Search Results (293)

L360QS pipeline steel, due to its high toughness, high strength, resistance to sulfide stress cracking, and resistance to hydrogen-induced cracking, is increasingly being used in pipeline network construction. Its fracture behavior is a critical factor for safe operation in mountainous steep-slope environments, but it has not yet been widely studied. Therefore, this paper conducts extensive experiments on the ductile fracture of L360QS pipeline steel. The tests employed standard tensile, notched tensile, shear, and compression specimens, covering a stress triaxiality range from approximately −0.33 to 0.92. The study combined Ling’s iterative method to establish an elastoplastic constitutive model considering post-necking behavior, and incorporated it into finite element models to extract the average stress triaxiality and equivalent plastic strain at the moment of fracture initiation for each type of specimen. Based on the extracted data, a piecewise ductile fracture model was established: a simplified Johnson–Cook criterion is used in the high triaxiality range, while an empirical function is used to describe fracture behavior in the medium, low, and negative triaxiality ranges. The model was validated using a train–test split approach, predicting fracture displacements for an independent test set of specimens. The results showed all prediction errors were within 5%, demonstrating the model’s high accuracy. Furthermore, a Spearman correlation analysis quantified the influence of geometric factors, revealing that notch curvature has the strongest monotonic relationship in controlling average stress triaxiality and fracture strain. The fracture model established in this paper can accurately predict the fracture behavior of L360QS pipeline steel and provides a reliable basis for failure prediction and safety assessment under complex service conditions (such as mountainous steep slopes). Full article

In spite of great successes of the inertial step approach (ISA) in various fields, we are investigating the converse inertial step approach (CISA) for the first time. First, the classical Picard iteration for solving nonexpansive mappings converges weakly with CISA integration. Its analysis is based on the newly developed weak quasi-Fejér monotonicity under mild assumptions. We also establish $O(1/k^{\gamma })$ ($\gamma \in (0,1)$) and linear convergence rate under different assumptions. This extends the $O(1/k)$ convergence rate of the Krasnosel’skiĭ–Mann iteration. A generalized version of CISA is then studied. Second, combining CISA with over-relaxed step approach for solving nonexpansive mappings leads to a new algorithm, which not only converges without restrictive assumptions but also allows an inexact calculation in each iteration. Third, with CISA integration, a Backward–Forward splitting algorithm succeeds in accepting a larger step-size, and a Peaceman–Rachford splitting algorithm is guaranteed to converge. Full article

Process industries increasingly face large-scale nonlinear programs with high dimensionality and tight constraints. This study reports on the design and implementation of a reduced-space sequential quadratic programming (RSQP) solver for such settings. The solver couples a column-reordering space-decomposition strategy with sparse-matrix storage/kernels, and is implemented in a modular C++ framework that supports range/null-space splitting, line search, and convergence checks. We evaluate six small-scale benchmarks with non-convex/exponential characteristics, a set of variable-dimension tests up to 128 k variables, and an industrial reverse-osmosis (RO) optimization. On small problems, RSQP attains an accuracy comparable to a full-space sequential quadratic programming (SQP) baseline. In variable-dimension tests, the solver shows favorable scaling when moving from 64 k to 128 k variables; under dynamically varying degrees of freedom, the iteration count decreases by about 62% with notable time savings. In the RO case, daily operating cost decreases by 4.98% and 1.46% across two scenarios while satisfying water-quality constraints. These results indicate that consolidating established RSQP components with column reordering and sparse computation yields a practical, scalable solver for large-scale process optimization. Full article

In addition to the performance parameters of a mechanical manipulator—such as precision, repeatability, payload and maximum speed—path optimization can bring significant improvements in terms of cycle time and energy consumption. In this paper, a method is proposed for post-processing trajectories initially generated by spline interpolation in joint space (cubic or quintic interpolation), so that the distances traveled are shorter. The principle of least action is used as a theoretical foundation trying to find the best cost function in terms of trajectory lengths using. In the pursuit of minimizing this cost function, an iterative method is applied. Initial trajectories are split into multiple internal nodes that are displaced little by little from their initial positions, recomposing trajectories that pass through these displaced nodes at every iteration. The purpose of this paper is to demonstrate that by post-processing trajectories initially generated by the usual spline interpolation in joint space, alternative, shorter variants can be obtained. Full article

In many different applications such as retail, energy, and tourism, forecasts for a set of related time series must satisfy both linear and non-negativity constraints, as negative values are meaningless in practice. Standard regression-based reconciliation approaches achieve coherence with linear constraints, but may generate negative forecasts, reducing interpretability and usability. This paper develops and evaluates three alternative strategies for non-negative forecast reconciliation. First, reconciliation is formulated as a non-negative least squares problem and solved with the operator splitting quadratic program, allowing flexible inclusion of additional constraints. Second, we propose an iterative non-negative reconciliation with immutable forecasts, offering a practical optimization-based alternative. Third, we investigate a family of set-negative-to-zero heuristics that achieve efficiency and interpretability at minimal computational cost. Using the Australian Tourism Demand dataset, we compare these approaches in terms of forecast accuracy and computation time. The results show that non-negativity constraints consistently improve accuracy compared to base forecasts. Overall, set-negative-to-zero achieve near-optimal performance with negligible computation time, the block principal pivoting algorithm provides a good accuracy–efficiency compromise, and the operator splitting quadratic program offers flexibility for incorporating additional constraints in large-scale applications. Full article

Prefabricated building projects represent industrialized and intelligent construction through factory production, standardized design, and mechanized assembly. This study presents a multi-agent simulation approach to model the prefabricated construction process, allowing for the concurrent optimization of the prefabricated component (PC) splitting design and the construction organization plan through iterative simulation. (1) Employing a questionnaire survey, it identifies critical factors affecting schedule and cost from a design–construction coordination perspective. (2) Based on these findings, an agent-based model was developed incorporating PC installation, crane operations, and storage yard spatial constraints, along with interaction rules governing these agents. (3) Data interoperability was achieved among Revit, NetLogo3D and Navisworks. This integrated environment offers project managers digital management of design and construction plans, simulation support, and visualization tools. Simulation results confirm that a hybrid resource allocation strategy utilizing both tower cranes and mobile cranes enhances resource leveling, accelerates schedule performance, and improves cost efficiency. Full article

As part of non-terrestrial networks (NTN), the Low Earth Orbit (LEO) plays a critical role in supporting high-throughput wireless communication. However, the high-speed mobility of LEO satellites, coupled with the high density of user terminals, makes efficient user assignment crucial in maintaining overall wireless performance. The suboptimal assignment from LEO satellites to user terminals can result in frequent unnecessary handovers, rendering the user terminal unable to receive the entire downlink signal. Consequently, it reduces user rate and user satisfaction metrics. However, finding the optimum user assignment to reduce handover issues is categorized as a non-linear programming problem with a combinatorial number of possible solutions, resulting in excessive computational complexity. Therefore, this study proposes a distributed user assignment for the LEO networks. By utilizing message-passing frameworks that map the optimization problem into a graphical representation, the proposed algorithm splits the optimization problem into a local mapping issue, thereby significantly reducing computational complexity. By exchanging small messages iteratively, the proposed algorithm autonomously determines the near-optimal solution. The extensive simulation results demonstrate that the proposed algorithm significantly outperforms the conventional algorithm in terms of user rate and user satisfaction metric under various wireless parameters. Full article

Background: The accurate classification of brain tumor subtypes from MRI scans is critical for timely diagnosis, yet the manual annotation of large datasets remains prohibitively labor-intensive. Method: We present SSPLNet (Semi-Supervised Pseudo-Labeling Network), a dual-branch deep learning framework that synergizes confidence-guided iterative pseudo-labelling with deep feature fusion to enable robust MRI-based tumor classification in data-constrained clinical environments. SSPLNet integrates a custom convolutional neural network (CNN) and a pretrained ResNet50 model, trained semi-supervised using adaptive confidence thresholds ($\tau = 0.98 \to 0.95 \to 0.90$) to iteratively refine pseudo-labels for unlabelled MRI scans. Feature representations from both branches are fused via a dense network, combining localized texture patterns with hierarchical deep features. Results: SSPLNet achieves state-of-the-art accuracy across labelled–unlabelled data splits ($90:10$ to $10:90$), outperforming supervised baselines in extreme low-label regimes (10:90) by up to 5.34% from Custom CNN and 5.58% from ResNet50. The framework reduces annotation dependence and with 40% unlabeled data maintains 98.17% diagnostic accuracy, demonstrating its viability for scalable deployment in resource-limited healthcare settings. Conclusions: Statistical Evaluation and Robustness Analysis of SSPLNet Performance confirms that SSPLNet’s lower error rate is not due to chance. The bootstrap results also confirm that SSPLNet’s reported accuracy falls well within the 95% CI of the sampling distribution. Full article

Background: Differentiating Parkinson’s disease (PD) from essential tremor (ET) is often challenging, especially in early or atypical cases. Dopamine transporter (DAT) single-photon emission computed tomography (SPECT) with ¹²³I-Ioflupane supports diagnosis, and semi-quantitative tools such as DaTQUANT^® and BasGanV2™ provide objective measures. This study compared their diagnostic performance when integrated with supervised machine learning. Methods: We retrospectively analysed ¹²³I-Ioflupane SPECT scans from 169 patients (133 PD, 36 ET). Semi-quantitative analysis was performed using DaTQUANT^® v2.0 and BasGanV2™ v.2. Classification tree (ClT), k-nearest neighbour (k-NN), and support vector machine (SVM) models were trained and validated with stratified shuffle split (250 iterations). Diagnostic accuracy was compared between the two software packages. Results: All classifiers reliably distinguished PD from ET. DaTQUANT^® consistently achieved higher accuracy than BasGanV2™: 93.8%, 93.2%, and 94.5% for ClT, k-NN, and SVM, respectively, versus 90.9%, 91.7%, and 91.9% for BasGanV2™ (p < 0.001). Sensitivity and specificity were also consistently higher for DaTQUANT^® than BasGanV2. Class imbalance (PD > ET) was addressed using Synthetic Minority Over-sampling Technique (SMOTE). Conclusions: Machine learning analysis of ¹²³I-Ioflupane SPECT enhances differentiation between PD and ET. DaTQUANT^® outperformed BasGanV2™, suggesting greater suitability for AI-driven decision support. These findings support the integration of semi-quantitative and AI-based approaches into clinical workflows and highlight the need for harmonised methodologies in movement disorder imaging. Full article

Background/Objectives: Type 2 Diabetes (T2D) computable phenotypes, which leverage electronic health records (EHRs) and administrative claims data, provide the basis for T2D population health research. Our study investigates how T2D phenotypes affect downstream healthcare utilization prediction, specifically inpatient (IP) and emergency room (ER) admissions. Methods: This study included 15,338 adult patients from a large academic medical center with both EHR and claims data from 2017 to 2019. We compared widely adopted and locally developed T2D phenotypes. EHR predictors and claims-based outcomes were used for utilization prediction. Models were developed using a 70/30 training-and-test split over 100 iterations. Mean area under the curve (AUC), odds ratios (ORs), positive predictive values, and negative predictive values were reported. Results: Models had comparable performance in concurrent predictions. Impact of phenotypic variation impact was more apparent in prospective predictions. The CMS Chronic Conditions Data Warehouse (CCW) phenotype was more discriminatory in predicting concurrent IP and ER admissions (AUCs of 0.80 and 0.74) than prospective IP and ER visits (0.70 and 0.73) in 2019. Conclusions: Our study demonstrated how phenotypic variations and data sources impact healthcare utilization prediction in T2D patients. Furthermore, we highlight the significance of phenotype selection for targeted T2D population health initiatives and management strategies. Full article

This work presents an extragradient-type iterative process combined with the viscosity method to find a common solution to a split generalized variational-like inequality, a variational inequality, and a fixed point problem associated with a family of $\epsilon$-strict pseudo-contractive mappings and a nonexpansive operator in Hilbert spaces. Strong convergence of the proposed algorithm is established, with some remarks derived from the main theorem. Numerical experiments are carried out to verify the applicability of the method and provide comparative observations. The results broaden and unify a range of existing contributions in this field. Full article

Background: Accurate differentiation of Parkinson’s disease (PD) from healthy aging is crucial for timely intervention and effective management. Postural sway abnormalities are prominent motor features of PD. Quantitative stabilometry and machine learning (ML) offer a promising avenue for developing objective markers to support the diagnostic process. This study aimed to develop and validate high-performance ML models to classify individuals with PD and age-matched healthy older adults (HOAs) using a comprehensive set of stabilometric parameters. Methods: Thirty-seven HOAs (mean age 70 ± 6.8 years) and 26 individuals with idiopathic PD (Hoehn and Yahr stages 2–3, on medication; mean age 66 years ± 2.9 years), all aged 60–80 years, participated. Stabilometric data were collected using a force platform during quiet stance under eyes-open (EO) and eyes-closed (EC) conditions, from which 34 parameters reflecting the time- and frequency-domain characteristics of center-of-pressure (COP) sway were extracted. After data preprocessing, including mean imputation for missing values and feature scaling, three ML classifiers (Random Forest, Gradient Boosting, and Support Vector Machine) were hyperparameter-tuned using GridSearchCV with three-fold cross-validation. An ensemble voting classifier (soft voting) was constructed from these tuned models. Model performance was rigorously evaluated using 15 iterations of stratified train–test splits (70% train and 30% test) and an additional bootstrap procedure of 1000 iterations to derive reliable 95% confidence intervals (CIs). Results: Our optimized ensemble voting classifier achieved excellent discriminative power, distinguishing PD from HOAs with a mean accuracy of 0.91 (95% CI: 0.81–1.00) and a mean Area Under the ROC Curve (AUC ROC) of 0.97 (95% CI: 0.92–1.00). Importantly, feature analysis revealed that anteroposterior sway velocity with eyes open (V-AP) and total sway path with eyes closed (TOD_EC, calculated using COP displacement vectors from its mean position) are the most robust and non-invasive biomarkers for differentiating the groups. Conclusions: An ensemble ML approach leveraging stabilometric features provides a highly accurate, non-invasive method to distinguish PD from healthy aging and may augment clinical assessment and monitoring. Full article

Conventional commuting mode split models are characterized by inherent limitations in dynamic adaptability, primarily due to persistent dependence on periodic survey data with significant temporal gaps. A dominant transportation distance-based modeling framework for commuting mode choice is proposed, formalizing a generalized cost function. Through the application of random utility theory, probability density curves are generated to quantify mode-specific dominant distance ranges across three demographic groups: car-owning households, non-car households, and collective households. Empirical validation was conducted using Dongguan as a case study, with model parameters calibrated against 2015 resident travel survey data. Parameter updates are dynamically executed through the integration of big data sources (e.g., mobile signaling and LBS). Successful implementation has been achieved in maintaining Dongguan’s transportation models during the 2021 and 2023 iterations. Full article

The detection of citrus separation lines is a crucial step in the citrus processing industry. Inspired by the achievements of line-structured light technology in surface defect detection, this paper proposes a method for detecting citrus separation lines based on line-structured light. Firstly, a gamma-corrected Otsu method is employed to extract the laser stripe region from the image. Secondly, an improved skeleton extraction algorithm is employed to mitigate the bifurcation errors inherent in original skeleton extraction algorithms while simultaneously acquiring 3D point cloud data of the citrus surface. Finally, the least squares progressive iterative approximation algorithm is applied to approximate the ideal surface curve; subsequently, principal component analysis is used to derive the normals of this ideally fitted curve. The deviation between each point (along its corresponding normal direction) and the actual geometric characteristic curve is then adopted as a quantitative index for separation lines positioning. The average similarity between the extracted separation lines and the manually defined standard separation lines reaches 92.5%. In total, 95% of the points on the separation lines obtained by this method have an error of less than 4 pixels. Experimental results demonstrate that through quantitative deviation analysis of geometric features, automatic detection and positioning of the separation lines are achieved, satisfying the requirements of high precision and non-destructiveness for automatic citrus splitting. Full article

Terahertz (THz) communications and simultaneous wireless information and power transfer (SWIPT) hold the potential to energize battery-less Internet-of-Things (IoT) devices while enabling multi-gigabit data transmission. However, severe path loss, blockages, and rectifier nonlinearity significantly hinder both throughput and harvested energy. Additionally, high-power THz beams pose safety concerns by potentially exceeding specific absorption rate (SAR) limits. We propose a sensing-adaptive power-focusing (APF) framework in which a reconfigurable intelligent surface (RIS) embeds low-rate THz sensors. Real-time backscatter measurements construct a spatial map used for the joint optimisation of (i) RIS phase configurations, (ii) multi-tone SWIPT waveforms, and (iii) nonlinear power-splitting ratios. A weighted MMSE inner loop maximizes the data rate, while an outer alternating optimisation applies semidefinite relaxation to enforce passive-element constraints and SAR compliance. Full-stack simulations at 0.3 THz with 20 GHz bandwidth and up to 256 RIS elements show that APF (i) improves the rate–energy Pareto frontier by 30–75% over recent adaptive baselines; (ii) achieves a 150% gain in harvested energy and a 440 Mbps peak per-user rate; (iii) reduces energy-efficiency variance by half while maintaining a Jain fairness index of 0.999;; and (iv) caps SAR at 1.6 W/kg, which is 20% below the IEEE C95.1 safety threshold. The algorithm converges in seven iterations and executes within <3 ms on a Cortex-A78 processor, ensuring compliance with real-time 6G control budgets. The proposed architecture supports sustainable THz-powered networks for smart factories, digital-twin logistics, wire-free extended reality (XR), and low-maintenance structural health monitors, combining high-capacity communication, safe wireless power transfer, and carbon-aware operation for future 6G cyber–physical systems. Full article