Search Results (2,322)

Computer-aided modeling and mathematical optimization of energy systems are essential for improving operational efficiency and achieving emission reductions, particularly for steam turbine systems with part-load-dependent efficiency characteristics. Mixed-Integer Linear Programming (MILP) is the state of the art, due to its short computational times and reliable convergence. However, its simplifications often reduce model accuracy. Mixed-Integer Nonlinear Programming (MINLP) offers high accuracy but faces long computational times and potential convergence issues. Recent advancements in Mixed-Integer Quadratically Constrained Programming (MIQCP) offer a promising approach for more accurate energy system modeling by enabling quadratic and bilinear representations while avoiding the full complexity of nonlinear programs. This study compares the optimization methods MILP, MINLP and MIQCP for the operational optimization of a steam turbine system. The parameterization of the models is based on hourly measurement data of two real-world steam turbines. Key evaluation criteria include accuracy, computational time, implementation complexity and the deviation in the calculated optimum. The results show that MIQCP improves accuracy compared with MILP while requiring lower computational time than MINLP. Overall, the results demonstrate that MIQCP provides a suitable compromise between model accuracy and computational efficiency for the operational optimization of steam turbine systems. Full article

In terminal flight trajectory, significant dispersion poses a challenge for accurate localization, as the velocity vector of a supersonic flying object increasingly deviates from the normal vector of the measurement plane under gravitational and aerodynamic effects. Therefore, in this study, an impact point localization method, utilizing a dual-rectangular-ring linear optical microphone array based on apparent shock-wave velocity, was developed. A shock-wave measurement array was developed using a dual rectangular ring composed of linear optical microphone arrays. A time-equivalent model, derived from shock-wave propagation, was introduced to analyze the apparent velocity of the shock-wave within the measurement plane. The time difference in the shock-wave arrivals at the dual rectangular ring, combined with the distances between the inner and outer rectangular rings, was used to calculate the non-uniform apparent shock-wave velocity, thereby enabling the localization of supersonic flying objects. The method’s constraints were examined, and its measurement errors were evaluated. The simulation and experimental results showed that the error was less than 0.5 mm. The proposed novel and cost-effective method for impact point localization aids in the effective dispersion assessment of flying objects. Full article

Soil corrosion is a critical durability and cost factor for metallic foundations in photovoltaic (PV) power plants, yet it is still addressed with fragmented criteria compared with atmospheric corrosion. This paper reviews the main soil corrosivity drivers relevant to PV installations—moisture and aeration dynamics, electrical resistivity, pH and buffer capacity, dissolved ions (notably chlorides and sulfates), microbiological activity, hydro-climatic variability and geological heterogeneity—highlighting their coupled and non-linear effects, such as differential aeration, macrocell formation and corrosion localization. Building on this mechanistic basis, an engineering-oriented methodological roadmap is proposed to translate soil characterization into durability decisions. The approach combines soil corrosivity classification according to DIN 50929-3 and DVGW GW 9, tiered estimation of hot-dip galvanized coating consumption using AASHTO screening, resistivity–pH correlations and ionic penalty factors, and verification against conservative NBS envelopes. When coating life is insufficient, a traceable steel thickness allowance based on DIN bare-steel corrosion rates is introduced to meet the target service life. The framework provides a practical and auditable basis for durability design and risk control of PV foundations in heterogeneous soils. The proposed framework shows that, for soils exceeding AASHTO mild criteria, zinc corrosion rates may increase by a factor of 1.3–1.7 when chloride and sulfate penalties are considered, potentially reducing coating service life by more than 40%. The methodology proposed enables designers to estimate the penalty factors for sulfates ($f_p\left({SO}_4^{2-}\right)$) and chlorides ($f_p\left({Cl}^{-}\right)$) in each specific project, calculating the appropriate values of $K_{{SO}_4^{2-}}$ and $K_{{Cl}^{-}}$ using electrochemical techniques—ER/LPR and EIS—to estimate the effect of the soluble salts content in the ${Zn}_{Corr Rate}$, not properly catch by the proxy indicator $V_{corr}\left(ER, pH\right)$ when sulfate and chloride content are over AAHSTO limits for mildly corrosive soils. Full article

Background/Objectives: The triglyceride-to-High-density lipoprotein cholesterol (TG/HDL) ratio is an established marker of atherogenic dyslipidaemia and insulin resistance. Although its association with subclinical atherosclerosis has been reported, the relative contributions of long-term TG/HDL burden and visit-to-visit variability to carotid intima media thickness (CIMT) remain unclear. This study aimed to evaluate the differential associations of the longitudinal mean and temporal variability of the TG/HDL ratio with CIMT. Methods: This retrospective single-center observational cohort study included 260 adult patients with at least three years of longitudinal lipid measurements and a standardized carotid ultrasonography assessment. The longitudinal mean TG/HDL ratio and variability indices, including standard deviation, coefficient of variation, average real variability and variability independent of the mean, were calculated. CIMT was measured using B-mode ultrasonography. Associations were assessed using correlation analyses, multivariable linear regression, joint category analyses and stratified analyses according to statin therapy. Results: The longitudinal mean TG/HDL ratio was independently associated with increased CIMT after adjustment for traditional cardiovascular risk factors. In contrast, TG/HDL variability indices showed no independent association with CIMT and did not improve model performance beyond the mean TG/HDL ratio. Restricted cubic spline analysis demonstrated a significant non-linear association between TG/HDL mean and CIMT, suggesting a threshold-dependent relationship. Joint category analyses demonstrated higher CIMT values in groups with elevated TG/HDL mean regardless of variability status. A significant interaction was observed between TG/HDL variability and statin therapy (p for interaction = 0.011). Conclusions: These findings indicate that cumulative exposure to atherogenic dyslipidaemia, reflected by the long-term mean TG/HDL ratio, is more strongly associated with subclinical carotid atherosclerosis than short-term lipid fluctuations. Full article

Unsupervised anomaly detection has been extensively studied. However, most existing methods are designed for either numerical or nominal data, which struggle to detect anomalies effectively in real-world mixed-type datasets. Fuzzy information granulation is a key concept in granular computing, which offers a potent framework for managing uncertainty in mixed-type data and provides a viable pathway for unsupervised anomaly detection. Nevertheless, conventional fuzzy information granulation-based detection methods often model only simple, linear fuzzy relations between samples. This limitation prevents them from capturing the complex, nonlinear structures inherent in the data, leading to a degradation in detection performance. To address these shortcomings, we propose a Hybrid Kernelized Fuzzy Density-based anomaly detector (HKFD). HKFD pioneers a hybrid kernelized fuzzy relation by integrating a hybrid distance metric with kernel methods. This new relation allows us to define a hybrid kernelized fuzzy density for each sample within every feature subspace, effectively capturing the local data dispersion. Crucially, we introduce an information-theoretic weighting mechanism. By calculating the fuzzy information entropy of each feature’s distribution, HKFD automatically assigns higher weights to more informative feature subspaces that contribute more to identifying anomalies. The final anomaly factor is then calculated by the weighted fusion of these densities. Comprehensive experiments on 20 datasets demonstrate that HKFD significantly outperforms state-of-the-art methods, achieving superior anomaly detection performance. Full article

Background: There exists some inconsistent evidence on the relationship between altered cardiac morphology, its function, and frailty. Therefore, this study aimed to assess the associations among frailty, lean body mass, central arterial stiffness, and cardiac structure and geometry in older people with a normal ejection fraction. Methods: A total of 205 patients >65 years were enrolled into this ancillary analysis of the FRAPICA study and were assessed for frailty with the Fried phenotype scale. Left ventricular dimensions and geometry were assessed with two-dimensional echocardiography. Fat-free mass was measured using three-site skinfold method. Parametric and non-parametric statistics and analysis of covariance were used for statistical calculations. Results: Frail patients were older and women comprised the majority of the frail group. Frail men and women had comparable weight, height, fat-free mass, blood pressure, central blood pressure, and carotid–femoral pulse wave velocity to their non-frail counterparts. There was a linear correlation between the sum of frailty criteria and left ventricular end-diastolic diameter (Spearman R = −0.17; p < 0.05) and relative wall thickness (Spearman R = 0.23; p < 0.05). In the analysis of covariance, frailty and gender were independently associated with left ventricular mass (gender: β of −0.37 and 95% CI of −0.50–−0.24 at p < 0.001), the left ventricular mass index (gender: β of −0.23 and 95% CI of −0.37–−0.09 at p < 0.001), and relative wall thickness (frailty: β of −0.15 and 95% CI of −0.29–−0.01 at p < 0.05; gender: β of 0.23 and 95% CI of 0.09–0.36 at p < 0.01). Frailty was associated with a shift in heart remodeling toward concentric remodeling/hypertrophy. Conclusions: Frailty is independently associated with thickening of the left ventricular walls and a diminished left ventricular end-diastolic diameter, which are features of concentric remodeling or hypertrophy. This association appears to be more pronounced in women. Such adverse cardiac remodeling may represent another phenotypic feature linked to frailty according to the phenotype frailty criteria. Full article

Pipeline leakage is a common issue in many pressurized pipeline systems, with significant hazards, making it a current research hotspot. To reveal the fundamental characteristics of leakage in straight pipelines and 90° elbows transporting different media and thereby predict leakage locations, this paper conducts numerical calculations of the internal flow, while also predicting the pipeline leakage location monitoring model. The study finds that under air medium conditions, the nonlinear function model demonstrates excellent prediction accuracy, with R² > 0.99 for the water₃ condition. Under water medium conditions, the model’s fitting performance gradually weakens with increasing inlet pressure, with R² dropping to 0.77. For a bent pipe, when air is used as the medium, the pressure peak at the large bend angle increases significantly under high inlet pressure. In contrast, when water is the medium, the local pressure reconstruction effect in the bent pipe exhibits a linear strengthening trend as the inlet pressure increases. Full article

The photovoltaic field has seen significant development in recent years, with continuously expanding installation capacity and increasing grid integration. However, due to the intermittency of solar energy and meteorological variability, PV output power poses serious challenges to grid security and dispatch reliability. Traditional forecasting methods largely rely on modeling historical power and meteorological data, often neglecting the consideration of cloud movement, which constrains further improvement in prediction accuracy. To enhance prediction accuracy and model interpretability, this paper proposes a dual-branch attention-based PV power prediction model that integrates physical features from ground-based cloud images. Regarding input features, a cloud segmentation model is constructed based on the vision foundation model DINO encoder and an improved U-Net decoder to obtain cloud cover information. Based on deep feature point detection and an attention matching mechanism, cloud motion vectors are calculated to extract cloud motion speed and direction features. For feature processing, feature attention and temporal attention mechanisms are introduced, enabling the model to learn key meteorological factors and critical historical time steps. Structurally, a parallel architecture consisting of a linear branch and a nonlinear branch is adopted. A context-aware fusion module adaptively combines the prediction results from both branches, achieving collaborative modeling of linear trends and nonlinear fluctuations. Comparative experiments were conducted using two years of engineering data. Experimental results demonstrate that the proposed model outperforms the benchmarks across multiple metrics, validating the predictive advantages of the dual-branch structure that integrates physical features under complex weather conditions. Full article

Background/Objectives: Given the high prevalence of obesity and abdominal obesity in Indigenous adults from Sonora (IAS) and its strong association with diet, this study evaluates the association of dietary inflammatory index (DII) with obesity and abdominal obesity and its indicators, such as body mass index (BMI) and waist circumference (WC), respectively. Methods: This cross-sectional study included data from 559 adults across two Indigenous populations (Seris and Yaquis) collected in two separate studies. Obesity and abdominal obesity were classified according to the definitions established by the World Health Organization and the International Diabetes Federation. The DII was calculated with data from population-specific food frequency questionnaires. Multiple linear regression was used to assess the association between the DII variable (expressed as both numeric and categorical) and BMI and WC, separately; multiple logistic regression was used to evaluate the association between obesity and abdominal obesity. Results: The prevalence of obesity and abdominal obesity was 34.1% and 78.2%, respectively. There was a positive association between the DII and BMI (DII as numeric: β = 0.53, p = 0.001; tertile3 of DII vs. tertile1: β = 1.86, p = 0.001) and WC (DII as numeric: β = 1.15, p = 0.002; tertile3 of DII vs. tertile1: β = 3.81, p = 0.005). Similar results were found for both types of obesity. Conclusions: Higher DII scores were associated with increased obesity indicators (BMI and WC) and a higher risk of obesity and abdominal obesity in IAS. Promoting anti-inflammatory diets represents a feasible approach for preventing non-communicable diseases. Full article

Background/Objective: Pulmonary impairments have been identified as some of the most complex and debilitating post-acute sequelae of SARS-CoV-2 infection (PASC) or long COVID. This study identified and characterised the specific forms of pulmonary impairments detected using pulmonary function tests (PFT), chest X-rays (CXR), and computed tomography (CT) scans in patients with long COVID symptoms. Methods: We conducted a single-centre retrospective study to evaluate 60 patients with long COVID who underwent PFT, CXR, and CT scans. Pulmonary function in long COVID patients was assessed using defined thresholds for key test parameters, enabling categorisation into normal, restrictive, obstructive, and mixed lung-function patterns. We applied exact binomial (Clopper–Pearson) 95% confidence intervals to calculate the proportions of patients falling below the defined thresholds. We also assessed the relationships among spirometric indices, lung volumes, and diffusion capacity (DLCO) using scatter plots and corresponding linear regressions. The findings from the CXRs and CT scans were categorised, and their prevalence was calculated. Results: A total of 60 patients with long COVID symptoms (mean age 60 ± 13 years; 57% female) were evaluated. The cohort was ethnically diverse and predominantly non-smokers, with a mean BMI of 32.4 ± 6.3 kg/m². PFT revealed that most patients had preserved spirometry, with mean Forced Expiratory Volume in 1 Second (FEV1) and Forced Vital Capacity (FVC) above 90% predicted. However, a significant proportion exhibited reductions in lung volumes, with total lung capacity (TLC) decreasing in 35%, and diffusion capacity (DLCO/TLCO) decreasing in 75%. Lung function pattern analysis showed 88% of patients had normal function, while 12% displayed a restrictive pattern; no obstructive or mixed patterns were observed. Radiographic assessment revealed that 58% of chest X-rays were normal, whereas CT scans showed ground-glass opacities (GGO) in 65% of patients and fibrotic changes in 55%, along with findings such as atelectasis, air trapping, and bronchial wall thickening. Conclusions: Spirometry alone is insufficient to detect impairment of gas exchange or underlying histopathological changes in patients with long COVID. Our findings show that, despite normal spirometry results, many patients exhibit significant diffusion impairment, fibrotic alterations, and ground-glass opacities, indicating persistent lung and microvascular damage. These results underscore the importance of comprehensive assessment using multiple diagnostic tools to identify and manage chronic pulmonary dysfunction in long COVID. Full article

Promoting cultivated land green utilization efficiency (CLGUE) through agricultural scale operation is critical for reconciling the conflict between food security and sustainable land use. Based on panel data from 30 provinces in China (2007–2022), this paper calculates CLGUE using the Super-efficiency SBM-Undesirable model and empirically examines the impact mechanisms and nonlinear characteristics of scale operation using Tobit and threshold models. The findings reveal that: (1) Agricultural scale operation has a significant positive impact on CLGUE, but this effect is non-linear and characterized by diminishing marginal returns, validating the “moderate scale” operation theory. (2) Substantial heterogeneity exists across different functional grain production zones and geographic regions. (3) Mechanism analysis identifies technological innovation as a key transmission channel through which scale operation boosts CLGUE. (4) A significant double-threshold effect is observed in fiscal support for agriculture; specifically, the positive enabling effect of scale operation is maximized only when fiscal support intensity is maintained within a specific rational range. Consequently, this study suggests that policymakers should prioritize “moderate scale” strategies, tailor policies to regional conditions, and optimize the allocation of fiscal funds to foster a transition toward green and sustainable agriculture. Full article

Background: The Remnant Cholesterol Inflammatory index (RCII) has been proposed as a marker of insulin resistance and systemic inflammation. However, its associations with incident stroke, incident heart disease, and all-cause mortality among individuals with cardiovascular–kidney–metabolic (CKM) syndrome stages 0–3 remain uncertain. Methods: This longitudinal cohort study used data from the China Health and Retirement Longitudinal Study (CHARLS). The remnant cholesterol inflammatory index (RCII) was calculated as [RC (mg/dL) × hs-CRP (mg/L)]/10. Outcomes included incident stroke, incident heart disease, and all-cause mortality. Covariates were prespecified based on established risk factors. Cox proportional hazards models and restricted cubic spline (RCS) analyses were used to evaluate associations between RCII and each outcome. Long-term RCII patterns were identified using k-means clustering. Robustness was assessed using subgroup and sensitivity analyses. Results: The final study involved 6994 participants in the stroke and heart disease cohort and 7245 participants in the all-cause mortality cohort, all within CKM syndrome stages 0–3. Higher baseline RCII was associated with increased risks of stroke (HR = 1.55, 95% CI: 1.14–2.12) and all-cause mortality (HR = 1.67, 95% CI: 1.37–2.04) compared with the lowest quantile. Cumulative RCII showed a stronger association with all-cause mortality (HR for Q3 = 2.18, 95% CI: 1.54–3.11). RCS analysis suggested a J-shaped, non-linear association between cumulative RCII and all-cause mortality. (p for non-linearity < 0.05). K-means clustering further indicated that, relative to the reference group, cluster 2 (high-to-higher) had the highest risk of incident heart disease, whereas cluster 3 (high-to-moderate) had the highest risk of all-cause mortality. Conclusions: Higher RCII levels were associated with higher risks of stroke, heart disease, and all-cause mortality among individuals with CKM stages 0–3. RCII may serve as a promising biomarker for early risk stratification in clinic and prevention efforts in this population. Full article

In this study, the technical, economic, and environmental performances of a Linear Fresnel Reflector (LFR) integrated with an Organic Rankine Cycle (ORC), designed with a non-storage approach, and a monocrystalline photovoltaic (PV) system were comparatively evaluated in meeting a building’s 10 kW electricity demand. Solar-based electricity generation systems play a critical role in reducing carbon emissions and increasing energy self-sufficiency in buildings, yet small-scale, storage-free LFR-ORC applications remain relatively underexplored compared to PV systems. The optimal areas for both systems were determined using the P₁–P₂ methodology. The electricity generation of the LFR-ORC system was calculated based on experimentally measured thermal power output and ORC efficiency, while the production of the PV system was determined using panel area, efficiency, and measured solar irradiation data. System performance was assessed through self-consumption and self-sufficiency ratios, and the economic analysis included life cycle savings (LCS), payback period, and levelized cost of electricity (LCOE). The results indicate that the PV system is more advantageous economically, with an optimal payback of 4.93 years and lower LCOE of 0.053 €/kWh when the economically optimal panel area is considered. On the other hand, the LFR-ORC system exhibits up to 35% lower life-cycle CO₂ emissions compared to grid electricity under grid-connected operation (15.86 tons CO₂-eq for the standalone LFR-ORC system versus 50.57 tons CO₂-eq for PV over 25-year lifetime), thus providing superiority in terms of environmental sustainability. In this context, the study presents an engineering-based approach for the technical, economic, and environmental assessment of small-scale, non-storage solar energy systems in line with the United Nations Sustainable Development Goals (SDG 7: Affordable and Clean Energy and SDG 13: Climate Action) and contributes to the existing literature. Full article

Predicting core loss under high-frequency non-sinusoidal excitation is crucial for power electronics equipment design. Temperature significantly affects core loss, and traditional core loss prediction models typically incorporate temperature corrections to enable accurate loss estimation across varying temperatures. Based on the Modified Steinmetz Equation (nonT-MSE) model, this study considers the temperature effect by employing a combination of the Tanh function and a linear term to modify the three empirical parameters, with the Tanh function capturing the nonlinear saturation of the loss coefficient k with increasing temperature. This leads to the establishment of the temperature-corrected non-TMSE (T-MSE) model for predicting magnetic core loss under high-frequency non-sinusoidal excitation. During model derivation, training data undergo logarithmic transformation processing. Subsequently, with T-MSE empirical parameters as variables and the minimum mean squared error between T-MSE predicted values and experimental values as the objective function, a single-objective optimization model is established. Finally, the empirical parameters of T-MSE are calculated using the training data and the single-objective optimization model. Comparing the core loss experimental results of the four materials, the average MSE values for the T-MSE model, the nonT-MSE model, and the square-root temperature-corrected non-TMSE model proposed by Zeng et al. (Zeng) are 0.0082, 0.0459, and 0.0110, respectively; with average MAPE of 1.57%, 1.87%, and 2.17%, respectively; and average R² of 0.9862, 0.9807, and 0.9731. Compared to the nonT-MSE model and the Zeng model, the T-MSE model demonstrated higher prediction accuracy. Full article

This paper presents a simulation-based artificial neural network (ANN) model to predict bit-rock interaction forces during drilling. Drill string vibration poses a significant challenge in the oil, gas, and geothermal industries, leading to non-productive time and substantial financial losses. This research addresses the challenge of modelling bit-rock interaction excitation forces, which is crucial for predicting vibration and component fatigue life. For a PDC bit with multiple cutters, the cutter tangential velocities at various drilling speeds are calculated, and individual cutter forces are predicted with a two-dimensional discrete element method simulation in which a single cutter moves in a straight line through rock modelled as bonded particles. This data is then used to train an ANN model that characterizes the bit-rock force time series in terms of frequency, amplitude, and distribution of force peaks. Once inserted into a dynamic simulation of the drill string, the algorithm reconstructs the expected bit-rock force time series. A case study using a rigid segment axial and torsional drill string model was used to show that the bit-rock model outputs lead to the expected bit-bounce and stick-slip under certain drilling conditions. Next, the model was implemented in a 3D deviated well drill string simulation with non-linear friction and contact, generating complex stress states with good computational efficiency. Full article