Search Results (286)

In the era of intelligent assessment, psychometric tests are becoming increasingly important for personnel selection, career development, and mental health assessment. Forced-choice tests are common in personality assessments because they require participants to select from closely related options, lowering the risk of response distortion. However, traditional latent trait models for forced-choice tests suffer from severe computational bottlenecks in high-dimensional settings. Furthermore, existing deep learning-based cognitive diagnosis models are primarily designed for independent items in educational scenarios (predicated on absolute scoring), making them structurally maladapted to the ipsative data (relative preference comparisons) generated by forced-choice tests. To address these challenges, this study presents a deep learning-based Forced-Choice Neural Latent Trait (FCNLT) Model that overcomes the limitations of traditional models and is applicable to the three most common item block types found in forced-choice tests. To account for the unidimensionality of items, participants’ latent trait levels and item characteristics are represented as interpretable latent embeddings. FCNLT mines these features through nonlinear mapping and introduces a weighted BPR-based ranking loss to natively align with the relative-scoring nature of forced-choice data. Additionally, the monotonicity assumption is utilized to improve the interpretability of the trait estimates. The FCNLT’s effectiveness is validated by experiments on real-world and simulated datasets that show its accuracy, interpretability, and robustness. Full article

Galloping of iced transmission lines under complex microclimates poses a severe threat to power grid security, whereas traditional full-span de-icing strategies suffer from excessive energy redundancy and limited spatial precision. To address the spatial uncertainty of actual ice accretion, a three-dimensional nonlinear aeroelastic finite element model is established by considering geometric nonlinearity and eccentric ice-induced added stiffness. A state-space Monte Carlo framework is then used to evaluate the galloping probability under different wind speed regimes and spatially non-uniform ice distributions. The results reveal a distinct non-monotonic instability characteristic: the galloping probability decreases to 33.0% at 8.0 m/s, forming a clear probability trough and indicating an aerodynamic self-stabilization effect associated with the shift in the baseline effective angle of attack. To map spatial ice heterogeneity to global dynamic instability, a galloping sensitivity index (GSI) based on the Spearman rank correlation coefficient is proposed to identify the dominant sensitive sections responsible for inducing galloping-prone responses. Based on this index, a GSI-guided targeted ultrasonic de-icing decision strategy is constructed. Under the assumption of identical rated power for each section, the proposed strategy activates only 40% of the physical sections and reduces the number of activated sections, as well as the associated operational energy demand, by 60% compared with the full-span de-icing strategy. This framework provides a quantitative basis for linking stochastic ice distribution, galloping probability evaluation, and energy-efficient targeted de-icing decisions. Full article

Fused deposition modeling (FDM) is a material-extrusion additive manufacturing technology that is widely used in rapid prototyping, complex product modeling, and functional part fabrication. However, process-parameter drift and environmental disturbances may induce underfilling, overfilling, warping, delamination, and other defects, thereby reducing part quality or interrupting the manufacturing process. Since FDM is characterized by point-wise extrusion and layer-by-layer deposition, layer-surface images naturally contain both spatial morphology and temporal evolution information. Existing image-based diagnostic methods often treat layer images as independent samples, and the selection of the image-stream length is still insufficiently supported by experimental evidence. Moreover, spatial compression in spatiotemporal neural networks may remove local defect information that is important for distinguishing similar process-parameter drifts. This study provides a deployment-oriented analysis of FDM image-stream diagnosis by systematically examining how layer-window length, spatial feature preservation, and strict data partitioning influence process-parameter drift recognition. To address these issues, this paper studies ConvLSTM-based FDM image-stream process-parameter drift diagnosis. Continuous region-of-interest image streams are constructed for one nominal condition and six process-parameter drift conditions. In this paper, the time step T denotes the number of consecutive layer-surface images, or, equivalently, the number of consecutive printed layers, contained in one diagnostic image stream. A ConvLSTM-Flatten baseline is first developed to preserve complete spatial feature maps and to evaluate the effect of different time-step lengths. Then, a ConvLSTM model with adaptive spatial pooling and temporal attention (ASP-TA) is constructed to analyze the influence of spatial pooling granularity and temporal feature fusion. The experiments show that the ConvLSTM-Flatten model achieves the highest average test accuracy of 0.7288 at $T=9$, whereas $T=3$ is identified as a practical optimal time step when test accuracy, image-frame computation, diagnosis latency, and convergence behavior are considered together. The paired trial-wise accuracy difference between $T=9$ and $T=3$ is small and not statistically significant over ten repeated trials. Thus, the diagnostic window corresponding to $T=3$ covers three consecutive deposited layers; after the initial window is available, stride-one stream construction allows the diagnosis to be updated with each newly acquired layer image. ASP-TA with a pooling size of eight consistently outperforms ASP-TA with a pooling size of four, but both are lower than the Flatten baseline, indicating that preserving sufficient spatial information is essential for distinguishing FDM process-parameter drift states. The results reveal the non-monotonic influence of time-step length and clarify the tradeoff between spatial feature preservation and model compactness in FDM image-stream process-parameter drift diagnosis. Full article

GPS-derived floating car data (FCD) provide spatially continuous urban traffic observations without fixed-sensor infrastructure. This study develops a spatiotemporal baseline framework jointly modelling day type and precipitation for 1189 junction-level nodes in Budapest. A six-phase pipeline—GPS preprocessing, coordinate reprojection, FME (Feature Manipulation Engine, Safe Software Inc., Surrey, BC, Canada)-based map-matching, junction-level aggregation, Voronoi meteorological allocation, and dataset assembly—was applied to 44.1 million 10 s records from approximately 1100 probe vehicles (November 2024–December 2025). Public holidays form a structurally distinct traffic flow pattern compared to Sundays (r = 0.71) and to regular workdays (r = 0.42); morning peak shifts to 09:00–11:00 and pooling holidays with Sundays introduces reference errors of 15–25%. Precipitation raises morning peak volumes by 6–17% across all zones while afternoon peaks remain statistically unchanged, consistent with commuter inertia; Saturday volumes fall by 7–15%. Rainy Wednesdays reach 109–112% of the Monday dry reference in inner zones, attributed to hybrid workers advancing their office day. Pairwise junction correlations show a non-monotonic distance-decay pattern, and time-lagged cross-correlation identifies 23 anticipative junction pairs with 60–90 min lead times. The results could potentially help decision making when developing city-wide infrastructure and tuning traffic signals so that traffic can be optimised and adapt to both real-time natural and social effects. The resulting baselines map onto DATEX II (Data Exchange standard, CEN EN 16157) ElaboratedDataPublication fields, supporting metadata publication on the Hungarian National Access Point under EU Regulation 2022/670/EU. Full article

We propose a foundational framework in which internal temporal ordering, the low-entropy boundary of the observable branch, the compatibility of a local thermodynamic arrow with a global partition lifecycle, and a qualitative late-time dark-energy sign relation are organized as projections of a single internal-access architecture. The observable universe is treated as an internally accessible partition of a larger pure atemporal quantum state satisfying the Wheeler–DeWitt constraint. The ordering parameter is not identified with partition entropy itself; it is interpreted as an algebraic readout-depth parameter associated with a nested tower of admissible factor-like subalgebras, each inclusion adding one unit of autonomous distinguishability to the accessible sector. The reduced entropy S(rho_S) is then the Page-like scalar profile evaluated along this depth. This separates the internal ordering structure from the entropy being measured while retaining Page complementarity between accessible and inaccessible capacities. A minimal cosmological bridge is introduced: in the semiclassical Friedmann–Lemaitre–Robertson–Walker regime, if the effective Hubble rate is monotonic in partition entropy and readout depth is monotonically oriented with observer time, standard kinematics imply a sign correspondence between entropy change and the effective dark-energy equation of state. The metric map remains open. Full article

Across the world’s electrical substations, water-treatment plants, and manufacturing lines, a single engineer with valid credentials and a laptop can today push new control logic to a programmable logic controller (PLC) and change the physical behaviors of safety-critical equipment within minutes. Firmware and ladder-logic updates on SCADA and industrial IoT systems are privileged operations: an attacker installing a malicious update controls the physical process. Existing protections concentrate install authority in a single party with no externally verifiable record; compromise of the vendor key, the engineering workstation, or any operator credential suffices to deliver an attacker-chosen payload to a PLC. CertiFlash binds every update to four independent approvals: a vendor signature, a FROST-Ed25519 threshold signature from an operator quorum, a per-session nonce from the PLC, and a monotonic counter. Every decision is recorded in an append-only Merkle transparency log. The PLC verifies the aggregate with a standard RFC 8032 Ed25519 verifier, requiring no FROST-specific device code. Four security properties (authenticity, authorization, rollback resistance, auditability) are machine-checked in Tamarin under a Dolev–Yao adversary with up to t − 1 compromised operators and corroborated through ten attack scenarios. The implementation runs with concurrent Modbus TCP and Siemens S7 traffic, blocks all attacks, signs in 27–192 ms (k = 3–10), keeps ML-DSA-65 within 6% of Ed25519 from 1 KiB to 10 MiB, and sustains 30.1 updates/s on 100 PLCs. The operator-quorum signature remains FROST-Ed25519: the design is partially post-quantum in the evaluated version. The framework maps to IEC 62443-3-3 SR 3.4 and NIS2 Article 21(2)(d–e). Full article

Efficient detection of aircraft surface defects (ASD) is a cornerstone of aviation safety. However, ASD detection is challenged by microscopic defect scales, extremely low contrast, and severe background interference. This paper proposes the Multi-Scale Discriminative Edge Enhancement Network (DEE-Net) based on an improved YOLO11. First, to mitigate feature dissipation of tiny defects, a lossless reassembly mechanism using space-to-depth convolution (SPD-Conv) is introduced, safeguarding sub-pixel topological information through space-to-depth conversion. Second, an adaptive selective edge-enhancement (ASE) module, integrating a dual-domain selection mechanism (DSM), is designed to suppress non-target redundant information on the fuselage skin. Finally, a Wise-CIoU loss function with a non-monotonic focusing mechanism is introduced to enhance localization stability under stringent IoU thresholds. Experimental results demonstrate that DEE-Net outperforms the baseline, improving mAP50 by 7.15% and mAP50-95 by 2.43%. To provide a more reliable evaluation, a 5-fold cross-validation experiment is further conducted on the original non-augmented images, and the results are reported as mean ± standard deviation. The cross-validation results provide a more conservative estimate and indicate that the proposed method achieves competitive performance across different data partitions. Full article

Fatigue health monitoring of engineering structures requires continuous degradation assessment, yet ground-truth health labels are unavailable during run-to-failure tests. Existing self-supervised approaches rely on monotonic degradation assumptions that are violated by the structured non-monotonic behaviour of acoustic emission signals during fatigue. A self-supervised framework called Cross-Modal Degradation Rivalry (CMDR) is proposed, which introduces the Modal Rivalry Index (MRI) as a directional measure of cross-modal predictability between heterogeneous sensor modalities. CMDR comprises a label-free representation-learning stage trained via the Cross-Modal Prediction Asymmetry (CMPA) pretext task, followed by a lightweight supervised stage that maps MRI features to scalar health indicators (HIs) using normalised lifecycle labels. The MRI is conceptually related, under the stated assumptions only loosely met in practice, to the Transfer Entropy difference between sensor latent channels. Experiments on a structural fatigue dataset with seven specimens under two loading conditions demonstrate that CMDR achieves competitive trendability and prognosability, as well as the lowest remaining useful life (RUL) error in three of four scenarios. RUL evaluations are additionally repeated under a fully online estimator that uses only training specimens. A strictly inductive ablation that re-pre-trains the self-supervised stage within each leave-one-specimen-out fold confirms a bounded transductive-vs-inductive gap, and CMDR remains the best against three further self-supervised baselines on the within-condition and mixed-condition scenarios. Ablation studies confirm the necessity of directional asymmetry, bottleneck architecture, and momentum-updated target encoders. Full article

Salt rock exhibits pronounced viscoelastic creep, continuously imposing radial extrusion loads on casing and threatening long-term well integrity. Field observations in the Missan Oilfield, Iraq, show that casing damage is concentrated near salt–non-salt interfaces, where lithologic contrasts intensify stress redistribution and mechanical coupling. This study integrates triaxial creep experiments, a calibrated modified Burgers model, UMAT implementation, and three-dimensional finite element simulations to investigate casing stress evolution and failure mechanisms. The calibrated model reproduces salt rock creep with a maximum relative strain error of 16.8%. Results show that post-cementing salt creep amplifies non-uniform radial loading at the interface, causing progressive casing stress concentration. At low inclination, the interface–casing intersection evolves into an elliptical annulus; the circumferential variation in equivalent wall thickness and stress-peak migration jointly weaken local stress concentration. However, when the inclination angle reaches approximately 45° at β = 0°, the peak Mises stress begins to exceed that under the vertical-well condition. When α ≥ 65°, the peak stress no longer decreases monotonically with azimuth but exhibits a decrease–increase trend. This indicates that eccentric loading and the additional bending moment dominate the transition from radial extrusion to coupled bending–shear–extrusion loading. A casing stress risk map and grade-selection chart are developed to support casing design in salt-interbedded formations. Full article

Cross-material continuous tool wear prediction is difficult because a model must preserve the physical wear scale, not only align high-dimensional sensor features. This limitation is critical in milling, where the target variable is the continuous flank wear width ($\mathrm{VB}$) and material shift can distort the mapping from sensor response to wear magnitude. We address this problem by recasting cross-domain tool wear prediction as monotone wear-scale adaptation. We propose Multi-Physics Monotone Score Transport (MPMST), a monotone score transport framework that constructs a tool-wear-oriented score from sensor-derived candidate cues, transports the target-domain score onto the source-domain wear scale, and then predicts wear through isotonic regression. We also evaluate One-Physics Monotone Score Transport (OPMST), a force-only variant that uses the same score-transport pipeline with a restricted cue family. On Mondragon Unibertsitatea–Tool Condition Monitoring (MU-TCM) with two cross-material transfer tasks, the validation-driven MPMST configuration reduces mean absolute error by approximately 63% relative to Correlation Alignment (CORAL) and by approximately 31% relative to a physics-informed Gaussian process baseline. The results support monotone score construction and score transport as practical mechanisms for continuous tool wear prediction under domain shift, while also showing that MU-TCM is strongly force dominated. Full article

Additive–subtractive hybrid manufacturing (ASHM) allows for the rapid manufacturing of metal components with complex and precise geometries for ready-to-use or near-ready-to-use applications. Laser wire-directed energy deposition (LW-DED) can be used to quickly manufacture metal components, while CNC machining can achieve precise geometric tolerances. In this study, Ti-6Al-4V alloy specimens were fabricated using an LW-DED process combined with CNC machining and tested to evaluate the effects of ASHM on mechanical performance. Post fabrication, the Ti-6Al-4V material was evaluated through hardness mapping, monotonic tensile testing, and fully reversed axial fatigue testing. Vicker’s micro-hardness mapping showed a range of hardness results from 300 to 350 HV in the ASHM Ti-6Al-4V that remained consistent throughout the build. Tensile results showed a similar response to cast and wrought Ti-6Al-4V, with an average yield stress of 819.4 MPa, ultimate tensile strength of 935.5 MPa, and modulus of 119 GPa. When tested in fatigue, the material had a reduced life compared to wrought Ti-6Al-4V, which is attributed to defects originating from the additive process. While no run-outs were observed from the testing, the fatigue results remain aligned with trends reported for other methods of additively manufactured Ti-6Al-4V. Fully reversed high-cycle fatigue loading revealed that the ASHM-fabricated Ti-6Al-4V fell into a Basquin power-law fit with a fatigue strength coefficient of 1942 MPa with a fatigue strength exponent of −0.115. The fatigue life of the ASHM material is found to be dependent on the resulting porosity of the material that stems from the LW-DED process used in the ASHM process described. Full article

We study positive–negative guarded systems of language equations over a fixed finite alphabet. The ambient space is the complete ultrametric space of all formal languages equipped with a length-based distance, where two languages are close whenever they agree on all words up to a sufficiently large length. The systems considered here contain both positive recursive dependencies and negative dependencies expressed through language complements. To handle this mixed structure, we introduce a suitable product order on pairs of languages and prove that the associated system operator has the weak monotone property. We show that the complement is an isometry for the length-based ultrametric and establish a signed wrapping estimate for guarded positive and negative language terms. These estimates lead to an ordered contraction principle for comparable pairs. As a consequence, the canonical lower and upper Picard iterations converge to the same limit, which is the unique fixed pair of the system. We also derive an explicit convergence rate and a finite-depth certification result: after a prescribed number of iterations, the approximants agree with the fixed-point semantics on all words below a given length. Additional symmetry assumptions are shown to force the unique fixed pair to be diagonal, reducing the system to a single language equation. Finally, we discuss an application to trace-based policies for tool-using AI agents. In this interpretation, finite executions of an agent are represented as words over an alphabet of observable tool-events, and the two components of the fixed point provide a stable semantics for policy-defined admissible and risky trace classes. The resulting framework gives a mathematically certified method for finite-depth analysis of recursive trace-based policies based on ultrametric fixed-point techniques. Full article

Spaceborne precipitation products such as NASA IMERG V07 provide sub-hourly data required for hydrological modelling, but systematic biases in wet-event frequency and extreme-intensity representation limit their reliability for applications sensitive to precipitation extremes. This study develops a three-stage calibration pipeline combining probability-of-precipitation frequency adaptation, empirical quantile mapping of the distribution body, and Generalised Pareto Distribution tail modelling with constrained blending. The approach is calibrated against 202 Roshydromet stations using 3-hourly observations and evaluated on 15 spatially independent stations over a 9-year validation period. At the station-optimal blending weight, the proposed pipeline reduces median absolute percentage bias at the P99 quantile from 43.9% to 10.2%, while maintaining comparable volume balance (|PBIAS| 6.5%). To suppress a disaggregation artefact arising from amplification of multi-hour accumulations, the operational gridded R-factor product instead adopts a more conservative blend (|PBIAS@P99| = 24.9%) together with an empirically constrained accumulation cap, although the absence of sub-hourly calibration data remains the principal limitation. The calibrated dataset is applied to derive a 24-year (2001–2024) rainfall erosivity climatology for the Volga region, yielding a domain-mean R-factor of 254 ± 55 MJ mm ha⁻¹ h⁻¹ yr⁻¹ with no detectable monotonic trend. The proposed framework improves the representation of precipitation extremes and provides a transferable preprocessing approach for hydrological modelling applications. Full article

Cobalt ferrite (CoFe₂O₄) magnetostrictive ultrasonic elliptical vibration cutting (UEVC) tools have recently emerged as a low-cost, low-eddy-loss alternative to piezoelectric and rare-earth-driven cutting heads. The structural design and resonance characterisation of such a dual-bending CoFe₂O₄ UEVC tool was reported in our previous work. The present paper builds directly on that platform and addresses a different objective: to determine how the four primary process variables—feed rate, cutting speed, cutting depth, and inter-channel phase difference—should be set to obtain the best surface quality on a representative ductile metal. Using H62 brass as the workpiece and a single-crystal diamond tool with a 0.2 mm nose radius and 60° included angle, single-factor experiments are run on a custom 5-axis precision lathe, and surface roughness is mapped in both the cutting and the feed direction with a Keyence VK-X1000 confocal microscope (Keyence, Osaka, Japan). The speed ratio K = Vc/(2πfA) is computed for every test point so that each result can be classified as belonging to the continuous-contact or to the intermittent-contact UEVC regime. The results show: (i) feed rate has a non-monotonic effect, with an optimum at 1 μm where ductile-mode separation is achieved without secondary tool-trajectory overlap, reducing the cutting direction roughness by up to 45% with respect to conventional cutting (CC); (ii) the UEVC advantage shrinks at high cutting speeds because the speed ratio approaches unity and the intermittent regime collapses, but is still 12.6%–38% over the 50–375 mm/s range tested; (iii) the relative improvement is largest at low depth and decreases as the depth grows, retaining 11.5%–49% gain over CC across 0.5–10 μm; (iv) the inter-channel phase difference, which controls the geometry of the tool-tip ellipse, is the strongest single lever—at 60°, the trajectory becomes an oblique ellipse whose major axis is tilted with respect to the cutting direction, bringing the cutting direction roughness down to 1.21 μm against 2.82 μm for CC, a 57% reduction. A simple kinematic argument links this optimum to a maximum effective separation duration per cycle and offers a design rule for analogous UEVC tools. Full article

This paper investigates the optimal harvesting of a nonlinear, size-structured population governed by a first-order transport equation with nonlocal environmental crowding feedback and exogenous inflow. First, we establish finite-horizon well-posedness for the controlled state system in an $L^1$ framework, proving the existence, uniqueness, positivity, and continuous dependence of weak solutions. Second, we show that the infinite-dimensional stationary problem reduces exactly to a scalar nonlinear closure equation, yielding existence and conditional uniqueness results for stationary states. Within this equilibrium framework, we distinguish the persistence of the forced system from intrinsic demographic self-replacement and introduce size-continuous per-recruit and spawning-potential diagnostics. Finally, we formulate a partial differential equation (PDE)-constrained optimal harvesting problem. Under a compactness assumption on the control-to-state map, we establish the existence of optimal controls. We then formally derive a Pontryagin-type first-order optimality system for the harvesting problem. The variation of the nonlocal environmental feedback produces a coupled integral source term in the adjoint equation. The associated pointwise maximization condition yields a bang–bang harvesting structure, while a monotone size-threshold policy is shown to require an additional single-crossing assumption on the switching function. These hypotheses are illustrated using a fisheries model with density-dependent von Bertalanffy growth. Full article