Search Results (1,286)

The Newton Downhill Optimizer (NDO) combines a derivative-free downhill relation with population differences. However, its Hybrid-Guided Operator uses a random reference and persistent best-solution guidance, which can cause directional fluctuations and premature population contraction. This study proposes the Adaptive Fitness–Distance-Guided Newton Downhill Optimizer (AFDNDO). Fitness–Distance Balance selection identifies guiding individuals that account for both solution quality and spatial diversity. Stage protection, elite protection, and historical success-rate feedback regulate activation of the improved branches. A tripodal heavy-tailed update and a wave-weighted masked differential update reconstruct the two original branches. A non-uniform mutation is also triggered for low-quality individuals when the global best value stagnates. Across 30 independent runs on CEC2017, CEC2020, and CEC2022, AFDNDO attained the lowest mean rank in all six formal benchmark configurations. Its mean ranks on the 10-, 30-, and 50-dimensional CEC2017 tests were 1.172, 1.241, and 1.276, respectively. Dynamic path-planning environments included rigid obstacles, three levels of soft-risk regions, and moving obstacles. In the single-target environments, AFDNDO–DWA achieved a 100% execution success rate without collisions. In the multi-target environments, it reduced the mean objective value by 4.46–6.64% relative to NDO. It also increased the success rate from 63.33% to 70.00% in the most constrained environment. These findings indicate that AFDNDO improves cross-landscape optimization performance while retaining the basic NDO framework. They also support its use as a global planner within the tested dynamic multi-task environments. Full article

Storm-surge residuals represent one of the most destructive marine-coastal hazards, and reliable short-term surge residual prediction is critical for coastal disaster preparedness. Conventional empirical forecasting approaches suffer from limited cross-regional generalization, while high-fidelity physics-based hydrodynamic models such as ADCIRC-SWAN can reproduce complete storm-surge physical processes but demand substantial computational resources. In this study, a three-layer back-propagation neural network (BPNN) for storm-surge residual forecasting is constructed, which is driven by output datasets from the validated ADCIRC-SWAN coupled hydrodynamic model. Wind speed, significant wave height, sea-surface atmospheric pressure, and the simulated current-time storm-surge residual are selected as input predictors. Simulation-derived samples are pre-processed via data cleaning and Min-Max normalization, and two different dataset partitioning strategies (random mesh-point-based partition and time-sequential partition) are implemented for comparative experiments. After hyperparameter sensitivity tests, the optimal network configuration with 30 hidden-layer neurons is determined. Model predictive performance is quantitatively evaluated via multi-station time-series comparison and universal statistical metrics including R, NSE, and RMSE. The results show that the BPNN achieves satisfactory performance under random mesh-point-oriented partitioning, yet obvious performance degradation occurs under time-sequential temporal extrapolation, with prominent underestimation of surge peaks. On the basis of BPNN-predicted spatial surge residual fields, storm-surge intensity grading is carried out following the Chinese national standard GB/T 39418-2020. Statistical comparisons between the full computational domain and the Pearl River Estuary sub-region reveal strong spatial aggregation of high-intensity storm-surge grids within the estuary driven by funnel-shaped topographic amplification. This work demonstrates the feasibility of using a BPNN as a surrogate emulator for hydrodynamic outputs under a given typhoon condition; however, limitations in temporal extrapolation performance still need to be addressed before this approach can be practically used in operational early-warning applications. Full article

This paper proposes a novel outpainting technique for quadri-wave lateral shearing interferograms which employs Quadri-Wave Generative Adversarial Networks (QW-GANs). QW-GAN is adversarially trained to capture the high-order statistics of real interferograms. It thereby avoids the Gibbs-ringing artifacts and spectral leakage caused by deterministic interpolation or single-frame extrapolation. The recovered fringes remain physically consistent with preserved Fourier support, enabling more accurate wavefront phase retrieval. Numerical simulations and comparisons with aberrations (defocus, astigmatism, coma, spherical, and random), along with real experimental results, demonstrate the enhanced reconstruction precision of our method, providing an efficient solution for wavefront reconstruction in optical applications. Full article

Depressive symptoms, alcohol use, and body mass index (BMI) frequently co-occur, yet their longitudinal associations from adolescence into adulthood remain unclear. This study examined their prospective associations by distinguishing between-person differences from within-person changes and testing variations by gender and race. Data came from four waves of the National Longitudinal Study of Adolescent to Adult Health (Add Health). The analytic sample comprised 5734 participants (51.4% female; 56.7% non-Hispanic white) followed from ages 11–19 to ages 24–32. Multigroup cross-lagged panel models (CLPMs) and random intercept cross-lagged panel models (RI-CLPMs) assessed associations across four gender–racial groups: white males, white females, non-white males, and non-white females. All three variables demonstrated significant stability over time. Higher adolescent depressive-symptom levels were associated with higher subsequent alcohol-use scores at both the between-person and within-person levels. Higher BMI in young adulthood was associated with higher subsequent depressive-symptom levels at the between-person level. At the within-person level, higher-than-usual depressive-symptom levels were associated with lower subsequent BMI only among white females during adolescence. Within-person associations between alcohol use and BMI also varied developmentally: higher-than-usual alcohol-use scores were associated with higher subsequent BMI earlier in development, whereas negative associations emerged in both directions in adulthood. Several associations differed across gender–racial groups. Overall, the findings demonstrate complex, developmentally specific associations among depressive symptoms, alcohol-use frequency, and BMI, underscoring the importance of distinguishing between-person from within-person processes and considering gender, race, and developmental stage. Full article

Distributed acoustic sensing (DAS) converts fiber cables into dense strain-rate sensor arrays, capturing direct, reflected and guided seismic phases with ultra-fine spatial sampling. However, DAS interrogators suffer far stronger random noise than traditional geophones, limiting its microseismic imaging capacity. This work adopts curvelet-transform denoising to suppress noise. Curvelets partition the frequency–wavenumber plane into multiscale directional sectors; coherent wave energy concentrates in limited angular wedges, while stochastic noise disperses evenly across all transform coefficients, enabling noise-signal separation via wedge-wise thresholding. We test four default threshold schemes on synthetic downhole DAS microseismic data. Parameter tuning proves all methods deliver comparable performance, so we compare their out-of-box reliability for shale reservoir monitoring. Three noise-statistic-based strategies perform stably: median-absolute-deviation (MAD), quiet-window and empirical-cumulative-distribution-Function (ECDF percentile) thresholding. By contrast, the default knee-point algorithm from mainstream DAS toolboxes fails, as its preset threshold falls within noise components and barely removes interference. We propose MAD as a robust default for the tested downhole DAS microseismic setting for it estimates thresholds directly from noisy traces without blank reference windows and offers superior operational stability. Applied to field DAS records from a southwest China shale-gas horizontal monitor well, the MAD curvelet workflow greatly enhances microseismic arrivals with negligible spurious events. Benchmarks against standard 2D Daubechies-4 wavelet and adaptive Goldstein FK filtering verify curvelet denoising as a physically interpretable, efficient tool for DAS wavefields. Full article

Insufficient physical activity (PA) and problematic smartphone use (PSU) frequently co-occur during adolescence, but whether their prospective association reflects stable differences between adolescents or within-person fluctuations remains unclear. This three-wave longitudinal study examined reciprocal prospective associations between PA and PSU in 1015 Chinese adolescents aged 12–18 years across 12 months, with assessments separated by approximately six months. PA was assessed using the Physical Activity Rating Scale-3, and PSU was assessed using the Smartphone Addiction Scale–Short Version. A random-intercept cross-lagged panel model (RI-CLPM) was specified as the primary analysis, with a conventional cross-lagged panel model (CLPM) estimated secondarily for comparison with previous longitudinal research. In the RI-CLPM, the stable between-person components of PA and PSU were negatively associated (β = −0.681, p = 0.004). At the within-person level, higher-than-usual PA was prospectively associated with lower-than-usual PSU at the subsequent wave (β = −0.117 and −0.108), whereas higher-than-usual PSU was prospectively associated with lower-than-usual subsequent PA (β = −0.257 and −0.189); all four cross-lagged paths were statistically significant. The secondary CLPM showed reciprocal negative cross-lagged associations in the same directions. By separating stable between-person differences from within-person deviations, this study extends longitudinal evidence on the PA–PSU relationship and indicates that the two behaviors are prospectively coupled within adolescents over time. These findings provide a basis for future research and intervention trials that consider PA and problematic smartphone behavior jointly, while causal mechanisms remain to be established. Full article

Objectives: This study examined the within-person longitudinal associations between obesity-related eating behaviors and physical activity among community-dwelling Chinese older adults. Methods: Three waves of data were collected at three-month intervals from 1256 adults aged 65 years or older across 17 urban and rural communities. Physical activity was measured using the Physical Activity Rating Scale-3, and obesity-related eating behaviors were assessed using the seven-item Sakata Eating Behavior Scale Short Form. The eating-behavior scale was reverse-scored so that higher scores represented fewer problematic behaviors. The analysis examined longitudinal measurement invariance, gender differences, and prospective within-person associations using a random-intercept cross-lagged panel model. Results: The analysis identified small reciprocal within-person associations between obesity-related eating behaviors and physical activity. Occasions characterized by fewer problematic eating behaviors than usual were followed by higher-than-usual physical activity at the next wave. Higher-than-usual physical activity was likewise followed by fewer problematic eating behaviors. These associations remained similar after adjustment for the available demographic covariates. Men reported higher average physical activity, while women reported fewer problematic eating behaviors, but changes over time did not differ by gender. Conclusions: Physical activity and obesity-related eating behaviors showed small bidirectional within-person associations across three-month intervals. This reciprocal pattern suggests that the two behaviors are modestly connected within everyday routines and supports examining them together when studying health behavior dynamics in later life. Full article

Mathematical modeling has played a crucial role in epidemiological research, its importance amplified during the COVID-19 pandemic. This comprehensive review provides a five-year analysis of mathematical models developed to understand and control COVID-19 transmission. The reviewed studies include deterministic models such as SIR and SEIR frameworks, stochastic models incorporating random effects, age-structured models accounting for demographic heterogeneity, within-host models describing viral dynamics and immune responses, and hybrid, multi-strain, agent-based, and network-based approaches. A literature search was conducted across major databases, and studies were categorized by model structure, analytical methods, and data integration strategies. The review examines the predictive capabilities of deterministic models, the role of stochasticity in capturing uncertainty, and the importance of demographic structure in explaining transmission and mortality patterns. The effectiveness of non-pharmaceutical interventions and vaccination strategies is also assessed. The integration of real-time epidemiological data into modeling efforts is discussed, highlighting insights into hospitalization demand, ICU utilization, and mortality trends across pandemic waves. By analyzing the strengths and limitations of each approach, this review aims to inform future pandemic management, concluding with emerging trends, including the integration of genomic data, and future directions for the field. Full article

Background: Upper limb spasticity after stroke occurs due to neural hyperexcitability and secondary alterations in muscle properties. Repetitive peripheral magnetic stimulation (rPMS) is a non-invasive technique used to reduce spasticity. This study aimed to compare the effects of rPMS versus sham stimulation on upper limb spasticity and muscle stiffness using shear wave elastography (SWE). Methods: This prospective, double-blind randomized controlled trial included 32 stroke patients with upper limb spasticity (Modified Ashworth Scale (MAS) ≥ 2 in the elbow flexors), who received rPMS or sham stimulation in addition to conventional rehabilitation (n = 16 per group). Spasticity was assessed using MAS, and muscle stiffness using SWE. Results: The rPMS group showed a transient short-term reduction in upper limb spasticity compared with the control group at the end of treatment (p = 0.045); however, this effect was not sustained at the 2-week follow-up. No significant between-group differences were observed in muscle stiffness, upper limb motor function, or activities of daily living. Conclusions: This study provides preliminary evidence that rPMS may produce a transient, short-term reduction in upper limb spasticity after stroke. However, this effect was not sustained at follow-up or accompanied by significant improvements in muscle stiffness or functional outcomes. Full article

Background/Objectives: Concern that COVID-19 vaccination causes pulmonary nodules persists because vaccination coincided with expanded computed tomography (CT), low-dose CT (LDCT) screening, post-COVID imaging and artificial intelligence (AI)-assisted detection. This review asks whether the current literature supports causality and how vaccination history should inform thoracic imaging. Methods: A focused search of PubMed, PubMed Central and the Cochrane Library was performed through 15 July 2026. Evidence was classified as direct or contextual; no PRISMA screening, risk-of-bias scoring or quantitative synthesis was performed. Results: Direct evidence remains sparse. One case report was too confounded for inference. A two-sample Mendelian randomization study found no broad lung disease risk signal, but nodules were not modeled and the heterogeneous endpoints were exploratory. An ecological study of 1,616,750 samples linked rising detection to SARS-CoV-2 infection waves and AI-assisted reading; lacking individual vaccination data, it cannot establish whether vaccination affected detection. Screening interruption produced the opposite pattern: Lung-RADS 4 nodules rose from 8% to 29%. The most reproducible post-vaccination thoracic finding is regional lymph-node activation on [18F]fluorodeoxyglucose positron emission tomography/computed tomography ([18F]FDG-PET/CT), an expected immune response rather than a parenchymal nodule. Conclusions: Current evidence is insufficient to establish vaccination as an independent, population-level cause of pulmonary nodules. Vaccination history should guide [18F]FDG-PET/CT interpretation; CT-detected parenchymal nodules warrant standard risk stratification. Full article

Traditional particle swarm optimization (PSO) easily falls into premature convergence, while differential evolution (DE) is highly sensitive to fixed control parameters. Existing PSO-DE hybrid frameworks suffer from static population sizes and insufficient cross-population information exchange. This paper proposes PSO-DE-ADP, a hybrid optimizer with sinusoidal adaptive parameters, elite-guided mutation, ring neighborhood-weighted PSO and fitness-driven dynamic dual-population allocation. Four complementary mechanisms are integrated: (i) sine-wave perturbation superimposed on linear decay adaptively adjusts PSO inertia weight, acceleration factors and DE scaling/crossover coefficients to balance search stages; (ii) global elite individuals are embedded into DE mutation to reduce blind random search; (iii) ring topology with weighted learning realizes bidirectional information interaction between PSO and DE subpopulations; (iv) the proportion of PSO/DE individuals is dynamically adjusted according to elite ratio to allocate computing resources. Experiments adopt the CEC2017 30-dimensional benchmark with 30 test functions covering unimodal, multimodal, hybrid and composite landscapes. Compared with 8 state-of-the-art metaheuristics, PSO-DE-ADP achieves the lowest Friedman rank (1.08 vs. 2.23–4.90 for PSO variants; 1.53 vs. 2.07–5.00 for non-PSO algorithms). Ablation tests prove each component significantly boosts accuracy; The algorithm only costs 0.172 s average runtime, superior to all competitors. Statistical Wilcoxon and Friedman tests verify its significant superiority. Future work extends this method to multi-objective, constrained and real engineering optimization tasks. Full article

Background/Objectives: Nonsurgical treatment for pancreatolithiasis is widely performed. However, treatment success remains difficult to predict before treatment initiation, complicating the selection of an appropriate treatment strategy. This study aimed to predict pancreatic stone clearance after nonsurgical treatment for pancreatolithiasis associated with chronic pancreatitis by integrating pretreatment computed tomography (CT) images and clinical information using deep learning and machine learning models. Methods: Of 195 patients with pancreatolithiasis associated with chronic pancreatitis who underwent nonsurgical treatment, including extracorporeal shock wave lithotripsy, at our institution between 1992 and 2024, only 91 (47%) had extractable pretreatment noncontrast abdominal CT images and were included in the AI analysis. Multiple deep learning models (VGG16/19, InceptionV3, ResNet50, DenseNet121/169/201, Vision Transformer, and Swin Transformer) were trained using CT images, and their predictive performance was compared. Imaging-derived and clinical predictors selected using only the training data in each patient-level cross-validation fold were combined and used as inputs for conventional machine learning models, including random forest, support vector machine, naïve Bayes, neural network, and gradient boosting. Results: Successful pancreatic stone clearance was achieved in 54 of 91 patients (59%). Compared with the 104 patients without extractable CT data, the analyzed cohort had a higher proportion of asymptomatic pancreatolithiasis (43% vs. 15%) and a markedly lower pancreatic stone clearance rate (59% vs. 88%), indicating potential selection bias. Asymptomatic pancreatolithiasis and a pancreatic stone size of ≥15 mm, defined using a data-derived exploratory cutoff, were significantly associated with unsuccessful stone clearance. Among the deep learning models, ResNet50 achieved the highest performance (area under the receiver operating characteristic curve [AUC], 0.718), followed by Vision Transformer (Large model, 16 × 16 patches) (AUC, 0.700). When image-derived features were combined with clinical features, the neural network achieved the best performance, with a mean AUC of 0.757, a median sensitivity of 0.568, a median specificity of 0.704, and a median accuracy of 0.659. Conclusions: A neural network integrating CT-derived image features with clinical information showed moderate internal predictive performance for pancreatic stone clearance. Because this was a single-center retrospective study without external validation, the present model should be regarded as a preliminary predictive model requiring validation in independent cohorts before clinical application. Full article

Reverse osmosis (RO) desalination is a major technology for freshwater production in arid regions, but its energy demand becomes more challenging when the system is supplied by variable renewable energy. This study presents an offline digital-twin-assisted decision-support framework for dynamic RO under Kuwait solar-availability conditions. Within this framework, the predictive models are driven primarily by the dynamic RO process variables, while NASA Prediction Of Worldwide Energy Resources (POWER) data provide the Kuwait solar-availability context, and the PV power margin serves as a scenario-level energy indicator. The purpose is to predict instantaneous permeate flow rate, estimate specific energy consumption, and identify energy-efficient operating conditions using machine learning. Kuwait City was used as the solar case-study location. Hourly solar and meteorological data were obtained from NASA POWER, while dynamic RO membrane data were obtained from the open experimental wave desalination dataset published by the National Renewable Energy Laboratory (NREL) through Data.gov and the Marine and Hydrokinetic Data Repository. The RO dataset includes steady-state, ramp, sinusoidal, and Wave Energy Converter SIMulator (WEC-Sim) pressure/flow experiments. The process-flow image used in the system description was also taken from the same NREL dataset and is cited in the figure caption. The raw RO files were cleaned, harmonized, and transformed into a process-informed modeling dataset. Derived features included pressure rate, recovery ratio, salt rejection, estimated pump power, specific energy consumption (SEC), PV power margin, and rolling pressure/flow features. Three supervised regression models were tested: Gradient Boosting, Random Forest, and XGBoost. A representative subset of 60,000 records was used to preserve the main experimental conditions while reducing redundancy in the densely sampled sequential data. Results show that permeate flow rate can be predicted with high accuracy using Gradient Boosting (R² = 0.981; RMSE = 0.161 L/min). The moderate energy prediction performance yielded an R² of 0.654 and RMSE of 7.570 kWh/m³ for Random Forest. The accuracy of permeate conductivity predictions was lower (R² = 0.257; RMSE = 245.44 µS/cm) because membrane and feed characterizing parameters should be included for an adequate water quality control. The proposed approach is best suited as an offline decision-support framework for dynamic RO process analysis. Full article

Rock cohesion ($c$) and angle of internal friction ($\phi$) are core parameters for rock mass stability analysis and engineering design; however, traditional triaxial tests are costly and time-consuming, limiting their availability in preliminary engineering assessments. To address this limitation, this study develops a machine learning framework that predicts these parameters from easily measurable physical properties, enabling rapid and cost-effective estimation without the need for complex laboratory testing. Based on a total of 199 sets of measured data from four rock types (shale, limestone, quartzite, and quartz-mica schist) in the Himalayan region, this study uses P-wave velocity ($V_p$), density ($\rho$), uniaxial compressive strength (UCS), and tensile strength (TS) as input variables. It employs four models: Support Vector Regression (SVR), Random Forest (RF), Multi-Layer Perceptron (MLP), and extreme gradient boosting (XGBoost) to predict $c$ and $\phi$. Hyperparameters were tuned using grid search and Bayesian optimization. We compared unified modeling with rock-type-specific modeling, performed interpretability analysis using SHapley Additive exPlanations (SHAP), and tested robustness by introducing Gaussian noise. The results show that XGBoost produced the best predictions at $c$ (test set $R^2$ = 0.9901, RMSE = 0.512 MPa), while the Bayesian-optimized SVR model yielded the best results at $\phi$ ($R^2$ = 0.9776, RMSE = 0.744°). Rock-type-specific modeling improved the $R^2$ for limestone at $\phi$ by 0.3541; the SHAP contribution for UCS and TS exceeded 70%; Random Forest demonstrated the best noise resistance, with a decrease in $R^2$ of less than 0.04 under 10% noise. In summary, the strategy proposed in this paper allows for the selection of prediction schemes based on data quality and lithological differences, providing a feasible approach for rapidly obtaining rock strength parameters. Full article

With the continued advancement of low-carbon campus transformation and the increasing penetration of renewable energy, campus integrated energy systems have become key infrastructure for green campus development. However, the highly random nature of student behavior causes dynamic fluctuations in electricity, heating, and cooling loads, creating major challenges for real-time supply-demand balance and economic system scheduling. To address this problem, this paper takes student behavior uncertainty as the core disturbance factor and proposes a flexible architecture-driven autonomous adaptation and multi-energy complementary optimization strategy. A closed-loop operation paradigm of signal–response–complementarity–regulation is established, in which dynamic electricity price signals, comfort-oriented guidance, and campus functional energy-zone division are combined to form a multi-level autonomous response chain. To improve solution efficiency, the electromagnetic wave propagation algorithm is further enhanced, and a Multi-Objective Electromagnetic Wave Propagation Algorithm (MEMWPA) is developed. Wave-impedance matching and energy-flux-density feedback mechanisms are introduced to strengthen convergence performance in complex multi-objective optimization problems. Comparative case studies show that the proposed strategy can effectively smooth the net load curve, reduce the campus peak load by 26.73%, and increase the load factor by 14.533 percentage points, thereby improving both operational flexibility and energy efficiency. Full article