Search Results (260)

Communication latency is one of the main factors limiting the practical scalability of unmanned aerial vehicle (UAV) swarms operating with distributed formation control. In real-time UAV missions, such as coordinated swarm navigation, autonomous inspection, and aerial monitoring, delayed information exchange directly affects formation stability and operational safety. In practical aerial networks, inter-UAV communication latency is influenced by stochastic effects including jitter, burst delays, and multi-hop propagation, which are rarely captured by the simplified deterministic delay assumptions commonly adopted in analytical formation-control studies. This paper introduces a measurement-informed stochastic delay model and a communication–control delay-feasibility framework that jointly account for per-link latency behavior, multi-hop delay accumulation, and controller-level delay tolerance. The proposed framework is evaluated using an attractive–repulsive distance-based potential field (ARD–PF) formation controller, for which the maximum admissible end-to-end delay is quantified as a function of swarm size and inter-UAV separation. The delay model is calibrated and validated using more than 15,000 in-flight communication delay samples collected from a multi-UAV LoRa platform operating under realistic flight conditions. The results show that different mechanisms limit swarm operation under different operating scenarios. In some configurations, stochastic communication latency becomes the dominant constraint, whereas in others, formation geometry or network load determines the feasible operating region. Based on these elements, the proposed framework characterizes delay-feasible operating regions and predicts the maximum feasible swarm size under distributed formation control and realistic multi-hop communication latency. Full article

The increasing operational variability in distribution networks (e.g., abrupt load changes and distributed generation integration) increases the demands on voltage regulation devices and, in particular, on transformers with on-load tap changers (OLTCs). This paper develops and validates a discrete supervisory control scheme based on Petri nets, implemented in Stateflow and coupled to an electromagnetic model of the OLTC transformer in Simulink/Simscape. The Petri net formalizes the conditional and sequential logic of OLTC operation, enabling state- and time-dependent decisions (e.g., delays between maneuvers) to improve voltage regulation and reduce unnecessary tap operations. The evaluation is performed by simulation under transient scenarios that include sudden load variations anda phase-to-ground fault in the IEEE 13-node standard network, specifically at node 634. In the base case, the controller maintains the voltage within the tolerance band $\pm 1.875\%$ during 96% of the simulated time, with an 88% reduction in RMS error (from 1.92% to 0.23%) and 100% operational efficiency (16 effective maneuvers, with a single hunting event). Subsequently, the scheme is validated on the standard IEEE 13-node network, with four disturbances applied over 600 s (two load increments, photovoltaic injection, and a temporary line disconnection). In this case, regulation remains within a precision zone of $\pm 0.3\%$ for 96.8% of the time, with an average RMS error of 0.23% and 100% efficiency, with no hunting events. The results confirm that a Petri net-based supervisory logic can simultaneously improve the OLTC’s voltage quality and switching efficiency, providing a reproducible alternative for distribution network automation. Full article

Impact artificial seismic sources are gaining popularity in reflection seismic exploration. However, challenges arise due to the uncertain delay between the hammer’s acceleration and its impact on the interface, as well as the strong vibrations or pulsed magnetic fields produced during the acceleration process. These factors complicate the synchronous triggering methods typically used in traditional explosive and sledgehammer artificial seismic sources, often resulting in temporal misalignment of the acquired data. To tackle this issue, this study introduces a high-precision synchronous triggering method based on non-contact audio detection. Utilizing an STM32F4 microcontroller as the core hardware, the system collects ambient audio and extracts 39-dimensional acoustic features via Mel-frequency cepstrum coefficients (MFCC). A lightweight convolutional neural network (CNN) model is employed to accurately identify hammer impact events. Additionally, a synchronization time compensation mechanism is implemented to address system processing delays. Results from 300 field tests conducted in three environments—open ground, construction site, and mining tunnel—demonstrate that the system achieves a triggering accuracy of up to 94.6%, with compensated triggering time errors controlled within ±125 μs, thereby meeting the minimum tolerable synchronous triggering error requirement. This study significantly enhances the reliability of impact-type Artificial Seismic Source exploration data and offers insights for the application of sound recognition in engineering surveying and other related fields. Full article

Drought severely limits maize production. Basic helix-loop-helix (bHLH) transcription factors act as key regulators of plant drought responses; however, the precise regulatory networks they coordinate in maize remain largely unclear. Here, we functionally characterized ZmbHLH81, a drought- and abscisic acid (ABA)-responsive bHLH transcription factor in maize. Subcellular localization confirmed that ZmbHLH81 is a nuclear protein. Overexpression of ZmbHLH81 in Arabidopsis enhanced drought tolerance, whereas CRISPR/Cas9-mediated targeted mutagenesis in maize significantly increased plant sensitivity to drought stress. Physiologically, these mutant lines exhibited accelerated water loss, delayed stomatal closure, compromised antioxidant enzyme activities and elevated malondialdehyde (MDA) accumulation under drought stress. DAP-seq analysis demonstrated that ZmbHLH81 specifically recognizes the conserved G-box motif (CACGTG). Furthermore, integrating DAP-seq and transcriptomic data successfully identified the key downstream targets governed by ZmbHLH81. Molecular assays confirmed that ZmbHLH81 directly targets and transactivates the core ABA signaling kinase gene ZmSnRK2.9 and stress-responsive transcription factor genes ZmNAC20 and ZmHDZ4. Taken together, ZmbHLH81 positively regulates maize drought tolerance by directly activating a specific regulatory module that orchestrates ABA-mediated stomatal closure and reactive oxygen species (ROS) scavenging, providing a promising genetic target for breeding climate-resilient crops. Full article

The advent of 5G/6G technologies and the pervasive deployment of IoT devices are driving the emergence of demanding applications that necessitate ultra-low latency, high bandwidth, and significant computational power. Traditional cloud computing models fall short in meeting these stringent requirements. To address this, Software-Defined Edge Networks (SDENs) have emerged as a promising architecture, yet efficiently managing their heterogeneous and geographically distributed resources poses substantial challenges for optimal application provisioning. In response, this paper proposes a novel framework for intelligent task offloading, which reframes the intricate multi-component application task offloading problem as a Virtual Network Embedding (VNE) challenge within a SDEN environment. We introduce a comprehensive model where complex applications are represented as Virtual Network Requests (VNRs). In this model, each VNR consists of virtual nodes that demand specific computing and storage resources, as well as virtual links that demand specific bandwidth and must adhere to maximum tolerable delay constraints. To dynamically solve this NP-hard VNE problem in the face of stochastic VNR arrivals and dynamic network conditions, we leverage Deep Reinforcement Learning (DRL). Specifically, a Soft Actor-Critic (SAC) agent is employed at the SDN controller. This agent learns a sequential decision-making policy for mapping virtual nodes to physical edge servers and virtual links to network paths. To guide the agent towards efficient resource utilization, we define the reward for each successful embedding as the long-term revenue-to-cost ratio. By learning to maximize this reward, the agent is naturally driven to find economically viable allocation strategies. Comprehensive simulation experiments demonstrate that our SAC-based VNE approach significantly outperforms other baselines across key metrics, affirming its efficacy in dynamic SDEN environments. Full article

The integration of blockchain technology into Cyber–Physical Systems (CPS) offers decentralized resilience against data manipulation. This also introduces stochastic consensus latencies that threaten real-time control stability. We present a Stochastic-Aware Blockchain Predictive Control (SAB-PC) framework, which models blockchain-induced jitter as a state-dependent Markovian process, and embeds it within a Markovian Jump Linear System (MJLS) formulation. Using mode-dependent Linear Matrix Inequalities (LMIs), we derive Mean Square Stability (MSS) conditions, which capture the interaction between decentralized consensus dynamics and closed-loop control behavior. The framework is validated on the Tennessee Eastman Process (TEP) benchmark, using a calibrated stochastic delay model that reflects realistic blockchain congestion patterns. Our results show that standard blockchain-mediated control architectures become unstable under Practical Byzantine Fault Tolerance (PBFT)-induced quadratic latency growth, whereas SAB-PC maintains stable operation across decentralized networks up to 60 validator nodes. The predictive Safety Runway effectively masks long-tail delay distributions, ensuring real-time feasibility and preserving safe Reactor Pressure trajectories. Under coordinated False Data Injection (FDI) attacks, SAB-PC limits pressure deviations to only 1.2 kPa despite an 8.0 kPa adversarial bias, demonstrating cryptographic and control-theoretic resilience. Full article

Limited terrestrial network coverage in rural and remote areas constitutes a significant barrier to the digital transformation of the agricultural sector. Smart and precision farming applications, ranging from conventional environmental monitoring systems to advanced Digital Twin solutions, rely on the reliable transmission of sensor data, images, and video streams from geographically isolated farms. Such data-intensive services cannot be effectively supported without a robust communication infrastructure. Non-Terrestrial Networks (NTNs), particularly satellite systems, offer both narrowband and broadband connectivity, enabling the transmission of low-rate sensor measurements, as well as high-throughput multimedia data from the field. This paper presents an experimental performance evaluation of two satellite backhauling solutions: a Geostationary Earth Orbit (GEO) system provided by SES and a Low Earth Orbit (LEO) system from Starlink. The networks were first deployed and tested in a laboratory environment and subsequently validated in an operational agricultural field setting. Their performance is benchmarked against a terrestrial cellular network to assess their suitability for supporting advanced agricultural applications. The performance assessment results indicate that both satellite backhauling solutions are reliable and capable of meeting the bandwidth and latency requirements of delay-tolerant agricultural applications. In addition to the technical evaluation, this work presents a cost–benefit analysis that further underscores the advantages of NTN-based solutions. Despite higher initial expenditures, they provide extended coverage in remote areas and enable cost sharing across multiple users, improving overall economic viability. Full article

Background: The optimal duration of neoadjuvant therapy for high-risk colorectal liver metastases (CRLM) remains debated. While prolonged chemotherapy may enhance response, it also increases toxicity and risks delaying potentially curative resection. These considerations have raised the question whether a short-course neoadjuvant strategy might achieve sufficient oncologic selection and response while minimizing treatment-related morbidity. Methods: Patients with synchronous or metachronous CRLM who received two cycles of neoadjuvant CAPOX plus bevacizumab followed by curative-intent liver resection treated between 2014 and 2024 at Health Network Vienna, Austria, were included. Clinicopathologic characteristics, treatment tolerability, response assessments (biochemical, radiologic, and pathologic), and survival outcomes were collected and analyzed. Results: A total of 57 patients were included (65% synchronous, 35% metachronous), with the rectum being the most frequent primary tumor site (45.6%). Most liver lesions were <5 cm (84.2%), and 47% had bilobar disease. Minor hepatectomy was performed in 65% of cases, predominantly via open surgery (72%). Grade ≥3 treatment-related adverse events occurred in 6 patients (10.6%), mainly neutropenia and diarrhea. Biochemically, 53.7% achieved >50% tumor marker reduction. Radiologic assessment showed partial response in 31.6% and complete response in 1.7%. Pathologic evaluation revealed TRG 3 as the most common finding (57.1%), followed by TRG 2 in 22.5%. Subgroup analyses demonstrated significantly improved OS and RFS in patients receiving adjuvant therapy and in those with tumors < 5 cm. Conclusion: A two-cycle, short-course regimen of CAPOX plus bevacizumab proved both effective and safe in high-risk CRLM, achieving meaningful biochemical, radiologic, and pathologic responses with acceptable toxicity. This abbreviated approach allowed delivery of neoadjuvant therapy while limiting cumulative treatment-related toxicity, supporting its feasibility as a neoadjuvant strategy in selected high-risk CRLM patients. Full article

Reliable Internet connectivity is essential for latency-sensitive services such as video conferencing, media streaming, and online gaming. Round-trip time (RTT) is a key indicator of network performance and is central to setting retransmission timeout (RTO); inaccurate RTT estimates may trigger unnecessary retransmissions or slow loss recovery. This paper proposes an Enhanced Regularized Extreme Learning Machine (RELM) for RTT estimation that improves generalization and efficiency by interleaving a bidirectional log-step heuristic to select the regularization constant C. Unlike manual tuning or fixed-range grid search, the proposed heuristic explores C on a logarithmic scale in both directions ($\times 10$ and /10) within a single loop and terminates using a tolerance–patience criterion, reducing redundant evaluations without requiring predefined bounds. A custom RTT dataset is generated using Mininet with a dumbbell topology under controlled delay injections (1–1000 ms), yielding 1000 supervised samples derived from 100,000 raw RTT measurements. Experiments follow a strict train/validation/test split (6:1:3) with training-only standardization/normalization and validation-only hyperparameter selection. On the controlled Mininet dataset, the best configuration (ReLU, 150 hidden neurons, $C={10}^2$) achieves $R^2=0.9999$, $MAPE=0.0018$, $MAE=966.04$, and $RMSE=1589.64$ on the test set, while maintaining millisecond-level runtime. Under the same evaluation pipeline, the proposed method demonstrates competitive performance compared to common regression baselines (SVR, GAM, Decision Tree, KNN, Random Forest, GBDT, and ELM), while maintaining lower computational overhead within the controlled simulation setting. To assess practical robustness, an additional evaluation on a public real-world WiFi RSS–RTT dataset shows near-meter accuracy in LOS and mixed LOS/NLOS scenarios, while performance degrades markedly under dominant NLOS conditions, reflecting physical-channel limitations rather than model instability. These results demonstrate the feasibility of the Enhanced RELM and motivate further validation on operational networks with packet loss, jitter, and path variability. Full article

In recent years, flying ad hoc networks (FANETs) have attracted attention as aerial communication platforms for large-scale disasters. In wide, city-scale disaster zones, survivors’ devices often form multiple isolated clusters, while battery-powered unmanned aerial vehicles (UAVs) must periodically return to a ground station (GS). Under such conditions, conventional delay/disruption-tolerant networking (DTN) routing (e.g., PRoPHET) often traps bundles in clusters or UAVs, degrading the bundle delivery ratio (BDR) to the GS. This study proposes a DTN-based FANET architecture that integrates (i) a mobility model assigning UAVs to information–exploration UAVs that randomly patrol the disaster area and GS–relay UAVs that follow spoke-like routes to periodically visit the GS, and (ii) an extended PRoPHET-based routing protocol that exploits exogenous information on GS visits to bias delivery predictabilities toward GS–relay UAVs and UAVs returning for recharging. Simulations with The ONE in a 10 km × 10 km scenario with multiple clusters show that the proposed method suppresses BDR degradation by up to 41% relative to PRoPHET, raising the BDR from 0.27 to 0.39 in the five-cluster case and increasing the proportion of bundles delivered with lower delay. These results indicate that the proposed method is well-suited for relaying critical disaster-related information. Full article

Background: Chronic diabetic wounds, particularly diabetic foot ulcers (DFUs), are prone to recurrent infection and delayed healing, resulting in substantial morbidity, mortality, and economic burden. Multifunctional wound dressings that combine antibacterial, antioxidant, conductive, and self-healing properties may help to address the complex microenvironment of chronic diabetic wounds. Methods: In this study, ε-poly-L-lysine and amino-terminated polyethylene glycol were grafted onto carboxylated single-walled carbon nanotubes (SWCNTs) via amide coupling to obtain ε-PL-CNT-PEG. Aminated chondroitin sulfate (CS-ADH) and a catechol–metal coordination complex of protocatechualdehyde and Fe³⁺ (PA@Fe) were then used to construct a dynamic covalently cross-linked hydrogel network through Schiff-base chemistry. The obtained hydrogels (Gel0–3, Gel4) were characterized for photothermal performance, rheological behavior, microstructure, swelling/degradation, adhesiveness, antioxidant capacity, electrical conductivity, cytocompatibility, hemocompatibility, and antibacterial activity in the presence and absence of near-infrared (NIR, 808 nm) irradiation. Results: ε-PL-CNT-PEG showed good aqueous dispersibility, NIR-induced photothermal conversion, and improved cytocompatibility after surface modification. Incorporation of ε-PL-CNT-PEG into the PA@Fe/CS-ADH network yielded conductive hydrogels with porous microstructures and storage modulus (G′) higher than loss modulus (G′′) over the tested frequency range, indicating stable gel-like behavior. The hydrogels exhibited self-healing under alternating strain and macroscopic rejoining after cutting. Swelling and degradation studies demonstrated pH-dependent degradation, with faster degradation in mildly acidic conditions (pH 5.0), mimicking infected chronic diabetic wounds. The hydrogels adhered to diverse substrates and tolerated joint movements. Gel4 showed notable DPPH• and H₂O₂ scavenging (≈65% and ≈60%, respectively, within several hours). The electrical conductivity was 0.19 ± 0.0X mS/cm for Gel0–3 and 0.21 ± 0.0Y mS/cm for Gel4 (mean ± SD, n = 3), falling within the range reported for human skin. In vitro, NIH3T3 cells maintained >90% viability in the presence of hydrogel extracts, and hemolysis ratios remained below 5%. Hydrogels containing ε-PL-CNT-PEG displayed enhanced antibacterial effects against Escherichia coli and Staphylococcus aureus, and NIR irradiation further reduced bacterial survival, with some formulations achieving near-complete inhibition under low-power (0.2–0.3 W/cm²) 808 nm irradiation. Conclusions: A dynamic, conductive hydrogel based on PA@Fe, CS-ADH, and ε-PL-CNT-PEG was successfully developed. The hydrogel combines photothermal antibacterial activity, antioxidant capacity, electrical conductivity, self-healing behavior, adhesiveness, cytocompatibility, and hemocompatibility. These properties suggest potential for application as a wound dressing for chronic diabetic wounds, including diabetic foot ulcers, although further in vivo studies are required to validate therapeutic efficacy. Full article

This paper proposes a large-scale, self-healing multipoint fiber Bragg grating (FBG) sensor network that employs reinforcement learning (RL) techniques to enhance the resilience and efficiency of optical wireless communication networks. The system features a mesh-structured, self-healing ring-mesh architecture employing 2 × 2 optical switches, enabling robust multipoint sensing and fault tolerance in the event of one or more link failures. To further extend network coverage and support distributed deployment scenarios, free-space optical (FSO) links are integrated as wireless optical backhaul between central offices and remote monitoring sites, including structural health, renewable energy, and transportation systems. These FSO links offer high-speed, line-of-sight connections that complement physical fiber infrastructure, particularly in locations where cable deployment is impractical. Additionally, RL-based artificial intelligence (AI) techniques are employed to enable intelligent path selection, optimize routing, and enhance network reliability. Experimental results confirm that the RL-based approach effectively identifies optimal sensing paths among multiple routing options, both wired and wireless, resulting in reduced energy consumption, extended sensor network lifespan, and improved transmission delay. The proposed hybrid FSO–fiber self-healing sensor system demonstrates high survivability, scalability, and low routing path loss, making it a strong candidate for future services and mission-critical applications. Full article

With the advancement of 6G networks, terrestrial centralized network architectures are evolving toward integrated space–air–ground network frameworks, imposing higher requirements on the efficiency of computation offloading and multi-objective collaborative optimization. However, existing single-decision strategies in integrated space–air–ground networks find it difficult to achieve coordinated optimization of delay and load balancing under energy tolerance constraints during task offloading. To address this challenge, this paper integrates communication transmission and computation models to design a two-stage computation offloading model and formulates a multi-objective optimization problem under energy tolerance constraints, with the primary objectives of minimizing overall system delay and improving network load balance. To efficiently solve this constrained optimization problem, a two-stage computation offloading solution based on a Hierarchical Cooperative African Vulture Optimization Algorithm (HC-AVOA) is proposed. In the first stage, the task offloading ratio from ground devices to unmanned aerial vehicles (UAVs) is optimized; in the second stage, the task offloading ratio from UAVs to satellites is optimized. Through a hierarchical cooperative decision-making mechanism, dynamic and efficient task allocation is achieved. Simulation results show that the proposed method consistently maintains energy consumption within tolerance and outperforms PSO, WaOA, ABC, and ESOA, reduces the average delay and improves load imbalance, demonstrating its superiority in multi-objective optimization. Full article

Background/Objectives: The poultry industry faces severe heat-stress challenges that threaten both economic sustainability and animal welfare. Embryonic thermal manipulation (ETM) has been proposed as a thermal programming strategy to enhance chick heat tolerance, yet its efficacy in layers requires verification, and its effects on growth performance and neurodevelopment remain unclear. Methods: White Leghorn embryos at embryonic days 13 to 18 (ED 13–18) were exposed to 39.5 °C (ETM). Hatch traits and thermotolerance were recorded, and morphometric and histopathological analyses were performed on brain sections. Transcriptome profiling of the whole brains and hypothalami was conducted to identify differentially expressed genes (DEGs). Representative pathway genes responsive to ETM were validated by RT-qPCR. Results: ETM reduced hatchability, increased deformity rate, and decreased hatch weight and daily weight gain. During a 37.5 °C challenge, ETM chicks exhibited delayed panting and lower cloacal temperature. Histopathology revealed impaired neuronal development and myelination. Transcriptomic analysis of ED18 whole brains showed DEGs enriched in neurodevelopment, stimulus response, and homeostasis pathways. RT-qPCR confirmed hypothalamic sensitivity to ETM: up-regulation of heat-shock gene HSP70, antioxidant gene GPX1, the inflammatory marker IL-6, and apoptotic genes CASP3, CASP6, CASP9; elevated neurodevelopmental marker DCX, indicative of a stress-responsive neuronal state; and reduced orexigenic neuropeptide AGRP. Conclusions: ETM improves heat tolerance in layers but compromises hatching performance and brain development, with widespread perturbation of hypothalamic stress responses and neurodevelopmental gene networks. These findings elucidate the mechanisms underlying ETM and provide a reference for enhancing thermotolerance in poultry. Full article

Vehicle remote driving services are increasingly used in urban settings. Yet, vehicle-operator communication time delays may pose a challenge for teleoperators in maintaining safety and efficiency. The purpose of this study was to examine whether Predictive Displays (PDs), which show the vehicle’s predicted real-time position, improve performance, safety, and mental workload under moderate time delays typical of 4G/5G networks. Twenty-nine participants drove a simulated urban route containing pedestrian crossings, overtaking, gap acceptance, and traffic light challenges under three conditions: 50 ms delay (baseline), 150 ms delay without PD, and 150 ms delay with PD. We analyzed the counts of crashes and navigation errors, task completion times, and the probability and intensity of braking and steering events, as well as self-reports of workload and usability. Results indicate that though descriptive trends indicated slightly sharper steering and braking under the 150 ms time delay conditions, the 150 ms time delay did not significantly degrade performance or increase workload compared with the 50 ms baseline. In addition, the PD neither improved performance nor reduced workload. Overall, participants demonstrated tolerance to typical 4G/5G network time delays, leaving little room for improvement rendering the necessitating of PDs. Full article