Search Results (17,039)

Cheese whey, a low-value by-product of cheese production, has gained renewed attention within the transition toward sustainable and circular food systems. Despite posing environmental challenges due to its high biochemical and chemical oxygen demand, whey retains a substantial proportion of milk nutrients, notably high-quality proteins that can be converted into bioactive peptides with potential health benefits. These peptides have been shown to modulate key biological pathways, including angiotensin-converting enzyme inhibition, nitric oxide bioavailability, oxidative stress balance, and inflammatory signaling, providing mechanistic plausibility for cardioprotective and immunomodulatory effects. However, the translation of promising in vitro and animal findings into consistent human health outcomes remains constrained by variability in peptide composition, processing conditions, bioavailability, and study design. This narrative review critically synthesizes current evidence on the functional properties of whey-derived peptides, with particular emphasis on cardiovascular and immunomodulatory outcomes across experimental models. In addition, the review situates whey upcycling within the Portuguese agro-food context, highlighting regional cheese production as both an environmental challenge and an opportunity for sustainable innovation. By integrating mechanistic evidence with sustainability-driven valorization strategies, this review aims to clarify the translational potential of whey-derived peptides as functional food ingredients. Full article

The use of end-of-life tyre (ELT) rubber as a partial aggregate replacement in concrete represents a promising route for waste valorisation; however, its durability-related behaviour and long-term performance remain insufficiently characterised, particularly under combined environmental exposures. This study addresses these limitations by combining a targeted literature review encompassing more than 4500 data points from over 150 published studies with a laboratory-based experimental assessment of rubberised concretes aimed at clarifying key knowledge gaps. The experimental programme investigates concretes incorporating 5–50% ELT rubber (0/4 mm) as a selective replacement of a specific sand fraction, rather than of the total fine aggregate content, with particular emphasis on performance under coupled freeze–thaw cycling and sulphate attack. A reference mix (>50 MPa at 28 days) and seven rubberised concretes were characterised in terms of mechanical behaviour and selected durability-related indicators. Specimens were subsequently exposed for 270 days to freeze–thaw cycles (−20/+20 °C) in a 10% MgSO₄ solution, and surface damage and compressive strength loss were quantified. Increasing rubber content resulted in the expected reductions in mechanical performance, accompanied by lower electrical resistivity and increased porosity and carbonation depth. However, the selective replacement of a single sand fraction led to more gradual deterioration than typically reported for global sand substitution. Under combined freeze–thaw and sulphate exposure, concretes with low rubber contents (5–15%) exhibited no observable surface damage and retained most of their mechanical capacity, with compressive strength losses below 8%, whereas mixtures with ≥30% replacement showed pronounced surface degradation and strength losses exceeding 50%. Full article

Accurate river discharge estimation is fundamental for water resource management under increasingly variable hydrological conditions. While conventional in situ techniques remain hydrometric reference standards, their operational deployment is constrained by cost, accessibility, and limited spatial coverage. Advances in remote sensing and artificial intelligence (AI) have introduced non-contact discharge estimation frameworks based on image-derived observations. This systematic review, conducted in accordance with Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 reporting guidelines, examines the evolution of river discharge measurement methods between 2004 and 2024 through a structured two-stage design. An initial search in Web of Science and Scopus identified 2809 records, of which 249 were retained for first-stage synthesis. A focused second-stage screening isolated seven studies that directly integrate image-based data with machine learning or deep learning architectures for discharge estimation. The analysis reveals a methodological transition from instrument-based hydrometry toward computationally assisted, image-driven approaches. The retained studies employ close-range and satellite imagery combined with Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), and related models. Although reported validation metrics indicate strong predictive capability under specific conditions, performance remains dependent on site-specific calibration and reference discharge records. Broader operational deployment requires improved transferability, uncertainty integration, and cross-basin validation. Full article

Introduction: Muscle function is influenced by hydroelectrolytic mechanisms that regulate cellular volume beyond isolated plasma electrolyte concentrations. However, the role of integrated hydration and electrolyte regulation profiles in muscle function among older adults remains insufficiently understood. Objective: To identify which physiological domains of hydroelectrolytic regulation are most strongly associated with muscle strength and functional performance in community-dwelling older adults. Methods: A cross-sectional study was conducted in 96 community-dwelling individuals aged ≥ 70 years. Markers of cellular hydration and membrane integrity were assessed using bioelectrical impedance analysis, together with first-morning fasting plasma and urinary sodium and chloride concentrations. Principal component analysis (PCA) was applied as a data-driven approach to identify latent domains of coordinated hydroelectrolytic regulation. Associations between component scores and handgrip strength and Timed Up and Go (TUG) were examined using two sequential multivariable regression models: Model 1 adjusted for sex and fat-free mass index (FFMI); Model 2 additionally adjusted for age, hypertension, and diuretic use. Results: Three principal components were retained, explaining 77.5% of total variance: PC1 (renal–cellular domain), PC2 (plasma electrolyte domain), and PC3 (cellular volume domain). For handgrip strength, Model 1 showed significant associations for PC3 (β = 0.152; p = 0.025) and PC1 (β = 0.180; p = 0.050). In Model 2, only PC3 remained independently associated (β = 0.146; p = 0.036). For TUG, Model 1 showed associations for PC1 (β = −0.262; p = 0.049) and PC3 (β = −0.238; p = 0.015). In Model 2, PC1 (β = −0.308; p = 0.019) and PC2 (β = −0.190; p = 0.046) remained independently associated, whereas PC3 was not. Conclusions: Maximal force production appears primarily associated with cellular volume regulation, whereas functional performance reflects broader multi-compartmental hydroelectrolytic integration involving renal and plasma domains. These findings suggest that multidimensional hydration profiling may complement isolated biochemical markers in the functional assessment of older adults, warranting validation in longitudinal studies. Full article

Stratigraphic forward modeling (SFM) is a numerical approach used to reconstruct sedimentary basin evolution by simulating the infilling and tectonic evolution process of strata. The challenge is that existing approaches inevitably require trade-offs among modeling fidelity and computational cost. We present a novel clastic stratigraphic forward modeling (CSFM) approach to reducing computational cost while retaining key flow and transport behaviors relevant to stratigraphic architecture. In CSFM, Lagrangian water particles affect momentum and sediment, while a fixed Eulerian grid stores topographic elevation and lithologic fractions. A simplified form of the Navier–Stokes equations is proposed to compute the trajectories of fluid particles, which can greatly reduce the computational cost. Sediment dynamics are represented by coupled suspended load and bedload modules. To validate CSFM, we constructed a synthetic alluvial fan model and performed stratigraphic forward modeling on it. Five lake-level cycles were imposed and results showed that cyclic sand–clay couplets and isolated channel sand bodies were formed during repeated progradation and backstepping. These results are consistent with established sedimentological knowledge, confirming the geological plausibility of CSFM. Full article

Magnetoplasmadynamic thrusters (MPDTs) are becoming increasingly viable as electric propulsion (EP) technology for space missions, yet their complex plasma behaviour, intricate thrust-generation process, and nonlinear multi-physics thrust–field interactions prove difficult for conventional modelling approaches, including empirical techniques. Traditional empirical modelling shortcomings include failure to predict accurately across wide operational regimes. This paper introduces a physically interpretable, artificial intelligence (AI)-powered thrust model for Applied-Field Magnetoplasmadynamic Thrusters (AF-MPDTs), developed using symbolic regression (SR) to address the gap between data-driven prediction and physics-based understanding. The proposed method, an alternative to traditional black box AI methods, incorporates physics-aware composite-term operators, ensuring that the resulting analytical expressions are bounded by known physical behaviours while retaining the flexibility to discover previously overlooked nonlinear couplings. A comprehensive dataset of AF-MPDTs undergoes rigorous preprocessing to ensure dimensional consistency and noise robustness. The SR model then evolves candidate equations, balancing predictive accuracy with interpretability through Tree-Structured Parzen Estimator (TPE) optimisation. The results, closed-form surrogate correlations with 95.98% of accuracy as goodness of fit, root mean square error of 0.0199, mean absolute error of 0.0143, and mean absolute percentage error reduction of 28.91% against the benchmark model in the literature. A post-discovery protocol for numerical robustness and physical consistency is implemented, with Shapley Additive Explanations (SHAP) providing insight into the influence of each composite-term in the developed correlation, followed by a numerical robustness and physical consistency validation using a Monte Carlo (MC) envelope. A StabilityScore is calculated for all developed correlations, enabling explicit accuracy–complexity–stability comparisons. In doing so, we demonstrated that SR can systematically recover known physical relationships—such as the scaling of thrust with discharge current and applied magnetic field—while proposing interpretable higher-order corrections that improve fit quality. The resulting SR-based thrust models not only achieve competitive accuracy relative to state-of-the-art numerical and empirical methods but also offer more explainable and interpretable results capable of revealing compact formulations that capture essential acceleration mechanisms with transparency. Overall, this paper, using SR, advances explainable AI (XAI) methodologies capable of generating trustworthy, analytically transparent models for next-generation electric propulsion systems. Full article

Non-intrusive load monitoring (NILM) can provide actionable feedback for demand response (DR) when direct measurements of device states are unavailable. We propose a reproducible, engineering-oriented pipeline for detecting ON/OFF states of end-use load groups from an aggregated active power time series. The method uses robust hysteresis-based labeling with adaptive thresholds derived from the median and median absolute deviation, followed by compact feature engineering restricted to global active power (GAP). After removing collinear features (|r| > 0.98), permutation importance is used to retain informative predictors. Probabilistic binary classifiers (LGBM, Histogram-based Gradient Boosting, XGBoost, and CatBoost) are trained for each target load, and the decision threshold is optimized via F_β to balance missed events and false alarms. A post-processing stage stabilizes predictions by smoothing probabilities and suppressing spurious triggers. Model quality is assessed with both sample-wise metrics and event-based metrics that credit the correct detection of switching intervals within a time tolerance. Experiments on the open “Individual Household Electric Power Consumption” dataset (1-min resolution, 2007–2010) demonstrate that lightweight gradient boosting models, particularly LGBM, deliver reliable and interpretable state estimates suitable for practical DR integration and edge deployment. Full article

This study examines whether inward foreign direct investment (FDI) has ultimately supported or hindered sustainable economic growth in Türkiye by analyzing the impact of FDI stocks on output per worker within an endogenous-growth framework. Using annual data for 1970–2024 and an ARDL approach, the study estimates the short- and long-run effects of FDI stock, distinguishes these effects from those of domestic capital stock, and assesses whether they remain stable across different sub-periods. The results show that FDI stock has a robustly negative impact in the long run, and this adverse long-run effect persists when the sample is split into pre- and post-2001 crisis subsamples, while domestic capital does not emerge as a significant driver of growth. Human capital—measured by years of schooling—also displays a negative long-run association with output per worker, whereas institutional quality has a strong positive effect. The study contributes to the Turkish FDI–growth literature by providing, to the best of available knowledge, the first time-series evidence that focuses on FDI stocks and jointly models foreign and domestic capital stocks, and by documenting a long-run-negative effect of FDI stock that is robust over time. The findings imply that policy in Türkiye should shift from maximizing the volume of FDI toward improving its sectoral structure and governance, upgrading education quality and retaining high-skilled workers, and implementing concrete rule-of-law reforms. Full article

Single-phase ground faults account for more than 80% of total faults in distribution networks. However, the introduction of distributed generation complicates power grid topology, leading to strong nonlinearity and non-stationarity in the zero-sequence current. This limits the accuracy of traditional faulted line selection methods. To address this problem, a CNN–ResNet-based method for faulted line selection for single-phase ground faults in distribution networks is proposed. Firstly, a 10 kV arc ground fault simulation test platform is built to analyze the nonlinear distortion characteristics of fault current. The WOA–VMD algorithm, optimized by permutation entropy, is used to denoise the zero-sequence current signal. The Gram Angular Difference Field (GADF) is then adopted to convert the one-dimensional signal into a two-dimensional image that retains its temporal characteristics. A hybrid deep learning model is constructed by fusing the one-dimensional time-domain features extracted by CNN and the two-dimensional time-frequency image features extracted by ResNet34. Matlab/Simulink simulations and physical experimental verification demonstrate that the proposed method achieves a training accuracy of over 97%, with zero misjudgments recorded in 15 arc grounding fault tests, representing a significant improvement in accuracy compared with existing diagnostic algorithms. It can adapt to complex scenarios such as high-resistance grounding and changes in neutral point grounding mode, effectively improving the accuracy and robustness of faulted line selection and providing technical support for the safe operation of distribution networks. Full article

A high strength Al-12Si has been prepared through mechanical alloying and press forming without any additional alloy components. The alloy exhibited a high tensile strength of 458 MPa at room temperature and retained excellent tensile properties at elevated temperatures with a UTS of 118 MPa at 350 °C after 1000 h of exposure. Furthermore, after 1000 h of heat exposure testing, the mechanical properties of the alloy showed no significant decrease. X-ray diffraction characterizations indicated that the alloy consists solely of an Al matrix and Si phase. Microstructural characterization through HRTEM revealed that the grain size of the Al matrix was approximately 300 nm, with a high-density of stacking faults present. The grain refinement strengthening and stacking fault strengthening contributed to the alloy’s excellent mechanical properties at both room temperature and elevated temperatures. Full article

Bio-inspired metaheuristic optimization offers flexible search mechanisms for high-dimensional predictive problems under operational constraints. In administrative risk prediction settings, class imbalance and feature redundancy challenge conventional learning pipelines. This study evaluates a wrapper-based metaheuristic feature selection framework for post-compliance income declaration prediction using real longitudinal administrative records. The proposed approach integrates swarm-inspired optimization with supervised classifiers under a weighted objective function jointly prioritizing minority-class recall and subset compactness. Robustness is assessed through 31 independent stochastic runs per configuration. The empirical results indicate that performance effects are learner-dependent. For variance-prone classifiers, substantial minority-class recall gains are observed, with recall increasing from 0.284 to 0.849 for k-nearest neighbors and from 0.471 to 0.932 for Random Forest under optimized configurations. For LightGBM, optimized models maintain high recall levels (0.935–0.943 on average) with low dispersion, suggesting representational stabilization and dimensional compression rather than large absolute recall improvements. Optimized subsets retain approximately 16–33 features on average from the original 76-variable space. Within the evaluated experimental protocol, the findings show that metaheuristic-driven wrapper feature selection can reshape predictive representations under class imbalance, enabling simultaneous control of minority-class performance and feature dimensionality. Formal institutional deployment and cross-domain generalization remain subjects for future investigation. Full article

Trace amounts of H₂O are inevitably introduced during lithium battery manufacturing processes, which induces the hydrolysis of LiPF₆, leading to HF formation, which triggers a cascade of deleterious reactions that degrade the solid electrolyte interphase (SEI) and corrode electrode materials. In this work, a water-scavenging electrolyte was constructed by employing a boroxine-linked covalent organic framework (COF) as the suspended phase. The ring-opening reaction of the boroxine ring units in COFs can effectively capture H₂O, thereby suppressing the hydrolysis of PF₆⁻ and mitigating electrode corrosion caused by HF. Consequently, a Li-metal battery with a high-nickel cathode retained 73% of its initial capacity after 500 cycles at 1 C, and a silicon-based lithium-ion battery with a high-nickel cathode sustained stable cycling over 500 cycles at a high rate of 10 C. This suspension strategy, leveraging a boroxine-linked COF with dual H₂O-scavenging capability, offers a scalable and versatile platform for electrolyte engineering toward practical next-generation lithium batteries. Full article

Biogas production from animal manure has huge potential in mitigating greenhouse gas emissions and replacing the higher environmental footprint energy sources. This study aimed to assess the technical functionality, environmental benefits, and economic advantages of low-cost biodigesters suitable for rural areas, which can produce biogas from animal manure. Four low-cost polyethylene tubular biodigesters with a concrete retaining wall with capacities ranging from 4 to 14 m³ were installed in small dairy production units in Chiapas, Mexico. Four profitability indicators were calculated. The IPCC’s methodology was used to calculate emissions from biogas and firewood burning, and the emission reduction from manure management. These biodigesters generate between 526 and 1993 m³ of biogas year⁻¹ and represent a savings of USD 197–744 year⁻¹ in energy costs. The four profitability indicators were favorable. Moreover, these biodigesters reduce 70–73% of greenhouse gas (GHG) emissions through manure management, that is, between 1.5 and 5.1 t CO₂e year⁻¹, and 1.3–5.1 t CO₂e year⁻¹ from firewood displacement. These findings provide critical insights into the potential of sustainable and low-cost biodigesters that can be implemented effectively in small-scale dairy farms in rural areas in many parts of the world. Full article

Aqueous zinc-ion batteries have emerged as a research hotspot due to their advantages of safety, environmental friendliness, low cost, and high capacity. At the same time, there are some problems with anode materials, such as zinc dendrite growth and corrosion reactions. In this work, silica fume, a byproduct of industrial silicon smelting, was selected as a coating material for the Zn anode (SF@Zn). This material is not only cost-effective and widely available but also exhibits superior hydrophilicity, enhancing the electrolyte’s wettability on the anode. Additionally, it serves as an ion shunt, preventing uneven deposition of Zn²⁺, and it was demonstrated that the symmetrical cell achieved a cycle life of up to 1800 h at 0.5 mA·cm⁻². The full cell delivered a capacity of 246.2 mAh·g⁻¹ at 1 mA·cm⁻² and retained a capacity of 100.4 mAh·g⁻¹ after 1800 cycles. Full article

Human civilization embodies a rich cultural heritage shaped over long historical periods by numerous ethnic groups, each employing distinctive motifs and patterns in religious spaces, architecture, clothing, utensils, and other artifacts. Such motifs commonly originate from elementary geometric primitives that are organized through symmetric or asymmetric compositions to convey symbolic and esthetic meaning. This study focuses on Mongolian patterns derived from the nomadic heritage of Mongolia and still prevalent in contemporary design. These patterns draw inspiration from nature, geometry, animals, plants, and symbolic forms. This article proposes a mathematical modeling-driven digitization framework for the systematic analysis and digitization of Mongolian patterns, with the objective of generating accurate digital representations in the form of computer-aided design (CAD) models. A concise review of related work is first presented, followed by a structured digitization framework and a taxonomy of representative Mongolian motifs. A case study demonstrates that, when combined through distance-preserving and shape-preserving geometric operations such as translation, rotation, and reflection, four fundamental geometric entities, namely the circle, circular arc, spiral, and astroid, are sufficient to retain the intrinsic symmetry and compositional coherence of complex patterns observed in selected artifacts. Furthermore, the proposed analytical modeling approach enables the generation of vector-based line drawings that support precise CAD model construction. Accordingly, this study establishes a computational design workflow that integrates cultural heritage patterns into CAD-based modeling environments, thereby supporting digital preservation and fabrication with high geometric fidelity. Full article