Search Results (1,735)

As the rapid evolution and expansion of Internet of Things (IoT) devices continues to accelerate, modern infrastructures face increasing cyber risks, largely driven by device inter-connectivity, limited security maturity, and interdependent attack propagation across networks. Traditional cyber insurance models often overlook these IoT-specific characteristics, relying on uniform or simplified risk assumptions that fail to capture real-world vulnerabilities. To address this gap, this paper presents a maturity-aware cyber insurance optimization framework tailored for interconnected IoT environments. The framework integrates organizational security maturity, interdependent risk propagation modeled through a modified Susceptible–Infected–Susceptible (SIS) process, and a Stackelberg game formulation that captures strategic interactions between the insurer and the defender. Through numerical studies on representative IoT topologies, we demonstrate that maturity-aware, risk-sensitive premium structures quantitatively outperform uniform pricing baselines in cost-efficiency and insurer sustainability. Specifically, our experimental results reveal that operating at an optimal intermediate maturity level ($M=3$) reduces the defender’s total expected cost by approximately $40\%$ (from $255.38$ k to $152.36$ k) compared to the baseline state ($M=1$). Furthermore, this structural hardening triggers an $88.3\%$ reduction in full-coverage insurance premiums (from $225.38$ k to $26.36$ k). In contrast, our uniform-pricing baseline exhibits reduced profitability in our experiments due to cross-subsidization effects, reinforcing the value of tiered, risk-proportional pricing for mitigating adverse-selection incentives. In summary, this work establishes a tractable, economically viable framework for cyber insurance in IoT ecosystems and provides a foundation for future extensions to richer network settings. Full article

The quality of cement clinker is strongly linked to its free calcium oxide (f-CaO) content. Therefore, real-time detection of f-CaO content is crucial for reducing energy consumption and stabilizing clinker quality. This work presents a Temporal Convolutional Network (TCN) that incorporates a self-attention mechanism for handling coupled time-series data from process variables. This model utilizes TCN to capture the time series coupling relationship among multiple input variables and extract multivariable time series features that affect f-CaO content. On this basis, a self-attention mechanism is introduced to focus on nonlinear features that have a significant impact on the output variable. The self-attention mechanism enhances the model’s ability through three key aspects: dynamic feature weighting, global context awareness, and interpretable feature selection. Combined with TCN’s time feature extraction, a robust f-CaO content prediction framework is constructed. Finally, a mapping relationship between nonlinear features and output is established through a fully connected layer, enabling real-time measurement of f-CaO content. Experimental comparisons with existing deep learning-based soft sensors demonstrate the superior performance of our model. Full article

With the increasing proportion of intelligent assisted vehicles in traffic flow, the existing primary highway traffic management measures exhibit insufficient adaptability to mixed traffic flows with high penetration of such vehicles. This study proposes a simulation analysis method based on SUMO for the primary highway traffic involving mixed flows of vehicles and conventional human-driven vehicles. It elaborates on the simulation configuration, network construction, demand generation, data output and visualization, and selection strategies. A Python-based post-processing tool for simulation results was developed. Gradient control simulation experiments (5% coarse adjustment → 1% fine analysis) were designed to investigate the impact of Connected and Automated Vehicle (CAV) penetration rates and the configuration of a dedicated CAV lane on the inner side of a bidirectional four-lane primary highway on the network Level of Service (LOS). Results indicate that when the CAV penetration rate ranges between 18% and 52%, setting one dedicated lane on the inner side can improve the LOS. However, if the penetration rate is below 18%, such a lane configuration reduces the LOS. When the penetration rate exceeds 52%, the impact becomes negligible. This study establishes a simulation framework for analyzing mixed CAV/conventional vehicle flows on the primary highways, systematically quantifying the penetration rate threshold (18–52%) for CAV-dedicated lanes. This provides a strategic basis for phased implementation based on actual CAV penetration rates and offers a strategic basis for the phased implementation of dedicated CAV lanes on inner lanes of four-lane highways, depending on the actual CAV penetration rate. Full article

The growing demand for IoT applications in highly dynamic environments with multiple connected devices introduces significant scalability and low-latency challenges. In the context of software-defined networking (SDN) integrated with Edge environments, adopting machine learning (ML) techniques has emerged as a promising approach to meet these requirements. This study presents a Systematic Literature Review (SLR) that identifies and analyzes ML-based solutions applied to Software-Defined Internet of Things (SD-IoT) infrastructures in Edge environments, emphasizing improving low latency and scalability. Following established methodological best practices, we conducted the review, including a clear definition of research questions, well-defined inclusion and exclusion criteria, a structured search protocol, and multiple scientific databases. Based on the analysis of selected studies, the main strategies employed to enhance network performance are categorized, along with the level of fidelity and complexity of the experimental environments used, and the realism and applicability of the proposed solutions are discussed. Furthermore, drawing from the context of the selected studies, the most recurrent ML approaches are presented—including supervised, unsupervised, and reinforcement learning methods—along with a discussion of their advantages and limitations in dynamic network scenarios. By compiling and organizing the contributions from the literature, this paper provides a comprehensive overview of the state of the art in applying ML to SD-IoT networks, shedding light on current trends, existing gaps, and research opportunities aimed at building more intelligent and adaptable solutions for IoT environments. Full article

Modern power networks contain an increasing amount of renewable energy resources that are connected to the grid via inverters (Inverter-Based Resources, IBR). As highlighted in the recent IEEE Standard 2800-2022, these resources behave differently compared to conventional power plants, which impact protection systems. For networks with a high proportion of IBR, existing protection systems may no longer be dependable and reliable. This research project investigated the behaviour of commercially available relays for distance protection applied to a power grid with a high proportion of IBR. A detailed numerical model was established for the power grid of the Gaspesian Peninsula in Québec, Canada, where there are numerous wind farms. Five power lines with different characteristics were selected, and 700 fault events were generated in COMTRADE format. These events were then converted into analog signals, applied to commercially available relays, and their tripping actions were analyzed. Several misoperations could be identified and classified. Proposals for improving protection performance were developed and validated with the experimental setup. This project highlights the importance of validating and eventually adapting the protection systems in power grids with a high proportion of IBR, as existing protection systems may be prone to misoperate. Various solutions are proposed to ensure the dependability and reliability of protection systems in modern power grids. Full article

With the advancement of UAV (Unmanned Aerial Vehicle) technology, flying ad-hoc networks (FANETs), composed of multiple coordinating UAVs, demonstrate tremendous application potential in disaster relief, environmental monitoring and intelligent logistics. However, inherent resource constraints and unpredictable operating environments make UAV failures a frequent and critical challenge. Particularly in mission-critical applications, simultaneous or consecutive failures of multiple UAVs can severely disrupt network topology, leading to catastrophic consequences such as network fragmentation and service interruptions. Furthermore, traditional topology reconstruction algorithms suffer from high computational overhead and significant communication delays. Primarily designed for single-node failure recovery, they are ill-equipped to address the challenge of concurrent multi-node failures. To address these challenges, this paper proposes a topology reconstruction algorithm tailored for multi-node failure scenarios in FANETs. The core objective of this algorithm is to minimize communication overhead and secondary damage to the network during the reconstruction process while ensuring basic reconstruction results, thereby improving the system’s energy efficiency and robustness. The proposed framework integrates three key phases: First, overlapping communication coverage areas among neighbors of failed nodes are leveraged to define first and second regions, enabling rapid identification of connection restoration candidate positions and avoiding computationally intensive global calculations. Second, a comprehensive importance evaluation mechanism is constructed based on the topological and functional attributes of node, categorizing nodes into different importance types. For failed nodes of varying importance, differentiated search ranges and retry strategies are employed to ensure the most suitable nodes are selected for reconstruction tasks. Third, the inflexibility of repulsion ranges in traditional artificial potential field (APF) method is addressed by introducing dynamic repulsion influence zones and a composite repulsion model. The improved APF algorithm enhances safety in high-speed scenarios and reduces the probability of UAVs becoming trapped in local minima. Finally, extensive simulations validate that the proposed algorithm accurately identifies critical network nodes and promptly implements effective reconstruction measures to minimize network damage. Full article

Objective: The purpose of this research is to examine how motivational music immediately impacts the brain’s functional connectivity patterns in male athletes following a single session of intense endurance exercise, utilizing resting-state electroencephalography (EEG) and brain network analysis methods. Methods: The study involved 34 healthy male athletes who were tasked with performing incremental cycling exercises until exhaustion, both with and without music. Their resting-state EEG was recorded before and after the exercise. Brain functional networks were analyzed in the theta, alpha, and beta frequency bands based on changes in phase locking value (PLV). Specifically, the study examined the central executive network (CEN), default mode network (DMN), salience network (SN), sensorimotor network (SMN), and dorsal attention network (DAN), assessing their topological properties using graph theory methods. Results: Music significantly prolonged the time to exhaustion. Across frequency bands, the music condition exhibited higher global and local efficiency compared with the no-music condition. Following exhaustion without music, beta-band connectivity significantly increased, suggesting compensatory hyper-synchronization under fatigue. In contrast, music led to reduced alpha- and beta-band global connectivity post-exercise, accompanied by selective strengthening of functionally relevant couplings, particularly between SMN and CEN, and enhanced DAN–DMN coordination. Additionally, music prevented maladaptive connectivity shifts observed under fatigue, including excessive SN–CEN coupling. Conclusions: Exhaustive exercise without music induces widespread beta-band hyper-connectivity, reflecting increased neural cost under central fatigue. Music, however, promotes a more efficient and selectively integrated network configuration, supporting the neural efficiency hypothesis. These findings provide neurophysiological evidence that music optimizes large-scale brain network organization under physical stress, thereby contributing to enhanced endurance performance. Full article

It is proposed that the process of learning may be represented as a trajectory within the phase space of long-term memory. The research uses an artificial neural network design to explore, in theory, if starting from different points within the phase space affects how quickly learning occurs. Using a Monte Carlo method, 1000 virtual agents were trained using the Levenberg–Marquardt algorithm to recognise a large set of Arabic digits at ten different skill levels. The simulations replicated the typical learning curves observed in human learning and were successful in distinguishing ten levels of skill. First, and in line with previous research, the results provide convincing evidence that learning consolidates a selected set of pathways within the network. Second, and critical to the hypothesis, the distance in the phase space, calculated as the difference in average connectivity between skill levels, is highly predictive of both learning time and performance. The findings strongly support the hypothesis that learning represents progression along a trajectory connecting two points within the phase state landscape. As these properties may be more pronounced in biological systems because of their greater complexity, these results shed new light on individual variance in learning. Full article

With the deep integration of the modern automotive industry and artificial intelligence technologies, connected and automated vehicles (CAVs) have emerged as a key breakthrough for improving traffic safety and operational efficiency. This study proposes a distributed multi-vehicle cooperative trajectory planning and control framework for ramp merging and diverging scenarios, integrating Deep Neural Networks (DNNs) with Model Predictive Control (MPC). The methodology consists of three key components: First, a distributed cooperative architecture based on dynamic topology is constructed to effectively reduce communication loads; second, a feature point-based Cubic Bézier Curve trajectory generation method is proposed, enabling flexible path planning with reduced reliance on high-precision maps; finally, a DNN-accelerated MPC solving strategy (NN-MPC) is designed. This strategy employs an offline-trained deep neural network to approximate the online optimization process, supplemented by a terminal Safety Check mechanism and a dynamic surrounding vehicle selection algorithm. Experimental results demonstrate that the proposed method successfully reproduces the planning capability of offline high-precision MPC in ramp merging and diverging scenarios while reducing computation time to the millisecond level. It effectively overcomes the myopic decision-making problem of traditional real-time algorithms, achieving smoother conflict resolution and higher traffic efficiency. Notably, quantitative validation confirms that this cooperative framework achieves an approximate 30% reduction in average travel delay compared to the non-cooperative baseline. This study confirms the engineering advantages of the hybrid architecture under dynamic high-density traffic flows, significantly enhancing the system’s real-time response capability while balancing the safety and riding comfort of cooperative driving. Full article

The global push to advance smart and digital forestry relies on emerging technologies to support efficient, AI-assisted, and data-driven forest management, but many forest operations occur in remote forests where reliable internet connectivity is unavailable. Low Earth Orbit (LEO) satellite constellations such as Starlink may provide reliable connectivity where cellular networks are unavailable. The performance of LEO-based solutions remains poorly understood under forest canopies, and empirical evaluations linking canopy characteristics to connectivity performance are largely lacking. In this study, the effect of forest vegetation on Starlink performance below the canopy was evaluated by placing a satellite receiver at thirty randomly selected permanent single tree inventory plots on the University of Idaho Experimental Forest and measuring connection success, connection time, and upload and download speeds along 50 m transects in all cardinal directions. LiDAR-derived stand density index (SDI), leaf area index (LAI), rumple index (RI), and vegetation cover (VC) were used to quantify canopy structure. Principal Component Analysis and survival analysis showed that higher values of PC1, primarily driven by SDI, LAI, and RI, reduced the probability of establishing a connection. Linear regression analysis indicated that higher SDI increased connection time, indicating that denser stands slowed or prevented connectivity. Linear mixed-effects models demonstrated that internet speed primarily declined with increasing distance, with download and upload rates dropping beyond 40 m from the router. LAI, RI, and VC did not influence connection time or speed, suggesting that overall stand density rather than leaf area per unit ground area has a greater impact on signal obstruction. Overall, dense forest structure and distance are the main constraints on LEO satellite connectivity and performance, and understanding these limitations supports the development and deployment of satellite-based networking to advance smart forestry operations. These results provide one of the first quantitative assessments of LEO satellite connectivity constraints in operational forest conditions, offering practical guidance for deploying satellite-based networks to support smart forestry applications in remote environments. Full article

Intrusion detection aims to effectively detect abnormal attacks in Internet of Things (IoT) networks, which is crucial for cybersecurity. However, it is difficult for traditional intrusion detection methods to effectively extract data features from traffic data, and most existing models are too complex to be deployed on edge servers. Addressing this need, this paper proposes a hybrid feature selection method and a lightweight deep learning intrusion detection model. Firstly, the data feature space is reduced using variance filtering, mutual information, and the Pearson Correlation Coefficient, thereby reducing the computational cost of subsequent model training. Then, an intrusion detection model based on a Temporal Convolutional Network (TCN) is constructed. This model utilizes dilated causal convolutions to effectively capture long-term temporal dependencies in network traffic. Simultaneously, the residual connections are used to mitigate the vanishing gradient problem, making the model easier to train and converge. Finally, experiments are conducted on the newly released Edge-IIoTset dataset. The results show that the proposed feature selection algorithm maintains good detection performance despite a significant reduction in feature dimensionality. Furthermore, compared with other models, the proposed TCN-based approach achieves higher classification accuracy with lower computational overhead, demonstrating its suitability for deployment in resource-constrained edge computing environments. Full article

Automated production lines are increasingly being expanded with Industrial Internet of Things (IIoT) devices, creating complex Cyber-Physical Systems (CPSs) that connect physical production with control and information infrastructure. However, the convergence of Information Technology (IT) and Operational Technology (OT) layers creates new entry points for attacks targeting communication availability. Most existing studies analyze Distributed Denial of Service (DDoS) attacks primarily in simulation or testbed environments, with limited experimental verification of their impact on real-world production systems. This article presents an experimental evaluation of the impact of DDoS and Distributed Reflection Denial of Service (DRDoS) attacks carried out directly on a physical automated production line with integrated IIoT infrastructure during real operation. Three attack scenarios (TCP SYN flood, TCP ACK flood, and ICMP reflected attack) were implemented, targeting Programmable Logic Controllers (PLCs), Radio-Frequency Identification (RFID) subsystems, and selected IIoT devices. The results showed rapid degradation of deterministic PROFINET communication, disruption of the link between the OT and IT layers, loss of digital product representation, and physical interruption of the production process. Based on the findings, a minimally invasive security solution based on perimeter protection was designed and experimentally verified. The results emphasize the need to design IIoT-based manufacturing systems with an emphasis on network segmentation and architectural separation of the IT and OT layers. Full article

Background/Objectives: Baichuan Baile (BCBL), a novel functional dietary formula, has been shown to exert antidepressant-like effects through modulation of the 5-HT system in our prior studies. Given the close neurobiological connections between depression and insomnia, along with its pharmacodynamic profile guided by TCM theory and nutritional assessments, BCBL is likely to possess beneficial effects against insomnia. However, this hypothesis and its underlying mechanisms require further validation. Methods: The chemical constituents of BCBL were analyzed by UPLC-Q-TOF-MS, and network pharmacology was applied to predict potential sleep-relevant targets and pathways. Subsequently, BCBL was evaluated for sedative-hypnotic effects using pentobarbital-induced hypnosis, locomotor activity, and polysomnography (EEG/EMG). Its therapeutic efficacy was further assessed in insomnia models induced by environmental stress, serotonin depletion, and rotarod-based sleep deprivation. The rotarod-induced chronic model was selected for mechanistic studies due to its sustained insomnia-like phenotype. Finally, key network-predicted targets were validated in this model through histopathology, Western blotting, and ELISA. Results: Pharmacological evaluation confirmed that BCBL significantly promoted sleep at both behavioral and EEG levels, confirming its sedative-hypnotic properties. BCBL mitigated environmental stress-triggered impairments in NREM sleep continuity and duration, and exerted protective effects against body weight loss and sleep disturbances in a serotonin depletion-induced insomnia model. In the rotarod sleep deprivation model, BCBL treatment increased spontaneous alternation rates and recognition indices, ameliorated hippocampal pathological alterations, and reduced hippocampal levels of HIF-1α, TNF-α, and IL-1β. Furthermore, BCBL elevated the p-GSK3β/GSK3β ratio and enhanced SIRT₁ expression in the hypothalamus. It also modulated the activity of key sleep–wake neurotransmitters/neuromodulators (serotonin, dopamine, adenosine, and glutamate) and key circadian rhythm regulators (BMAL₁, PER₂, and CLOCK) in this region. Conclusions: BCBL exhibits significant therapeutic efficacy against insomnia, indicating its potential as a dietary supplement for managing insomnia. Its mechanisms appear to involve anti-inflammatory effects, rebalancing of neurotransmitters/neuromodulators, and stabilization of circadian rhythm gene expression. Full article

Marrow-isolated adult multilineage inducible (MIAMI) cells are a subpopulation of mesenchymal stem/stromal cells (MSC) with enhanced self-renewal, multilineage plasticity, and anti-inflammatory properties, suggesting that their extracellular vesicles (MIA-EVs) may confer advantages over conventional MSC-EVs. MIAMI cells were transcriptionally profiled and expressed regenerative markers, including PDGFRB, CDX2, and TERT. We report the first successful isolation and characterization of MIA-EVs. EVs were isolated by ultracentrifugation and characterized by nanoparticle tracking analysis, transmission electron microscopy, flow cytometry, and surface markers. Cargo analysis identified growth factors (IGFBP-1, HGF, VEGF-D) and 19 highly expressed miRNA targeting survival, regenerative, and immune regulatory pathways. MIA-EVs were efficiently internalized, enhanced keratinocyte wound closure and suppressed osteosarcoma proliferation in vitro. Conditioned MIA-EVs reshaped pathway weighting without altering core regulatory identity, as a conserved 15-miRNA backbone persisted across naïve, irradiated, and cytokine-primed states. In contrast, a 9-miRNA core shared with MSC-EVs defined a basal mesenchymal framework, while MIA-EVs expanded regenerative, survival, and immune network connectivity. Similar to embryonic stem cell (ESC)-EVs, both MIA- and cytokine-primed EVs promoted M2 macrophage polarization, selectively upregulating IL1R2 and PPARG/STAT1, respectively. Meanwhile, MSC-EVs induced heterogeneous responses. These findings establish MIA-EVs as a conditioning-resistant, systems-regulated, cell-free platform with regenerative, immunomodulatory, and cytoprotective potential under hostile microenvironments. Full article

This study is dedicated to the application of neural network technologies for determining aeration parameters in order to predict the efficiency of flotation separation. Within the framework of the research, digital technology solutions were actively employed, including a neural network for segmentation at the stage of determining the granulometric characteristics of bubbles and a convolutional neural network module for determining the froth layer height. An analysis was conducted to examine the variation in the statistical parameter d₃₂, which characterizes the bubble size distribution, as a function of flotation time and measurement height. The analysis revealed that the d₃₂ values determined by neural network processing remained within the range of acceptable dispersion and are therefore suitable for subsequent analytical procedures. Furthermore, a comparative evaluation of the obtained size distributions indicated the absence of statistically significant differences between the neural network measurements and manually labelled data with a p-value equal to 0.64. A neural network for object detection was used to record the height of the froth layer during the experiment to obtain a time series, that were subsequently processed with data processing approaches including Savitzky–Golay and Singular Spectra Analysis. Based on the analysis of the sum of the obtained dependences, a criterion is proposed and modeled for evaluating the selectivity of frother by connecting the diameter of bubble in pulp and bubble in froth. Based on the modeling results, it was determined that the optimal range of bubble sizes and froth size ratios for MIBC is constrained to d₃₂ values ranging from 1.058 to 1.089 mm, with the ratio of froth bubble radius to d₃₂ ranging from 1.302 to 2.098, depending on the floatability ratios of the respective fractions. When employing OPF, the values for d₃₂ fall within the interval of 0.868 to 1.113 mm, while the Dₓ parameter ranges from 0.559 to 0.931. Full article