship between attitude concerning entrepreneurship (ACE), perceived social norms (PSN), entrepreneurial education (EE), and EI, reinforcing its role in bridging individual and educational influences with entrepreneurial behavior. However, PIS does not significantly moderate the EM-EI relationship, suggesting institutional support alone is insufficient to enhance motivation’s impact on EI. This challenges assumptions about institutional effectiveness and highlights the importance of entrepreneurial ecosystems, social capital, and mentorship networks as alternative enablers. Implications—The study extends TPB by incorporating mediation and moderation effects, offering a deeper understanding of personal, social, and institutional influences on EI. This study contributes by simultaneously modeling entrepreneurial motivation as mediator and perceived institutional support as moderator within a TPB framework. Such integration remains rare, particularly in Southern European higher education contexts, and our findings nuance current assumptions by revealing when institutional supports may fail to strengthen motivational pathways. The findings emphasize the need for education policies that integrate experiential learning, entrepreneurial ecosystems, and mentorship to foster entrepreneurial mindsets. Originality/Value—This research challenges the assumed role of institutional support, highlighting motivation as a key driver of EI and providing new insights into policy-driven entrepreneurship promotion in higher education. Full article

Enhancing productivity while reducing environmental impact presents a major engineering challenge in sustainable dairy farming. This study proposes an engineering-oriented explainable machine learning and digital twin framework for multi-objective optimisation of milk yield and nitrogen-related emissions. Using the CowNflow dataset, which integrates individual-level nitrogen balance, feeding, and production data collected under controlled experimental conditions, the framework combines data analytics, feature selection, predictive modelling, and SHAP-based explainability to support decision-making in dairy production. The stacking ensemble model achieved the best predictive performance ($R^2$ = 0.85 for milk yield and $R^2$ = 0.794 for milk urea), providing reliable surrogates for downstream optimisation. Predicted milk urea values were further transformed using empirical equations to estimate urinary urea nitrogen (UUN) and ammonia (NH₃) emissions, offering an indirect yet practical approach to assess environmental sustainability. Furthermore, the predictive models are integrated into a digital twin platform that provides a dynamic, real-time simulation environment for scenario testing, continuous optimisation, and data-driven decision support, effectively bridging data analytics with sustainable dairy system management. This research demonstrates how explainable AI, machine learning, and digital twin engineering can jointly drive sustainable dairy production, offering actionable insights for improving productivity while minimising environmental impact. Full article

Machine learning algorithms are essential tools for developing Mineral Prospectivity Models (MPMs), enabling a data-driven approach to mineral exploration. This study integrated airborne geophysical, topographic, and geological data with a mineral system framework to build MPMs for iron oxide–copper–gold (IOCG) and hydrothermal nickel deposits in the Southern Copper Belt of the Carajás Province, Brazil. Seven machine learning algorithms were tested using stratified 10-fold cross-validation: Logistic Regression, k-Nearest Neighbors, AdaBoost, Support Vector Machine (SVM), Random Forest, XGBoost, and Multilayer Perceptron. SVM delivered the highest classification accuracy and robustness, highlighting new mineralized zones while minimizing false positives and negatives, and accounting for geological complexity. SHapley Additive ExPlanations (SHAP) analysis revealed that structural controls (e.g., faults, shear zones, and geochronological contacts) exert a stronger influence on mineralization patterns than lithological factors. The resulting prospectivity maps identified geologically distinct zones of IOCG and hydrothermal nickel mineralization, with high-probability closely aligned with major structural corridors oriented E–W, NE–SW, and NW–SE. Results also suggest an indirect association with volcanic units, Orosirian A1-type granites and Neoarchean A2-type granites. Full article

Reactive oxygen species (ROS) function as critical signaling molecules in cancer biology, promoting proliferation, angiogenesis, and metastasis at controlled levels while inducing lethal damage when exceeding the cell’s buffering capacity. To survive under this state of chronic oxidative stress, cancer cells become dependent on a hyperactive antioxidant shield, primarily orchestrated by the Nrf2, glutathione (GSH), and thioredoxin (Trx) systems. These defenses maintain redox homeostasis and sustain oncogenic signaling, notably through the oxidative inactivation of tumor-suppressor phosphatases, such as PTEN, which drives the PI3K/AKT/mTOR pathway. Targeting this addiction to a rewired redox state has emerged as a compelling therapeutic strategy. Pro-oxidant therapies aim to overwhelm cellular defenses, with agents like high-dose vitamin C and arsenic trioxide (ATO) showing significant tumor-selective toxicity. Inhibiting the master regulator Nrf2 with compounds such as Brusatol or ML385 disrupts the core antioxidant response. Disruption of the GSH system by inhibiting cysteine uptake with sulfasalazine or erastin potently induces ferroptosis, a non-apoptotic cell death driven by lipid peroxidation. Furthermore, the thioredoxin system is targeted by the repurposed drug auranofin, which irreversibly inhibits thioredoxin reductase (TrxR). Extensive preclinical data and ongoing clinical trials support the concept that this reliance on redox adaptation is a cancer-selective vulnerability. Moreover, novel therapeutic strategies, including the expanding field of redox-active metal complexes, such as manganese porphyrins, which strategically leverage the differential redox state of normal versus cancer cells through both pro-oxidant and indirect Nrf2-mediated antioxidative mechanisms (triggered by Keap1 oxidation), with several agents currently in advanced clinical trials, have also been discussed. Essentially, pharmacologically tipping the redox balance beyond the threshold of tolerance offers a rational and powerful approach to eliminate malignant cells, defining a novel frontier for targeted cancer therapy. Full article

The beverage industry is undergoing a dynamic transition in terms of how and where consumers buy products. In an era of rapid digitalization and shifting consumer behaviors, this study investigates how Germany’s wine producers reach consumers and how the distribution landscape of German wine has transformed. A survey of more than 1000 German wine producers allowed us to explore multichannel strategies. Home-country distribution stands for 84% of the production, while export represents 16% of sales. Indirect sales via food retail safeguard a large portion of distribution, but direct sales to consumers matter in value-driven sales. The findings confirm the continued dominance of indirect retail, particularly food retail, while also highlighting a rebound in direct-to-consumer sales, value market approaches, and on-premises distribution. The results of this study contribute to closing data gaps by underlining that gastronomy has been re-established as a relevant distribution channel and that German wine has not profited from global growth in wine trading. Multichannel strategies are increasingly common, but they vary significantly in their depth and reach depending on different business models. We conducted a cluster analysis and identified three strategic groups: (1) consumer-centric, predominantly direct-to-consumer-oriented estates (63%); (2) industrial, multichannel producers with a strong presence in food retail and export (8%); and (3) hybrid operators balancing value and volume strategies (29%). This study contributes to the development of a more nuanced understanding of multichannel distribution in the wine sector and provides empirical insights into the strategic implications of firm heterogeneity. Full article