Search Results (354)

High-frequency induction hardening enhances the surface wear resistance and contact fatigue life of high-speed linear guides, but simultaneously produces an inhomogeneous, layered cross-sectional structure comprising a high-strength, low-ductility outer hardened layer and a low-strength, high-ductility inner core. This structural heterogeneity renders conventional straightening stroke prediction models—predicated on homogeneous material assumptions—fundamentally inadequate. Moreover, the iterative trial-bending operations ubiquitous in industrial practice progressively accumulate plastic strain, causing guide rails to exhibit erratic positive-to-negative deflection reversal during sequential straightening passes. To address these critical challenges, this study proposes a novel two-stage hybrid straightening strategy based on inverse finite element analysis (FEA) and closed-loop experimental feedback. An equivalent hardened layer depth (HD₀) is introduced as a parametric descriptor to construct a layered elastoplastic finite element model, and an inverse simulation strategy is developed to generate a comprehensive three-dimensional stroke–residual deflection prediction dataset encompassing both vertical and lateral straightening conditions across multiple support spans. Displacement-controlled three-point bending experiments validate the layered model and elucidate the mechanism by which cumulative plasticity progressively amplifies cross-sectional plastic sensitivity under repeated loading. Grounded in this physical insight, a hybrid straightening algorithm is formulated, combining dataset-driven initial stroke prediction for rapid large-deformation elimination with an upper-bound constraint and a measurement-feedback-driven sequential reduction compensation scheme for fine-tuning. Comparative experiments demonstrate that the proposed strategy effectively suppresses the oscillatory over-straightening characteristic of conventional empirical trial-and-error approaches, consistently reducing residual deflection below 0.05 mm within two to three loading cycles. This work bridges the gap between theoretical simulation and the complex physical state of actual machining, substantially improving both the efficiency and precision of straightening for guide rails with induction-hardened layers. Full article

Inland container depot (ICD) truck schedules must absorb new orders, service delays, appointment changes, congestion, and port cut-offs without destabilizing an already executed plan. This study asks whether event-triggered local repair can be separated into an explicit business-rule audit and a learned ranking of feasible task–vehicle actions. The proposed decision-support framework connects a static baseline, candidate task chains, six modeled hard-feasibility predicates, a Transformer encoder trained with proximal policy optimization (Transformer-PPO), and discrete-event execution logs. A five-seed, 120-episode confirmation gave Transformer-PPO a held-out online completion proxy (α_online) of 0.3226 and reward of 110.58, compared with 0.2581 and 61.87 for the matched multilayer perceptron (MLP); deterministic rules and search remained competitive. An independent audit of 4,968,000 action cells across 552 decision states found no disagreement with an independently coded oracle for the implemented hard predicates, while a reward-weight screen exposed the expected efficiency-stability trade-off. Together with a rolling-horizon comparator and a three-scale by three-disturbance stress test, the evidence supports an auditable system-integration contribution, not a new generic reinforcement learning (RL) algorithm or universal performance superiority. Claims are limited to synthetic simulation-based decision support. Full article

Cross-chain bridges represent one of the most damaging attack surfaces in decentralized finance, with major exploits (e.g., Ronin, Wormhole, Nomad, Multichain) arising not from broken signature schemes but from failures in proof verification, replay protection, and signer-set management, gaps that conventional threshold-signature multisignature wallets do not address. This study presents an incident-aware multisignature architecture combining three on-chain predicates—block-height freshness windows, epoch-bound signer sets, and Merkle inclusion-proof verification—with a non-authoritative off-chain LightGBM classifier that generates SHAP-attributed risk explanations to support governance actions such as pausing, vetoing, or rotating signers, without directly blocking or approving execution. The framework was evaluated on a simulated benchmark of 78,600 Ethereum testnet transactions containing six injected anomaly classes (gas spikes, nonce jitter, malformed call data, stale intents, proof-delivery delays, and epoch-rotation replays). The LightGBM advisor achieved ROC-AUC 0.92 (95% CI [0.906, 0.926]) and F1 0.73 ([0.712, 0.749]), outperforming five baselines—logistic regression, Random Forest, XGBoost, isolation forest, and a rule-based detector—with the highest F1 (0.731) and PR-AUC (0.799), while the rule-based detector, which by construction covers only the anomaly classes addressed by the deterministic predicates, attained F1 0.282. Differences were statistically significant except for the LightGBM–XGBoost PR-AUC comparison. The deterministic layer itself is verified through 28 property-level contract tests covering all seven modeled attack objectives, with measured per-function gas costs (execute_Intent: 118,756 gas, of which 28,432 gas is Merkle-proof verification). Within this controlled setting, the results indicate that a machine learning advisor can extend anomaly-prioritization coverage beyond the scope of the deterministic predicates while leaving execution control fully deterministic. This work is presented as a controlled proof of concept: the reported metrics quantify recovery of scripted injection patterns, and validation against real-world exploit traces remains future work. Full article

The logarithmic law of the wall is a foundational element in turbulence modeling. Its classical derivation, rooted in Prandtl’s mixing-length theory, is predicated on the existence of a constant shear stress layer. This paper demonstrates that this foundational assumption is not strictly satisfied for two-dimensional, uniform open-channel flows, where the Reynolds shear stress profile exhibits a distinct peak near the bed and never forms a true constant stress zone. We present a novel mechanistic derivation that circumvents this inconsistency. By reframing the bed shear stress as the time-averaged momentum flux from discrete, wall-coherent eddy impacts, we recover the log law through a mechanistic framework. Our model starts from the physical definition of the Reynolds stress at the bed, employs kinematic scaling for the velocity fluctuations, and incorporates the geometric constraint of eddy size. This approach does not require a constant stress layer and provides a more physically defensible explanation for the emergence and robustness of the log-law, directly linking it to the underlying structure of wall turbulence. The derivation resolves the long-standing paradox between the theory’s assumption and the empirical reality in open channel flows. Full article

Traffic inside a cloud Virtual Private Cloud (VPC) is filtered by two layers with fundamentally different semantics: a stateless, ordered, first-match Network ACL (NACL) and a stateful, unordered, allow-only Security Group (SG). Existing analyzers decide point-to-point reachability using satisfiability solvers, Datalog engines, or binary decision diagrams, but do not identify, at the rule level, which rules are dead, redundant, or dominated, nor explain why. We provide a closed-form set algebra over the two layers. Representing each rule field by its boundaries makes a rule a hyper-rectangle and a layer a union of boxes; the effective admitted region Φ = A(N) ∩ A(G) is then a finite union of disjoint boxes computable from rule endpoints alone. We define a taxonomy of single- and cross-layer anomalies—shadowed NACL rules, dead SG rules, Φ-redundant rules, Φ-ineffective NACL allows, and layer disagreement—characterize each by a decidable region predicate, and prove an exact iff-condition for SG Φ-redundancy. A boundary-only detection algorithm is sound and complete for the exactly decidable anomaly classes, running in O((k + t)^d) time for fixed dimension d, and the disjoint box decomposition of Φ gives a minimal anomaly-free form that is unique up to merging adjacent coplanar boxes. A single-file implementation matches brute force on millions of packets, staying orders of magnitude below the worst-case bound; the parametric model extends unchanged to IPv6 and ICMP. Full article

Permanent lunar habitation will require robotic systems that can maintain infrastructure, recover from local failures, and acquire new operational capabilities under limited Earth supervision. Existing planetary robots are largely fixed-function specialists, while end-to-end foundation-model policies remain difficult to validate and extend for safety-critical surface operations. We present SELENE (Self-Evolving Lunar Embodied ageNt Ecosystem), an architectural proposal for contract-governed lunar robotic autonomy centered on a shared Atomic Action Library $\mathbb{A}$. The key abstraction is the Atomic Action Contract: a typed skill interface that specifies parameters, preconditions, goal predicates, execution bindings, safety envelopes, runtime reports, and validation metadata. Through this contract, a VLM-driven Cognitive Agent plans over executable skills, a multi-modal Execution Agent realizes them through optimization-based controllers, Vision–Language–Action (VLA) policies, Vision–Language–Navigation (VLN) policies, or reinforcement-learned policies, and an offline Evolutionary Agentic Framework synthesizes and registers new candidate contracts without modifying the planner or the execution interface. This paper presents an architecture-level validation of that contract mechanism. We instantiate SELENE across two heterogeneous pathways on LunarBot and its simulation counterpart, with optimization-based control supported as a third execution modality. A pre-trained VLA policy adapted from 100 teleoperated demonstrations achieves 29/30 task success (96.7 percent) in in-domain trials on the physical LunarBot. A curriculum–RL policy instantiates the traversal pathway in simulated lunar-gravity terrain. Together, these results show that the Atomic Action Contract can serve as a common registration and dispatch interface across heterogeneous control modalities. The same contract layer also defines the path toward runtime gap-triggered self-evolution, mission-grade admission, and lunar-environment validation in subsequent system-level studies. Full article

The role of Chapman’s hydrostatic solar wind model (based on a hydrostatic force balance condition) in Parker’s hydrodynamic solar wind theory is investigated by invoking the de Laval nozzle analogy for the production of flow acceleration in the latter model. The action of solar gravity in Parker’s hydrodynamic solar wind model is shown to be geometrically equivalent to a renormalization of the actual wind channel area and the renormalization factor is exactly Chapman’s hydrostatic radial density profile, which is totally predicated on the hydrostatic force balance condition. This result appears to be traceable to the encapsulation of the solar gravity effects in Parker’s hydrodynamic solar wind model by Chapman’s hydrostatic corona model, even beyond the coronal base. Furthermore, this result is shown to be robust by considering both isothermal gas and polytropic gas models as well as an n-dimensional ($n=$ 1, 2, or 3) underlying space for the solar wind. Full article

With the global climate governance development, climate policy uncertainty has shifted as one of the most important institutional factors influencing corporate sustainability. However, existing studies have shown mixed evidence regarding the effects of governance and have paid little attention to the boundary conditions that climate policy uncertainty shapes corporate sustainability. The relationship between climate policy uncertainty and corporate sustainability performance will be investigated in this study using a sample of Chinese A-share listed firms from 2016 to 2025, which is proxy measured through ESG (environmental, social, and governance) performance scores. We utilized a two-way fixed-effects model, including various robustness, endogeneity, and heterogeneity tests. The empirical findings suggest that the uncertainty of climate policies boosts corporate sustainability. Moreover, this positive impact is particularly significant for enterprises situated in southern China, firms engaged in sectors benefitting from industrial policy support and large enterprises. According to this finding, climate policy uncertainty’s governance impact is predicated on the interaction of external institutional settings and firms’ organizational features. By offering a more comprehensive clarification of how climate policy uncertainty affects governance, this study contributes to the literature on corporate sustainability and climate policy uncertainty and advances our knowledge of firms’ sustainability responses in the context of policy uncertainty. Full article

In Internet-of-Things (IoT)-based rehabilitation, patient data from wearable sensors (IMU, EMG, heart rate), clinical assessments, and surveys differ in format and granularity, complicating unified patient-state construction. Existing AI-based systems often omit fatigue level or rehabilitation stage, or they place large language models (LLMs) in the clinical decision-making role, exposing patients to hallucination risk. We propose an IoT-ready framework comprising three components: an input-source-independent context abstraction pipeline, a digital twin that simulates clinical-score trajectories, and an LLM Agent that interprets the outputs of a deterministic algorithm and a digital twin as (subject, predicate, object) Triplets to produce a retrieval-augmented clinical report. The algorithm—not the LLM—selects the 13-exercise sequence from Shoulder Pain and Disability Index (SPADI) item-level responses. Explicit per-layer schemas let IoT-sensor branches be added without changing the downstream interface. Here we evaluate the framework on the clinical-score path (three patient-reported outcome measures and six range-of-motion measures); the multi-modal IoT branches maintain deployment-target functionality. On 48 IRB-approved shoulder rehabilitation patients (144 longitudinal records; augmented to 7200 only to train the trajectory generator), real-only leave-one-subject-out evaluation gave VAS RMSE 0.658 (0–10) and SPADI RMSE 6.499 (0–100). Ablation across four surface forms revealed a fidelity–accuracy trade-off—Narrative highest on template fidelity (BERTScore F1 0.250), raw JSON highest on judge-rated accuracy—and the framework adopts the balanced-midpoint Triplet form. A claim-level audit of the Triplet-form reports left 22–31% of atomic claims unsupported (Claude Sonnet and GPT-4o judges) regardless of guideline retrieval. A raw-LLM control never reproduced the algorithm-defined sequence exactly (0 of 48), supporting deterministic, auditable sequence selection and the need for clinician review before clinical use. Full article

Large language models (LLMs) frequently generate physically impossible parametric specifications in computer-aided design (CAD). This paper presents FMforME, a three-layer runtime constraint verification framework that integrates LLM-based generation with six engineering standard-grounded defect detection predicates (D1–D6) and execution in Autodesk Fusion 360. To address detected violations, a Self-Examine subsystem iteratively provides structured, standard-referenced feedback to the LLM, enabling verifier-guided error correction. We evaluated the framework across 564 test cases using four LLM backends from three model families. The system showed consistent buildability within the evaluated benchmark: across all models, every specification validated by the Monitor successfully built in Fusion 360 (548/548). Across the four evaluated backends, realized seeded defect detection ranged from 95.5% to 95.8%, and three backends showed an observed 100% natural pass rate in this benchmark. Crucially, this runtime safety barrier adds little computational overhead relative to LLM inference, averaging only 0.1 ms per in-memory verification check. These results show that standards-referenced runtime verification with structured self-correction can serve as a practical safeguard for LLM-generated CAD specifications within the tested single-part workflows, while broader validation on complex assemblies remains future work. Full article

The core of the Persian tense-aspect system revolves around two verbal stem forms and the so-called durative prefix mi-. We argue that Stem II encodes a perfective meaning, requiring that the event time is contained in the topic time, whereas the prefix mi- encodes that a viewpoint is temporally contained in the event time. Temporal operators, in turn, relate the viewpoint time and the topic time to the utterance time, thus relating the event time to the utterance time. At least one of these methods has to be applied in order to make the predication interpretable. These meanings are modeled within a framework of branching time, which captures facts about the completion or non-completion of events, and provides a novel way for modeling the future. We also treat the use of the prefix mi- in generic sentences and the complex tenses that are expressed with auxiliaries: the modal future, the progressive, and the perfect. Full article

Reliable wearable activity recognition requires not only a class label but also an auditable indication of whether that label is supported by historical sensor evidence. We present CC-NSIEA, a label-preserving neural-plus-rule-based class-contrast evidence audit for cross-subject wearable activity recognition. A Temporal Residual Perception Network supplies the sole activity label, posterior probabilities, and a normalized temporal embedding. A read-only Training-Subject Evidence Memory retrieves global, predicted-class, and competing-class records. A rule-based Evidence Consistency Audit combines data validity, dynamic/static motion coherence, retrieval support, and class separation. When first-round evidence is insufficient, Class-Contrast Evidence Refinement performs one deterministic contrast between the predicted class and the strongest posterior competitor; the audit cannot change the neural label. The term neuro-symbolic is used only in this restricted architectural sense: a neural predictor is coupled to explicitly represent deterministic predicates and a finite rule-based controller; the method does not perform symbolic inference, theorem proving, or knowledge-graph reasoning. On five subject-disjoint outer folds of the UCI HAR official training partition, the shared perception model achieved 90.13% accuracy and 90.55% macro-F1 across 7352 out-of-fold windows from 21 subjects. Relative to a matched dynamic deterministic controller, CC-NSIEA increased Error AUPRC from 0.423802 to 0.433057 and reduced AURC from 0.035941 to 0.035913. The 10,000-resample subject-cluster bootstrap interval for the AUPRC difference was [0.001595, 0.019547]. CC-NSIEA provides an evidence-centered complement to confidence-based reliability estimation. Full article

Natural-language access to spatial databases requires relation interpretation, entity grounding, metric normalization, and database-specific execution semantics. Direct generation of Structured Query Language (SQL) by large language models (LLMs) can therefore return executable but spatially wrong SQL, especially for Chinese urban questions with aliases, abbreviated place names, and geometry-dependent predicates. This paper presents GeoIR-Compiler, a spatially specialized framework that maps a Chinese question to a typed geospatial intermediate representation (GeoIR), grounds mentions and attributes to database objects, and deterministically compiles the grounded representation into SQL for PostGIS, a spatial database extension for PostgreSQL. The contribution is the specialization of intermediate representations for Chinese urban spatial question answering through explicit spatial relations, metric constraints, grounding records, and PostGIS execution templates. We construct two controlled executable benchmarks, NJ-GeoIR-700 and WH-GeoIR-700, covering retrieval, topology, distance, nearest-neighbor, aggregation, compositional, and alias/noisy-mention queries. Across seven locally served backbones, GeoIR-Full reaches mean execution accuracies of 0.7271 on Nanjing and 0.7363 on Wuhan, outperforming Direct-SQL, Data-Augmented In-Context Learning (DAIL)-SQL-style, and Linking-SQL under the fixed evaluation protocol. Ablations are consistent with grounding contributing strongly to the observed gains, while verification mainly trades coverage for answer reliability. Full article

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

Within the framework of argument realization theory, Semantic Role Lists, Participant Roles, and Predicate–Argument Structures represent competing models of verb semantics, each yielding distinct predictions regarding mental representation and processing complexity. This study formalizes these perspectives into two competing accounts: the Role-Quantity Hypothesis (H1), which posits that processing load is driven by the number of event participants, and the Structure-Quantity Hypothesis (H2), which attributes complexity to the multiplicity of syntactic templates. To evaluate these hypotheses, a lexical decision task was conducted on Chinese verbs. The results revealed a significant main effect of role quantity: two-role verbs elicited longer reaction times and lower accuracy than one-role verbs. Conversely, no significant differences were found between one-structure and two-structure verbs. These findings provide robust empirical support for H1, indicating that role-based representations possess greater psychological reality in the Chinese mental lexicon. We argue that for an isolating language like Chinese, verb processing is primarily event-driven, where role information serves as a predictive heuristic during early lexical access. This study offers new insights into the role-based nature of Chinese verb representation, its psychological reality in real-time processing, and the value of integrating argument realization theory with experimental psycholinguistics. Full article