Search Results (981)

The SAE J2841 utility factor (UF) estimates the fraction of driving expected to occur in charge-depleting (CD) mode for plug-in hybrid electric vehicles. Emerging in-use data suggest that real-world electric usage is lower than assumed, motivating a reassessment of how charging behavior and related factors should be incorporated into the UF curve. Using trip-level data from approximately 1000 PHEVs observed over one year, we develop a charging model that captures both population-level heterogeneity in charging frequency and day-to-day characteristic temporal patterns in individual charging. The charging behavior modeling is applied to NHTS driving data to generate UF curves spanning 5 to 200 miles (8 to 322 km) of CD range. When key behavioral features are included, the resulting CD driving fractions align closely with industry-provided data. Sensitivity analysis indicates that the assumed share of habitual non-chargers is among the most influential parameters affecting the gap between the original UF and in-use data. Multiple modeling approaches were used to explore the problem and compare results, including machine learning, logistic regression, and parametric methods. Additional factors such as blended CD operation and temperature effects are discussed within a modular framework for refining J2841. These findings inform ongoing discussions on PHEV utility representation in analytical and regulatory contexts. Full article

Bamboo plantations are increasingly recognized as significant terrestrial carbon sinks, yet accurate estimation of biomass and carbon stocks requires species-specific, regionally validated allometric models. Bambusa emeiensis L.C.Chia & H.L.Fung (ci bamboo) is among the most ecologically and economically important clump-forming bamboo species in southwestern China, but robust multi-regional allometric models are lacking. Using destructive sampling data from 127 culms across two major production areas—Sichuan Province (n = 82) and Guizhou Province (n = 45)—we developed additive biomass and carbon storage model systems enforcing mathematical additivity via nonlinear seemingly unrelated regression (NSUR). Allometric equations used diameter at breast height (D), culm height (H), and compound variables (DH, D²H) as predictors. Regional models achieved R_a² of 0.0879–0.8320 total relative error (TRE): −0.99% to 0.04% for biomass and R_a² of 0.0923–0.8282 (TRE: −1.01% to 0.03%) for carbon storage; culm and total aboveground models attained R_a² ≥ 0.52. Organ-level carbon content (40.79%–44.46%) was significantly lower than the intergovernmental panel on climate change (IPCC) default of 50% (one-sample t-test, p < 0.01 for all organs), with Sichuan values exceeding Guizhou values (independent-samples t-test, p < 0.01), indicating that use of the default would overestimate carbon stocks by 12%–22%. Cross-regional validation revealed prediction biases of up to ±19.24% when applying single-region models outside their training area, whereas the combined model held errors within ±11.36% for biomass and ±8.49% for carbon storage. External validation using 32 independent culms from Hunan, Yunnan, and Chongqing confirmed the robustness of the combined model (TRE: −6.30% to 4.27%). A key limitation is that belowground biomass was not measured. The established models provide scientifically rigorous and practically applicable tools for regional carbon accounting of B. emeiensis plantations under China’s national greenhouse gas inventory framework and for informing sustainable bamboo management planning, and demonstrate that species- and region-specific carbon fractions are essential for accurate carbon stock assessments. Full article

The global transition toward carbon-neutral energy solutions has established Solid Oxide Fuel Cells (SOFCs) as a key technology for next-generation power generation. This work presents a comprehensive numerical study and multi-factor statistical analysis of an anode-supported SOFC fueled by synthetic diesel. A three-dimensional computational fluid dynamics model, validated against experimental data, was integrated with a Taguchi L₂₇ orthogonal array to systematically evaluate the influence of six key parameters: temperature, fuel mass flow rate, operating pressure, current load, flow channel configuration, and methane molar fraction. Statistical analysis through the signal-to-noise ratio and analysis of variance identified the operating current as the most significant factor affecting cell voltage, followed by the fuel mass flow rate and temperature. The experiments showed that the highest levels of all factors (except for the current, which had the lowest level) maximize electrochemical performance while maintaining a steam-to-carbon ratio (S/C) within a range of 0.83 to 0.92, calculated based on total carbon content, ensuring sufficient humidification for internal reforming across all tested fuel compositions. Furthermore, a multiple linear regression model was developed as a computationally efficient surrogate, demonstrating exceptional predictive accuracy with an R² of 0.9954 and a mean relative error of 1.76% across independent validation cases. These results provide a robust methodology for rapid design and sensitivity analysis of internal-reforming SOFCs, offering a precise tool for optimizing fuel utilization in high-temperature electrochemical systems. Full article

This study investigates the dry pneumatic separation of wheat flour using a newly designed rotating air classifier to obtain starch- and protein-enriched fractions. The process is based on differences in particle density and size, enabling separation without water or chemical reagents. The influence of key process parameters, including air flow velocity 6–12 m/s, classifier geometry, and particle size distribution, was investigated. Statistical analysis confirmed that the air flow velocity and orifice diameter significantly affect the separation efficiency. The optimal conditions of 9–10 m/s and 1.8 mm resulted in a starch fraction with a purity of about 89% and a protein-enriched fraction containing approximately 45% protein. Regression models (R² > 0.99) demonstrated a strong relationship between the process parameters and fraction yield. Compared with conventional wet fractionation, the proposed method reduces energy consumption by approximately 28% and eliminates water use. These results confirm the feasibility of dry pneumatic classification as a sustainable and efficient technology for producing functional wheat-based ingredients. All experiments were conducted in triplicate (n = 3), and the results are presented as mean ± standard deviation. The reported yields correspond to the fraction mass, while the composition values indicate component purity within each fraction. Full article

Food waste valorization represents a critical challenge and opportunity for sustainable food systems. This study investigated the reuse of sauce production by-products through two approaches: (i) solvent-free recovery of an oil-rich fraction and (ii) development of polymeric films for potential edible or biodegradable packaging. Centrifugation recovered approximately 10 g per 100 g of by-product. The recovered oil was characterized for total polyphenols and fatty acid composition, showing a profile consistent with vegetable oils (mainly olive oil), with minor contributions attributable to cheese and meat components. A full factorial design was used to prepare and test films and to study the effects of the three ingredients used, namely pectin, carvacrol, and sauce by-products, on their mechanical, surface, and antibacterial properties. Chemometric analysis based on principal component analysis (PCA) and regression-based modeling (multiple linear regression and response surface analysis) was applied to identify the relationships among the responses and the most influential factors. Among the tested formulations, N3 (low pectin and by-product; high carvacrol) showed the most favorable overall balance, combining the strongest antibacterial activity (mean inhibition halo diameter of 14.8 mm and 17.8 mm against Escherichia coli ATCC 11229 and Staphylococcus aureus ATCC 6538, respectively) with favorable mechanical performance, including the highest maximum force (0.53 ± 0.01 MPa) and elastic modulus, (6.8 ± 0.01 MPa) and intermediate elongation (12 ± 3%) and work at maximum force (11.9 ± 0.9 N mm). Full article

Internal combustion engines fueled by fossil fuels are major contributors to greenhouse gas (GHG) emissions. This study analyzes and predicts GHG emissions from hydrogen-enriched compressed natural gas (HCNG)-fueled spark-ignition (SI) engines. Experiments were conducted under stoichiometric conditions, and emissions before and after the three-way catalytic converter (TWC) were analyzed by varying hydrogen fraction (0–50%), EGR ratio (0–23%), engine speed (900 rpm–1500 rpm), engine load (25–75%), and spark timing (8–49 °CA bTDC). Before the TWC, increasing the hydrogen fraction from HCNG0% to HCNG40% at 1500 rpm, 50% load, and 23% EGR reduced total GHG emissions from 184.3 to 65.17 g/kWh. Similarly, for HCNG20% at 900 rpm and 30% load, the TWC reduced the CO₂-equivalent emissions of CO, CH₄, and NOx from 18.531, 8.149, and 9.057 g${\mathrm{C}\mathrm{O}}_{2eq}$/kWh to 7.013, 1.626, and 0.429 g${\mathrm{C}\mathrm{O}}_{2eq}$/kWh, respectively. Pearson correlation analysis revealed strong linear relationships between operating parameters and GHG emissions. Furthermore, emissions were predicted using four Gaussian process regression (GPR) models: Squared, Exponential, Matern 5/2, and Rational. Among these, the Exponential GPR demonstrated the highest predictive accuracy, achieving RMSE values of 0.098, 0.022, and 0.035, with corresponding R² values of 0.999, 0.807, and 0.996 for CO, CH₄, and NOx, respectively. The findings of this study offer valuable insights into GHG emissions and support the development of cleaner, more efficient HCNG engines. Full article

Predicting the concrete breakout strength of an expansion anchor embedded in short carbon fiber-reinforced concrete (SCFRC) is challenging due to the nonlinear and heterogeneous nature of fiber–matrix interaction. This study develops an Artificial Neural Network (ANN) model to estimate the breakout capacity of a single expansion anchor installed in SCFRC. Experimental data from 48 cases covering variations in compressive strength, tensile strength, fiber volume fraction, and fiber length were used to train and validate multiple ANN architectures in MATLAB’s Regression Learner. A 4-4-1 trilayered ANN with Rectified Linear Unit (ReLU) activation and 5-fold cross-validation achieved the most reliable performance, yielding R² values of 0.6726 (validation) and 0.9376 (test), with correspondingly low RMSE, MAE, and scatter index (SI < 0.1). SHAP-based sensitivity analysis identified tensile strength as the dominant predictor, contributing 70.78% to model output influence. ANN predictions were compared with the Concrete Capacity Design (CCD) model adopted by ACI and the National Structural Code of the Philippines (NSCP) and a multiple linear regression (MLR) model, showing that while the ANN is not the most precise model, it provides acceptable accuracy and captures nonlinear concrete breakout behavior more effectively than linear approaches. Results demonstrate that the ANN framework offers a viable data-driven tool for estimating concrete breakout strength in SCFRC anchorage systems. Full article

This study addresses the need for applied sentiment analysis in engineering decision-support systems by presenting a hybrid framework for domain-specific engineering text. This study presents a hybrid sentiment classification framework by integrating transformer-based semantic embeddings with fractional-order feature modeling. The proposed BERTLR framework combines BERT and RoBERTa representations with Grünwald–Letnikov fractional derivative–enhanced TF-IDF features and logistic regression within a unified soft-voting architecture. Unlike conventional ensemble sentiment models that merely aggregate embeddings and handcrafted features, the proposed method introduces fractional-order feature transformation to capture non-local dependency patterns and memory-aware lexical variations that are often overlooked in technical review text. This design provides a structured fusion of contextual semantic information and fractional statistical representations, supported by SHAP-based explainability and ablation analysis. Experiments conducted on six real-world engineering application domains show consistent improvements over conventional TF-IDF models, LSTM baselines, and non-fractional transformer variants. The framework achieves up to 91% accuracy, together with strong precision, recall, and F1-score performance. These results demonstrate that fractional-order feature augmentation can provide a meaningful complementary signal to transformer embeddings, offering an interpretable and effective sentiment analysis solution for engineering and industrial decision-support applications. Full article

Background: Despite advances in reperfusion therapy, ST-segment elevation myocardial infarction (STEMI) remains associated with substantial morbidity and mortality. Early identification of predictors of adverse outcomes is essential for improving risk stratification. Methods: This retrospective study included 512 STEMI patients who underwent coronary revascularization within 6 h of symptom onset. Clinical, laboratory, angiographic and echocardiographic variables were analyzed. The primary endpoint was in-hospital mortality. Secondary outcomes included reduced left ventricular ejection fraction (LVEF < 40%) and moderate-to-severe ischemic mitral regurgitation (IMR). Independent predictors of in-hospital mortality were identified using multivariable logistic regression, while secondary outcomes were described to characterize the study population. Model performance was evaluated using ROC analysis. Results: In-hospital mortality occurred in 9.4% of patients. Reduced LVEF was present in 26.2%, and IMR in 10.9%. Independent predictors of mortality included LVEF < 40% (OR 5.72, 95% CI 2.77–11.80, p < 0.001), IMR (OR 2.61, 95% CI 1.14–5.97, p = 0.023), lower hemoglobin levels (OR 0.74, 95% CI 0.61–0.91, p = 0.003), and reduced glomerular filtration rate (OR 0.96, 95% CI 0.95–0.98, p < 0.001). The model demonstrated good discrimination (AUC 0.88). Complete revascularization was not independently associated with mortality. Conclusions: Left ventricular dysfunction, IMR, anemia, and renal impairment are strong predictors of in-hospital mortality in STEMI patients. Integrating echocardiographic and laboratory parameters may improve early risk stratification and guide clinical decision-making. Full article

Background: Malnutrition and systemic inflammation are increasingly recognized as important determinants of prognosis in patients with heart failure. Several immunonutritional indices, including the Prognostic Nutritional Index (PNI), the Controlling Nutritional Status (CONUT) score, and the C-reactive protein–albumin–lymphocyte (CALLy) index, have been proposed as markers of nutritional and inflammatory status. However, their prognostic value in elderly patients with heart failure with preserved ejection fraction (HFpEF) remains incompletely defined. This study aimed to evaluate the prognostic significance of these immunonutritional indices in elderly patients with HFpEF over a long-term follow-up period. Methods: This retrospective observational study included 200 elderly patients hospitalized with HFpEF (mean age 86.6 ± 6.5 years). Clinical, laboratory, and echocardiographic parameters were collected at admission. Nutritional status was assessed using PNI, CONUT score, and CALLy index. Patients were followed for mortality during long-term follow-up. Survival analyses were performed using Cox regression models, receiver operating characteristic (ROC) curves, and Kaplan–Meier analysis. Median follow-up was 3.8 years (IQR 2.1–5.9). Results: During follow-up, 123 patients (61.5%) died, while 77 patients (38.5%) were alive at the end of observation. In univariate analysis, PNI, CONUT score, left ventricular ejection fraction (LVEF), and the tricuspid annular plane systolic excursion to systolic pulmonary artery pressure (TAPSE/sPAP) ratio were significantly associated with mortality. In multivariate analysis, the CONUT score, LVEF, and the TAPSE/sPAP ratio remained independent predictors of mortality. ROC analysis showed strong prognostic performance for the TAPSE/sPAP ratio (AUC 0.932), CONUT score (AUC 0.925), and LVEF (AUC 0.897). Optimal cut-off values for mortality prediction were CONUT ≥ 6, LVEF ≥ 65%, and TAPSE/sPAP ≤ 0.55 mm/mmHg. Kaplan–Meier analysis confirmed significantly reduced survival among patients with higher CONUT scores, higher LVEF, and an impaired TAPSE/sPAP ratio. Conclusions: In elderly patients with HFpEF, nutritional status and cardiopulmonary functional parameters are important determinants of long-term prognosis. The CONUT score emerged as the most informative immunonutritional index, while echocardiographic parameters reflecting ventricular function and right ventricular–pulmonary arterial coupling provided additional prognostic information. Integrating nutritional assessment with echocardiographic evaluation may improve risk stratification in elderly patients with HFpEF. Full article

Landmark epidemiological studies and clinical trials, such as the Seven Countries Study, the Lyon Diet Heart Study, the PREDIMED Study and the CORDIOPREV Study, have shown significant reductions in cardiovascular events in those following the Mediterranean diet (MD). The aim of the present work is to summarize the most robust available evidence and the major biological pathways underlying the protective effects of the MD, with particular emphasis on the role of PAF inhibitors. Mechanistically, MD functions through a complex synergy of antioxidant, anti-inflammatory, and antithrombotic effects that collectively improve lipid profiles, enhance endothelial function, optimize postprandial metabolism and cell membrane signaling, making it a functional model for human longevity. The PAF-Implicated Atherosclerosis Theory has emerged as a key unifying framework, proposing that Platelet-Activating Factor (PAF)—a highly potent lipid inflammatory mediator—plays a central role in the initiation and progression of atherosclerosis. Oxidized LDL promotes the production of PAF and PAF-like lipids, leading to endothelial dysfunction, vascular inflammation, and atherosclerotic plaque formation. Traditional Mediterranean foods are rich in natural PAF inhibitors, particularly the polar lipid fractions of extra virgin olive oil, as well as wine, fish, vegetables, onions, and garlic. Animal studies demonstrate that these compounds can reduce or even regress atherosclerotic lesions, independently of serum cholesterol levels. Human dietary interventions have further shown that MD-based meals and functional foods enriched with PAF inhibitors reduce PAF activity and improve thrombosis-related biomarkers. This mechanistic framework helps explain phenomena such as the “French Paradox” and the cardio-protective effects associated with fish consumption. Moreover, the extraction of PAF inhibitors from Mediterranean food by-products, such as olive pomace, offers promising ecological and economic advantages. Collectively, targeting PAF and increasing dietary intake of PAF inhibitors represent promising strategies for the prevention and management of atherosclerosis and other inflammatory diseases, supporting the view that PAF may function as a major, modifiable risk factor in these conditions. Full article

Parkinson’s disease (PD) may benefit from non-pharmacological motor–cognitive rehabilitation, but sensitive neuroimaging markers of training-related brain changes remain limited. This study investigated whether 4 weeks of daily Quadrato Motor Training (QMT) modulate resting-state functional connectivity (FC) in PD and secondarily explored whether whole-brain radiomic features derived from T1-weighted and fractional anisotropy (FA) images could detect pre–post differences over this short intervention interval. Fifty patients with idiopathic PD were randomized to QMT or a SHAM repetitive stepping condition, and 48 completed the protocol (25 SHAM, 23 QMT). MRI was acquired at baseline and after 4 weeks and included resting-state fMRI, 3D T1-weighted imaging, and diffusion-derived FA maps. Resting-state fMRI was analyzed using independent component analysis and dual regression, whereas an IBSI-compliant radiomics workflow and machine-learning models were used for exploratory scan-level classification. Compared with baseline, the SHAM group showed reduced synchronization across several resting-state networks, whereas the QMT group showed increased synchronization in the right sensorimotor and frontoparietal networks and no significant reductions. Between-group analyses showed lower delta-FC in SHAM than QMT in the cerebellar and sensorimotor networks. In contrast, radiomics showed limited discrimination between pre- and post-QMT scans; the best model achieved a ROC-AUC of 0.65 with near-chance accuracy, and no selected predictor remained significant after multiple-comparison correction. These findings suggest that QMT may support short-term functional network stability or task-relevant reorganization in PD relative to the SHAM condition, whereas whole-brain structural radiomics appears less sensitive for detecting early training-related effects in this setting. Full article

The building façade serves as the primary interface between the built environment and external climate, marking the transition from static regulation to dynamic response in climate-adaptive design. While existing research predominantly addresses periodic climatic elements such as temperature and solar radiation, the highly stochastic wind environment and its potential for internal acoustic problems remain systematically unexplored. This study investigates the acoustic modulation mechanism of building façades under dynamic wind conditions through a simulation-based methodology. The primary aim is to demonstrate the use of active control to mitigate the influence of fluctuating wind on the internal acoustic environment of buildings with open windows or semi-open boundaries, focusing on the coupling between stochastic wind fields and architectural acoustics in humid subtropical climates. We propose a wind-responsive adaptive acoustic façade system employing fractal geometry and configurable delay strategies, and develop a high-fidelity simulation framework to quantify how façade geometry and activation logic regulate acoustic parameters under varying wind conditions (1–8 m/s). Results indicate that: (1) support vector regression-based mapping of wind speed to delay strategies maintains key sound-field parameters (Lateral Fraction (LF), Speech Clarity (C₅₀), and Early Decay Time to Reverberation Time ratio (EDT/RT₃₀)) within 10% fluctuation across wind regimes; (2) fractal configurations achieve balanced wide-band (125 Hz–8 kHz) performance, with SPL fluctuation <3 dB, spectral tilt (+0.3 dB), and reverberation time slope <0.3; (3) configurational switching between column (high LF) and row (high C₅₀) arrangements enables dynamic trade-off between spatial impression and speech clarity. This work establishes an integrated framework coupling wind dynamics, façade morphology, and acoustic modulation to regulate objective indoor acoustic parameters. Based on the simulated omnidirectional point-source model, the results show that key acoustic indicators remain stable across varying wind conditions, providing a theoretical and quantifiable basis for climate-responsive acoustic envelope design. Future work will include empirical prototype testing and listening tests to determine whether these simulated acoustic parameters translate into improved comfort and well-being for occupants. Full article

This study examines how structural constraints and institutional support are associated with the configuration of agricultural production systems in Durango, Mexico. Using survey data from 362 farmers, multidimensional indices capturing productive restrictions, export barriers, technification gaps, and support needs were constructed using binary indicators and normalized to a 0–1 scale, with internal consistency assessed through the Kuder–Richardson coefficient. To analyze structural dynamics, Ordinary Least Squares (OLS) and fractional logit models were used to estimate determinants of structural pressure, while a logistic regression model was applied to examine differences in structural positioning across production regimes identified through cluster analysis. The results show that larger productive scale is associated with higher exposure to regulatory and commercialization pressures, indicating that farm expansion tends to coincide with increased structural complexity rather than automatic improvements in structural conditions. More importantly, institutional linkage remains positively and statistically significant in the logistic model (OR = 9.085; p = 0.015), even after controlling for technification, structural constraints, export barriers, labor intensity, and farming experience. These findings indicate that institutional connectivity is positively associated with differentiated structural configurations among production units and are consistent with the interpretation that institutional support is associated with variation in structural conditions in semi-arid agricultural systems, beyond the effects of technological endowment or scale alone. Full article

In the semi-arid Batna Belezma region of northeastern Algeria, groundwater is a vital resource for agriculture and drinking water. However, the climate leads to intense evaporation, which affects its quality. This study aims to identify the key hydrogeochemical processes that control groundwater composition in the Merouana Basin and to evaluate the predictive performance of machine learning (ML) models. A total of 30 groundwater samples were analyzed using multivariate statistical techniques, including Principal Component Analysis (PCA), and were modeled using PHREEQC to assess mineral saturation states. Additionally, ML-based regression models, including K-Nearest Neighbors (KNN), Support Vector Machine (SVM), Random Forest (RF), and Extreme Gradient Boosting (XGB),were employed to predict groundwater chemistry. The results indicate that the dominant ion distribution follows the following trend: Ca²⁺ > Mg²⁺ > Na⁺ and HCO₃⁻ > SO₄²⁻ > Cl⁻. Alkaline earth metals (Ca²⁺ and Mg²⁺) constitute the major fraction of total dissolved cations, reflecting carbonate equilibrium and dolomite dissolution processes. In contrast, Na⁺ represents a smaller proportion of the cationic load; however, its hydro-agronomic significance is substantial due to its influence on sodium adsorption ratio (SAR) and soil permeability. The PHREEQC modeling showed that calcite and dolomite precipitation promote evaporite dissolution, while most samples remain undersaturated with respect to gypsum. The PCA results reveal high positive loadings of Mg²⁺, Cl⁻, SO₄²⁻, HCO₃⁻, and EC, suggesting that ion exchange and seawater mixing are the primary controlling processes, with carbonate weathering playing a secondary role. To enhance predictive assessment, several supervised machine learning models were tested. Among them, the Random Forest model achieved the highest predictive performance (R² = 0.96) with low RMSE and MAE values, confirming its robustness and reliability. The results indicate that silicate weathering and mineral dissolution are the primary mechanisms governing groundwater chemistry. The integration of multivariate statistics and machine learning provides a comprehensive understanding of groundwater evolution and offers a reliable predictive framework for sustainable water resource management in semi-arid environments. Geochemical model performance showed a high global accuracy (GPI = 0.91), confirming a strong agreement between observed and simulated chemical data. However, the HH value (0.81) indicates some discrepancies, particularly for specific ions or extreme conditions. Full article