Search Results (7,048)

Electrical resistivity is a sensitive indicator of rock damage, yet the quantitative link between pore-structure change and resistivity response under varied hydro-thermo-mechanical conditions remains poorly constrained. In this study, triaxial compression tests were conducted on saturated limestone at confining pressures of 5–20 MPa, pore pressures of 0–9 MPa and temperatures of 20–80 °C, with synchronous measurement of electrical resistivity and pore volume; in situ X-ray computed tomography (X-CT) was performed on miniature specimens at six characteristic loading stages to resolve the accompanying mesoscale pore-structure evolution. Resistivity tracked pore volume closely, rising to at most 1.16 times its initial value as pre-existing microcracks closed, then falling once crack propagation restored fluid connectivity, with the turning point occurring at approximately 50% of the peak deviatoric stress. A resistivity change rate per unit pore volume (ρ′) is introduced to separate the two contributions: its stage-averaged magnitude during unstable crack propagation is about 8.5 times that during linear compression, showing that crack opening alters resistivity far more efficiently than closure of an equivalent pore volume. Consistently, the cementation exponent decreased monotonically throughout loading while the electrical tortuosity reversed from increasing to decreasing at crack initiation, indicating that deformation reorganizes the topology of the conductive network rather than merely its volume. X-CT reconstruction shows pore orientation migrating progressively toward the loading axis, with 93% of the pore volume concentrated within the 18–27° interval after failure, matching the macroscopic shear angle. Crack connectivity, rather than pore fluid content, therefore governs resistivity in damaged limestone, providing a quantitative basis for resistivity-based early warning of rock mass instability in underground engineering. Full article

This paper presents an integrated modelling and decision-support reanalysis of a published 24-run experiment on diamond-spark grinding (electrical-discharge diamond grinding) of two hard alloys—the tungsten-free cermet TN-20 and the WC–TiC–Co alloy HS123—machined jointly with C45 steel; no new experiments are performed. Established components are deliberately combined into one reproducible workflow: quadratic response-surface models fitted by least squares and by minimax (Chebyshev) approximation, validation by prediction-oriented criteria including nested leave-one-out cross-validation of the entire model-selection pipeline, the addressable DEFMOT representation of the 9⁴-factor grid formalized as an ε-constraint procedure, and benchmarking against desirability-function and Pareto analyses. Minimax fitting reduces the maximum absolute residual by 22.5–36.9% at the cost of higher aggregate errors. Nested validation exposes model-selection instability for the TN-20 responses, and a dedicated sensitivity analysis shows that the surrogate-model choice can change the recommended regime: the TN-20 compromise is efficient or one grid step from efficient under all three surrogate families, whereas the preferred HS123 regime shifts qualitatively (including a reversal of the wheel-speed setting) between least-squares and minimax surrogates. A residual-bootstrap analysis propagates data uncertainty through the complete optimization and quantifies how frequently each recommended regime is re-selected. Within the legacy cost basis, point estimates indicate comparable productivity (difference below 9%), an approximately 35% lower specific machining cost for TN-20 and approximately 1.8 times higher diamond consumption; the 95% confidence intervals for the between-material contrasts include zero, so experimental confirmation is required before industrial substitution. The framework quantifies, rather than hides, how surrogate uncertainty propagates into the engineering decision. Full article

X-band rainfall radar systems offer advantages such as high spatial resolution, flexible deployment options, and strong near-surface detection capabilities. However, due to the short electromagnetic wavelength in this band, the radar base data are highly susceptible to multiple coupled factors—including clutter from mountain vegetation, tall buildings, and other terrain features; electromagnetic interference from surrounding radio-frequency equipment; beam obstruction caused by complex topography; and attenuation of rainfall intensity along the precipitation path—resulting in pronounced distortion of raw echoes. This distortion significantly hinders the accuracy of quantitative precipitation estimation in mountainous regions and makes it difficult to meet the operational requirements for precise mountain torrent early warning systems. To address the problem, this study utilizes real-time observational data from field deployments of X-band rainfall radars in mountainous regions to construct a comprehensive, progressive quality control system comprising: refined removal of terrain clutter; electromagnetic interference pre-suppression; dynamic beam obstruction correction; and adaptive rainfall intensity attenuation correction. The terrain clutter suppression algorithm is optimized in this study, and an adaptive beam-blockage compensation algorithm based on the terrain-blockage fraction is further adopted. However, beam-blockage correction exhibits limited improvement in this case, which is likely attributed to the complementary observational coverage provided by higher-elevation radar scans. The echo-missing regions are dynamically corrected according to the terrain-blockage coverage ratio at different elevation angles and azimuths to realize differentiated compensation corresponding to blockage severity so as to effectively restore the true echo intensity obscured by terrain. Furthermore, considering the prominent rain-induced attenuation of radar electromagnetic waves along the propagation path, a dynamic attenuation correction scheme based on path-integrated reflectivity is introduced. The precipitation attenuation coefficient is dynamically calculated point by point from the variation characteristics of real-time echo intensity along the beam path to compensate echo loss, which addresses the limitation that fixed correction parameters cannot adapt to attenuation differences under variable rainfall intensities. Based on the Z-R power-law relationship, the radar echo-derived rainfall is inverted, using hourly measurements from dense ground-based rain gauges as the reference values, and precision is verified using the MB and RMSE—industry-standard meteorological evaluation metrics. Experimental results demonstrate that clutter suppression dominates RMSE reduction (7.37 to 1.91 mm/h). Beam blockage correction shows negligible impacts on RMSE and MB. Attenuation correction delivers marginal MB improvement (−0.52 to −0.51 mm/h) with no measurable RMSE response. After full-chain quality-control optimization, the aggregate RMSE between radar-derived rainfall and gauge observations is 1.91 mm h⁻¹, representing an approximately 74% reduction compared with the raw dataset (from 7.37 to 1.91 mm h⁻¹). Elevated RMSE values of 4.2–5.0 mm h⁻¹ are observed for rainfall intensities between 5 and 10 mm h⁻¹. In addition, the discrepancy between radar and gauge estimates grows as rainfall intensity increases. As the distance between radar and rain gauge increases, radar QPE systematically underestimates light precipitation, while persistent underestimation occurs across all ranges for heavy rainfall. This study enhances the differentiated quality control framework for X-band radar systems in complex mountainous regions, effectively improves the observation quality of radar-derived base data, and provides data support and technical references for dynamic monitoring of flash floods in mountainous river basins. Full article

With the large-scale integration of electric vehicles (EVs), distributed charging scheduling has become a key enabler for coordinated charging management. However, its reliance on information exchange makes it susceptible to cyberattacks, including False Data Injection (FDI), Denial of Service (DoS), and replay attacks. Such attacks may compromise privacy and corrupt or interrupt communication, leading to incorrect consensus prices and degraded scheduling performance. To mitigate these threats, this paper proposes a distributed resilient charging scheduling strategy. Specifically, anomalous nodes are detected through neighbor-based observations, while a belief-degree-based trust mechanism is employed to isolate low-trust nodes and suppress attack propagation. In addition, an individual price resetting mechanism is developed to restore convergence to the optimal price of the remaining EVs following node isolation. Simulations on communication networks with 5 to 100 EVs show that, under all three attacks, the compromised node is detected at the second and isolated at the third consensus iteration after attack onset, no healthy node is falsely isolated, and the remaining fleet converges to the optimum of the reduced scheduling problem with a price deviation below $8.2\times {10}^{-3}$. A buffered detection envelope extends these guarantees to asynchronous communication, heterogeneous time-varying delays, packet losses, and intermittent attacks: in 245 randomized stress runs on 5-to-100-EV networks, every attacker is isolated, no healthy node is isolated outside the harshest composite scenario, and the final price deviation remains below $1.1\times {10}^{-2}$. Extensive simulations under representative cyberattack scenarios verify the effectiveness and robustness of the proposed strategy within the stated assumptions. Full article

The influence of mechanical noise on impact identification using fiber Bragg grating (FBG) accelerometers in complex environments remains insufficiently explored and quantitatively characterized. This study investigates how the intensity and propagation path of mechanical noise affect the output stability and impact-detection capability of a self-developed FBG accelerometer. A quantitative evaluation approach is established using wavelength-variation statistics, impact-response amplitude, and signal-to-noise ratio (SNR). Experimental results show that mechanical noise markedly increased the background wavelength fluctuation, with the maximum standard deviation reaching 16.71 pm, approximately 11 times the no-additional-noise value. By contrast, the RMS of the target impact response increased only slightly, from 0.0300 nm to 0.0353 nm, indicating that mechanical noise primarily affects baseline stability rather than the amplitude of the target impact response. Under the highest-intensity disturbance tested, the SNR remained at 22.98 dB, and the impact event remained distinguishable. The proposed multi-indicator approach provides a quantitative characterization of the FBG accelerometer response under different mechanical-noise conditions. A commercial 941B accelerometer was used as an external reference for sensitivity calibration, and its measurements were not used for direct performance comparison with the FBG accelerometer. The results provide experimental evidence of the impact-identification behavior of the proposed sensor under the investigated mechanical-noise conditions. Full article

Advanced Metering Infrastructure (AMI) underpins bidirectional communication in smart grids, a foundational layer of smart city energy systems, facilitating the flow of data, real-time monitoring, and demand-responsive control. But its connectivity exposes smart meters to data tampering, denial-of-service attack, and false-data-injection attacks. Most intrusion detection systems (IDSs) for AMI only report that an intrusion has occurred but cannot reconstruct how it propagated or where it originated, leaving the operators without the forensic evidence to perform containment. This paper proposes a multi-stage approach coupling detection with forensic analysis. A recurrent neural network (RNN) extracts temporal features, a support vector classifier (SVC) performs binary classification, and ant colony optimization (ACO) serves two purposes: feature selection before classification and a backward path reconstruction after an intrusion is confirmed. The proposed framework is evaluated on a simulated AMI network with forensic ground truth and further validated on the public UNSW-NB15 benchmark. The detection accuracy exceeds $96\%$, while ACO reduces the feature set from 40 to 14. A McNemar’s test ($p=0.265$) indicates that this feature reduction does not significantly alter the per-sample error pattern. With the use of the improved tracer, the Path Overlap Score increases from $0.29$ to $0.40$, while the False-Positive Path Rate decreases from $0.39$ to $0.19$, relative to the centroid baseline tracer used for forensic tracking. This improvement in the Path Overlap Score is statistically significant ($p=4.39\times {10}^{-8}$). However, the Source Localization Rate remains relatively low ($9\%$–$11\%$) for both methods, owing to the intrinsic difficulty of identifying the true source meter from incomplete alert data. Therefore, the proposed framework not only reliably detects intrusions but also significantly outperforms the baseline tracer in path overlap and false-positive rate, although precise source localization remains an open challenge. Full article

Hyperbolic interface problems are widely applied to model wave propagation and shock transmission across discontinuous media, such as acoustic waves in layered materials, seismic waves in the Earth’s crust, and stress or electromagnetic waves in composite structures. This study introduces a novel computational framework for hyperbolic interface problems, specifically designed to unify and extend the treatment of regular interfaces within partial differential equations. The proposed hybrid approach combined Haar wavelet-based spatial discretization with finite difference schemes for temporal integration. By employing truncated Haar series to approximate spatial derivatives and leveraging finite difference techniques for time evolution, the method delivers accurate solutions for both linear and nonlinear systems regardless of whether the governing coefficients are constant or spatially variable. In addressing linear problems, the resulting algebraic equations are solved efficiently using Gaussian elimination. For nonlinear formulations, the method incorporates a quasi-Newton linearization strategy, effectively transforming the system into a linear one. Extensive validation is performed through a suite of benchmark problems, with performance assessed via metrics including maximum absolute errors (MAEs), root mean square errors (RMSEs), and convergence behavior as a function of collocation point (CP) density. Numerical experiments highlight the method’s superior stability and accuracy, particularly in scenarios marked by discontinuities or sharp gradients in the solution. The approach proves especially effective in bridging inconsistencies between boundary and initial conditions, offering a robust alternative to existing techniques. Theoretical soundness, strong convergence properties, and comprehensive numerical validation collectively underscore the method’s reliability and adaptability across a broad spectrum of applications. Full article

Single-factor experiments were conducted with sensory score set as the evaluation index to investigate individual effects of sugar substitution dosage, mass ratio of xylitol (Xyl) to isomaltooligosaccharide (IMO), and osmotic soaking duration on the comprehensive quality of low-sugar strawberry preserves. Box–Behnken design (BBD)-based response surface methodology (RSM) and back-propagation neural network (BPNN) models were subsequently established for parallel process optimization and comparative modeling analysis based on single-factor experimental datasets. Physicochemical indicators including total sugar, titratable acidity, total flavonoids, total phenolics, chromatic aberration, moisture content and water activity of low-sugar strawberry preserves manufactured under optimized technological parameters were determined and systematically compared with those of sucrose-controlled counterparts. The RSM and BPNN models predicted maximum sensory scores of 81.62 and 82.62, respectively. Experimental verification conducted under the practically adjusted optimal conditions derived from the RSM and BPNN models yielded sensory scores of 81.63 and 82.72, respectively, with the BPNN-derived condition achieving a higher verified sensory score than the RSM-derived condition. Based on these results, the optimal processing parameters were determined as a 40% (w/w) addition amount of compound sugar substitute, a Xyl/IMO mass ratio of 1.5, and a soaking time of 5.5 h. Lower total sugar and titratable acidity, yet higher contents of total phenolics and flavonoids, were detected in sugar-substituted strawberry preserves in comparison with sucrose-preserved samples. Meanwhile, stronger red–yellow chromaticity was observed in sucrose-based preserves, while sugar-substituted products delivered higher lightness. Superior predictive capacity of the BPNN model to RSM was validated in the formulation optimization of strawberry preserves. Our experimental findings show that partially replacing sucrose with functional sweeteners improves the nutritional attributes of fruit preserves without impairing their sensory acceptability. Full article

This paper studies how a localized defect migrates through the $D_4$ root lattice. Its spatial slice is the face-centered cubic lattice, and the tetrahedral voids of that slice form a simple cubic sublattice, two-colored by coordinate sum modulo four. Four results follow. First, the exclusion geometry of a void closes its four face channels and leaves six edge channels open, so the coordination number six is derived and not assumed. Second, each void class admits exactly one bond-direction set, so a void carries one bit of orientation information. The matter/antimatter grading therefore cannot be the void class, and the worldline must carry it instead. This corrects an earlier identification of inversion-related voids with matter and antimatter: those classes are propagation sublattices. Third, the rotational site symmetry is $T\cong A_4$, whose double cover has faithful irreducible representations only in dimension two. Among the half-integer spins, $j=\frac{1}{2}$ alone stays irreducible on restriction. This selects a spin; it does not determine one. Fourth, a hop induces a canonical bijection on the bounding atoms by a lattice translation, and baryon species is preserved if the winding labels follow that bijection. One dynamical postulate is used. Under it a hop carries exactly one time step, no update leaves a defect in place, and a defect at rest oscillates between two adjacent voids. A closing section records the conditions under which a localized continuum solution obeys $E=\gamma E_{\mathrm{rest}}$; no such relation is derived here for the lattice defect, and that section is conditional throughout. Every geometric claim is verified by an accompanying script. Full article

Neural surrogates of evolution equations replace many solver steps by one network evaluation, but the physical invariants they appear to respect are properties of the trained weights, and weights are what reduced-precision and integer inference perturb. We study a six-field reaction, diffusion and taxis model of angiogenesis coupled to a Dynamic Energy Budget reserve, whose fields are densities and must stay nonnegative, and we build a macro-step surrogate whose forward pass factors through operators with known sign structure. The diffusion propagator is assembled once as a dense nonnegative row-stochastic matrix, the haptotaxis step is written with explicitly nonnegative coefficients, and the learned block returns a production and a per capita destruction that enter a Modified–Patankar quotient. Every operation is then a sum or a product of nonnegative numbers, or a quotient of a nonnegative number by a positive one, so the state stays nonnegative under any arithmetic whose rounding preserves the sign of nonnegative reals. Positivity survives half precision, bfloat16 and integer quantization without clamping and without retraining, which we confirm against an unstructured surrogate sharing the backbone, the parameter count and the training protocol. Measurements on a graphics processor quantify what each format costs in accuracy, time and memory. Full article

IoT deployments handle credential hygiene reactively: cloned, replayed, or stale credentials are typically discovered only after misuse, and revocation state is often propagated through centralized lists whose integrity cannot be independently verified. This article introduces the Yang–Baxter IoT Consistency Gateway (YB-IoT-CG), a Zero-Trust admission-control framework that pushes an algebraic layer of credential screening to the edge and anchors security evidence on a modeled permissioned-ledger architecture. YB-IoT-CG operates after conventional secret-based authentication and Yang–Baxter equality is used as an execution-consistency invariant rather than as proof of device identity or message authenticity. Each authenticated message is hashed into a digest and reduced to an algebraic transcript that is verified over two Yang–Baxter traversal paths. Beyond equality checking, chain-proximity metrics inspired by time–memory trade-off analysis, nonce and timestamp freshness heuristics, and contextual risk scoring identify credentials that should be proactively challenged or revoked before telemetry is admitted. The proactive risk component is deterministic and policy-based rather than a learned predictive model. Decisions are batched into Merkle trees whose roots are represented through a permissioned-ledger model, credential revocation is propagated through a modeled on-chain registry, and device identities are bound to W3C Decentralized Identifiers (DIDs) with verifiable credentials. This design provides tamper-evident audit support while keeping ledger interaction off the packet decision path. Validation is based on a controlled Python 3.13.7 packet-level simulation and a parameterized ledger model, not on a physical IoT or live Hyperledger Fabric deployment. Across 60 seeded simulation runs, the complete post-authentication screening pipeline obtained a median incremental decision time of 7.8 $\mathsf{\mu }$s, 99.4% aggregate decision accuracy for the modeled attack classes, a 0.43% false rejection rate, and a 31.4 ms amortized ledger service-time equivalent per decision for a batch size of 64. The 99.4% value is a property of the composed freshness/context/registry/YB policy and is not a YB-only detection rate; the YB-specific positive guarantee is limited to the isolated fault class of Proposition 7. These results provide simulation-based evidence supporting a lightweight, explainable, auditable, and proactive approach to credential hygiene at the edge. Full article

Sustainable production planning requires the simultaneous optimization of economic, environmental, and social objectives under conditions where key parameters are not only imprecise but also variably reliable. This study proposes a Z-number-based multi-objective linear programming (Z-MOLP) framework that addresses this dual uncertainty. The framework incorporates eight conflicting objectives-profit maximization alongside minimization of particle board consumption, energy use, carbon emissions, production time, water usage, metal usage, and edge-band consumption-spanning economic, environmental, and social sustainability dimensions. Objective weights are determined through a Z-number-based reciprocal pairwise comparison procedure, and optimal production plans are obtained via weighted aggregation and integer linear programming. The framework is validated using real monthly production data from three furniture manufacturing enterprises in Azerbaijan. A comparative analysis between the full eight-objective model and a reduced six-objective model-excluding carbon emissions and production time-reveals that sustainability-specific objectives consistently constrain production volumes and reduce profit (+17.45%, +4.67%, and +0.57% profit increases when excluded for each of the three enterprises) but that these economic gains are driven entirely by production-scale expansion rather than efficiency improvement, with resource consumption rising in near-exact proportion. These findings confirm the existence of a genuine trade-off between economic performance and sustainability in developing-economy manufacturing contexts and demonstrate that Z-number representations add practical value by propagating data reliability through both the optimization and the output-reporting stages of production planning. Full article

Highway soft rock slope deformation monitoring produces nonlinear, non-stationary, and multi-scale time series that are strongly affected by rainfall and field noise. This study proposes an adaptive hybrid framework that combines optimal variational mode decomposition (OVMD), the sparrow search algorithm (SSA), and gated recurrent unit (GRU) networks. High-precision BeiDou Global Navigation Satellite System (GNSS) observations collected hourly over a 120-day K55 monitoring campaign (late 2022 to early 2023) are cleaned using a cumulative-sum (CUSUM) change-point detector and cubic-spline reconstruction, while rainfall-related hydro-mechanical variables derived from seepage and slope-stability analyses are incorporated as external inputs. To prevent future-information leakage during blind testing, OVMD is recomputed causally at each one-day-ahead forecast origin using only observations available up to that origin; it then separates the deformation signal into physically interpreted trend, periodic, and high-frequency components, and SSA adaptively optimizes component-specific GRU hyperparameters for parallel prediction and reconstruction. On the K55 strongly weathered shale slope, across five independent runs the proposed model achieved a mean root-mean-square error (RMSE) of 0.04 ± 0.01 mm and a mean absolute percentage error (MAPE) of 0.18 ± 0.05%, outperforming standard GRU, long short-term memory (LSTM), and back-propagation neural network (BPNN) baselines that received an equivalent validation-based hyperparameter search. Cross-scenario evaluation on a geologically distinct K14 marl slope, independently retrained on its own record, yielded a mean RMSE of 0.14 ± 0.02 mm and a mean MAPE of 0.42 ± 0.08%. The results indicate that the proposed framework improves prediction accuracy while retaining useful cross-scenario robustness, supporting intelligent monitoring and early warning of rainfall-sensitive highway slopes. Full article

Agentic large language model (LLM) networks are increasingly used in safety-critical settings where autonomous agents invoke tools, exchange context, and coordinate decisions. Prompt-injection attacks remain a significant threat to these multi-agent pipelines because they can compromise data flows between agents, bypass instruction hierarchies, and corrupt output integrity. Although defenses against injected prompts and mechanisms for cryptographically verifying model-related computations have been studied independently, no common framework unifies these complementary security perspectives in a protocol suitable for real-time agentic deployments. From the perspective of symmetry, secure inter-agent communication requires the preservation of an invariant integrity relationship between a message at its source and the corresponding message accepted at its destination. A benign communication path therefore exhibits a form of integrity symmetry, whereas prompt injection or message manipulation creates an asymmetric state in which the received payload, its semantic effect, or the receiving model’s internal activation pattern deviates from the trusted reference state. In this paper, we propose SecurePrompt-IntegrityNet (SPI-Net), a prompt-injection-resilient data integrity verification protocol that combines cryptographic attestation with anomaly-aware activation monitoring. SPI-Net provides three closely related mechanisms: a Merkle-tree-based commitment system that verifies the provenance and integrity of data payloads exchanged between agents; a layer-wise Mahalanobis-scoring Activation Anomaly Detector (AAD) that identifies distributional shifts in the intermediate representations of LLMs; and a Trust Propagation Consensus (TPC) mechanism that combines cryptographic and behavioral evidence into per-payload integrity verdicts. In this formulation, the Cryptographic Attestation Module (CAM) tests whether message-level structural symmetry is preserved between the sender and receiver, whereas the AAD detects behavioral symmetry breaking in activation space. Experiments on three multi-agent benchmarks under five adaptive attack strategies show that SPI-Net achieves a 96.8% detection rate with a 1.7% false positive rate, reduces the attack success rate by 94.3% relative to undefended baselines, verifies data integrity with 99.2% accuracy, and introduces only 38 ms of median per-message latency. These results demonstrate that jointly preserving cryptographic integrity symmetry and identifying activation-level asymmetry provides substantially stronger prompt-injection resilience than either verification mechanism alone. Full article

Recovering time-varying 3D scenes from monocular dynamic videos is challenging because each frame provides only a single view of a changing scene. Fast, large-magnitude motion further weakens geometric stability and image fidelity under monocular observations, especially when depth, motion, and visibility must be inferred from imperfect frame-wise priors. This paper proposes SurfelFlow, a surface-aware 2D Gaussian streaming 4D reconstruction method for this setting. SurfelFlow reduces the downstream geometric amplification of residual monocular-depth and optical-flow errors by changing how propagated primitives represent local surface support, rather than by replacing the external priors themselves. It represents local rendering primitives as 2D Gaussian surface patches aligned with the currently visible surface while retaining depth- and flow-guided frame-wise point propagation. It also introduces single-frame surface geometric regularization and streaming Gaussian visibility management to stabilize local surfaces and suppress residual historical dynamic Gaussians in disoccluded regions. Experiments and ablation studies on the public real-world DAVIS2017-dev dataset show that SurfelFlow improves the per-sequence macro-average PSNR over the GFlow baseline by 4.16 dB and outperforms the evaluated representative baselines in image reconstruction quality. Furthermore, evaluation against held-out registered sensor depth on four dynamic Bonn RGB-D sequences shows lower visible-surface errors, reducing scale-aligned absolute relative depth error (AbsRel) from 0.1241 to 0.1129 and surface normal mean angular error (MAE) from ${58.55}^{\circ }$ to ${54.51}^{\circ }$. Full article