Search Results (2,323)

Configurable intelligent design is formalized here as the selection of a consistent configuration from interdependent alternatives under constraints of budget, quality, risk, and compatibility. It is examined not as a universal solver but as a class of domain-specific systems, each defined for a task family with explicitly formalizable variables, hard constraints, and objectives. Locally correct predictions for individual components do not guarantee a globally feasible solution. This study compares direct neural-network inference, the exact HIM-D solver, and a procedural hybrid whose network candidate is checked by an independent verifier, with an exact search invoked whenever a constraint is violated. A reproducible synthetic corpus of 24,000 problems across educational, engineering, and commercial scenarios was assessed for feasibility, optimality, robustness to distribution shift, repeatability, and latency. Pure neural-network models were feasible in only 17.40% and 19.12% of cases and degraded sharply out of distribution, whereas the hybrid maintained 100.00% feasibility and 83.08% joint optimality. A staged ablation identifies pairwise incompatibility density as the dominant source of that degradation, and constraint-aware correction alone raises feasibility without reaching a guarantee. The results therefore support separating probabilistic candidate generation from independent formal verification within a bounded class of specialized configuration problems, not a universal solver. Full article

This paper presents a stage-complete programmable-logic architecture for pairwise monocular structure from motion using calibrated, pre-undistorted 1920 × 1080 video. It integrates streaming feature extraction, Block-RAM-backed Top-K selection, spatial-bucket matching, two-pass essential-matrix estimation and pruning, fixed-point pose recovery, and triangulation with point-coordinate output. Bounded feature storage, local correspondence search, and mixed floating- and fixed-point arithmetic support the complete pairwise chain without processor-side geometry computation. The complete VCU118/XCVU9P design was synthesized, placed, routed, and compiled to a bitstream in Vivado 2026.1, meeting setup and hold timing with $+0.031$ ns and $+0.010$ ns slack, respectively. It uses 12.09% of the device’s logic lookup tables and 32.94% of its Block RAM tiles. Controlled numerical tests characterize the operating domain of the geometry stages. The cycle-based model estimates a 13.13 ms geometry-back-end subtotal at 1000 matches; 60 fps is the architectural input target. To the authors’ knowledge, within the directly comparable literature surveyed, this is the first reported stage-complete mapping of the listed pairwise chain entirely to programmable logic. Full article

Background: Modern-type depression (MTD), first described in Japan, reflects a sociocultural form of depression characterized by avoidance of social roles, complaint tendencies, and low self-esteem. While increasingly recognized across Asian societies, no Malay-language adaptation of the TACS-22 has previously undergone psychometric evaluation. Objectives: This study aimed to translate and culturally adapt the Tarumi’s Modern-Type Depression Trait Scale (TACS) into Malay and to evaluate the reliability, factorial structure, discriminant validity, and convergent associations of the Malay version among Malaysian adults. Methods: Following standardized forward and backward translation and expert review, the TACS-22-Malay was administered to convenience sample of 250 predominantly young Malaysian adults together with the Malay Depression, Anxiety, and Stress Scale. Internal consistency was examined using Cronbach’s alpha, ordinal McDonald’s omega, and the Greatest Lower Bound (GLB). Because the TACS items comprise five ordered-response categories and multivariate nonnormality was present, the primary confirmatory factor analysis used the weighted least squares mean and variance-adjusted estimator with ordered indicators. The complete 22-item-correlated three-factor model was evaluated, with robust maximum likelihood and conventional maximum likelihood models used as sensitivity analyses. A one-factor model was also examined. Convergent associations were evaluated using Spearman’s ρ with bootstrap confidence intervals. Results: Internal consistency was high for the total score (alpha = 0.804, ordinal omega = 0.839, GLB = 0.877), but varied across Avoidance of Social Roles (alpha = 0.617, omega = 0.677), Low Self-Esteem (alpha = 0.711, omega = 0.760), and Complaint (alpha = 0.412, omega = 0.496). The primary three-factor WLSMV model showed poor fit, chi square (206) = 794.922, p < 0.001, CFI = 0.587, TLI = 0.537, RMSEA = 0.117, 90% CI [0.110, 0.125], SRMR = 0.108, and produced an inadmissible latent covariance matrix. Latent correlations were 0.748, 0.959, and 1.050, with the Low Self-Esteem and Complaint correlation exceeding 1. The one-factor WLSMV model also fit poorly. Robust and conventional maximum likelihood sensitivity analyses led to the same substantive conclusion. All TACS scores were positively associated with DASS 21 Depression, Anxiety, and Stress, with Spearman’s ρ ranging from 0.235 to 0.644. Conclusions: The Malay TACS-22 demonstrates useful internal consistency at the total score level and meaningful associations with psychological distress, but the hypothesized three-factor measurement model was not supported and discriminant validity between factors was inadequate. The poor fit of the one-factor model also indicates that high total score reliability should not be interpreted as evidence of unidimensionality. The present findings therefore support the Malay version as a provisional adaptation requiring further item refinement and independent validation rather than as a fully validated instrument. Full article

Persistent angle-only tracking with Walker low-Earth-orbit constellations requires repeated geometric assessment, but optimal sensor-subset selection is computationally costly. To support efficient assessment, a symmetry-reduced differentiable neural information field is proposed to approximate the A-optimal potential of the best feasible sensor subset of a prescribed size. Its two-stage network replaces absolute time with bounded phases derived from axial and finite-permutation symmetries of a relative-periodic Walker constellation under $J_2$. For a $25\times 25$ constellation, symmetry reduction decreased the three-seed mean root-mean-square error from $7.664\times {10}^{-3}$ to $1.154\times {10}^{-3}$ relative to a matched two-body encoding; altitude-gradient correlation reached 0.9960. Finite-symmetry closure was verified for Delta, Star, and Rosette. Field accuracy remained within prescribed thresholds for 30 days under matched $J_2$ dynamics. Without orbit maintenance, high-fidelity propagation yielded median and minimum sampled validity horizons of 1.50 and 1.25 days. On historical Iridium trajectories reconstructed from orbital records, three independently trained fields maintained the prescribed accuracy for at least 30 days, demonstrating long-horizon applicability to an operational Walker constellation. Neural triggering reduced penalized mean regret by 91.6% and candidate-pool searches by 93.4%. Full-grid neural inference achieved 17.5-fold CPU and 5567-fold GPU speedups over CPU candidate-pool evaluation. Full article

Retrofit condition monitoring of industrial manipulators should distinguish actual mechanical anomalies from signal changes produced by payload, speed, program phase, and transient motion. This study presents a hybrid detector for a six-axis machine-tending robot using a forearm-mounted inertial measurement unit and an auditable two-stage decision architecture. Engineered descriptors support two branches: a Random Forest estimates similarity to reviewed abnormal patterns, while a PCA representation measures context-compatible geometric novelty. The branch scores are combined via a linear fusion coefficient selected on grouped validation runs. Operating context selects a pre-validated, controlled context-compatible PCA reference and modifies the final decision through a bounded threshold correction; it does not update the nominal model online. An H-of-K persistence rule converts repeated window-level exceedances into event-level maintenance evidence. All data-dependent transformations are fitted after complete physical acquisition runs have been assigned to training, validation, or held-out testing. In the run-grouped archive, the complete adaptive hybrid achieved 97.1 ± 0.8% accuracy and an F1-score of 0.96 ± 0.01 across 18 held-out runs. The resulting framework prioritizes leakage-controlled validation, constrained adaptation, and computationally modest retrofit deployment; further developments will enable online adaptation. Full article

Predicting the future occupied regions of non-cooperative space objects is critical for close-range situational awareness, on-orbit servicing, and collision risk assessment. Yet, conventional target-wise estimators discard the cross-target uncertainty correlations induced by a shared chaser state, whereas reachable-set propagation methods often prescribe disturbance bounds independent of current observations. With known target identities, this paper presents a joint multi-target state estimation and probabilistic reachable-region prediction method based on a local Markov factor graph. The graph unifies chaser and target states, relative-position measurements, equivalent maneuver variables, and history-supported behavior and pair-interaction factors. Schur complement analysis demonstrates how marginalizing the shared chaser state induces cross-target covariance and transfers information through common observations. The finite-graph posterior is then mapped to a corrected-dynamics inferred-control (CDIC) framework to produce target- and time-indexed probabilistic position regions for evaluating orbital safety. Monte Carlo simulations with common random numbers demonstrate that the joint formulation falls back to independent target estimation when relation evidence is absent, while persistent shared evidence improves prediction-center accuracy and empirical trajectory containment. Parameter-sensitivity and randomized-configuration tests delimit this evidence-conditioned benefit. These findings identify the operating regime where cross-target information improves multi-object state estimation, equivalent-maneuver inference, and probabilistic reachable set prediction. Full article

LLM-based conversational agents generate fluent responses but remain limited in adapting their supportive style to individual personality and emotional needs. We present a Detect–Regulate–Evaluate (D–R–E) architecture that performs turn-by-turn Big Five detection and applies Zurich Model-inspired behavioural regulation, orchestrated with PROMISE. The novelty is this integrated, reproducible detection→regulation→evaluation architecture for controlled simulation—not a claim of clinical effectiveness. In a GPT-4 simulation comparing personality-adapted (regulated) and standard (non-adaptive) assistants, scored by a structured LLM-based evaluator with author review retained as an internal audit, the main finding is the mixed-personality comparison with identical user text (input replay; outcome-scoring protocol not independently archived in full): dimension-level accuracy fell to 58.1%, yet three cross-family LLM judges retained a Personality Needs advantage, locating the extreme-profile result as an upper bound. Under extreme boundary-condition profiles, strict all-five-trait recovery was 21/60 (35.0%) and dimension-level accuracy was 83.3%; regulation adherence was 100%, and the Personality Needs Yes rate rose from 8.3% (5/60) to 100% (60/60) as an upper-bound selective-enhancement check. A two-rater verify-and-revise audit on an $n=66$ overlap showed substantial agreement on detection labels (mean linear $\kappa =0.731$) but covered detection labels only; outcome rubrics remain LLM-rated. Safety behaviours (crisis handling, unsafe advice, hallucination, escalation) were not evaluated. The Zurich mapping is implemented as a design choice rather than validated as psychological theory; the system is not evaluated as a therapeutic or clinical intervention. Full article

In geographically dispersed and multilingual Vietnamese lower-secondary English language teaching contexts, digitally mediated professional development can widen access to peer learning, yet less is known about how dialogue, facilitation, and practical resources are configured under curriculum, workload, and infrastructure constraints. This bounded qualitative case study examined the implementation-relevant components of digitally mediated Teacher Activity Groups and the contextual conditions shaping their operation. The primary dataset comprised eight semi-structured facilitator interviews, supplemented by 24 documentary sources: 18 facilitator-authored reports and six structured observation forms. Interviews were analysed through a hybrid deductive–inductive thematic approach, while documents were analysed abductively to corroborate, qualify, and complicate interview interpretations. Three interrelated components were identified: structured peer dialogue across dispersed professional contexts; facilitation routines as human infrastructure; and modelling with adaptable digital resources. Their reported operation was shaped by workload and time scarcity, curriculum and textbook pressures, infrastructure instability, uneven participation, conceptual uncertainty, and institutional support. The findings illuminate digitally mediated teacher professional learning as a socio-technical arrangement in which professional exchange, facilitation, resources, and context are relationally configured. Instead of quantifying theme prevalence, we present these findings as a contextually grounded interpretation, deliberately avoiding claims regarding programme effectiveness, causal mechanisms, or generalisable outcomes. Full article

In large-scale plant construction projects, integrating Building Information Modeling (BIM) with construction schedules is essential for detailed planning and advanced practices such as Advanced Work Packaging (AWP), yet a granularity mismatch remains between schedule activities and object-level BIM components. This study proposes a method that combines lightweight axis-aligned bounding box (AABB) geometry with piping-specific semantic rules to derive object-level connectivity for installation grouping and sequencing. The method was implemented as a custom Autodesk Navisworks add-in and evaluated using an industrial process piping system comprising 705 physical objects. Geometric adjacency relationships were refined through semantic false-positive filtering, achieving 99.02% edge-level precision and 98.37% port-count agreement. The validated connectivity was then used to restructure 20 initial semantic partitions into 16 dimensionally feasible installation groups. A candidate installation sequence was subsequently generated by prioritizing equipment-connected main-line piping and maintaining continuity along connected piping routes. The results demonstrate that lightweight geometry combined with piping-specific semantic constraints can reliably support object-level installation planning without requiring predefined installation grouping or sequencing information in the BIM model. The proposed method provides an intermediate planning structure that links detailed BIM objects with broader construction schedule activities and supports subsequent detailed 4D BIM planning. Full article

Decision making for complex products often depends on subjective user experience, while competing alternatives may be supported by strongly unequal numbers of observations. This study develops an evidence-weighted multi-criteria decision-making methodology for such sparse and unbalanced information. A seven-category rating scale is aggregated at the respondent level and transformed affinely to [0, 1]. Domain-specific variance components are estimated by restricted maximum likelihood and used for empirical Bayes partial pooling, so that sparse estimates are moderated without excluding valid alternatives. AHP preference weights are kept conceptually separate from evidence weights, inherent product quality is separated from non-inherent ownership attributes, and uncertainty is propagated through Monte Carlo simulation. The empirical demonstration comprised 140 unique questionnaire records for 21 agricultural tractor brands with sample sizes from 1 to 50. Compared with direct averaging, the empirical Bayes ranking was highly preserved (Spearman $\rho =0.992$) while unsupported extremes were reduced. A controlled simulation showed lower RMSE for empirical Bayes than for the arithmetic mean, median, and fixed shrinkage under both Gaussian and bounded non-Gaussian data generation, with the largest benefit at $n=1$. Sensitivity analyses showed high ranking stability to the upper-level quality weight, perturbations of AHP weights, and bounded score transformations. The framework therefore provides reproducible uncertainty-aware decision support without treating weak evidence as either absent or equally strong as data-rich evidence. Full article

Feature distributions shift under variable-speed gearbox operation, which can cause a model trained only on healthy samples to misclassify normal operating changes as anomalies. A semi-supervised anomaly detection method is proposed in this study. Using healthy data, the method first fits speed-dependent trends for the selected time- and frequency-domain statistics, and the deviations from these trends form the statistical residuals. Computed order tracking then converts the vibration signal to the angular domain, where five mechanism features describe meshing energy, harmonic structure, sideband modulation, and order-spectrum entropy. Removing the corresponding healthy speed trends yields the mechanism residuals. Robust Bounded Health-Consistency Weighting (RB-HCW) weights these residuals according to their variability in healthy data before they are fused with the statistical residuals and modeled by Deep Support Vector Data Description (DeepSVDD). The Sequential Bayesian Queue-Based Alarm (SBQA) module then confirms whether abnormal decisions persist across successive windows. Across the four fault types under the two separately modeled load conditions, the proposed method achieved macro-averaged true positive rate (TPR), accuracy (ACC), and F1-score values of 93.31%, 92.58%, and 94.02%, respectively. Full article

Reliable in-line estimation of slurry rheology could improve the safety and control of pipeline-transfer operations, particularly in radioactive-waste processing where frequent manual sampling is undesirable. This study presents SW-RheoPINN, a physics-informed inverse pipe-rheometry framework for estimating pipe-effective yield stress and plastic viscosity from short windows of pressure-drop, mass-flow-rate, density, and pipe-geometry measurements. The framework combines a permutation-invariant sensor-window encoder with an analytical Bingham pipe-flow backbone, radial momentum balance, a regularized constitutive relation, cross-sectional mass conservation, and a tightly bounded velocity-profile correction for limited model discrepancy. SW-RheoPINN was evaluated using 20 two-state kaolin–water flow-loop experiments comprising 40 hydraulic states. In matched-physics synthetic tests with 2% multiplicative mass-flow noise, yield-stress recovery improved from $R^2=0.787$ for two-state windows to $R^2=0.923$ and $R^2=0.955$ for three- and four-state windows, respectively; plastic-viscosity recovery improved from $R^2=0.937$ to $R^2=0.967$ and $R^2=0.961$. For the experimental data, complete sensor-window reconstruction achieved a MAPE of 0.80% and $R^2=0.990$. Because measured mass flow is an encoder input in this reconstruction, these metrics characterize inverse self-consistency rather than prospective prediction. A separate target-flow-withheld evaluation, in which the withheld mass flow was not supplied to the inverse model, achieved an RMSE of $0.108$ kg.s-1, $R^2=0.790$, and a median absolute percentage error of 3.55%. Prediction was strongest for compositions with repeatable hydraulic behavior and degraded when nominally similar experiments occupied distinct response states. Ablation and sensitivity analyses showed that strongly resolved high-yield conditions were largely insensitive to composition-related regularization, Papanastasiou sharpness, and correction capacity, whereas low-yield estimates were more model dependent. Misspecified-physics tests further showed that small hydraulic residuals do not necessarily imply unbiased rheological parameters. The inferred pipe-effective yield stresses retained the broad composition-dependent trend observed by offline rheometry, although absolute cross-scale agreement was limited. These results support SW-RheoPINN as a physics-constrained inference and diagnostic framework for identifying both well-supported and weakly resolved rheological states from standard process measurements. Full article

In roadside surveillance imagery, shadows, vegetation, vehicles, exposed pavement, and water stains may exhibit local textures and morphological characteristics similar to those of rockfalls, causing a standalone You Only Look Once (YOLO) real-time object detector to generate frequent false-positive detections. To address this issue, this study proposes a serial cascaded detection method that integrates an improved YOLO detector with machine-learning-based secondary verification. In the YOLO branch, channel-prior convolutional attention (CPCA) and learnable weighted multiscale feature fusion are introduced to enhance target representation under complex background conditions and generate candidate bounding boxes. In the machine-learning branch, handcrafted features describing texture, color, shape, edges, morphology, and frequency-domain characteristics are extracted from the candidate regions. A verifier selected through multi-model comparison and ensemble evaluation is then employed to confirm the YOLO-generated candidates. For parameter optimization, the operating point of the standalone YOLO detector with the highest $F_1$-score is first selected as the baseline. A two-dimensional grid search is subsequently performed over 95 threshold combinations consisting of five YOLO candidate-confidence thresholds and nineteen machine-learning confidence thresholds. The optimal configuration is determined using a weighted improvement score defined according to the relative changes in precision, recall, and the $F_1$-score with respect to the baseline. The best overall performance is achieved when the YOLO and machine-learning confidence thresholds are set to 0.25 and 0.75, respectively. Compared with the standalone YOLO detector, the proposed cascaded model improves accuracy from 93.3% to 94.1%, precision from 90.1% to 92.6%, and the $F_1$-score from 93.4% to 94.0%, while recall decreases slightly from 97.0% to 95.5%. These results demonstrate that interpretable local features can effectively filter out false-positive YOLO candidates, thereby suppressing false alarms and improving overall discrimination performance at the cost of only a limited reduction in recall. The developed system has been deployed on rockfall-prone sections of highways G210 and G108, providing technical support for real-time road rockfall monitoring and early warning. Full article

During sudden-onset disasters, social media text and images provide time-critical evidence for emergency awareness, damage assessment, and humanitarian response, but their cues are often incomplete, noisy, or conflicting. Direct equal-status fusion of heterogeneous pretrained representations can introduce semantic misalignment and allow unreliable evidence to influence the classifier. This paper proposes foundation-augmented reliability-conditioned dynamic adaptive fusion (FA–RC–DAF) for multimodal disaster classification. The model first preserves CLIP as a unit-weight text–image alignment base, maintaining a stable cross-modal decision space. It then projects BERTweet and SigLIP features into the CLIP-aligned space as bounded residual corrections, enabling domain-specific linguistic and complementary vision–language cues to refine the base without overwriting it. A confidence–agreement router estimates sample-level residual reliability from normalized-entropy predictive concentration and cross-encoder agreement, selectively admitting each residual before fusion. Explicit cross-modal interaction is performed only after the two streams have been reliability-refined. On CrisisMMD, FA–RC–DAF achieves 92.26% accuracy, 92.25% weighted F1, and 91.26% macro F1 under the retained five-class protocol. The protocol-aware published comparison is reported separately from the five-seed internally matched architectural comparison, which provides the primary evidence for method-level claims. Additional evaluations show differentiated behavior under event- and disaster-type shifts, stronger difficulty under forward temporal drift, and condition-dependent sensitivity to corrupted or unavailable inputs, providing a more fully characterized basis for multimodal disaster decision support. Full article

Four-wheel electromechanical braking (EMB) systems may receive nominal brake torque commands that exceed transient actuator capability under low-voltage supply or degradation. This study proposes a brake torque allocation method based on wheel-end dynamic reachable sets, defined here as one-step torque intervals permitted by voltage-dependent magnitude and application/release rate limits from the previous actual output. A single-wheel EMB model identifies the capability boundaries. Each interval is updated using the wheel’s bus voltage, capability coefficient, previous-cycle actual torque, and control period. Nominal commands are projected onto these intervals, and residual demand is redistributed according to the remaining margins. MATLAB/Simulink–CarSim simulations compare the method with fixed-ratio, static saturation, and dynamic-bound quadratic programming (QP-D) allocation. Under a synchronized 24 V supply, DRA reduces integrated brake demand deficit by 28.8% relative to the fixed QP-D benchmark. Under rear-left degradation, it reduces the peak yaw rate by 78.0% relative to static allocation. These results support capability-aware command allocation in the tested model. Strongly unequal voltages nevertheless produce the largest yaw response with DRA, identifying yaw-aware coordination as a necessary extension for this operating regime. Full article