Search Results (2,318)

Closed droplet contours provide the geometric basis for area estimation in vision-based droplet observation. In OLED inkjet printing, droplets are deposited into pixel wells with predefined geometry; projected area is therefore a primary geometric quantity for assessing whether the deposited liquid sufficiently fills the well or risks overflow. This work formulates closed-contour sampling under a fixed sampling budget as an area-driven sampling problem. A leading-order analysis of the local arc–chord area error shows that the dominant cubic term depends jointly on curvature and segment length. Minimization of the resulting leading-order area-error functional yields an asymptotically optimal area-driven sampling density proportional to the cube root of curvature, together with a sampling-budget estimate under a target area-error tolerance. The derived sampling density is implemented on the fitted closed contour through cumulative-weight inversion. Experiments on random closed curves and the droplet dataset provide a systematic quantitative comparison with representative methods under identical fixed-budget settings, complemented by statistical analysis and evaluations of geometric fidelity, sensitivity, and computational efficiency. The proposed method achieves lower area estimation error under the tested sampling budgets, with the improvement being most pronounced at lower sampling budgets, while the reported geometric-fidelity metrics show no disproportionate degradation of contour fidelity. These results demonstrate the effectiveness of area-driven sampling for closed-contour area estimation under limited sampling budgets. Full article

Stabilisation of autonomous vehicle platoons becomes challenging in the presence of uncertain delays in actuator dynamics and vehicle-to-vehicle communication. This paper presents a distributed fractional-order proportional derivative (FOPD) control protocol for a platoon operating under various communication topologies, with bounded uncertainties in actuator lag and communication delay, respectively. It builds on an auxiliary function-based robust stability approach that provides a geometric interpretation of the delay-uncertain characteristic family and eliminates the need for exhaustive parameter gridding. This is followed by a numerical design procedure that evaluates the derived robust stability conditions on explicitly stated frequency grids. Numerical analysis of heterogeneous platoons indicates that the FOPD strategy produces bounded stable responses for the sampled uncertainty realisations and exhibits improved tracking behaviour compared with the integer-order PD baseline using the same numerical values of $K_P$ and $K_D$. It also provides better damping of oscillations and reduced tracking errors under uncertain operating conditions. Full article

To address the difficulty in accurately evaluating the vertical bearing behavior of prestressed high-strength concrete (PHC) pipe pile-group foundations, this study investigated three single piles and an eight-pile group with a Wang-shaped irregular pile cap through field static loading tests and theoretical analysis. The measured ultimate bearing capacities of single piles D1/D2 and D3 were 7040 and 3000 kN, respectively. For the pile-group foundation, the ultimate bearing state was not reached under the maximum applied load of 15,000 kN, at which the settlement was only 3.52 mm. The pile-head load distribution followed the order corner piles > side piles > inner piles, with corresponding load proportions of approximately 13.9%, 12.9%, and 9.4%. The calibrated API and hyperbolic models predicted the single-pile bearing capacities with errors ranging from 0.23% to 3.40%. The API model better represented the steep-drop portion of the Q-s curve, whereas the hyperbolic model more accurately predicted the initial stiffness and low-load response. For pile-group foundations, the combined equivalent-pier and load-transfer method showed good applicability. The main contribution of this study is to provide field evidence for the vertical bearing and load-transfer behavior of a large-diameter PHC pipe pile group with a Wang-shaped irregular pile cap, extending existing studies that have mainly focused on single piles or conventional symmetric pile groups. The results also provide a quantitative basis for the analysis and design of PHC pipe pile-group foundations in highway bridge engineering. Full article

Durability deterioration and interlayer bonding failure of ultra-thin overlays remain critical challenges under coupled environmental and mechanical actions. Although environmental damage to asphalt mixtures has been widely investigated, the relationship between pore-structure evolution and interlayer fatigue deterioration in polymer-composite-modified ultra-thin overlays incorporating styrene–butadiene–styrene (SBS) and micro carbon fiber (MCF) remains insufficiently understood. This study therefore extends existing research by clarifying this relationship under freeze–thaw cycling and water immersion. Three-point bending and direct shear fatigue tests were conducted to evaluate bending and interlayer shear fatigue performance, respectively, while nanoindentation and X-ray computed tomography (CT) were used to characterize micromechanical properties and three-dimensional pore-structure evolution. The results showed that five freeze–thaw cycles reduced the bending fatigue life by 75.3% and the interlayer shear fatigue life by 49.6%, while six days of water immersion reduced the interlayer shear fatigue life by 55.1%. Freeze–thaw cycling promoted open-pore and pore-throat development and increased total porosity by 23.9%, contributing to aggregate displacement and redistribution of the internal skeleton. In contrast, immersion increased the proportion of small and closed pores, while isolated pores concentrated near the interlayer weakened interlayer shear resistance. Although immersion caused greater reductions in hardness and modulus, freeze–thaw-induced pore development was associated with greater deterioration in bending fatigue performance. Furthermore, an adaptive-network-based fuzzy inference system (ANFIS) was developed to predict pore tortuosity from equivalent diameter, shape factor, and porosity, with testing errors ranging from 0.102 to 0.129 for untreated, freeze–thaw, and immersed specimens. An exponential relationship was further identified between tortuosity and the pore comprehensive effect index (PCEI), providing a quantitative approach for characterizing pore connectivity and evaluating environmental deterioration in polymer-composite-modified asphalt concrete. Full article

This paper presents an artificial intelligence (AI)-based energy management and control framework for renewable-powered smart microgrids operating under dynamic conditions. The proposed system integrates photovoltaic (PV) generation, battery energy storage, and grid interaction within a MATLAB/Simulink-R2024B environment to improve operational reliability, energy efficiency, and renewable energy utilization. Seven control scenarios were investigated, including baseline operation, Rule-Based Energy Management System (EMS), Proportional–Integral–Derivative (PID), Model Predictive Control (MPC), Fuzzy Logic Control (FLC), Artificial Neural Network (ANN)-assisted forecasting, and Reinforcement Learning (RL)-based optimization. Comparative results demonstrate progressive performance improvements with increasing controller intelligence. The RL-based EMS achieved the highest operational cost reduction (95%), voltage regulation performance (95%), battery state-of-charge management (95%), renewable energy utilization (92%), grid dependency reduction (92%), overall system efficiency (95%), and an overall performance score of 95.4%. The ANN forecasting model attained a forecasting accuracy of 96.2%, corresponding to a Mean Absolute Percentage Error (MAPE) of 3.8%, while achieving a Mean Absolute Error (MAE) of 1.84 kW, Root Mean Square Error (RMSE) of 2.37 kW, and coefficient of determination ($R^2$) of 0.982. For voltage regulation, the RL controller reduced the RMSE, settling time, and overshoot to 1.50 V, 1.8 s, and 0.5%, respectively, compared with 20.0 V, 15.0 s, and 10.0% for the baseline case. Ultimately, the results demonstrate that AI-driven control strategies substantially enhance microgrid stability, battery utilization, renewable energy penetration, and operational efficiency, providing a practical and scalable solution for next-generation intelligent microgrids. Full article

Objectives: To derive sex-specific Relative Body Mass Index (rBMI) cut-offs for classifying adolescents into four adiposity categories defined by dual-energy X-ray absorptiometry (DXA)-based body fat percentage (BF%), quantify their stability, and evaluate their classification performance against current BMI%-based classification and previously published ROC-derived rBMI cut-offs. Materials and Methods: Data from 567 observations in adolescents aged 11–19 years were analyzed. Adiposity categories (normal, mildly elevated, moderately elevated, severely elevated) were defined by sex-specific BF% thresholds. Classification and Regression Tree models with inverse class-frequency weighting were fitted in two formulations: sex-stratified, and a pooled-threshold model with shared lower boundaries and sex-specific upper boundaries. Threshold stability was assessed by bootstrap resampling. Performance was estimated by repeated nested cross-validation, with the full derivation pipeline repeated within each training fold; the previously published ROC-derived thresholds were re-derived within each fold so that both approaches carried the same correction for optimism. Results: Sex-stratified thresholds were 103, 118, and 162 in males and 100, 118, and 176 in females; three of six agreed with the previously published ROC-derived values to within 0.5 units and a fourth to within 3.2 units. Five of the six published values fell within the corresponding bootstrap intervals, the exception being the female moderately/severely elevated boundary (160.0; 95% CI: 161.4–218.7). The male boundary separating normal from mildly elevated adiposity was identified in 76.0% of bootstrap resamples; estimating the lower boundaries from the pooled sample raised this to 99.9% and improved ordinal agreement among males (quadratic-weighted kappa 0.812 vs. 0.759). Out-of-fold accuracy was 0.697, 0.694, and 0.694 for the sex-stratified, pooled-threshold, and ROC-derived approaches, respectively, with all paired differences including zero in the overall sample. All three exceeded BMI% classification (accuracy 0.608), which identified only 15.5% of adolescents with mildly elevated adiposity and produced more than twice the proportion of errors spanning two or more categories (6.0% vs. 1.8–2.1%). Conclusions: rBMI cut-offs aligned more closely with DXA-defined adiposity than BMI% classification, particularly in the intermediate categories. Tree-derived and ROC-derived thresholds converged and performed equivalently, indicating that threshold location does not depend on the derivation procedure. A pooled-threshold formulation provided a simpler and more stable rule. These are derivation-stage estimates requiring external validation before clinical adoption. Full article

Permanent Magnet Synchronous Motors (PMSMs) are widely employed in electric vehicle (EV) propulsion systems because of their high efficiency, high power density, and superior dynamic performance. The performance of field-oriented control (FOC)-based PMSM drives strongly depends on accurate tuning of the proportional–integral (PI) controllers governing the speed and current loops. Conventional tuning approaches often optimize a single performance index and therefore fail to simultaneously enhance the dynamic behavior of all control loops. This paper proposes a multi-error Integral of Time-weighted Absolute Error (ITAE)-based optimization framework for simultaneous tuning of the PI controllers by minimizing a composite objective function that incorporates the time-weighted absolute errors of the rotor speed, q-axis current, and d-axis current. To validate the effectiveness and optimizer independence of the proposed framework, five metaheuristic optimization algorithms—Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Gray Wolf Optimizer (GWO), Gazelle Optimization Algorithm (GOA), and White Shark Optimization (WSO)—are evaluated under identical optimization settings. MATLAB/Simulink simulations are performed for reference-speed tracking, load disturbance rejection, and variable-speed operation. The results demonstrate that the proposed optimization framework consistently improves tracking accuracy and dynamic response regardless of the selected optimizer, while WSO provides the best overall performance. In the variable-speed tracking scenario, WSO achieved the lowest RMSE of 0.96 rad/s and the minimum ITAE value of 0.1716, confirming its effectiveness as the most suitable optimizer for the proposed framework in high-performance PMSM drive applications. Full article

We set out to fix a “contamination” problem in reconstruction-based Temporal Convolutional Network VAEs (TCN-VAEs) for cross-dataset IoT flow anomaly detection: when attack flows share an encoder window with benign flows, the shared latent code is allegedly distorted, inflating benign reconstruction error and producing false positive rates (FPRs) of 22–65% despite an ROC-AUC above 0.93. Our proposed fix, TCN-Pred, excludes the target flow from the encoder and scores it by next-flow prediction error, reducing FPR to 0.65–13%. We subjected this causal explanation to a battery of controlled ablations, holding architecture, decoder, loss, and thresholding fixed while varying one factor at a time. Each one falsified the original hypothesis: target inclusion/masking changes FPR by at most 0.001; context shuffling/reversing/zeroing changes it by at most 0.003; a context-blind constant-output predictor matches TCN-Pred’s FPR and F1 to three decimal places on all three datasets. The actual cause, confirmed on the original trained models with no retraining, is a scoring-granularity mismatch: the TCN-VAE threshold is calibrated from per-window errors averaged over 20 flows but applied to per-flow errors at evaluation (standard deviation $\approx \sqrt{20}\times$ higher, measured ratio 4.46 against a predicted 4.47). Recalibrating the identical model at matching granularity drops FPR from 22.7/47.6/64.6% to 0.65/5.0/12.5% on BoT-IoT, IoT-23 and ToN-IoT, closing 89–97% of the reported FPR gap without changing a single model weight. We report this diagnostic chain, together with an attack-prevalence sensitivity analysis, sample-disjoint calibration, normality diagnostics, and label-free and redundancy-aware (mRMR) feature-selection benchmarks, as a methodology other work should apply before attributing fixed-threshold performance to architecture. The pipeline is supervised source-domain feature selection followed by benign-only detector training, not fully unsupervised, a distinction we quantify later in the paper. Investigating dataset representativeness, we found that all three provided files reduce to only ≈6000 genuinely distinct flows via an undocumented row-duplication procedure, causing 97.8% BoT-IoT train/test near-duplicate overlap; a leakage-free re-evaluation changes FPR by only 0.23 percentage points. We also found that the TLS-metadata columns are already transformed upstream of every available artefact, so the proportion of genuinely TLS-encrypted flows cannot be recovered, and we soften the paper’s encrypted-traffic framing accordingly. Full article

Satellite precipitation products like GPM IMERG are crucial for hydrological modeling and disaster prevention; yet, their reliability in complex mountainous monsoon regions remains challenging. While the latest IMERG V07 introduces key upgrades, including a Climatological Calibration Algorithm (CCA), its cross-scale error propagation mechanisms and performance heterogeneity in complex underlying surfaces are poorly understood. This study evaluates the daily and monthly performance of IMERG V07 and V06 (Early, Late, and Final Runs) from 2014 to 2020 against 91 rain gauges in Guangxi, China—a typical mountainous monsoon region. The evaluation employs multiple statistical metrics and a multi-dimensional stratification approach based on elevation, precipitation intensity, and seasonality to quantify error propagation and climate-topography coupling effects. The results reveal that V07, particularly the Late Run, enhances daily precipitation detection capabilities, it significantly increases the proportion of systematic positive bias from 62.3 to 64.8% (V06) to 67.2–68.9% (V07). Consequently, upon temporal aggregation to the monthly scale, this systematic overestimation is severely amplified, leading to degraded performance, with the Final Run suffering the most substantial accuracy loss. Furthermore, retrieval accuracy is heavily constrained by surface heterogeneity, with systematic overestimation surging in areas where relatively dry (mean annual precipitation < 1300 mm) and complex terrain (elevation 100–500 m) coincide. The introduced CCA effectively improved dry season estimations but failed during wet season by introducing substantial positive biases. Ultimately, while V07 better captures short-term precipitation dynamics, its structural systematic biases compromise long-term cumulative reliability, highlighting the necessity for physics-based bias correction in hydrological applications and dynamic calibration in future algorithm upgrades. Full article

Permanent magnet synchronous motors (PMSMs) operating at low speeds are susceptible to parameter mismatches, periodic harmonics, and various internal and external disturbances, which result in steady-state current errors and low-frequency speed oscillations. To address these issues and improve low-speed PMSM performance, an automatic tuning disturbance rejection incremental deadbeat predictive current control (AT-DR-IDPCC) method is proposed. First, an incremental extended-state observer (IESO) is incorporated into the incremental deadbeat predictive current control (IDPCC) framework to estimate and compensate for lumped disturbances caused by resistance and inductance mismatches, thereby improving parameter robustness. Meanwhile, a quasi-resonant controller (QRC) is connected in parallel with the current loop to selectively suppress sixth-order current harmonics induced by inverter nonlinearities and flux harmonics. Furthermore, a deep deterministic policy gradient (DDPG)-based parameter optimization scheme is introduced to automatically tune the controller parameters, overcoming the limitations of conventional trial-and-error tuning and achieving the coordinated optimization of dynamic response, steady-state accuracy, and disturbance rejection capability. Simulation and experimental results demonstrate that, compared with proportional–integral (PI) control and IDPCC incorporating the IESO (IESO-IDPCC), AT-DR-IDPCC reduces the phase current’s total harmonic distortion (THD) by 56.1% and 23.7% while also decreasing the speed fluctuation amplitude by approximately 50% and 20%, respectively. The proposed method significantly enhances the robustness, harmonic suppression capability, and low-speed control performance of PMSM drives. Full article

Background: Clinical pharmacists play a crucial role in identifying and resolving drug-related problems (DRPs) in hospitals. However, there is limited empirical evidence concerning their effect on service organizations in smaller regional facilities in Saudi Arabia. Methods: A retrospective serial cross-sectional study was conducted using three years of administrative data (2023–2025) from two public hospitals in the Al-Baha region. A total of 1812 different database records were identified after full-scale data cleaning, validation, and deduplication. The statistical framework included chi-square test of independence, pairwise two-proportions Z-test, three multivariate binary logistic regression models, Cramer’s V association analysis, and Ward’s hierarchical cluster modeling. Results: Active intervention rates significantly increased from 48.9% in 2023 to 72.4% in 2024 (z = −8.58, p < 0.001), stabilizing at 69.6% throughout 2025 (p < 0.001 versus the 2023 baseline), because 2023 data came from a different source system than 2024 to 2025 data, this pattern is reported descriptively rather as a programmatic integration. The most frequent clinical actions addressed therapy optimization (28.4%), inappropriate dosage regimens (23.6%), and missing drug orders (18.8%). Most entries recorded were for adult intensive care units (ICUs) (74.1%), and most involved patients aged 65 years or older (61.1% of records). ICU setting was independently associated with the intervention being classified as therapy optimization (adjusted odds ratio [aOR] 2.64, p < 0.001), but it was an inverse predictor of dosage-error tracking (OR 0.30, p < 0.001). Conclusions: This is among the first serial cross-sectional studies to build an evidence-based framework describing clinical pharmacy practices in the Al-Baha region, showing pharmacists’ roles and prioritizing advanced clinical training programs, reinforcing the need for critical analysis of health cluster policies across peripheral Saudi networks. Full article

Photovoltaic–thermal (PVT) collectors coupled to thermochemical energy storage (TCES) can turn intermittent low-grade solar heat into a dispatchable service, but solar intermittency poses a closed-loop control problem. A companion study established the feedback-only lower bound: a 937 kJ accumulated exergy-delivery-deficit benchmark under optimally tuned proportional–integral–derivative (PID) flow control. The corresponding upper bound is quantified here by means of a deliberately idealised search-based predictive controller that, at each 10 s step, enumerates 51 candidate pump rates, predicts the reactor-inlet temperature by a single forward-Euler step, and is granted perfect future irradiance. On the experimentally validated shared plant (matched to the companion baseline), against an optimally tuned PID, the perfect-foresight advantage is marginal: +0.96% daily exergy on synthetic days and +0.07–0.24% on two measured Walailak University monsoon days, all controllers tracking within 6–13 K on the measured days. Under tropical-monsoon irradiance, the 95 °C desorption setpoint is rarely sustained, so the delivered exergy is nearly controller-independent: the perfect-foresight upper bound lies just above the feedback-only lower bound, and together the two results bracket the exergy envelope available to any flow-rate controller of this system. A horizon sweep localises the bottleneck to internal-model fidelity, not anticipation depth. The eight-node plant is validated against measured module temperature (root-mean-square error 3.5 °C, coefficient of determination R² = 0.89) and a copper-tube PVT prototype (1.5 °C; peak hot water up to 79 °C). The central contribution is therefore a rigorously defined, experimentally grounded upper bound showing that, at this scale and latitude, deployability rather than anticipation is the effective design lever. Full article

Background: Accurate estimation of energy requirements is essential for appropriate nutritional therapy. Resting energy expenditure (REE) predictive equations are derived from specific populations and may therefore contain systematic individual-level biases when applied in different clinical settings. In this pilot exploratory study, we compared REE measured by portable indirect calorimetry with estimates obtained using multiple predictive methods in Japanese adults and characterized their association, absolute agreement, and error structure. Methods: Thirty-six university staff members and students were enrolled. The association and absolute agreement between REE measured by portable indirect calorimetry and estimates obtained using four predictive methods (Harris–Benedict, DRIs, Cunningham, and Ganpule) were evaluated using Spearman’s rank correlation coefficients, ICC(2,1), and Bland–Altman analyses. For equations demonstrating significant proportional bias, multivariate linear regression analyses were additionally performed using residual estimation error, calculated as (predicted REE-measured REE)/measured REE × 100, as the dependent variable. Results: Participants included 16 men and 20 women, with a mean age of 28.9 ± 10.3 years and BMI of 22.1 ± 3.1 kg/m2. Spearman’s rho ranged from 0.750 to 0.800, and ICC(2,1) estimates ranged from 0.736 to 0.766. Mean biases (predicted minus measured REE) were 50.7 kcal/day (95% CI, −20.1 to 121.4) for Harris–Benedict, −73.1 kcal/day (95% CI, −148.8 to 2.6) for DRIs, −94.7 kcal/day (95% CI, −161.7 to −27.8) for Cunningham, and −90.6 kcal/day (95% CI, −161.1 to −20.2) for Ganpule. The corresponding proportions of predictions within ±10% of measured REE were 41.7% (95% CI, 27.1–57.8%), 50.0% (95% CI, 34.5–65.5%), 55.6% (95% CI, 39.6–70.5%), and 52.8% (95% CI, 37.0–68.0%), respectively. Multivariate analyses suggested greater underestimation in females and a shift toward overestimation with higher BMI. Conclusions: Despite moderate-to-strong rank associations with measured REE, Bland–Altman analysis revealed proportional bias in three of the four predictive methods that was not apparent from the correlation coefficients alone. Exploratory analyses suggested that sex and BMI should be considered when interpreting the direction of prediction error. Because the precision of the limits of agreement was restricted by the small sample size, larger studies are needed to estimate individual-level agreement more precisely and to examine these error patterns in older adults and individuals with acute or chronic diseases. Full article

The Proportional-Integral-Derivative (PID) controller has been recorded as the most practical, reliable, and easy to implement feedback control system since its invention in the second decade of the twentieth century. Its adequacy has led to its utilization in approximately 90% of all industrial applications. In the last decade, Proportional-Integral-Retarded (PIR) controller has been introduced as a derivative-free controller to avoid the problem of noise amplification by the derivative term present in the PID controller. Instead of employing the error’s derivative in the control law, PIR controller employs an intentional retarded (exponential) term to provide damping for oscillations in systems with time-delay and enhance their stability. This paper develops, analyzes, and assesses the PIR controller for the heat diffusion system. Stability boundary loci are used to define the stability regions of the PIR controller’s parameters. Compared to a published study which applied PID and fuzzy logic to control the heat diffusion system, the developed PIR controller provides higher speed response by reducing the settling time by 77.082% and 74.992% as compared to PID and fuzzy-PID control systems in response to a step input of +18 °C. It also reduces the integral square error by 54.15% in comparison with the PID control system. Finally, the peak deviation in PIR control system response to an +18 °C disturbance is less than that of the PID control system by 0.167 °C. Full article

On-board three-phase two-level uninterruptible power supply systems serve as vital energy interfaces, ensuring high-fidelity power distribution for critical payloads in heavy-duty unmanned aerial vehicles. Therefore, this paper introduces a robust current-sensorless discrete-time sliding-mode control (DSMC) strategy in the stationary $\alpha \beta$ frame to simplify the system structure while maintaining high-quality dynamic voltage performance. A discrete-time extended-state observer (DESO) is implemented to precisely estimate the filter capacitor current, effectively addressing the voltage regulation issues stemming from load fluctuations and the absence of sensors. Furthermore, the DESO-based current estimate is incorporated as feedforward compensation into the DSMC architecture to significantly bolster the disturbance rejection and fault-tolerance capabilities of system. The simulation results verify that the proposed method outperforms typical cascaded proportional–resonant control, delivering superior voltage tracking accuracy and robust performance. Specifically, compared to the typical cascaded strategy, the proposed method reduces the steady-state RMS voltage tracking error by approximately 1.5 V across all load types, and decreases the THD by 0.08% under balanced loads and 0.15% under nonlinear loads. Full article