Search Results (701)

During software testing, memory errors in C/C++ can silently corrupt the program state. Address-based memory sanitizers, while offering practical performance and compatibility, are fundamentally unable to distinguish spatial errors that skip redzones or temporal errors that occur after memory reuse. Moreover, their reuse-delay quarantine mechanisms impose significant space and time overhead. We propose a taxonomy of memory sanitizers based on validity encoding and violation detection. Guided by this taxonomy, we introduce fused metadata, a single 8-byte word that encodes an object’s end address and a 6-bit identity tag. MixSan, a prototype built on RangeSanitizer (RSan), stores the same identity tag in pointer high bits through Intel Linear Address Masking (LAM) U57 and validates both the tag and the spatial bound with a unified 3-ALU-op check. On SPEC CPU2006, MixSan incurs a 1.58× geomean runtime overhead, comparable to RSan’s 1.61× overhead. On the Larson allocator benchmark, MixSan and the uninstrumented tcmalloc both scale with thread count, whereas RSan throughput falls; MixSan’s multi-thread plateau is more than 30× that of RSan. In a custom 97-test suite targeting post-reuse temporal errors and redzone-skipping spatial errors, MixSan’s mean single-run detection rate is 98.41% over ${10}^4$ independent executions per program, matching the theoretical $63/64$ rate given a uniform 6-bit tag distribution, whereas ASan and RSan do not detect these constructed cases. Full article

We consider impulsive stochastic delay differential equations with state-dependent impulse times, where the k-th impulse occurs at a time $\tau$ satisfying $\tau ={\tau }_k\left(u\left({\tau }^{-}\right)\right)$. In generalized Lipschitz and growth conditions for coefficients, establishing existence and uniqueness is conditional on the successive approximations sharing a common finite impulse-time structure. The proof of our main result is based on the successive approximation scheme along with Itô’s isometry and Bihari’s inequality. We prove that the approximating sequence is bounded and convergent in a suitable Banach space, which gives a unique local solution. Full article

This paper studies performance analysis and load frequency control (LFC) for a Takagi–Sugeno (T-S) fuzzy power system equipped with an energy storage unit. A corresponding T-S fuzzy representation of the system is constructed. Subsequently, a new lemma of slack Lyapunov function is constructed, which relaxes the conventional positive-definiteness requirement on the quadratic form, provides additional flexibility in the LMI formulation, and yields less conservative stability conditions. In addition, a genetic-algorithm-based outer search is employed to optimize the scalar parameter $\varrho$, while the associated LMIs are solved using the LMI solver, thereby reducing the conservatism of the stability conditions and enlarging the maximum allowable delay bound. Simulation cases are finally presented to confirm the feasibility and effectiveness of the proposed methods. The proposed method increases the maximum allowable delay bound by $2.6920\%$, $4.0220\%$ and $4.0528\%$ compared with the reference method for the tested controller parameters. Full article

Unauthorized low-altitude UAVs can challenge conventional fixed-parameter interception algorithms through agile maneuvers. This study develops a three-dimensional one-on-one terminal-interception simulation environment that incorporates protected-zone penetration, a within-step geometric interception criterion, and kinematic constraints. The intruder, denoted as the red UAV, combines six physically interpretable maneuver templates to generate structured evasive penetration behavior. The defender, denoted as the blue UAV, augments blended generalized proportional navigation (B-GPN) with a bounded residual corrective acceleration produced by deep reinforcement learning, thereby forming a hybrid architecture that combines a geometry-based nominal guidance command with reward-driven bounded compensation. In standardized tests on 1000 unseen scenarios, the implemented residual PPO pipeline increased the interception rate from 63.8% for nominal guidance to 94.1% (95% Wilson interval: 92.46–95.40%) and maintained at least 88.0% interception under the tested control-delay, kinematic, and noise perturbations. It also achieved the highest interception rate among the evaluated residual-learning implementations under both the stable-configuration comparison and the auxiliary task-side-controlled check; this result is limited to the reported implementations and is not a general ranking of algorithm families. These findings indicate that bounded residual learning can compensate for structural limitations of conventional guidance under the evaluated conditions. Full article

Multipath routing in wireless sensor networks (WSNs) improves reliability by providing alternative forwarding paths when a route fails. However, mobile sinks make path maintenance difficult because sink movement can invalidate previously constructed source-to-sink routes. Existing protocols typically depend on either global path reconstruction, which increases control overhead, or footprint-chaining, which accumulates detours through previous sink positions and may weaken path independence. To address this problem, this paper proposes QL-LGMPRP, a reliability-aware local-grid-based multipath routing protocol that combines a sink-centered local grid, two-path delivery, link-quality-aware forwarding, and lightweight tabular Q-learning for waypoint adaptation. Mobility-related route changes are confined to the sink-centered grid, whereas a compact tabular Q-learning policy adjusts the primary-path direction using grid, link-quality, and energy-related state variables. The sink constructs a local grid around its current position, with cells sized to keep in-grid forwarding locally bounded. When an event occurs, the source computes an entry point on the grid perimeter and constructs two greedy paths: a primary path through a Q-learning-selected waypoint near the grid boundary and a backup path toward the current sink position. The Q-learning agent uses a compact tabular state representation that includes the boundary-cell index, residual-energy level, sink-grid position, and local link-quality information, and learns waypoint offsets using a reward that combines delivery success, transmission energy, and delay. This design confines routing adaptation to the sink-centered grid while allowing the waypoint policy to respond to heterogeneous link conditions. Simulation results under different sink speeds and interference conditions show that QL-LGMPRP maintains high delivery reliability while reducing detour-related forwarding costs relative to footprint-chaining and showing lower weak-link exposure than the geometric-forwarding comparison schemes. 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

Closed-loop regulation of the bispectral index (BIS) during propofol–remifentanil co-administration is challenging because of nonlinear drug interactions, patient variability, model uncertainty, external disturbances, and infusion constraints. This paper proposes a residual-based fractional-order model predictive control (RB-FOMPC) method within the Extended Prediction Self-Adaptive Control (EPSAC) framework. FOMPC is obtained by introducing fractional-order weights into the EPSAC cost function, while an inverse Hill mapping handles the nonlinear BIS–drug relationship outside the online quadratic programming problem. RB-FOMPC further augments the FOMPC prediction with a bounded prediction of future residual variation generated by a delayed low-order residual model. During a predefined induction window, the one-sided correction is applied according to a filtered BIS-derived effect-site residual indicating nominal-model underestimation. A normalized infusion-ratio parameter coordinates the propofol and remifentanil inputs. Monte Carlo analysis showed that RB-FOMPC preserved the nominal FOMPC performance under inter-patient variability and substantially reduced excessive BIS undershoot under intra-patient model perturbations. Overall, the simulation results indicate that the residual-based predictive correction can selectively reduce induction-phase BIS undershoot under the evaluated nominal-model-underestimation conditions, while preserving the nominal performance of FOMPC. Full article

This paper investigates the qualitative and topological behavior of random solutions for a class of partial fractional random differential equations governed by the Darboux problem. Unlike classical configurations that rely on bounded or finite delays, our theoretical framework explicitly addresses systems involving unbounded infinite delay. The dynamics of the state transitions are formulated using left-sided mixed Riemann–Liouville fractional integrals and joint Caputo fractional derivatives of order $\epsilon =({\epsilon }_1,{\epsilon }_2)\in (0,1]\times (0,1]$. Because of the infinite historical horizon, the underlying model is constructed and analyzed within abstract, semi-normed axiomatic phase spaces defined over topological Fréchet spaces. By avoiding restrictive compactness assumptions on the nonlinear operational bounds, we establish novel random mild existence theorems. The structural proofs are achieved through a combination of a regular, sublinear family of axiomatic measures of noncompactness and an advanced generalization of the classical Darbo fixed-point theorem tailored for Fréchet domains. Finally, a concrete mathematical example is systematically analyzed to confirm the validity, consistency, and practical applicability of the established theoretical bounds. Full article

The quality and safety supervision of agricultural products at the grassroots level has long faced the triple superposition dilemma of small-sample sampling, multi-source evidence conflict, and risk chain evolution. Although existing data-driven models have considerable accuracy, they are difficult to leverage for intervention decisions, and the simple serial connection of traditional Bayesian networks and evidence theory cannot respond to dynamic scenarios. Aiming at this research gap, this paper constructs a dynamic risk assessment model, CIBE-DR, that deeply couples Bayesian networks with evidential reasoning. It contains three core innovations. First, the structure learning method of the causally identifiable Bayesian network embeds a graded do-calculus identifiability score covering both back-door and front-door criteria into the BDeu scoring function and combines this reward with an expert-prior divergence penalty that breaks Markov equivalence so as to realize the transition from relevance modeling to intervention decision modeling. Second, the conflict-aware adaptive evidence synthesis rule orthogonally decomposes multi-source conflict into an epistemic component and an ontological component, which are modeled respectively by Tsallis belief entropy and abductive inference over a discrete twenty-seven-point heterogeneity hypothesis space and are then fused under a reparameterized Dempster–Yager interpolation in which the two endpoints recover the two named rules under a single consistent interpretation. Third, the bidirectional closed-loop coupling mechanism between BN and ER realizes the mutual calibration between the conditional probability table and the evidence credibility prior under a Lyapunov monotone descent argument with the explicit Lipschitz bound $L_{\theta }$ ≤ 0.028 < 1, endowing the model with time-varying self-correction ability. Based on experiments on 156,847 sampling samples from counties and townships in East China, Central China, and Southwest China from 2021 to 2024, the proposed method achieved the best value in six of the seven evaluation indicators, with a minority recall of 0.864 ± 0.014, an intervention effect estimation error of 0.063 ± 0.005, and a dynamic response delay of 2.8 ± 0.3 days, significantly ahead of eleven mainstream baselines under the McNemar test on classification ($p$ < 0.001) and the Wilcoxon signed-rank test on intervention-effect estimation ($p$ < 0.001). The only indicator on which CIBE-DR does not lead is overall accuracy, which is 0.002 lower than that of Transformer; this difference does not reach statistical significance under the McNemar test ($p$ = 0.32) and does not weaken the value of grassroots supervision in the strong-imbalance scenario where the positive rate is only 1.04%. The robustness advantage of the model is particularly prominent in the scenarios of sparse data, adversarial perturbation, and prior-graph incompleteness, and the intervention-effect estimates were additionally validated against two post-2022 policy interventions with absolute deviations of 1.4 and 1.2 percentage points respectively. These results verify the product gain and grassroots deployability of the three mechanisms. Full article

We study a delayed semilinear evolution equation with a Caputo time derivative and the spectral fractional Dirichlet Laplacian on a bounded connected domain. The model separates two forms of memory: the Caputo operator retains the distributed Volterra history, whereas the nonlinear production samples the single past state $u(t-\tau )$. Working in the strongly continuous phase space $C_0(\Omega )$, we prove local well-posedness, positivity, a sup-norm continuation criterion, and a compatible weak formulation. In the delayed-source case with $\mu =0$, the solution exists globally and remains bounded on every finite time interval, while the first Dirichlet mode admits an explicit recursive sequence of positive lower bounds across successive delay windows. In the dissipative case $\mu >0$, $p>q>1$, histories satisfying the explicit smallness conditions remain in an invariant order interval and the $L^2$-energy decays at a Mittag–Leffler rate. The scalar computations are presented only as heuristic first-mode surrogate experiments. In addition, an independent spatially resolved sine spectral-Galerkin/L1 computation of the PDE, with temporal and spectral refinement studies, is included as a numerical illustration. Full article

Achieving high-accuracy path following remains challenging for an unmanned surface vehicle (USV) in narrow waterways with time-varying curvature and straight–curve transitions; fixed-parameter line-of-sight (LOS) guidance can cause delayed response, overshoot, and steady-state cross-track error. This paper proposes a curvature-coupled adaptive vector-field integral LOS (AVFILOS) guidance law. It incorporates curvature-adaptive guidance: a lookahead distance regulated by curvature and cross-track error and a field-source radius that contracts with curvature to strengthen centripetal correction in high-curvature regions. A fuzzy adaptive proportional–integral–derivative (PID) controller tracks surge speed and heading. A stability analysis establishes local exponential stability for straight and constant-curvature paths and local ISS with local uniform ultimate boundedness for time-varying curvature under a bounded-rate condition. Across six elliptical and sinusoidal cases, AVFILOS achieved an average root mean square error (RMSE($y_e$)) of 0.1325 m, reducing RMSE($y_e$) by 90.6%, 63.6%, and 37.2% compared with LOS, time-varying LOS (TLOS), and vector-field integral LOS (VFILOS), respectively. Its average maximum absolute cross-track error (Max($|y_e|$)) was 0.3478 m, with reductions of 88.2%, 48.2%, and 30.7%. The ablation and sensitivity results indicate that coupled adaptive mechanisms improve curved-path tracking and reduce overshoot. The simulations indicate that AVFILOS is promising for cross-track-error-sensitive USV navigation. Full article

This article establishes new retarded nonlinear integral inequalities that extend and generalize several known results in the literature, including recently reported estimates. These inequalities are used to derive explicit bounds for the unknown functions. These bounds are shown to be sharper and more general than existing ones, recovering earlier results as special cases. As an application, the derived inequalities are used to study the boundedness, uniqueness, and global existence of solutions to an initial value problem for a class of nonlinear integro-differential equations with delay. An illustrative example is presented to illustrate the applicability and effectiveness of the results. Full article

Battery-powered mobile robots that rely on an edge server for perception spend energy in three places at once: the on-board processor, the radio front end and the drivetrain. These budgets are normally optimised separately, which is a mistake, as lowering the processor clock pushes work onto the wireless link, transmitting into a poor channel costs far more than waiting for a better one, and where the robot drives determines what the channel will be. We formulate the co-management of all three as a minimisation of long-run average energy for a fleet sharing an access point and subject to task deadlines, a power budget and a mission-progress constraint that forces every policy under comparison to cover the same ground. The resulting stochastic mixed-integer non-convex program is made tractable by a Lyapunov drift-plus-penalty argument that decomposes it into four per-slot subproblems: a square-root clock rule, a water-filling transmit-power rule with an explicit on/off test, a join-the-shorter-queue offloading split, and a short lookahead over admissible speeds. The policy needs no channel or workload statistics and attains an A per-task deadline mechanism, feasibility floors on the clock and transmit decisions, and closes the gap between queue-stability guarantees and individual task deadlines, which drift arguments alone do not bound. The policy needs no channel or workload statistics and attains an $\left[O\right(1/V),O(V\left)\right]$ energy–delay tradeoff, stated under precisely qualified assumptions. In a per-task simulation study against six baselines, the policy reduced combined communication and computation power by 25% relative to the strongest deadline-compliant baseline ($p<{10}^{-4}$) at equal mission progress, and was the only scheme to hold deadline violations below 0.5% across the full load range, where every baseline exceeded 16% at high load. Full article

Reliable drinking-water production is difficult on remote islands where brackish-water delivery is intermittent, technical personnel are scarce, and reverse-osmosis plants are manually operated. This study presents an operational characterization of an Internet of Things (IoT) retrofit deployed in Santa Cruz, Galápagos; it is not a controlled before-and-after effectiveness evaluation. An ESP32-based M5Stack Tough controller, distributed ESP-NOW sensing nodes, relay–contactor interfaces, a binary pressure permissive, and a ThingSpeak cloud layer were integrated without replacing the existing pumps and membranes. The exported primary-flow channel contained 4,603,989 numeric observations, including 500 pre-official test readings. Operational analyses used 4,603,489 numeric observations from the official monitoring period; 4,603,340 values remained after nominal-range filtering, and positive flow had a median of 12 L/min (interquartile range: 11–15 L/min). Among 332 logged high-pressure commands, 326 were preceded by a low-pressure command (98.2% unbounded command-state consistency), whereas 275 occurred within a 120 s analytical bound (82.8%). The median low-to-high command delay was 27 s (interquartile range: 11–70 s). Four organizational representatives completed a published 41-item Industry 4.0 maturity instrument before and after deployment; the self-reported overall mean was 0.26 at baseline and 1.95 post-deployment, and these results are interpreted descriptively. Energy-consumption and production data were confidential and unavailable to the authors, while water-quality variables were not measured. The contribution is therefore a long-duration, local-first legacy retrofit with auditable telemetry and explicit limitations, rather than a claim of optimized desalination performance. Full article

This paper considers priority-aware partial computation offloading in an uplink mobile edge computing (MEC) network. Devices assigned to different groups occupy orthogonal subbands, whereas devices within each group use power-domain non-orthogonal multiple access (NOMA) with successive interference cancellation. Task-input size determines the transmitted and processed workload, while queue backlog and application urgency determine the service weight. The Gaussian multiple-access-channel rate region is convex, but the complete allocation problem is not jointly convex in the adopted variables because the offloaded workload is coupled with reciprocal transmission rate and reciprocal edge-CPU allocation. A structure-exploiting block-coordinate projected-gradient method is developed. It combines exact finite-candidate offloading updates, an exact edge-CPU allocation bounded below by deadline feasibility and above by local-path saturation, and an analytical projected power step with Armijo backtracking. For eight users at 23 dBm, pairwise group-based NOMA reduces the weighted delay–energy cost and device energy by 6.18% and 23.44%, respectively, relative to orthogonal access. Queue-aware weighting reduces upper-backlog-quartile delay by 2.69 ms (95% confidence half-width: 0.78 ms) while increasing lower-quartile delay by 8.34 ms (half-width: 2.07 ms). In a paired 15-iteration ablation, generic projected block-coordinate updates have a cost ratio of 1.0098 (half-width: 0.0086) relative to the structured method. A hybrid deep deterministic policy-gradient policy, evaluated over five training seeds, has an 11.77% higher cost while requiring 0.84% of the median online decision time. Of 432 allocations, 392 satisfy the residual-qualified stopping tests and 40 are explicitly reported as iteration-safeguard terminations. Full article