Search Results (901)

Enterprise strategic decisions must reconcile conflicting stakeholder interests under time pressure, yet the consulting that traditionally supports them remains out of reach for most small and medium-sized enterprises. Large language models make parts of this work automatable, but a single model speaks with one voice, and its reasoning can be neither inspected nor contested. As frontier models continue to improve, whether structured multi-agent deliberation is worth its additional cost has therefore become an empirical question rather than a design assumption. This study designs, prototypes, and evaluates Servi.AI, a personality-aware multi-agent intelligent decision support system for enterprise strategy. The system grounds every recommendation in a traceable evidence chain retrieved over a knowledge graph. It stages a statement–discussion–consensus roundtable in which role-specialized agents argue from conflicting professional stances. It also simulates how synthetic stakeholders, calibrated against a public personality dataset of 874,434 respondents, will experience the candidate decision. The roundtable characterizes how a decision is argued, whereas the sandbox characterizes how it will be experienced. A questionnaire with 133 screened decision-makers confirms these requirement priorities. We evaluate the system across five experimental axes: 2800 controlled simulation runs and a twelve-case benchmark judged blind across three model families. A strong single model attains the highest holistic scores (8.22–8.56/10 across judges), while deliberation contributes auditable role-grounded reasoning, conflict surfacing, and an executable blueprint. Ablating retrieval loses all 71 valid pairwise comparisons, while a heterogeneous five-family agent pool significantly improves risk coverage (Cliff’s $\delta =+0.75$). Retrieval thus drives the evidence-side qualities, and the role structure drives the deliberation-side ones: deliberative value is decomposable along architectural components, a middle-range design proposition. These findings support selective rather than default deployment. The released benchmark, judging protocol, and raw results provide a reusable basis for deciding when multi-agent decision support is worth its cost. Full article

Spaceborne synthetic aperture radar (SAR) needs point-like reference targets for co-registration, calibration, and absolute geolocation, but real corner reflectors (CRs) exist only at a few dedicated sites. We present a detection and three-dimensional (3D) positioning pipeline for naturally occurring corner-reflector-like (pseudo-CR) scatterers in KOMPSAT-5 stacks, combining constant false alarm rate (CFAR) detection ranked by local contrast rather than absolute power, a multi-scale template bank, multi-look range-Doppler bundle triangulation, and a per-target conditioning diagnostic vector. At the nine-CR Mongolia calibration field, all nine are recovered, with a mean horizontal error of 1.37 m and a mean 3D error of 2.01 m. At a CR-absent suburban site in Suncheon, Republic of Korea, 18 scenes yield 39,822 clusters, 26,659 of which pass the conditioning gate. Over 16 response-enriched poles retained by author adjudication, the difference to the nearest conditioning-filtered cluster averages 3.19 m horizontally and 1.45 m vertically relative to the surveyed pole base. Because part of that set was selected after inspecting preliminary responses, these are conditional nearest-cluster agreements, not verified target accuracies, and no detection probability is claimed. In an exploratory comparison on the same set, contrast ranking retains more evaluation targets than power ranking at equal budgets, 16 against 9 of 16. Removing robust weighting and pruning degrades the mean Mongolia 3D error to 7.39 m; the DEM prior is non-critical, whereas the clustering radius is consequential in the dense pole field, with the count within 20 m falling from 16 to 9 as the radius grows from 15 m to 30 m. In a leave-scene-out check, 85.8% of 254 evaluable clusters persist in a scene excluded from their solutions, against 35.2% of 270 evaluable random positions: a complete-case enrichment of 78%, or 53–82% under cross-arm worst-case imputation of the unevaluable targets; neither estimate measures catalogue precision. Limited KOMPSAT-5 stacks can therefore yield few-metre nearest-cluster agreement at selected pole locations where stable multi-scene responses form. Full article

Deploying prohibited-item detectors on resource-constrained X-ray security inspection terminals is not equivalent to selecting the smallest available YOLO scale: excessive compression can reduce feature capacity, whereas medium-scale detectors may retain avoidable computational redundancy. This study therefore investigates a deployment-oriented operating point through coordinated backbone compression, feature compensation, and class-sensitive optimization. YOLO-SMV and YOLO-EMV are developed from YOLOv8m and YOLO11m by combining MobileNetV3-Small backbone reconstruction, SE/ECA-based channel recalibration, and a VF-BCE objective for difficult and underrepresented categories. A three-seed full-factorial study on YOLO11m shows that VF-BCE provides the largest individual accuracy gain and that ECA repeatedly recovers part of the performance lost in the compressed VF-BCE pathway. The accuracy-oriented ECA+VF-BCE configuration reaches $0.93842\pm 0.00720$ mAP50, whereas the deployment-oriented YOLO-EMV reaches $0.92744\pm 0.00768$; the latter trades $1.098\pm 0.061$ percentage points of mAP50 for a reduction from 20.03 M to 12.04 M parameters and from 67.9 G to 28.8 G FLOPs. Under the common seed-41 SIXray protocol, YOLO-EMV also achieves higher mAP50 than standard YOLO11n, YOLO11s, and YOLO11m, demonstrating that the selected operating point is not reproduced simply by choosing a smaller baseline. Published SIXray results are reported separately as protocol-aware literature context rather than as a cross-paper ranking. Additional OPIXray and PIDray experiments provide multi-benchmark evidence for the component interactions under heavy occlusion and long-tailed class distributions. The deployment-oriented YOLO-EMV model has also been integrated into customs security inspection equipment. Full article

This paper proposes a process-mapped quality control framework for structural steel fabrication as a proof of concept for integrating normative requirements into a unified operational workflow. The study addresses the fragmentation of quality requirements in steel fabrication, where individual standards are often applied through separate inspections rather than as part of a coherent process model. The framework links material control, production preparation, assembly, welding, and anticorrosion protection through clearly defined quality gates. The framework is constructed through a qualitative design-oriented approach using SIPOC analysis and a detailed flowchart. It translates normative requirements into a coherent workflow that provides a structured basis for traceability, preventive quality assurance, and future integration with Construction 4.0. The paper suggests that process mapping can serve as a useful method for organizing quality control in steel construction projects and for transforming standards into an operational framework. Full article

‘Wellness-at-sea’ is an integral part of the cruise holiday service bundle. Marketed within an image of experiential diversity within a safe, controlled environment, it features medical centers, spa facilities and restoration-focused itineraries. Nevertheless, epidemiologically speaking, cruise ships rank amongst the riskiest transmission environments in tourism. This perspective paper proposes a typology of wellness cruising along two axes (i.e., offer scope: themed voyages versus onboard amenities and promotion focus: borrowed credibility versus narrative ‘storyscaping’) and uses it to develop the ‘wellness-risk paradox’, a boundary case for health-tourism literature. Three testable propositions emerge: reputational risk exposure increases with the offer’s wellness centrality; narrative-based promotion is more susceptible than credibility-based promotion and a quadrant-adjusted health-assurance scheme is more effective than current inspection standards. The argument draws on the 2026 Andes hantavirus outbreak aboard the MV Hondius (three deaths among thirteen cases), the 2020 Diamond Princess outbreak, and a decade of official outbreak surveillance, reporting an increase in gastrointestinal outbreaks from 0.34 to 0.62 per million passengers between 2019 and 2025 (rate ratio 1.84, 95% CI 0.87–3.86), which reverses the pre-pandemic downward trend although annual counts remain too small for the change to reach conventional statistical significance. Sanitation-inspection compliance cannot reliably predict outbreaks, nor does it facilitate trust. Four domains of action are proposed: continuous air-quality monitoring, wastewater surveillance, behavioral risk modeling and jurisdictional coordination. Full article

Cycling is increasingly promoted as part of sustainable transport strategies, but safety concerns remain an important barrier. Crash-based safety assessment is limited because crashes are reactive indicators and cycling crashes are often underreported. This study evaluates whether smartphone inertial sensor data can be used to detect selected potentially critical cycling manoeuvres that may support proactive safety screening. A two-stage workflow intended for possible future smartphone implementation was evaluated offline. In Stage 1, kinematic thresholds were applied to identify short candidate sensor windows when a high-dynamic manoeuvre was detected. In Stage 2, a machine learning classifier was applied to these candidate windows to classify the manoeuvre type and reduce false positives. The workflow was evaluated using three datasets collected in Germany from 10 riders, including controlled test-track manoeuvres and urban rides in Aachen and Bonn. The dataset included 148 hard-braking and 132 abrupt-swerving manoeuvres. In addition, 45 bicycle-only crash-like reference sequences were collected to compare low-speed impact-like signal patterns. In leave-one-dataset-out evaluation, the Support Vector Machine achieved mean precision–recall areas under the curve of 0.954 for hard braking and 0.884 for abrupt swerving. The corresponding mean F1-scores were 0.924 and 0.876. The offline evaluation showed that smartphone sensor data can support the detection of selected high-dynamic cycling manoeuvres with good classification performance under controlled and semi-controlled conditions. However, most target manoeuvres were controlled or rider-initiated proxy manoeuvres rather than confirmed traffic conflicts. The results should therefore be interpreted as manoeuvre detection, not as direct crash-risk validation. In future naturalistic studies, the workflow could help identify candidate locations where cyclists frequently brake hard or swerve abruptly. These locations can then be prioritised for site inspections, video-based conflict analysis, or comparison with crash records. Full article

This paper synthesises the available evidence on the evolution of statutory minimum and average wages across European Union Member States over the period 2019–2024. It examines patterns of wage convergence and wage adequacy, the implementation of Directive (EU) 2022/2041 on adequate minimum wages, and their implications for broader labour market developments. The core period of analysis is 2019–2024. Earlier years are used only as historical or institutional background, while selected 2025 references are used for contextual policy updates and not as part of the main comparative period. The analysis draws upon peer-reviewed academic literature alongside official institutional and statistical data from Eurofound, Eurostat, the European Commission, the OECD, the ECB, and national authorities. The central research question asks whether recent increases in statutory minimum wages and average wages have contributed to upward wage convergence and wage adequacy across EU Member States, and under which institutional and macroeconomic conditions these developments have affected inequality, employment, inflationary pressures, and competitiveness. Its theoretical framework is grounded in the concepts of wage spillover and wage compression effects, the wage–price spiral debate, and institutional approaches to collective bargaining and wage setting. Evidence indicates substantial nominal increases in statutory minimum wages in most EU countries, especially in Central and Eastern Europe, where upward wage convergence has reduced the gap with Western European countries. Despite concerns about inflationary pressures, the evidence does not support the existence of a generalised wage–price spiral. Instead, wage increases largely represent compensatory responses to externally driven inflation, producing moderate spillover effects on neighbouring wage levels and contributing to a partial compression of wage inequality at the lower end of the wage distribution. The study also identifies continuing challenges, including youth unemployment, precarious forms of employment, regional wage disparities and uneven collective bargaining coverage. It concludes that sustainable and adequate wage floors are best supported by transparent adjustment criteria, productivity growth, effective labour inspection, investment in skills and training, and inclusive collective bargaining institutions. Full article

Evaluating whether an intellectual property (IP) prototype faithfully preserves the visual identity and semantic intent of its original concept design is an important yet challenging task in product design and creative prototyping. Existing evaluation practices mainly rely on manual inspection or global image-level similarity comparison, which are subjective, difficult to reproduce, and insufficient for localizing identity-critical deviations. To address this problem, this paper proposes PartSense-IP, a part-aware vision–language sensor fusion framework for visual–semantic consistency evaluation of IP prototypes. The proposed framework takes a 2D concept image, an optional textual design description, and multi-view RGB-D sensor observations of a prototype as inputs. It first constructs a multi-view prototype representation and decomposes both the concept and prototype observations into design-relevant parts. Dense visual features, color and shape descriptors, and vision–language semantic embeddings are then extracted to evaluate part-level consistency. A Part-Aware Visual–Semantic Consistency Fusion (PVCF) algorithm is further developed to integrate shape, color, local visual similarity, semantic alignment, and cross-view stability into a unified IP consistency score. In addition to scalar scoring, PartSense-IP generates localized difference maps, 3D inconsistency visualization, and interpretable design feedback for prototype refinement. Experiments on the proposed IP-ProtoSense evaluation protocol demonstrate that PartSense-IP outperforms representative vision–language, dense-visual, segmentation-based, and 3D multimodal baselines in consistency scoring, inconsistency detection, localization, ablation, and robustness evaluation. Full article

The catenary support component (CSC) is a key part of the electrified railway system, and its operational status directly affects railway operational safety. These components’ images are collected using inspection equipment and detected using computer vision techniques. However, catenary network inspection faces the following issues: (1) due to limitations in the equipment’s shooting angle and changes in viewing distance, the collected images contain multi-scale and multi-class problems, and (2) the railway environment is highly variable, and adverse weather conditions such as fog, rain, and low light affect the imaging devices, leading to degraded image quality. To address these issues, this paper proposes a novel detection framework, FF-DEIM, for detecting catenary support components. First, a dual-channel fusion network (DCFNet) is introduced, which significantly improves image quality by removing foreground interferences such as fog, raindrops, and dynamic blur. Second, a pretraining framework based on contrastive learning, mask image modeling with contrastive learning (MIMCL), is designed to enhance the model’s focus on key regions of the catenary network components, optimizing feature extraction capabilities and improving model convergence speed. Then, a feature-focusing pyramid network (FFPN) is proposed, which uses the focus feature module to fuse cross-level contextual features, enhancing the ability to capture local details and improving the model’s small object detection performance. Finally, a drone-based catenary network image dataset, including scenes with fog, rain, and low light, is constructed, and experiments validate the effectiveness of the proposed method. Full article

Magnetic leakage detection is one of the most commonly used methods for pipeline inspection, which mainly uses magnetic sensors to detect the magnetic leakage field on the internal and external surfaces of the pipeline to determine whether there are defects in the pipeline. The defect quantification algorithm includes a forward model and an optimization algorithm, in which the estimation of target defects using optimization algorithms is one of the key aspects of defect inversion. Most of the existing optimization algorithms are based on particle swarm algorithms (PSOs) and genetic algorithms (GAs), which are prone to premature problems and have low convergence accuracy. To address the problems in the process of defect inversion, this paper proposes a new inversion algorithm, which obtains part of the prior knowledge from the application context of defect inversion, and adopts the decay oscillation function as the nonlinear convergence factor based on the whale optimization algorithm (WOA). In addition, referring to the concepts of “genetic” and “mutation” in the GA, a diversity variation strategy based on dynamic step size is designed. The algorithm designed has the advantages of fast operation and high search accuracy. At the end of the paper, two sets of experiments are designed to compare the improved WOA with other existing optimization algorithms. The results demonstrate that the algorithm is significantly superior to other algorithms, both in the ideal case of simulation experiments and in the practical application of defect inversion. Full article

The delay-and-sum total focusing method (TFM) for ultrasonic full matrix capture (FMC) depends on accurate ray path and travel time computation. In L-shaped carbon fiber-reinforced polymer (CFRP) corner parts, elastic anisotropy, multilayer stacking, and curvature-induced ray path non-uniqueness generate strong stripe-like coherent clutter (deterministic structural echoes), degrading focusing and sizing. To address this, we search multiple physically plausible candidate ray paths and propose a phase-consistency-adaptive multi-path fusion TFM (PCA-MPF-TFM) that performs pixel-wise path selection and fusion. The method is validated using pulse-echo FMC data acquired with a water-immersion linear array from a 6.4 mm-thick L-shaped CFRP specimen containing three 3 mm-diameter polytetrafluoroethylene (PTFE) inserts; the two within the concave-side inspection region were quantitatively evaluated. Compared with conventional isotropic TFM, an edge-adjacent delamination previously masked by structural noise is consistently detected with a 9.2 dB signal-to-noise ratio (SNR) and a 0.2 mm length error. For the second delamination, the SNR improves by 25 dB and the length error decreases from 0.6 mm to 0.2 mm. Experimental results demonstrate improved defect detectability and noise robustness under curved, anisotropic, and multilayer propagation while maintaining sub-millimeter sizing accuracy. 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

Dimensional accuracy and surface roughness in material extrusion (MEX) have been widely studied; however, the effect of part scale on the simultaneous fulfilment of geometrical tolerances and surface finish remains less clearly established. This study evaluates the scale-dependent quality of PLA parts manufactured by material extrusion (MEX) using a full 3 × 3 × 3 factorial design, combining three scales (1×, 0.75×, and 0.5×), three commercial PLA filaments, and three printing speeds (40, 60, and 80 mm/s). A dedicated test artefact was inspected by a coordinate measuring machine to quantify GD&T-related deviations, including flatness, perpendicularity, parallelism, angularity, circularity, cylindricity, and coaxiality. Surface roughness was characterized by 2D profilometry using Ra, Rz, and Rq on representative horizontal and vertical surfaces. Results showed that part scale was the dominant factor affecting geometrical conformity, especially for orientation- and location-related tolerances such as perpendicularity, parallelism, and coaxiality, which deteriorated markedly as specimen size decreased. Filament type also influenced several geometrical responses, whereas printing speed showed no significant main effect on geometrical tolerances within the evaluated range. Surface roughness exhibited clear anisotropy, with vertical surfaces showing higher values and stronger statistical dependence on process factors. Unlike studies focused only on dimensional accuracy or surface roughness, this work provides an integrated GD&T-based and surface-texture assessment of scale-dependent quality loss in PLA parts manufactured by MEX. These findings may help designers and manufacturers define inspection criteria, select suitable commercial PLA filaments, and identify critical geometrical features when scaled MEX parts are intended for functional applications. Full article

Road crack detection is an important part of the operation and maintenance of intelligent transportation infrastructure. However, the traditional convolution method has the problem of geometric mismatch when dealing with the slender linear structure of cracks, and the multi-scale adaptability and anti-interference ability of the detection model under a complex road background are still insufficient. Aiming at key problems such as the difficulty of linear feature extraction, poor multi-scale adaptability, and complex background interference, this paper proposes a road crack detection algorithm based on an improved RT-DETR multi-dimensional feature fusion method. This method improves the detection performance by introducing three core innovative modules. Firstly, a lightweight directional decoupled dynamic convolution (D3Conv) is designed, which makes the convolution kernel fit the crack direction through direction prediction and adaptive sampling, so as to improve the recall rate by 0.012 (from 0.647 to 0.659) and reduce the computational burden. Secondly, a multi-scale cross-attention enhancement module (MCAA) was proposed to fuse multi-scale convolution and direction-aware strip convolution, and the dual attention mechanism was combined to enhance the perception of crack morphology and scale. Furthermore, a context-guided feature reconstruction (CGFR) module was constructed, which effectively aggregated global semantics and local details through dynamic feature selection and a multi-branch refining mechanism to improve the accuracy of boundary location. Experiments on the public dataset SVRDD2024 show that the proposed algorithm achieves an mAP@50 of 0.724, which is 2.2% higher than the baseline RT-DETR-R18 in terms of mAP@50. Especially, the detection performance is significantly improved for small cracks and in complex environments. It provides a reliable and efficient solution for the automatic inspection of intelligent transportation infrastructure. Full article

This study examines the influence of the dynamic pressure control parameter in the nozzle during melt deposition onto the build platform (k-Factor), also known as Linear Advance or Pressure Advance in different firmware implementations, on the quality of parts produced using the Fused Deposition Modeling (FDM) technology. The investigation was conducted based on printed test specimens made from Ingeo Biopolymer 4043D, with k-Factor values varied in the range of 0.01 to 0.20 under comparable process conditions. Dimensional measurements were performed along the X and Y axes, and visual analysis was carried out to identify defects in the specimens. The results from statistical analysis and visual inspection reveal a clear correlation between the k-Factor value and part quality. An optimal balance between dimensional and geometric stability was achieved at k-Factor = 0.04, identifying it as a suitable value for the material and process conditions used. Full article