Search Results (7,686)

The accurate measurement of individual cognitive abilities in educational settings increasingly relies on personalized assessment systems that adapt to the learner’s current standing on the measured ability. These systems generate and use assessment items continuously and in real-time, bypassing the field-test cycle that traditional calibration requires. Item banks therefore contain a growing share of items whose parameters must be predicted first, and each test form must be assembled in real time to match the learner’s current ability level and content needs for effective personalized instruction. This study proposes an Ising framework that addresses both requirements by predicting item parameters from text with calibrated uncertainty estimates and assembling personalized test forms within the time budget of the assessment loop. This framework is validated through a factorial simulation and an empirical study on state-level English Language Arts items across Grades 3 through 12. In the simulation, the proposed uncertainty-aware method achieves a mean test information function (TIF) gap of 0.226, compared with 1.171 for the conventional baseline that ignores prediction uncertainty. The proposed method also produces feasible forms across the full operational region, whereas the chance-constrained and robust baselines become infeasible at high prediction error combined with a high share of predicted items. In the empirical study, predictive intervals achieve coverage within ten percentage points of the nominal level in the pooled condition, and the uncertainty-aware assembly reduces the TIF gap relative to the baseline, with the largest gains at the extremes of the ability distribution where accurate measurement matters most for instructional placement decisions. By separating prediction uncertainty from true individual differences in ability, this framework preserves the interpretability of cognitive ability estimates and provides a foundation for personalized assessment systems that support differentiated instruction under continuous item bank growth. Full article

Soil salinization constrains cotton production in arid regions, making the optimization of water and salt regulation in drip irrigation systems crucial for enhancing water use efficiency and yield in saline cotton fields. This study aimed to evaluate the synergistic effects of emitter parameters and irrigation quotas on water–salt regulation and cotton growth in a salinized field. Treatments included conventional and leaching irrigation quotas, combined with emitter flow rates of 1.4–3.0 L/h and emitter spacings of 20–30 cm. Soil water–salt dynamics, cotton growth, and yield responses were monitored. The results indicated that the irrigation quota was the dominant factor regulating root-zone water retention and salt leaching, with leaching quotas significantly enhancing soil moisture and desalination compared to conventional quotas. Emitter parameters significantly modulated water and salt distribution. A moderate flow rate of 2.4 L/h combined with a 30 cm spacing created more uniform soil moisture distribution and more efficient salt leaching within the root zone. Cotton growth and yield responded markedly to this water–salt regulation pattern. The combination of a 2.4 L/h flow rate, 30 cm spacing, and the leaching quota achieved the highest seed cotton yield of 6343.12 kg/hm². However, increasing the total seasonal irrigation quota reduced irrigation water productivity from 1.26 kg/m³ to 0.97 kg/m³. Based on the experimental data, optimization models for yield and salt leaching index were constructed and used for prediction. The model-predicted parameter combination for theoretical reference was an emitter discharge rate of 2.1–2.4 L/h, a spacing of 30 cm, and a per-application irrigation quota of 60–66 mm, with a predicted yield of 6356.68 kg/hm² and a salt leaching index of 6.48. This study provides a quantitative basis for optimizing drip irrigation system parameters and the management of water and salt in saline cotton fields in the arid region of Xinjiang. Full article

Background: Lipoprotein(a) [Lp(a)] is a genetically determined, pro-atherogenic lipoprotein enriched in oxidized phospholipids and increasingly recognized as a key potential contributor to residual cardiovascular and inflammatory risk. However, data on its distribution and cardiometabolic associations in Middle Eastern populations remain limited. This study aimed to evaluate the prevalence of elevated Lp(a) and its association with type 2 diabetes (T2D), systemic inflammation, and ethnic variation in a multiethnic cohort. Methods: In this cross-sectional study, data from 2083 adults aged ≥ 18 years residing in Kuwait were collected through the Kuwait Diabetes Epidemiology Program (KDEP) at Dasman Diabetes Institute between 2011 and 2014 using random sampling. Plasma Lp(a) concentrations, cardiometabolic parameters, and high-sensitivity C-reactive protein (CRP) were measured. Elevated Lp(a) was defined as ≥ 50 mg/dL according to established clinical guidelines. Associations of elevated Lp(a) with T2D and CRP were assessed using multivariable Poisson regression models to estimate adjusted prevalence ratios. Results: The overall prevalence of elevated Lp(a) was 41.2%, exceeding global estimates. Significant ethnic variation was observed, with Arabs exhibiting the highest prevalence. After adjustment for potential confounders, individuals with T2D had a higher prevalence of elevated Lp(a) (APR = 1.166) and diabetes status significantly modified the association between CRP and elevated Lp(a) interaction (APR = 1.015), thus supporting a link between Lp(a) and systemic inflammation. Conclusions: Elevated Lp(a) was highly prevalent in this multiethnic Middle Eastern population and was independently associated with T2D and systemic inflammation. These findings support the emerging concept that Lp(a) may function as a molecular-inflammatory mediator potentially contributing to residual cardiovascular risk and highlight the importance of incorporating Lp(a) assessment into cardiometabolic risk stratification, particularly in high-risk populations. Full article

Variable-rate pesticide spraying and fertilizer application require coordinated operation of heterogeneous agricultural machines, particularly in irregular fields where task demands, vehicle mobility, payload capacity, energy consumption, and resupply requirements vary spatially. However, most existing studies optimize aerial spraying or ground fertilization separately and do not jointly consider prescription-map demands, air–ground synchronization, pesticide-drift risk, and agricultural vehicle constraints. This study formulates collaborative UAV spraying and UGV fertilization as a multi-objective mixed-integer nonlinear programming problem with three objectives: minimizing system makespan, weighted energy consumption, and pesticide-drift penalty. A prescription-map-guided bi-level planning framework is proposed. At the upper level, the problem-specific TNSAOO solver determines UAV and UGV task sequences and collaborative resupply-point activation. At the lower level, adaptive Theta* and row-constrained Hybrid A* generate UAV spraying and UGV fertilization trajectories, respectively, while prescription-dependent application commands are assigned along active operation segments and a time-window mechanism detects and corrects residual air–ground conflicts. The framework was evaluated using 30 real farmland boundaries and 90 randomized prescription scenarios. Mean geometric coverage rates reached 98.82% for UAV spraying and 98.95% for UGV fertilization, while the mean prescription-compliance errors were 6.21% and 2.13%, respectively. In addition, 96.7% of the batch runs contained no more than one detected air–ground conflict, with a mean corrective waiting time of 1.07 s. Compared with traditional independent operation, collaborative planning reduced mean system makespan by 9.32%, weighted energy consumption by 7.21%, modeled drift penalty by 3.62%, and total path length by 5.79%. In the multi-objective comparison, TNSAOO obtained a mean hypervolume of 0.597 and a mean inverted generational distance of 0.375, showing competitive Pareto-search performance relative to established comparison algorithms, particularly NSGA-II. Additional terrain and drift sensitivity analyses produced systematic changes in energy, completion time, and modeled drift risk under controlled parameter perturbations. These findings demonstrate the simulation-based feasibility of jointly planning heterogeneous variable-rate spraying and fertilization under a shared prescription map. Physical field experiments remain necessary to validate spray deposition, fertilizer-distribution uniformity, terrain effects, and model calibration under environmental uncertainty. Full article

Automatic reseeding in spoon-chain potato planters requires reliable classification of miss-seeding, normal, and multiple-seeding events. Oblique viewing and continuous spoon motion cause scale changes, boundary truncation, blur, and occlusion, which destabilize single-frame predictions. The proposed system treats each seed-spoon passage as one control event. It combines lightweight detection, track-level learning, and multi-frame classification with air-blow clearing and missed-seed reseeding. Based on the target-size distribution, the detector removes the P3 head from YOLOv8n and restores shallow details through space-to-depth rearrangement and gated addition. Frame quality combines boundary distance, sharpness, adjacent-box intersection over union, and box-area stability. High-quality frames form a track prototype, while low-quality frames receive stronger constraints. A quality-gated temporal network classifies each spoon as containing 0, 1, or at least 2 seed potatoes. Each model was trained with three random seeds under the same data split. Compared with YOLOv8n, the detector reduced the parameter count and computational cost by 35.8% and 48.3%, respectively. Track-level learning improved event accuracy by 3.67 percentage points over frame-level training. The seven-frame model achieved 93.41% event accuracy and 93.80% macro-F1. At 0.5 m/s, the closed-loop recognition, air-blow, and reseeding success rates were 93.7%, 96.4%, and 97.1%, respectively. These results provide a practical basis for real-time, event-level planting-quality control in spoon-chain potato planters. Full article

Airborne dust plumes are difficult to localize in underground mine-face video because the scene is dark, illumination moves with machinery, and dust is confused with lamp bloom, reflective steel, and water-spray aerosol. We present DustVeil, a label-free two-stage system for image-space plume localization. Its software teacher combines background-referenced veiling (C1), local texture decay (C2), and absolute dark-channel response (C3) with a probabilistic soft-OR, then applies glare and chroma gates. The teacher returns a dimensionless response map in [0, 1], a binary plume mask and the corresponding image-area ratio; it does not estimate dust concentration, particle-size distribution, respirable exposure, or hazard categories. Teacher outputs from 342 frames in 114 clips/24 sessions supervise a 0.47 M parameter TinyU-Net. Evaluation uses a 144-image synthetic calibration set and a 72-frame real test set drawn from 72 clips in 18 sessions, with all roles separated at clip and session levels. Thresholds are selected only on synthetic masks and frozen before real scoring. After replacing per-image score normalization with fixed baseline-normal calibration and using reference implementations of the anomaly methods, DustVeil obtains IoU/F1 of 0.366/0.500 and the lowest clean-frame false-positive area (3.7% versus 13.9–59.9%). A separate water-spray set quantifies visual specificity. TinyU-Net runs at 610 FPS for network-only inference and 233 FPS aggregate in the measured six-stream decode-to-mask pipeline; optical flow is excluded from these figures. The validated scope is six fixed visible-light RGB cameras with camera-specific unlabelled calibration at one site, rather than concentration monitoring or camera-disjoint deployment. Full article

This paper investigates the distributed fusion estimation problem for multi-sensor cyber-physical systems (CPSs), where the communication channels from local estimators to the fusion center are subject to random packet dropouts. Packet dropouts induced by either network congestion or intermittent denial-of-service (DoS) attacks are modeled as Bernoulli random variables. When a local estimate is lost, a prediction compensation strategy is adopted at the fusion center, where the missing data are replaced by their one-step predictors. By constructing an augmented state consisting of the original state, local prediction errors, and virtual measurements, the multi-sensor system is transformed into a stochastic system with random parameter matrices and one-step autocorrelated noises. Based on the transformed system, a distributed state fusion (DSF) filter is proposed via the innovation analysis method, whose filter gain depends on the successful-reception probabilities. The stability of the proposed DSF filter is analyzed, and a sufficient condition for the existence of a steady-state filter is obtained. The steady-state gain can be pre-computed offline, thereby reducing the online computational burden. Simulation results validate the effectiveness of the proposed algorithm. Full article

The electrostatic rotary bell (ESRB) sprayer is widely used in the coating industry due to its ability to achieve uniform film thickness and reasonable paint transfer efficiency. However the efficiency of paint transfer and spraying coverage in ESRB systems remain highly sensitive to process parameters. Therefore, optimizing these parameters is essential to reducing paint consumption, energy use, and environmental impact. In this study, a simulation model of the ESRB spraying process was established using ANSYS/Fluent. The spraying flow field, paint deposition profile, and film thickness distribution were validated through the experiment. Based on a single-factor test and the Box–Behnken response surface method, a multi-parameter optimization framework was designed to investigate the effects of six spraying process parameters, including inner and outer shaping air flow rate, bell rotational speed, applied voltage, target distance, and paint flow rate, on coating pattern width and paint transfer efficiency. Based on the Z-score standardization, a mathematical model of the comprehensive score with six factors was established to evaluate spraying efficiency and paint transfer efficiency and predict optimal spraying process parameters. The results indicate that voltage and spray distance are significant factors affecting the comprehensive score, with the order of influence being voltage > spray distance. The optimal parameters were as follows: bell rotational speed X1, 40 kr/min; inner shaping air flow rate X2, 196 sl/min; outer shaping air flow rate X3, 298 sl/min; paint flow rate X4, 249 cc/min; applied voltage X5, 52 kV; and target distance X6, 154 mm. Validation tests showed deviation between the predicted comprehensive score and the actual value from simulation and experiment were 2.03% and 1.36%, respectively. These results demonstrate that the proposed optimization model has high reliability and can be used to optimize spraying process parameters. Full article

To address the excessive differential settlement and the bridge approach bump problem behind bridge abutments in binary strata under cyclic vehicle loading, a highway bridge abutment in western China was investigated as a case study. Through field sampling and laboratory dynamic triaxial tests, the dynamic parameters of the loess-like silt under cyclic loading were calibrated. A three-dimensional dynamic numerical model considering a pile–soil–structure interaction was established. The bridge approach settlement, horizontal displacement, and dynamic responses of abutment pile foundations before and after high-pressure jet grouting reinforcement were compared and analysed. Furthermore, the evolution of reinforcement effectiveness under different axle loads (40–150 [kN]) and vehicle speeds (40–100 [km/h]) was systematically investigated. The results indicate that high-pressure jet grouting can significantly control bridge approach settlement and horizontal displacement, with reductions of 80.5% in settlement and 78.0% in horizontal displacement at the bridge–embankment transition zone. Settlement and horizontal displacements of the pile foundation at shallow depths are effectively suppressed, the stress distribution along the piles becomes more uniform, and stress concentration at the soil–rock interface is notably alleviated. The influence of axle load on reinforcement effectiveness is far greater than that of vehicle speed, with 80 [kN] identified as the critical load for deformation control of the reinforcement system. Within the conventional speed range of 40–100 [km/h], the effect of speed variation on the deformation of the reinforced zone is limited, and the jet grouting reinforcement system maintains a stable control performance. The findings provide a theoretical basis and technical support for the design and maintenance of jet grouting reinforcement against bridge approach settlement in binary strata. Full article

The article is devoted to the current problem of optimizing transport flows implemented within a mechanical engineering enterprise, where the efficiency of logistics operations directly affects the productivity and competitiveness of production. Optimization of the parameters of transport flows implemented within a mechanical engineering enterprise is a crucial component of technical production planning. The paper presents the results of the cargo transportation process analysis, defining many of its structural elements. Using these structural components, a multi-level model for designing and comprehensive optimization of transport flows has been developed. A two-level model of comprehensive structural and parametric optimization of individual transport operations has been proposed. A multi-level system of indicators for assessing the effectiveness of individual transport operations and transport routes has been developed. The article presents the results of testing the proposed model. During the study, a structural analysis of transport flows at a mechanical engineering enterprise was conducted, and a system for their comprehensive optimization was developed. The key element of the system was a modular algorithm for designing transport operations, ensuring flexible planning taking into account actual production constraints. The use of a robust approach and vector optimization criteria made it possible to develop solutions that are robust to uncertainties: a reduction in the target indicator of uniformity of labor intensity distribution by 22.5% and in the indicator of uniformity of cargo weight distribution by 17.33% was achieved. Moreover, the efficiency coefficient of vehicle utilization in terms of carrying capacity increased by 21.67%, and the actual values of the target indicators exceeded the optimization ones by an average of 6.4% and 2.72%, respectively, which confirmed the operability and stability margin of the solutions. The practical significance of the approach lies in the possibility of autonomous use of the system or its integration into the general production management system. The implementation of the subsystem will improve the rhythm of transport operations, reduce downtime, and minimize the risk of disruptions to production cycles. Full article

The increasing demand for real-time monitoring systems has accelerated the adoption of distributed sensors operating in the ultra-low-power regime. However, in retrofittable and hard-to-access applications, providing a continuous external power supply is challenging, while batteries are limited by their lifetime and maintenance requirements. Energy harvesting represents a promising approach to enable autonomous sensor operation by exploiting ambient energy sources, including photovoltaic, thermal, mechanical, and electromagnetic sources. In this work, different circuit topologies for near-field electromagnetic energy harvesting are investigated through simulation and experimental validation. The proposed approach exploits the electromagnetic interference generated by shielded commercial power electronics, such as drivers and power supplies, as an available energy source. The results demonstrate the capability of compact and easily deployable circuits to capture and convert near-field electromagnetic energy into usable electrical power. The proposed methodology provides a flexible solution for powering low-power monitoring systems and can be adapted to different environments by tailoring the harvesting circuit parameters to the available electromagnetic source. Full article

Optical systems in the real world often create image problems, such as Gaussian blur from defocus or atmospheric turbulence, and radial vignetting caused by lens shape. Standard mixed-data fine-tuning helps the task head handle these issues, but it does not actually fix them. We introduce the Iterative Correction of Optical Perturbations ICOP framework, which corrects encoder feature representations before they reach the task head using a physics-aware plug-in module. ICOP does this by modeling blur as an isotropic-Gaussian point-spread function (PSF) and uses gradient-based, self-supervised optimization (Adam) to discover feature-space corrections that steer degraded representations toward their clean-data distribution. It includes a BlurEstimator that builds a degradation descriptor using fixed Laplacian and Sobel operators, and a PromptGenerator that turns this descriptor into modulation parameters for the frozen encoder output. The framework comes in two versions based on the task: an additive correction (ICOP-Add) for image classification, and a Feature-wise Linear Modulation correction (ICOP-FiLM) for object detection. We observe a convergence between clean-task performance and blur-induced degradation across datasets, consistent with greater reliance on high-frequency features in stronger backbones; we treat this as an empirical, cross-dataset observation rather than a demonstrated causal claim (task difficulty, category structure, texture, and object scale also differ across datasets). Independently of this, ICOP-FiLM’s corrective benefit does not scale with degradation severity, revealing a more nuanced relationship between backbone quality and robustness. For classification, ICOP-Add improves distorted-condition accuracy over strong mixed fine-tuning by $+2.4$, $+9.1$, and $+10.7$ percentage points on MNIST, FashionMNIST, and CIFAR-10, respectively (McNemar’s test, $p<0.001$ on all three, 5000 paired predictions per dataset). On three object detection datasets, ICOP-FiLM improves distorted-condition mAP over a mixed-fine-tuning null hypothesis by $+0.026$, $-0.005$, and $+0.003$ mAP, respectively (all values mean over 3 seeds). Against a matched-blur-ratio control that isolates the correction module’s own contribution, ICOP-FiLM wins by a consistent margin on two of the three datasets ($+0.037$ and $+0.024$ mAP, winning in every one of 3/3 seeds on each) and loses on the third ($-0.039$ mAP, losing in 3/3 seeds); it outperforms parameter-efficient (VPT, Adapter) baselines trained on identical data on the same two datasets. This dataset-dependent pattern is discussed in detail in the main text. ICOP-FiLM adds only 82,672 parameters to a 42-million-parameter Real-Time DEtection TRansformer (RT-DETR) detector. All reported results are obtained under synthetic Gaussian blur and radial vignetting applied to clean images from the six benchmark datasets studied. Full article

This paper addresses the design of an adaptive observer for a class of nonlinear cascade systems with partially measured states and unknown constant parameters. The considered systems have a cascade structure involving an unmeasured-state subsystem and a measured-state subsystem. The unknown parameter vector enters the measured state dynamics through a known distribution matrix, while the unmeasured state dynamics are described by a lower triangular subsystem satisfying suitable conditions. An adaptive observer is proposed to reconstruct the unmeasured state variables and to compensate for the effect of the unknown parameters using only the measured output and the known input. Sufficient gain conditions are derived through a Lyapunov-based analysis to guarantee asymptotic convergence of the state estimation errors in the absence of uncertainties, while ensuring boundedness of the parameter estimation error. The effect of bounded model uncertainties is then analyzed, and a $\sigma$-modified adaptive law is introduced to prevent parameter drift and guarantee uniform ultimate boundedness of the estimation errors. The proposed framework is illustrated through a reduced-order marine vessel model involving slowly varying environmental bias states and an unknown constant disturbance term. Numerical simulations illustrate the convergence properties and the robustness of the observer under bounded model uncertainties. Additional numerical tests with noisy output measurements are also reported to assess sensitivity to measurement noise. Full article

To improve renewable energy utilization and reduce fossil fuel consumption in building heating systems in Northwest China, this study proposes a solar–biogas hybrid heating system for the Jiuquan region. A dynamic simulation model was developed in TRNSYS, and multi-objective optimization was conducted using the NSGA-II algorithm. The optimization objectives were to maximize the solar fraction and minimize the initial system investment. The solar collector area, thermal storage tank volume, and biogas boiler start-up temperature were selected as decision variables. The results showed that the optimized solar fraction increased from 48.98% to 78.40% as the initial investment increased from 40,000 CNY to 80,000 CNY, although the marginal return gradually decreased. The NSGA-II algorithm generated a well-distributed Pareto front, revealing a clear trade-off between economic performance and solar energy utilization. Relative to the author-defined, proportionally scaled reference configurations, the optimized solutions increased the solar fraction by 18–21% across the examined investment levels. These improvement percentages are conditional on the adopted baseline definition. Considering both energy performance and economic feasibility, the scheme with a solar fraction of approximately 70% was identified as the compromise solution. Compared with the initial design, this scheme increased the initial investment by 15.52% and improved the solar fraction by 20.69%. These results indicate that appropriate configuration of key system parameters can substantially enhance renewable energy utilization. The proposed optimization method provides a useful reference for the design and operation of solar–biogas hybrid heating systems in Northwest China. Full article

An integrated Model-Opti-Scale (MOPS) computer system for the modeling, optimization, and scaling-up of plastic extrusion has been built. This enabled simulations of single-screw extrusion with conventional, gravitational feeding of the extruder and single-screw extrusion with metered feeding. This system enables predictions of the behavior of these processes using material, geometry, and operating data and contains numerous original and unique models and algorithmic solutions. The system co-operates with specially developed optimization and scaling procedures based on Genetic Algorithms, allowing process optimization/scaling-up. A unique, holistic approach to modeling flood-fed/starve-fed processes, with a possibility of transfer between these feed methods, is presented. The problem of optimizing the operating/geometry parameters of single-screw extrusion for maximizing process efficiency and minimizing specific energy use has been solved. Optimization has been achieved in a unique way, allowing the coupling of both methods of feeding. It has also been observed that an optimal solution may be obtained upon flood feeding or starve feeding, depending on the assumed weights of the optimization criteria. This optimized process was scaled up to increase process efficiency by enlarging the extruder diameter while keeping the length/diameter ratio constant. Scaling-up has been achieved using criteria comprising single parameters, including specific energy consumption, material temperature, and the plasticization rate, as well as criteria comprising functional parameters, such as temperature distribution and the material plasticization profile. Full article