Search Results (292)

Concurrency bugs originate from complex and improper synchronization of shared resources, presenting a significant challenge for detection. Traditional static analysis relies heavily on expert knowledge and frequently fails when code is non-compilable. Conversely, large language models struggle with semantic sparsity, inadequate comprehension of concurrent semantics, and the tendency to hallucinate. To address the limitations of static analysis in capturing complex concurrency semantics and the hallucination risks associated with large language models, this study proposes ConSynergy. This novel framework integrates the structural rigor of static analysis with the semantic reasoning capabilities of large language models. The core design employs a robust task decomposition strategy that decomposes concurrency bug detection into a four-stage pipeline: shared resource identification, concurrency-aware slicing, data-flow reasoning, and formal verification. This approach fundamentally mitigates hallucinations from large language models caused by insufficient program context. First, the framework identifies shared resources and applies a concurrency-aware program slicing technique to precisely extract concurrency-related structural features, thereby alleviating semantic sparsity. Second, to enhance the large language model’s comprehension of concurrent semantics, we design a concurrency data-flow analysis based on Chain-of-Thought prompting. Third, the framework incorporates a Satisfiability Modulo Theories solver to ensure the reliability of detection results, alongside an iterative repair mechanism based on large language models that dramatically reduces dependency on code compilability. Extensive experiments on three mainstream concurrency bug datasets, including DataRaceBench, the concurrency subset of Juliet, and DeepRace, demonstrate that ConSynergy achieves an average precision and recall of 80.0% and 87.1%, respectively. ConSynergy outperforms state-of-the-art baselines by 10.9% to 68.2% in average F1 score, demonstrating significant potential for practical application. Full article

A special class of complex adaptive systems—biological and social—thrive not by passively accumulating patterns, but by engineering coherence, i.e., the deliberate alignment of prior knowledge, real-time updates, and teleonomic purposes. By contrast, today’s AI stacks—Large Language Models (LLMs) wrapped in agentic toolchains—remain rooted in a Turing-paradigm architecture: statistical world models (opaque weights) bolted onto brittle, imperative workflows. They excel at pattern completion, but they externalize governance, memory, and purpose, thereby accumulating coherence debt—a structural fragility manifested as hallucinations, shallow and siloed memory, ad hoc guardrails, and costly human oversight. The shortcoming of current AI relative to human-like intelligence is therefore less about raw performance or scaling, and more about an architectural limitation: knowledge is treated as an after-the-fact annotation on computation, rather than as an organizing substrate that shapes computation. This paper introduces Mindful Machines, a computational paradigm that operationalizes coherence as an architectural property rather than an emergent afterthought. A Mindful Machine is specified by a Digital Genome (encoding purposes, constraints, and knowledge structures) and orchestrated by an Autopoietic and Meta-Cognitive Operating System (AMOS) that runs a continuous Discover–Reflect–Apply–Share (D-R-A-S) loop. Instead of a static model embedded in a one-shot ML pipeline or deep learning neural network, the architecture separates (1) a structural knowledge layer (Digital Genome and knowledge graphs), (2) an autopoietic control plane (health checks, rollback, and self-repair), and (3) meta-cognitive governance (critique-then-commit gates, audit trails, and policy enforcement). We validate this approach on the classic Credit Default Prediction problem by comparing a traditional, static Logistic Regression pipeline (monolithic training, fixed features, external scripting for deployment) with a distributed Mindful Machine implementation whose components can reconfigure logic, update rules, and migrate workloads at runtime. The Mindful Machine not only matches the predictive task, but also achieves autopoiesis (self-healing services and live schema evolution), explainability (causal, event-driven audit trails), and dynamic adaptation (real-time logic and threshold switching driven by knowledge constraints), thereby reducing the coherence debt that characterizes contemporary ML- and LLM-centric AI architectures. The case study demonstrates “a hybrid, runtime-switchable combination of machine learning and rule-based simulation, orchestrated by AMOS under knowledge and policy constraints”. Full article

To address the time–cost trade-off challenge in real-world practices, a bi-objective optimization model of the Multi-mode Resource-Constrained Project Scheduling Problem is proposed with simultaneously minimizing both the project makespan and the resource cost. A mode-oriented Non-dominated Sorting Genetic Algorithm II is developed to solve the formulated problem. Two key improvements are introduced: a mode-repair mechanism is incorporated during the initialization phase to generate feasible execution modes, thereby improving the quality of initial solutions and accelerating search efficiency, and four neighborhood structures based on mode and task execution lists are designed for local search, enabling fine-grained solution refinement in each iteration. Extensive experimental studies are conducted to verify the effectiveness of the proposed strategies, and comparative evaluations with state-of-the-art algorithms demonstrate that MNSGA-II achieves superior performance across multiple metrics, including lower mean ideal distance, better solution quality, improved diversity, and more uniform distribution of Pareto-optimal solutions. Full article

Background: As a labor-, capital-, and technology-intensive sector, shipbuilding supports water transportation, international trade, and marine development, driving economic growth and employment. Yet rising raw material/labor costs now bottleneck enterprise performance, making cost reduction and efficiency improvement urgent for shipbuilding and repair firms. It is an effective way to improve logistics transportation efficiency for reducing the cost of shipbuilding and repair firms. However, there are still few methods specifically designed for logistics transportation scheduling in shipbuilding and repair firms. Methods: In this paper, a “dual-cycle” strategy is proposed to optimize material transportation and cut logistics vehicles’ empty-load rate in the shipbuilding and repair process. A mixed-integer programming model is built to minimize total empty travel time, considering task priorities and time windows. A genetic algorithm-based scheduling method is proposed to solve this complex scheduling model. Results: Simulation with real shipyard logistics data shows the proposed model and algorithm can effectively address the shipbuilding logistics vehicle scheduling problem. In addition, the proposed algorithm performs better than two other compared algorithms in handling the studied problem. Conclusions: This study aids shipbuilding and repair logistics managers in making scheduling plans and determining optimal vehicle numbers, supporting cost-efficiency improvement. Full article

To address the limitations of the original Sand Cat Swarm Optimization (SCSO) algorithm—such as static strategy selection, insufficient population diversity, and coarse boundary handling—this paper proposes a multi-strategy enhanced version, namely the Modified Sand Cat Swarm Optimization (MSCSO). The algorithm improves performance through three core strategies: (1) an adaptive strategy selection mechanism that dynamically adapts to different optimization phases; (2) an adaptive crossover–mutation strategy inspired by differential evolution, in which mutation vectors are generated with the guidance of the global best solution and updated via binomial crossover, thereby enhancing both population diversity and local search capability; and (3) a boundary control mechanism guided by the global best solution, which repairs out-of-bound solutions by relocating them between the global best and the boundary, thus preserving useful search information and avoiding oscillation near the limits. To validate the performance of MSCSO, extensive experiments were conducted on the CEC2020 and CEC2022 benchmark suites under 10- and 20-dimensional scenarios, where MSCSO was compared with seven algorithms, including Particle Swarm Optimization (PSO) and Gray Wolf Optimizer (GWO). The results demonstrate that MSCSO consistently outperforms its competitors on unimodal, multimodal, and hybrid functions. Notably, MSCSO achieved the best Friedman ranking across all dimensions. Ablation studies further confirm that the three proposed strategies exhibit strong synergy, collectively accelerating convergence and enhancing stability. In addition, MSCSO was applied to multilevel threshold image segmentation, where Otsu’s criterion was adopted as the objective function and experiments were conducted on five benchmark images with 4–10 thresholds. The results show that MSCSO achieves superior segmentation quality, significantly outperforming the comparison algorithms. Overall, this study demonstrates that MSCSO effectively balances exploration and exploitation without increasing computational complexity, providing not only a powerful tool for global optimization but also a reliable technique for engineering tasks such as multilevel threshold image segmentation. These findings highlight its strong theoretical significance and promising application potential. Full article

Background/Objectives: The aim of the study is to first evaluate mid- to long-term changes in shoulder range of motion (ROM) and functional performance in activities of daily living (ADLs) after arthroscopic Bankart repair using three-dimensional (3D) motion analysis. Methods: We prospectively analyzed five patients (mean age: 31.8 years) pre- and postoperatively at 8.4 months and eight patients retrospectively at 12.1 years (mean age: 40.4 years). Fifteen asymptomatic adults served as controls. Shoulder kinematics were assessed using the Heidelberg Upper Extremity (HUX) model during maximum ROM and four ADL tasks (apron, neck, wash, and book). Results: At short-term follow-up, forward flexion improved by 31° (p < 0.05) and abduction improved by 70° (p < 0.05), while other movements showed non-significant trends toward improvement. Long-term follow-up demonstrated sustained or increased gains in flexion (+9°) and abduction (+7°) but significant declines in external rotation (−5°) and internal rotation (−30°). ADL analyses showed significant postoperative gains in abduction/adduction during “apron” (+6.7°) and “neck” (+49.8°) tasks. The long-term results remained comparable to or better than postoperative values in most planes, although external/internal rotation during the “wash” task decreased over time. Compared with normative controls, patients employed a larger ROM during some ADLs, suggesting compensatory mechanisms. Conclusions: Arthroscopic Bankart repair yields sustained mid- to long-term improvements in shoulder ROM and ADL performance. Rotational deficits persist despite maintained flexion and abduction in the long run, underscoring the need for targeted rehabilitation strategies. Full article

Accurate and reliable extraction of building footprints from LiDAR point clouds is a fundamental task in remote sensing and urban scene reconstruction. Building footprints serve as essential geospatial products that support GIS database updating, land-use monitoring, disaster management, and digital twin development. Traditional image-based methods enable large-scale mapping but suffer from 2D perspective limitations and radiometric distortions, while airborne or vehicle-borne LiDAR systems often face single-viewpoint constraints that lead to incomplete or fragmented footprints. Recently, backpack mobile laser scanning (MLS) has emerged as a flexible platform for capturing dense urban geometry at the pedestrian level. However, the high noise, point sparsity, and structural complexity of MLS data make reliable footprints delineation particularly challenging. To address these issues, this study proposes a Deep Line-Segment Detection–Driven Building Footprints Extraction Framework that integrates multi-layer accumulated occupancy mapping, deep geometric feature learning, and structure-aware regularization. The accumulated occupancy maps aggregate stable wall features from multiple height slices to enhance contour continuity and suppress random noise. A deep line-segment detector is then employed to extract robust geometric cues from noisy projections, achieving accurate edge localization and reduced false responses. Finally, a structural chain-based completion and redundancy filtering strategy repairs fragmented contours and removes spurious lines, ensuring coherent and topologically consistent footprints reconstruction. Extensive experiments conducted on two campus scenes containing 102 buildings demonstrate that the proposed method achieves superior performance with an average Precision of 95.7%, Recall of 92.2%, F1-score of 93.9%, and IoU of 88.6%, outperforming existing baseline approaches by 4.5–7.8% in F1-score. These results highlight the strong potential of backpack LiDAR point clouds, when combined with deep line-segment detection and structural reasoning, to complement traditional remote sensing imagery and provide a reliable pathway for large-scale urban scene reconstruction and geospatial interpretation. Full article

Person–job data are multi-source, heterogeneous, and strongly temporal, making knowledge modeling and analysis challenging. We present an automated approach for constructing a Human-Resources Temporal Knowledge Graph. We first formalize a schema in which temporal relations are represented as sets of time intervals. On top of this schema, a large language model (LLM) pipeline extracts entities, relations, and temporal expressions, augmented by self-verification and external knowledge injection to enforce schema compliance, resolve ambiguities, and automatically repair outputs. Context-aware prompting and confidence-based escalation further improve robustness. Evaluated on a corpus of 2000 Chinese resumes, our method outperforms strong baselines, and ablations confirm the necessity and synergy of each component; notably, temporal extraction attains an F1 of 0.9876. The proposed framework provides a reusable path and engineering foundation for downstream HR tasks—such as profiling, relational reasoning, and position matching—supporting more reliable, time-aware decision-making in complex organizations. Full article

IEA Wind Task 43 seeks to “unlock the full value of wind energy through digital transformation”. One mechanism to realize value is through enhanced data-driven decision-making and, while many areas in the wind sector can benefit from improved decision support, this case study focusses on a well-defined wind energy maintenance scenario involving blade inspection and repair. The solution concentrates on the specific damage category of blade leading edge erosion (LEE) and the optimum action to be taken for a given level of damage detected during periodic inspections. The key decision is whether to initiate repairs immediately or continue operating the turbine until the next inspection—and, if so, when that next inspection should take place. Even for such a specific damage type and decision option, the overall solution draws on multiple data types, ranging from damage classifications to cost drivers, and integrates a number of components including damage propagation, performance, and cost models. The core of the solution is a risk-based decision model using heuristic strategies, and Bayesian networks for optimized decision-making. This paper outlines the overall solution, expands on the data and modelling implementations, and discusses the results and conclusions arising from the investigation. Full article

This study examined the impact of early robotics experiences on kindergarten children’s self-efficacy and performance across multiple domains, including building, following visual instructions, problem-solving, and object repair. Ninety-seven children (ages 4–6) were assigned to either a research group (n = 46) receiving a year-long robotics curriculum or a control group (n = 51) following the standard curriculum. A quasi-experimental pre-test–post-test design was employed. Self-efficacy was measured using dichotomous questionnaire items, and performance was assessed through hands-on age-appropriate repair tasks. Baseline comparisons showed no significant differences between groups, supporting equivalence at the start of the study. Results indicated that children who participated in the robotics program reported greater confidence in building, following visual instructions, and solving problems compared to the control group. Importantly, children in the robotics group not only reported greater confidence in their repair abilities but also outperformed peers in the post-test repair task. These findings indicate that early robotics fosters both beliefs of capability and tangible problem-solving skills in early childhood. Embedding robotics into kindergarten curricula may therefore strengthen foundational self-efficacy and support transferable skills relevant for long-term learning and well-being. Full article

The promising biocontrol agent Brevibacillus brevis is a broadly dispersed bacterium exhibiting significant antibacterial properties against plant diseases. This study conducted a comprehensive comparative genomic analysis of 25 B. brevis strains to examine their taxonomic classification, genetic diversity, and biocontrol potential. The genome sizes, excluding strain NEB573, varied from 5.95 to 6.73 Mb, with GC content between 47.0% and 47.5%. Notably, strain NEB573 exhibited distinct genomic characteristics based on Average Nucleotide Identity (ANI), digital DNA-DNA hybridisation (dDDH), and phylogenetic analyses, suggesting it may represent a novel Brevibacillus species pending additional phenotypic confirmation. The remaining 24 strains were grouped into six phylogenetic clades. The pan-genome study demonstrated significant genomic flexibility, demonstrating an open architecture with 2855 core gene families (33.08%) and 1699 distinct genes. Functional annotations indicated that unique genes were enriched in tasks related to DNA repair and environmental adaptation, while core genes predominantly participated in amino acid metabolism and transcription. The examination of biosynthetic gene clusters (BGCs) identified multiple antimicrobial compounds, such as gramicidin and tyrocidine, which have been reported to exhibit both antibacterial and antifungal activities, thereby underscoring the broad-spectrum biocontrol potential of B. brevis. These findings endorse the application of biocontrol in sustainable plant disease management and offer novel perspectives on its genetic basis in B. brevis. Future investigations of its metabolic repertoire may unveil novel agro-biotechnological applications. Full article

Automated production plays a vital role in the long-term development of industrial enterprises, and automated production has high requirements for defect detection of industrial parts. In this study, we construct a complex atom network based on Swin Transformer—selected for its window-based multi-head self-attention (W-MSA) and shifted window-based multi-head self-attention (SW-MSA) mechanisms, which enable efficient cross-window feature interaction and reduce computational complexity compared to vanilla Transformer or CNN-based methods in multi-task scenarios—and after repairing and recovering the abnormally generated and randomly masked images in the industrial automated production environment, we utilize the discriminative sub-network to achieve real-time abnormality image detection and classification. Then, the loss function optimization model is used to construct a real-time multi-task image detection model (MSTUnet) and design a real-time detection system in the industrial automation production environment. In the PE pipe image defect detection for industrial automated production, the average recognition rate of this paper’s detection model for six kinds of defects can reach 99.21%. Practical results show that the product excellence rate and qualification rate in the industrial automated production line equipped with this paper’s detection system reached 15.32% and 91.40%, respectively, and the production efficiency has been improved. The real-time multi-task image inspection technology and system proposed in this paper meet the requirements of industrial production for accurate, real-time and reliable, and can be practically applied in the industrial automation production environment, bringing good economic benefits. Full article

Human rights frameworks have historically emphasised physical integrity, yet psychological integrity, the right to mental stability, identity, and emotional safety all remain neglected in health policy and law. This narrative review and commentary argues that psychological integrity is inseparable from ecological integrity, and that contemporary mental health crises are rooted in ruptured human–nature attachments. Drawing on Mother Nature Attachment Theory (MNAT) and supported by emerging empirical evidence, this review traces a trajectory from pre-attachment, through rupture via colonisation, displacement, and ecological collapse, to reconnection through cultural and ecological repair. Gaza exemplifies a contemporary site of deliberate ecological–psychological rupture, where environmental destruction compounds trauma and erodes cultural continuity. In contrast, Indigenous frameworks in Australasia, such as Te Whare Tapa Whā, provide culturally grounded models of reconnection that demonstrate how ecological repair and psychological restoration can proceed together. These contrasting cases illustrate MNAT’s trajectory and emphasise that safeguarding psychological integrity requires embedding ecological security into public health systems. The review concludes that planetary mental health depends on recognising healing of mind and Earth as an indivisible task. Healing mind and Earth must be understood as a single, urgent task within planetary public mental health. Full article

Fluoroscopy (real-time X-ray) images are used for monitoring minimally invasive coronary angioplasty operations such as stent placement. During these operations, a thin wire called a guidewire is used to guide different tools, such as a stent or a balloon, in order to repair the vessels. However, fluoroscopy images are noisy, and the guidewire is very thin, practically invisible in many places, making its localization very difficult. Guidewire segmentation is the task of finding the guidewire pixels, while guidewire localization is the higher-level task aimed at finding a parameterized curve describing the guidewire points. This paper presents a method for guidewire localization that starts from a guidewire segmentation, from which it extracts a number of initial curves as pixel chains and uses a novel perceptual grouping method to merge these initial curves into a small number of curves. The paper also introduces a novel guidewire segmentation method that uses a residual network (ResNet) as a feature extractor and predicts a coarse segmentation that is refined only in promising locations to a fine segmentation. Experiments on two challenging datasets, one with 871 frames and one with 23,449 frames, show that the method obtains results competitive with existing segmentation methods such as Res-UNet and nnU-Net, while having no skip connections and a faster inference time. Full article

Concrete diagnosis is an important task in making informed decisions about reconstructing or repairing buildings. Among the different approaches for evaluating its characteristics, methods based on electromagnetic waves have been proposed in the literature over the years. In this context, the characterization of concrete complex dielectric permittivity ${ϵ}_r\left(f\right)$ (where f is the frequency) has received considerable attention, taking into account that its values and its frequency behavior are both sensitive to a series of physical parameters, which in turn can significantly influence the mechanical performance of concrete. Recently, data-driven techniques have emerged as alternatives for modeling material properties due to their regression and generalization potential. Following this research line in this work, we investigated the potential of Gaussian Process Regression to model ${ϵ}_r\left(f\right)$ by comparing its performance with that of the model most employed to characterize the concrete dielectric permittivity: the universal Jonscher model. The inherent ability to provide predictions accompanied by confidence intervals, which allows the assessment of the reliability of the permittivity estimate across frequency, and the related error metrics demonstrate that GPR can effectively characterize ${ϵ}_r\left(f\right)$ in an effective manner, outperforming the Jonscher model in terms of accuracy in all the cases considered in our study. Full article