Search Results (2,556)

Efficient resource allocation and task scheduling remain fundamental challenges in cloud computing because of resource heterogeneity, dynamic workload characteristics, and the increasing demand for scalable, energy-efficient, and sustainable cloud infrastructures. Conventional scheduling approaches, including Min-Min and the Improved Sparrow Search Algorithm (ISSA), have improved resource utilization and load balancing. However, they still face limitations in scalability, execution efficiency, and adaptive scheduling under heterogeneous and dynamically changing cloud workloads. To overcome these limitations without introducing the computational overhead associated with iterative optimization techniques, this paper proposes a lightweight Greedy Predictive Scheduling (GPS) algorithm that combines predictive resource utilization estimation with greedy host selection to improve scheduling decisions across heterogeneous multi-region cloud environments. The proposed scheduler integrates predictive execution estimation, multi-resource awareness, adaptive greedy decision making, and utilization-aware energy consideration to improve scheduling decisions across heterogeneous multi-region cloud environments. The proposed approach is implemented and evaluated using the CloudSim Plus 5.0 simulation framework, and the experimental results demonstrate that GPS achieves better performance than ISSA across our experiments. GPS achieves a makespan reduction of up to 31.25% compared with ISSA while consistently improving execution efficiency, scalability, and balanced resource utilization across heterogeneous multi-region cloud environments, demonstrating that GPS provides an effective lightweight scheduling solution for large-scale energy-aware cloud computing. Full article

Binary logistic regression relies on the symmetric logit link, whose fixed inflection point cannot adapt to asymmetric response mechanisms and so may yield miscalibrated probabilities when the true response curve is asymmetric—a problem that can be particularly consequential in rare event or imbalanced applications. This study proposes a flexible, asymmetric link derived from the von Bertalanffy growth equation and tests a single, calibration-oriented hypothesis: estimating an asymmetric link improves the calibration of the predicted probabilities under asymmetric response mechanisms while leaving discrimination unchanged. The single shape parameter ν (the reparameterized von Bertalanffy shape, ν = m − 1; the growth curve baseline parameter is absorbed into the intercept and is not separately identifiable) is estimated jointly with the regression coefficients by L2-penalized maximum likelihood, giving a coherent generalized linear model rather than a post hoc re-thresholding of logistic scores; the logistic link is nested exactly at ν = 1 (m = 2), so the model reduces to ordinary logistic regression whenever the logit is adequate. The link was benchmarked on identical out-of-sample partitions against four competitors—logistic regression at the 0.5 cut-off, threshold-optimized logistic regression, ridge-penalized logistic regression at the same penalty (which separates the link from the regularization it requires), and Stukel’s generalized logistic model—across a correctly specified logistic mechanism and two asymmetric mechanisms (a complementary log–log mechanism and a von Bertalanffy-type best-case reference), three class imbalance ratios (0.10, 0.25, and 0.50) and five sample sizes (100–10,000), for 45,000 iterations in total. Paired differences were summarized by the Hodges–Lehmann median difference, its 95% confidence interval, and the rank-biserial correlation, with Benjamini–Hochberg control of multiplicity. The results supported the hypothesis. At moderate-to-large sample sizes, and increasingly as prevalence approached 0.50, the proposed link achieved lower Brier score and Log–Loss values than the three logistic competitors under both asymmetric mechanisms—for example, a median Log–Loss reduction against the matched-penalty ridge model of about 0.004 at balanced prevalence and the largest sample (95% confidence interval excluding zero; rank-biserial ≈ −1)—an advantage small in absolute size but, at the larger sample sizes, consistent across essentially every replication, and increasing with sample size and prevalence. Discrimination showed no practically important differences at moderate-to-large sample sizes: the area under the ROC curve and overall accuracy were essentially the same across the five methods, so the flexible link improves the quality of the probabilities without degrading class separation. Under the correctly specified logistic mechanism, the method showed no material deterioration, which is consistent with its exact nesting of the logistic model. The contribution is a growth curve-motivated asymmetric link that improves probability calibration under response asymmetry while preserving discrimination and recovering the logistic model when it is adequate. Full article

Stock markets are complex adaptive systems whose feature dependencies evolve over time, and identifying which signals genuinely drive returns is fundamentally a problem of information. Traditional feature selection methods and linear models rely on statistical correlations and overlook causality, which weakens their predictive performance in non-linear, evolving markets. This paper develops an information-theoretic approach to causal feature selection based on Markov Blanket discovery. We apply the Iterative Parent–Child-based search of Markov Blanket (IPCMB), whose conditional-independence tests are conditional mutual information measures, to recover the Markov Blanket of next-month returns—the minimal feature set that carries all Shannon information about the target. We then pair it with a Classification and Regression Tree (CART), whose impurity-based splitting admits an information-gain interpretation, to predict the direction of Chinese A-share returns non-linearly. Using data on 2760 stocks, IPCMB selects 13 causal features from 72 candidates, and CART forecasts whether the next month’s return is positive. The empirical results show an accuracy of 58.0%, 7.7 percentage points above the all-features CART benchmark, and a 13-month cumulative return 35.88% higher than that of the CSI 300 index. The findings indicate that selecting features according to their causal information content and combining them with interpretable tree-based prediction can support more reliable investment decisions in emerging markets. Full article

With advances in emerging material technologies, intelligent reflecting surface (IRS)-assisted vehicular networks have been gaining growing interest. By adaptively shaping the wireless propagation environment, IRSs can improve vehicular network quality of service (QoS). However, most IRS-assisted vehicular network studies are limited to individual RF or VLC frameworks, while only a few investigate IRS-assisted aggregated VLC-RF vehicular networks that combine wide RF coverage with high VLC data rates. In this paper, aggregated VLC-RF vehicular networks are supported by both optical IRSs (OIRSs) and RF IRSs, and a resource allocation scheme is developed to improve the total achievable rate. First, we establish a system model for IRS-assisted aggregated VLC-RF vehicular networks, and then formulate a problem to maximize the total achievable rate. Furthermore, we decompose the maximization of the total achievable rate into five subproblems and solve them iteratively via an efficient alternating optimization scheme based on block coordinate descent (BCD). Moreover, simulation results validate the convergence and efficiency of our algorithm, while highlighting the effects of crucial parameters on system performance, providing valuable insights for resource allocation in IRS-assisted aggregated VLC–RF vehicular networks. Full article

This conceptual paper revisits the learning styles debate in the context of generative and adaptive artificial intelligence in higher education. It does not seek to rehabilitate classical learning style taxonomies, whose prescriptive claims have been widely challenged as a neuromyth. Instead, it argues that AI-mediated learning creates new conditions under which patterns of regulation, delegation, verification, iteration, epistemic control, and ethical responsibility may become more observable and pedagogically relevant. Drawing on research on learning styles, neuromyths, AI literacy, adaptive learning, assessment, self-regulated learning, metacognition, and epistemic agency, the paper proposes a conceptual framework for regulation and agency styles in AI-mediated learning. In this framework, style is not treated as a fixed psychological trait or as a category into which learners should be sorted, but as a situated and modifiable profile of decisions and actions distributed across learners, tasks, and intelligent systems. Three analytical dimensions are proposed: epistemic control and metacognitive orchestration, adaptive regulation and generative iteration, and socio-algorithmic agency and ethical governance. The paper concludes with implications for task design, assessment, teacher education, and equitable AI integration. Full article

Online flatness detection of aircraft rudder surfaces serves as a pivotal core procedure for ensuring the manufacturing precision, aerodynamic performance and operational safety of aeronautical components. Traditional plane fitting-based detection approaches are constrained by low detection efficiency, susceptibility to local optimal solutions, weak anti-noise robustness and limited automation capability, which fail to satisfy the micron-level high-precision online detection requirements for curved composite rudder surfaces in batch manufacturing scenarios. To address the aforementioned technical bottlenecks, this study proposes a bidirectionally coupled PSO-SA hybrid optimization algorithm for non-convex minimum zone flatness evaluation of curved rudder surfaces, which overcomes the unidirectional open-loop iteration limitation inherent in conventional serial PSO-SA composite frameworks. Two targeted algorithmic improvements are elaborated in this work: a residual-adaptive nonlinear inertia weight strategy, which dynamically balances global exploration and local exploitation capabilities based on the fluctuation characteristics of free-form surface measurement residuals; and a measurement noise-modified Metropolis acceptance criterion, which substantially enhances the algorithm’s anti-interference performance against on-machine trigger sampling noise. Integrating with the trigger-type on-machine detection hardware of computer numerical control (CNC) machine tools, an integrated online detection system is established to realize the full-process functions of point cloud data acquisition, error compensation, intelligent plane fitting and flatness error evaluation. Meanwhile, the complete technical workflow involving measurement path planning, probe calibration and algorithm iterative solution is systematically illustrated. Comparative simulation experiments implemented on the MATLAB platform demonstrate that the proposed algorithm exhibits superior performance in convergence speed, fitting accuracy and optimization stability over five mainstream algorithms, including standard particle swarm optimization (PSO), standard simulated annealing (SA), comprehensive learning PSO (CLPSO), adaptive cooling SA and conventional serial PSO-SA. On-machine physical measurement experiments are conducted on 24 aircraft rudder workpieces covering aluminum alloy skins and assembled riveted components. After multi-dimensional systematic calibration, the overall detection error of the developed system is controlled within 1 μm. The experimental results indicate that the average flatness error calculated by the proposed bidirectionally coupled PSO-SA algorithm is 29.7 μm, which is 30.1% and 38.5% lower than that of standard PSO and standard SA, respectively, fully complying with the aviation flatness tolerance specification of 0.1–0.3 mm. Moreover, the full detection cycle for a single workpiece is only 2.1 min, achieving a 34.4% reduction in detection time compared with standard PSO and effectively improving the efficiency of online in-process inspection. One-way analysis of variance (ANOVA) combined with Tukey’s posthoc test further verifies that the accuracy superiority of the proposed algorithm is statistically significant. This research provides a targeted theoretical basis and complete engineering implementation scheme for intelligent flatness detection of aerospace curved thin-walled parts, and offers a valuable technical reference for form and position error evaluation of irregular industrial components under noisy measurement conditions. Full article

To address blind search, frequent deadlocks, slow convergence, and poor path quality in conventional ant colony algorithms for mobile robot path planning, this study proposes an A*-guided multi-strategy improved ant colony algorithm (MIACO). First, a global guidance path is generated via A*, and pheromones are non-uniformly initialized to enhance directionality and reduce blind exploration. Second, an ant death penalty mechanism applies negative feedback to lower pheromone concentration on deadlocked paths, reducing deadlock occurrence. Third, the heuristic function incorporates Euclidean distance to the goal, and an adaptive dynamic adjustment factor is introduced into the state transition probability to reduce invalid searches and accelerate convergence. Finally, the path node optimization strategy removes redundant nodes satisfying the direct-connectivity condition through geometric evaluation and obstacle detection, thereby reducing the number of path nodes and turns and improving the geometric quality of the planned path in the grid environment. Simulation results show that, in the simple grid environment, compared with ant colony optimization (ACO) and improved ant colony optimization (IACO), the path lengths obtained by MIACO are reduced by 11.35% and 3.94%, respectively; the average numbers of convergence iterations are reduced by 66.88% and 32.05%, respectively; and the average convergence times are reduced by 67.79% and 77.98%, respectively. In addition, compared with ACO, the average number of iterations and the average time required to achieve zero ant deaths are reduced by 36.47% and 49.38%, respectively. In the complex orchard simulation environment, compared with ACO and IACO, the path lengths obtained by MIACO are reduced by 33.15% and 2.18%, respectively; the average numbers of convergence iterations are reduced by 85.14% and 84.00%, respectively; and the average convergence times are reduced by 66.29% and 95.12%, respectively. The experimental results demonstrate that MIACO exhibits favorable path search and convergence performance in the two constructed grid environments. Full article

Accurate crime spatiotemporal prediction is crucial for crime prevention. However, crime occurrences are influenced by diverse and interacting social factors, resulting in dynamically evolving distributions with non-stationarity and spatial heterogeneity. Most existing methods focus on data preprocessing or architectural enhancements and remain offline models, which limits their generalization capability. To address these challenges, we propose a novel spatial semantic-guided online learning framework. Specifically, we first compute the spatial semantic similarity between urban regions using points of interest. Based on this, we then introduce a contrastive learning objective guided by this similarity during training. This design aims to enhance the model’s ability to capture both the similarities and discrepancies among regions. During the prediction process, an iterative online learning strategy is employed to adapt to dynamically changing crime patterns. By continuously fine-tuning the model with streaming data, the proposed framework improves robustness and generalization under non-stationary crime spatiotemporal distributions. Finally, extensive experiments on real-world crime datasets indicate the effectiveness and stability of our proposed approach. Full article

The efficiency of ammunition support operations in the aircraft carrier intermediate ordnance staging deck is critical to sortie generation rates in naval aviation, yet joint scheduling and personnel allocation in this multistage, resource-constrained environment remains a challenging bi-objective optimization problem. This study develops a framework integrating an improved Nondominated Sorting Genetic Algorithm II (NSGA-II) with a marginal-benefit-based iterative feedback mechanism. The intermediate ordnance staging deck support process is decomposed into individual ammunition processing stations and formulated as a processflow model incorporating operation sequencing and personnel specialization constraints. A constraint decision model then dynamically reconciles the minimization of total makespan and personnel workload equilibrium through iterative marginal-benefit comparison across support teams. The NSGA-II is enhanced with an adaptive crossover-mutation mechanism and an improved elitism preservation strategy to strengthen global search capability. Validation on a typical carrier intermediate ordnance staging deck scenario demonstrates that the improved NSGA-II outperforms the conventional NSGA-II in convergence speed and Pareto front quality. Under the optimized configuration, the total makespan remains 3600 s with a workload balance metric of 1075 even as ammunition quantity doubles from two to four units. The proposed framework offers practical decision support for carrier ammunition operations and extends to other resource-constrained multi-objective scheduling domains. Full article

UAV inspection images of transmission line spacers often contain small-scale defect targets, complex backgrounds, and weak fine-grained features. We propose MFIA-YOLO, a small-object defect detection model for spacer defects. Using the lightweight YOLOv8n variant as the baseline, we introduce a Multi-scale Spatial Heterogeneous Convolution (MSHC) into the backbone network. This module enhances the extraction of multi-scale features from spacer defect targets. Before the SPPF module, we further construct a Feature Complementary module (FCM). The FCM embeds shallow spatial location information into deep semantic features, thereby alleviating spatial information degradation during small-object detection. In the detection head, a C2f_IAFF module is adopted to adaptively fuse features at different scales through iterative attention-based feature fusion. This design improves the representation of defect targets in complex scenes. In addition, a transmission line spacer defect dataset is constructed from UAV inspection images collected in Xilingol League, Inner Mongolia. Experimental results showed that MFIA-YOLO achieved an mAP50 of 96.53% and an mAP50–95 of 83.50%. Compared with representative YOLO-series models, MFIA-YOLO achieved a better balance between detection accuracy and model complexity. These results demonstrate its effectiveness for accurate spacer defect detection in transmission lines. Full article

Sustainable hydrogen policy and national strategy documents are rich in qualitative information whose systematic evaluation still relies largely on subjective SWOT frameworks. This study proposes a reproducible hybrid methodology that couples expert-supervised Natural Language Processing (NLP) with a stochastic techno-economic model of the Levelized Cost of Hydrogen (LCOH) to convert policy discourse into quantitative, evidence-based recommendations. TF-IDF vectorization, K-Means clustering, Shannon entropy and Correspondence Analysis (CA) are applied to a manually annotated corpus of 107 sentences drawn from Türkiye’s national hydrogen strategy documents (Cohen’s κ = 0.81, substantial agreement). CA positions Regulation/Legislation and Financing near the Weakness quadrant, Renewable Resource Potential in the Strength quadrant, and Export/Demand Risk near Opportunity—revealing structural bottlenecks that challenge the sustainable energy transition. These qualitative findings are subjected to a quantitative consistency check via a Monte Carlo simulation (N = 10,000 iterations) propagating joint uncertainty in CAPEX, electricity price, electrolyzer efficiency, annual operating hours, discount rate and plant lifetime. The deterministic 2025 LCOH baseline of 4.89 €/kg H₂ carries a P10–P90 interval of [3.95; 5.92] €/kg. Global Sobol sensitivity analysis identifies electricity price as the dominant driver (S₁ ≈ 0.52), suggesting that financing is discursively surfaced by the textual layer. Under business-as-usual technological learning, the probability of reaching a globally competitive LCOH (≤2 €/kg H₂) by 2050 is only 17.8%; a stylized proactive policy intervention (carbon pricing + subsidies) raises this probability to 78.4%. The framework is adaptable to other countries and languages (though the current implementation is Turkish-specific), providing a scalable, open methodology for evidence-based sustainable clean energy planning. Full article

This study addresses the problems of vessel congestion and inefficient resource utilization caused by the independent scheduling of port berths and locks in river–sea intermodal transport. Moreover, this study explores the joint scheduling optimization problem for a single port and a single lock under the direct transshipment mode of seagoing and river vessels. First, considering the spatiotemporal coupling characteristics of the port–lock system, a mixed-integer linear programming model integrating seaside and riverside berth allocation, lock batch scheduling, and 2D layout constraints of the lock chambers is constructed. The objective is to minimize the time costs of seagoing vessels in a port, river vessels in a port, and river vessels passing through the locks. Second, a hierarchical iterative collaborative optimization algorithm is designed. The outer layer uses an iterative algorithm framework, while the inner layer utilizes a heuristic algorithm to address the lock scheduling problem. An adaptive large neighborhood search algorithm is used to address the berth scheduling problem. Finally, the model and algorithm are validated through numerical instances. Compared with the commercial solver CPLEX, the proposed algorithm HICOA achieves up to 81.45% lower costs in medium-to-large instances. Compared with independent scheduling, joint scheduling reduces the total vessel dwell time cost by up to 66.22%, demonstrating the significant value of port–lock collaboration. Furthermore, the arrival time window of river vessels and the ratio of the number of vessels to the number of berths have a significant influence on system performance. Full article

Dysgeusia is a clinically consequential, but still under-standardized, toxicity of cancer treatment. In the immunotherapy era, taste disturbances are increasingly relevant for patients receiving immune checkpoint inhibitors, chimeric antigen receptor (CAR) T-cell therapies and T-cell-redirecting bispecific antibodies, with G protein-coupled receptor family C group 5 member D (GPRC5D)-directed treatment in multiple myeloma representing a particularly instructive high-burden model. We performed a structured critical narrative review with evidence mapping. PubMed/MEDLINE was searched from database inception to June 2026, complemented by citation tracking in Google Scholar, ClinicalTrials.gov searches and guideline documents relevant to oncology nutrition, oral supportive care and cancer-related taste dysfunction. Search concepts covered cancer-related dysgeusia, immunotherapy-associated oral toxicity, GPRC5D/talquetamab-associated dysgeusia, oncology nutrition, oral–gut microbiome biology, natural taste modulators and miraculin-based interventions. Dysgeusia can reduce appetite, food enjoyment, dietary diversity and protein energy intake, thereby contributing to weight loss, malnutrition risk, distress, social withdrawal and, in severe cases, treatment modification or discontinuation. Available evidence is heterogeneous: general cancer-treatment-associated dysgeusia is supported by broader observational and interventional literature; immunotherapy-associated dysgeusia is less systematically characterized; and GPRC5D/talquetamab-associated dysgeusia represents the most clinically visible and target-specific immunotherapy-associated phenotype. Emerging pilot data suggest that dried miracle berry or miraculin-containing products may improve selected taste perception and nutritional parameters in cancer-related dysgeusia, but direct evidence in immunotherapy-associated dysgeusia is not yet established. We, therefore, propose a claim-disciplined precision supportive-care framework integrating systematic taste phenotyping, early nutritional risk assessment, oral health evaluation, microbiome-aware but hypothesis-generating endpoints, individualized flavor and texture adaptation, cautious use of natural taste modulators in selected patients and iterative monitoring of patient-centered outcomes. Future trials should test whether dysgeusia-focused nutritional and taste-modulating supportive care interventions can improve intake, quality of life and treatment persistence without compromising immunotherapy safety or efficacy. Full article

Kadomtsev–Petviashvili (KP)-type nonlinear dispersive wave equations play a fundamental role in the description of weakly nonlinear waves in plasmas, fluids, and nonlinear optical systems. In strongly turbulent or heterogeneous media, however, transport processes often become nonlocal and exhibit anomalous scaling behavior. Such phenomena are naturally described using fractional differential operators. Recent work has significantly advanced the mathematical understanding of fractional Kadomtsev–Petviashvili models by proving the existence of periodically modulated solitary waves and lump solutions, and by analyzing instability and other significant properties. The present paper complements this line of research by combining a plasma-oriented modeling motivation with a variational existence framework, structural properties of traveling profiles, and a Fourier spectral computational pipeline for profile construction and dynamical validation. The mathematical properties of the resulting equation are investigated. In particular, we prove the existence of traveling-wave solutions using a variational formulation and concentration-compactness arguments. To compute these coherent structures numerically, we develop a Fourier pseudospectral Petviashvili iteration scheme adapted to the fractional KP operator. The computed profiles are validated through direct time integration of the governing equation using an exponential time-differencing spectral method. The results demonstrate that fractional dispersive effects (e.g., the dependence on $\alpha$) significantly modify the structure of nonlinear plasma waves and provide a natural framework for describing wave dynamics in turbulent plasma environments. The numerical results include a detailed verification of the predicted algebraic decay law, a convergence study of the Petviashvili iteration, validation against the exact KP soliton, and dynamical stability tests over long time intervals. A quantitative comparison with existing results in the literature is also provided. Full article

Parameter optimization of injection and production is an important method used to enhance recovery rate and reduce water cut, mainly aiming to determine the injection and production strategies during the development process to maximize the economic benefits throughout the reservoir development process. However, current optimization approaches for injection and production face challenges such as complex and inefficient optimization models and high-dimensional discrete variables, making it difficult to improve the global optimization ability of the algorithm (avoiding local optimal solutions during the optimization process) and the controllability of the time for completing the optimization of injection and production parameters under real and limited numerical simulations. This paper proposes a high-dimensional multi-discrete injection and production parameter non-gradient optimization method (MNOM), which combines the upper confidence bound (UCB) algorithm and effectively explores better injection and production systems and small-layer water-allocation schemes, achieving the maximization of net present value (NPV) over the entire development period. Specifically, this method models the injection and production parameter optimization problem as a Monte Carlo tree search process (Monte Carlo tree search, MCTS), and implements the optimization of injection and production cycles and small-layer water allocation through a genetic algorithm (CLGA) proxy optimized by a convolutional long short-term memory network (ConvLSTM). This method effectively overcomes the spatial and temporal limitations of the search process, maps production dynamics to the random strategies of injection and production parameters, and estimates the expected return of each policy. The CLGA proxy rapidly identifies suitable well-control schedules and water-allocation schemes in real time based on the production status at different development stages, thereby improving overall production performance. The proposed method has two innovative points. Firstly, MCTS can explore the large-scale discrete space of injection and production parameter optimization variables through tree decomposition, combined with the UCB incentive mechanism, to improve the global optimization ability. Secondly, the model training process is completely based on existing physical laws, with good temporal evolution, and the trained strategy can quickly adapt to the production status of the target layer without the need for a complete re-training from the beginning, enabling offline application and having good real-time controllability. In order to verify the effectiveness of the method proposed in the article, tests were conducted on a 3D reservoir actual model. Compared with gradient-based approaches, classical evolutionary algorithms, and proximal policy optimization (PPO), MNOM not only achieves stronger global search performance and requires 55–78% fewer iterations, but also improves the effective sweep volume by 1.1% to 5.5% compared to other optimization methods; furthermore, when compared with the PPO method, it is found that in offline optimization, if the production regime changes, the training strategy has better real-time controllability. The MNOM method can still maintain the original optimization effect compared to the PPO method when the production regime changes, demonstrating better engineering adaptability. The research results show that the proposed multi-strategy fusion high-dimensional multi-discrete injection and production parameter non-gradient optimization method can effectively improve recovery rate, expand effective sweep volume, and balance global optimization ability, optimization efficiency, and real-time controllability under the constraint of limited numerical simulations, and it has good engineering adaptability and application prospects. Full article