Search Results (2,014)

In reconfigurable array transmissions, a phased-array transmitter changes its characteristics, yet must still be able to control its transmission while optimizing its performance. To enable full reconfiguration while transmitting predictably, performing accurate, real-time measurements within the transmit chain is useful. This recently developed in-situ measurement approach, shown in multiple previous contributions, is summarized in this paper. It serves two purposes: (1) informing the real-time optimization algorithm whether changes in transmitter characteristics improve or worsen performance, and (2) updating the array calibration to obtain the desired transmit array pattern. This will enable real-time, “on the fly” optimizations of transmitters to coexist with other wireless devices in an increasingly congested spectral environment. A four-port coupler, with two monitoring outputs, is used to monitor the total voltage and current between a reconfigurable impedance tuner and the antenna in each element of a transmit array chain. Experimental work from the different prior contributions shows the overall trajectory, reliability, and proposed applications of this in-situ measurement technique. Less than 1 mV of error vector magnitude is shown in vector network analyzer methods compared with simulations using the in-situ coupler approach. The integration and calibration of a software-defined radio to perform antenna input current in-situ measurements has been implemented, with an average current error vector magnitude of 258 µA when comparing the software-defined radio measurements with simulations. Simulation results have shown that in-situ measurements can successfully correct input voltage waveforms for accurate directionally modulated transmissions, lessening reliance on fixed transmitter array pre-calibrations. Full article

Affected by coupled environmental factors including temperature and water activity, food storage is restricted by fungal contamination, quality degradation, and energy limits. Conventional microbial growth prediction models typically rely on given initial states and environmental parameters, making it difficult to account for the effects of prior-parameter errors and thereby limiting the accurate quantification of fungal growth risk and the real-time regulation of storage environments. This paper develops a fungal growth risk prediction and optimal regulation method for food storage based on the forward reachable set (FRS). The method combines a fungal growth kinetic model for Aspergillus flavus with FRS theory to calculate the reachable domains of colony radius and cell states within a finite time horizon, adopts a risk margin to describe the maximum colony expansion relative to deterministic growth trajectories, and constructs a multi-objective index covering energy cost, fungal growth risk, quality loss, and control switching cost to select the optimal environmental control scheme. Numerical simulation results show that the risk margin reflects the expansion of fungal growth risk caused by the propagation and accumulation over time of prior-parameter errors, while the selected regulation strategy exhibits stronger conservatism. Full article

UAVs increasingly rely on accurate SLAM for aerial mapping and inspection in GPS-denied environments. 3D Gaussian Splatting (3DGS) has opened a new direction for UAV mapping by allowing SLAM systems to build dense, photorealistic, and renderable maps. Yet in 3DGS SLAM the map is optimized from the pose graph, so a false loop closure can deform both the UAV trajectory and the Gaussian map consumed by downstream UAV autonomy. Reliable loop admission is therefore relevant to safe GPS-denied operation because it protects the state and map estimates on which autonomous functions depend. The present work evaluated this upstream estimation-integrity problem; it did not measure closed-loop guidance, control, or navigation-safety outcomes. We address the loop-admission problem that arises after a place-recognition (PR) module proposes a candidate loop and relative-pose seed. GSSeq is a rendered-reference sequential verifier that uses the current Gaussian map as active evidence before inserting a loop factor. It renders RGB-D references with the PR seed, checks LiDAR/rendered-depth consistency and image/rendered-reference consistency over active support, and propagates the seed through a short query trajectory window. A loop is admitted only when this evidence remains geometrically supported and photometrically stable. On fixed LiDAR-PR candidate sets spanning MARS-LVIG, MUN-FRL, and independent NTU-VIRAL aerial sequences together with ground-mobility benchmarks, GSSeq provides a competitive precision-oriented operating point while suppressing false loop admissions. Thresholds calibrated only on NTU-VIRAL spms_01 combine rendered RGB agreement with LiDAR-submap geometry and are then frozen for spms_02. On this held-out sequence, GSSeq rejects all seven false-positive BTC factors while retaining one of three true-positive factors. The trajectory-to-map experiment reduced ATE RMSE from $2.609\mathrm{m}$ to $1.417\mathrm{m}$ and improved selected-view PSNR from $13.80\mathrm{dB}$ to $16.46\mathrm{dB}$. These results show that rendered verification can preserve an aligned, renderable UAV trajectory-map pair before unsupported loop factors reshape the SLAM map. Full article

This paper proposes a unified invariant-set-based reliable control framework for Takagi–Sugeno (T–S) fuzzy systems subject to actuator saturation and faults. The considered model incorporates both matched actuator faults and mismatched external disturbances, which provides a more realistic control setting. To address these challenges, a unified control framework is developed to systematically account for input constraints and actuator fault effects. A sequence of nested invariant ellipsoidal sets, together with corresponding set-dependent control gains, are constructed to guarantee that state trajectories starting within the designed outer invariant sets progressively converge toward a minimized target set. Based on this structure, relaxed LMI-based conditions are derived to compute both the invariant sets and the associated control laws via convex optimization. Finally, numerical examples demonstrate the effectiveness of the proposed method. Full article

Emergency response missions employing heterogeneous multi-UAVs are challenged by both the tight coupling between task assignment and motion control and the inherent difficulty in obtaining full probability distributions of stochastic disturbances. In practice, only partial moment information—typically the mean, covariance, and support set—can be estimated. To address this, the paper proposes a distributionally robust integrated “decision–control” framework. A bi-level optimization model is established: the upper level minimizes the maximum mission completion time across all platforms, while the lower level solves minimum-time optimal control problems under kinematic constraints, with the two levels coupled through task execution times. Given only the mean, covariance, and support set of disturbances, an ambiguity set is constructed, and by leveraging duality theory and semidefinite programming, the distributionally robust chance constraints on site reachability are equivalently transformed into deterministic safety margins. A two-stage trajectory planning method is further designed to decouple accumulated time estimation from robust constraint enforcement, ensuring computational tractability. Simulation results across multiple disturbance configurations show that, whereas deterministic planning yields an overall mission success rate of only about 0.7%, the proposed framework consistently achieves success rates above 99.9% and effectively balances workload among multiple UAVs. These results validate the practical benefit of the framework in providing reliable emergency response plans under limited distributional information. Full article

Background: Manure-amended agricultural soil is a critical reservoir of antibiotic resistance genes (ARGs), posing escalating threats to environmental health and food safety. However, the temporal trajectories of ARG prevalence throughout the complete reproductive cycle of cash crops, and their mechanistic linkages with fertilization regimes and microbial community succession, remain inadequately understood. Methods: To bridge this knowledge gap, we conducted an in situ field experiment over the entire growth period of Chinese cabbage at a long-term manure-amended farm in Tianjin, China. Six contrasting fertilization strategies were evaluated: unfertilized control (CK1), unfertilized baseline control (CK2), traditional full-rate combined manure–chemical fertilization (TF), traditional half-rate combined manure–chemical fertilization (T1), half-dose sole manure fertilizer (T2), and half-dose sole chemical fertilizer only (T3). Results: Our results demonstrated that ARG abundance and associated mobile genetic elements (MGEs) exhibited a pronounced transient surge immediately post-fertilization, yet reverted to baseline levels by harvest, revealing a tangible resilience of the soil resistome. Notably, the optimized half-organic fertilization (T2) effectively curtailed the proliferation of manure-derived pathogenic taxa while preserving beneficial keystone phyla (e.g., Acidobacteria and Proteobacteria), indicating a trade-off between nutrient provisioning and ecological filtering. Co-occurrence network analysis further identified MB-A2-108, Saccharimonadales, and Rokubacteriales as pivotal hosts for multidrug-resistant ARGs, underscoring that microbial interspecific interactions—rather than taxonomic richness alone—are the primary drivers of resistome succession. Quantitative risk assessment confirmed that the T2 regimen reduced the composite ARG contamination index (CF_zone) by 25% relative to conventional full fertilization (TF), while maintaining comparable cabbage yields. Conclusions: Collectively, our findings advocate for precision organic fertilization as a nature-based solution that synchronizes nutrient supply with crop demand, curtails ARG propagation, and mitigates long-term agroecological risks. Full article

A pursuer and an evader can exploit low-energy, non-Keplerian trajectories in cislunar space, making it difficult to obtain the saddle point for impulsive orbital pursuit–evasion games (OPEG). To address this problem, this paper first establishes a zero-sum differential game model based on the circular restricted three-body problem (CR3BP), where the terminal interception time is taken as the performance objective. The necessary optimality conditions for impulsive maneuvers are then derived using Pontryagin’s Maximum Principle (PMP), which transforms the optimal control problem into multipoint boundary value problems (MPBVPs). Subsequently, to overcome the high sensitivity of the MPBVPs to initial costate vectors in shooting methods, a two-layer hybrid initial-guess strategy combining a genetic algorithm with a time-domain coarse-grid search method is proposed for the single-impulse case. Furthermore, a receding-horizon strategy is introduced to generate the initial impulse sequence guess stage by stage for multiple-impulse cases. Finally, numerical simulations demonstrate that the proposed initial-guess strategy can effectively obtain the Stackelberg equilibrium solution for representative cislunar scenarios, including distant retrograde orbits (DROs) and Halo orbits. Meanwhile, the effects of observation delay and three-dimensional orbital characteristics on the game outcomes are also discussed based on dynamic game theory. Full article

Uniform algorithmic stability is a classical route to non-vacuous generalization bounds, yet existing analyses typically track the forward optimization trajectory on adjacent training sets. This paper develops a different construction of the same stability parameter. We introduce support unlearning, a deletion axiom that aligns only the support of a post-deletion map with that of retraining and is strictly weaker than certified unlearning. Adjacent n-size datasets are realized as two deletions from a common enlarged sample. A support unlearning operator, assembled from an efficiency envelope and a minimizer transport map, then defines unlearning stability. We prove that unlearning stability coincides with classical uniform stability and controls the generalization gap. For efficient learners we obtain general Lipschitz upper bounds in terms of the displacement of empirical minimizers under deletion. Concrete rates follow under standard geometry: $O(1/n)$ in the strongly convex case and $O(1/\sqrt{n})$ in the convex and weakly convex cases, with no non-vanishing additive constant. The results establish sample deletion as a constructive path from machine unlearning to uniform stability. Full article

High penetration levels of renewable energy and power electronic apparatus create prominent obstacles for novel power grids, which mainly manifested as inadequate system inertia and a deteriorated stability margin. To overcome such drawbacks, this research develops an operational control method to maintain safe and steady grid operation with large-capacity grid-forming energy storage connected to the system. This paper first builds a dynamic voltage model covering grid-forming energy storage, distributed renewable generators, and distribution network frameworks. Then it explores how different control parameter settings of grid-forming storage affect dynamic voltage regulation capabilities under distinct R-L ratio scenarios. Since the correlation between energy storage control variables and voltage regulation features is highly nonlinear and complicated, trajectory sensitivity analysis is adopted to linearize these coupling constraints, which are further embedded into the power system security operation mathematical model. A chaotic particle swarm optimization (PSO) algorithm is used to solve the constructed optimization model. Simulation tests on a modified IEEE 33-bus test system ultimately prove that the proposed method is reliable and practically applicable. Simulation results on the modified IEEE 33-bus test system demonstrate that the proposed strategy restricts grid voltage fluctuation rate to only 2.41%, raises renewable energy accommodation rate up to 98.4%, and achieves a 30.6% reduction in overall system operation cost compared to traditional energy storage configuration schemes, which fully verifies the outstanding effectiveness and practical engineering feasibility of the proposed method. Full article

This paper proposes a simulation-driven hierarchical Stackelberg–distributed model predictive control framework (SHS-DMPC) for intercepting multi-wave UAV swarm attacks under limited defensive resources. The interaction between the defender and the attacker is modeled as a Stackelberg leader–follower game. At the strategy layer, a finite-response approximation of Stackelberg decision making is constructed under incomplete information: the attacker’s response type is inferred online from swarm-level motion features, and candidate defender strategies are subsequently evaluated through state-dependent short-horizon rollout simulations. This formulation avoids requiring explicit knowledge of the attacker’s utility function while retaining anticipatory leader–follower strategy evaluation. At the task-allocation layer, target value, threat level, spatial bias, and a reassignment penalty are incorporated into the allocation cost to translate the selected defense strategy into dynamic defender–attacker assignments. At the control layer, each defending UAV solves a local DMPC problem to generate continuous control inputs while satisfying kinematic, inter-UAV separation, and airspace-boundary constraints. Simulation results show that SHS-DMPC achieve a higher interception success rate, a lower value-weighted target loss rate, and fewer minimum-separation violations than the comparison methods under multi-wave heterogeneous attack scenarios, demonstrating the benefits of closed-loop coupling among response inference, strategy-conditioned allocation, and constraint-aware distributed trajectory optimization. Full article

Objective: This multicenter, prospective, single-arm study evaluated the safety and preliminary clinical performance of a flowable porcine urinary bladder matrix (UBM) particulate for managing tunneling and undermining features in complex wounds. These features are associated with delayed healing, chronic inflammation, and infection risk. Flowable UBM enables targeted delivery into wound tunnels and cavities. Method: Twenty-five subjects from 3 United States (U.S.) sites were enrolled, with 21 meeting protocol criteria for analysis (15 with undermining and 6 with tunneling wounds). Participants received flowable UBM applied directly to tunneling or undermining areas, along with additional UBM particulate or sheet forms applied to the wound surface. The primary endpoint was the percentage reduction in tunneling volume or undermining depth at 12 weeks, with secondary assessment of reduction in overall wound volume. Safety was evaluated through evaluation of device-related adverse events. Results: At 12 weeks, 90.5% of wounds demonstrated positive response, with mean ± standard deviation and median reduction in tunneling or undermining dimensions of 84.6 ± 29.3% and 100.0%, respectively. Complete resolution of these features occurred in 52.4% of wounds (53.3% in undermining and 50.0% in tunneling wounds) with higher closure rates in non-pressure wounds compared to pressure injuries. No device-related adverse events were reported. Conclusions: Overall, flowable UBM was well tolerated and associated with favorable clinical trajectories demonstrated by reduction in tunneling and undermining dimensions; however, comparative effectiveness cannot be inferred from this limited single-arm pilot study. Further controlled studies are needed to confirm comparative effectiveness and optimize clinical use. Full article

This study proposes a safety-enhanced path planning system that integrates a Control Barrier Function (CBF)-based Safety Shield with Deep Reinforcement Learning (DRL). This framework addresses the critical limitations of conventional DRL-based Unmanned Surface Vehicle (USV) navigation models, which can output hazardous control commands in edge cases and violate the International Regulations for Preventing Collisions at Sea (COLREGs). The proposed system continuously operates during navigation via an Encounter Classifier that identifies multi-vessel situations (such as Head-on, Crossing, and Overtaking) in real time. The nominal control inputs generated by the DRL policy are verified and safely filtered through a Control Barrier Function-Quadratic Programming (CBF-QP) optimization layer immediately prior to execution, incorporating ship safety radii and asymmetric COLREGs constraints. Furthermore, we introduce a ‘Shielded Training’ mechanism that penalizes the agent based on the magnitude of the shield’s interventions during the training loop. This effectively diminishes the policy’s over-reliance on the safety filter and guides the network toward discovering robust, inherently safe trajectories. Extensive simulations conducted under diverse single- and multi-vessel encounter scenarios quantitatively demonstrate that the proposed method substantially reduces collision and COLREGs violation rates compared to baseline DRL-only or reward-shaping methods, while maintaining excellent computational scalability and real-time responsiveness. Consequently, by unifying the adaptive environmental exploration of reinforcement learning with model-based runtime safety constraints derived from control theory, this study provides a practical runtime assurance framework for future marine deployment. Full article

Background/Objectives: Postharvest fungal decay caused by Botrytis cinerea and Monilinia fructicola poses major threats to global fruit security. Driven by the need for sustainable crop protection agents, this work presents the systematic synthesis, biological evaluation, and computational modeling of a comprehensive 33-compound library of 3-acyl-bromoindole regioisomers (series 4a–k, 5a–k, and 6a–k) to establish clear structure–activity relationship (SAR) design rules. Methods: The regioisomeric library was assembled via a microwave-assisted catalytic protocol in an ionic liquid, expanding the known chemical space with seven newly synthesized 4-bromoindole derivatives (4d–f, 4h–k). Primary in vitro data were modeled using Hansch QSAR and Principal Component Analysis (PCA). Postharvest in vivo efficacy was evaluated on fresh ‘Lapins’ sweet cherries inoculated with M. fructicola. Molecular docking and 100 ns molecular dynamics (MD) simulations were performed against succinate dehydrogenase (SDH) and M. fructicola catalase 2 (MfCat2). Results: In vitro screening demonstrated marked target selectivity: parent core 4 displayed high mycelial suppression against M. fructicola (EC₅₀ = 7.05 µg/mL), whereas C3-acylation with a four-carbon linear chain (4c) achieved optimal broad-spectrum dual action (98% and 86% spore germination inhibition). In vivo cherry bioassays proved that bromoindoles 4, 6a, and 6d significantly suppressed Brown Rot severity to 44–47% (a 20–27% reduction vs. untreated control). Docking and MD trajectories confirmed stable multi-target binding within SDH and MfCat2 active sites (RMSD < 2.0 Å). Conclusions: Bromine regiochemistry dictates pathogen selectivity and life-stage targeting. The novel 4-bromoindole derivatives and multi-target profile establish these scaffolds as promising leads for postharvest crop protection. Full article

Excessive N application in Camellia oleifera plantations exacerbates soil acidification and N₂O emissions, intensified by the input of Al-accumulating litter. Biochar is a promising amendment, yet how litter decomposition interacts with N and biochar to modulate acidification and greenhouse gas emissions remains unclear. To understand how decomposition of Al-accumulating litter interacts with N and biochar in the soil acidification process and gas emissions, a twelve-month laboratory incubation study was conducted using a fully factorial, three-factor completely randomized design to examine litter decomposition. The experimental factors included nitrogen fertilization, biochar amendment, and litter input level. The results showed that litter transiently activated biochar alkalinity, raising pH to 5.7–6.3, but subsequent organic acid release drove sustained re-acidification (ΔpH −0.4 to −0.5). This pH trajectory controlled denitrification: early high pH favored complete denitrification (nosZ > nirK), while later acidification inhibited N₂O reductase, boosting N₂O emissions under single litter and N. Litter-C primed native soil organic carbon, doubling cumulative CO₂ emissions. Biochar further elevated CO₂ emission rate by 7.6% under double litter input treatment via porous-microsite priming. These results demonstrated that litter quantity dictates a temporal switch from biochar alkali activation to organic acid overrun, creating an acid rebound that amplifies N₂O while sustaining CO₂ release. Optimizing litter retention and biochar application timing is essential to break the acid-N₂O feedback in intensively managed C. oleifera systems. Full article

We study an optimal control framework for chemotherapy administration in patients with chronic lymphocytic leukemia (CLL) while accounting for immune regulation and treatment-induced allergic reactions. The analysis is based on a previously developed nonlinear delay differential equation model describing the interactions between leukemic cells, immune populations, antigen-presenting cells, and cytokine dynamics, with three distinct biological delays. The chemotherapy infusion rate is introduced as a time-dependent control variable and optimized to reduce leukemic burden, shift the helper T-cell balance toward a Th1-dominant configuration associated with lower hypersensitivity risk, and preserve immune competence. Existence of an optimal control is established for arbitrary delays and horizon, without the commensurability hypothesis required by reductions in delay systems to higher-dimensional delay-free ones; the argument uses only that the control enters the dynamics affinely and the running cost concavely. Necessary optimality conditions are derived via Pontryagin’s Maximum Principle for systems with delays, and the resulting eleven-dimensional adjoint system, which carries advanced arguments generated by the three delays, is written out explicitly. A contraction estimate for the associated sweep operator yields both uniqueness of the optimal control on a short horizon and geometric convergence of the numerical scheme. The optimality system is solved by a forward–backward sweep adapted to the delayed setting, with documented convergence and grid independence and sensitivity analysis over kinetic parameters, delays, initial conditions and objective weights. The optimized schedule is compared not only with the untreated case and a low constant dose, but also with a constant infusion delivering the same cumulative exposure, so that the reported benefit is attributable to the temporal distribution of the dose rather than to its total amount. At equal exposure, the optimal schedule reaches each therapeutic milestone earlier—Th1 dominance 0.9 days sooner and a 90% leukemic reduction 1.6 days sooner—and attains a terminal leukemic burden lower by a factor of 2.25; a constant infusion of the same total dose reaches a comparable configuration later. The benefit of adaptive scheduling in this model is therefore principally one of rate of response at fixed drug exposure. We emphasize that the absolute Th2 population is not reduced by treatment; the reduction in hypersensitivity risk arises from the resulting Th1-dominant relative balance rather than from direct Th2 suppression. To characterize the therapeutic outcome in information-theoretic terms, we describe the two competing goals as distributional balances: an allergy axis, given by the Th1/Th2/Treg distribution, and a leukemia axis, given by the immune/leukemic distribution, each measured by its Shannon entropy and its Kullback–Leibler divergence to a healthy reference profile. These quantities are used in two roles. As diagnostics, they are evaluated along the computed trajectories, and the ordering of dosing strategies is shown to be robust across twenty alternative reference profiles. As an objective, the combined divergence is then taken as the running cost of a second optimal control problem; because it depends on the leukemic population only through a normalized fraction, it prescribes a markedly gentler schedule that administers 37% of the drug and still achieves a 93% leukemic reduction, against 98% for the population-based formulation. These results suggest that adaptive, immune-aware chemotherapy scheduling may accelerate disease control at fixed drug exposure, and that information-theoretic objectives offer a scale-free alternative formulation of the therapeutic goal. Full article