Search Results (541)

This study explores the emergent dynamics of knowledge sovereignty within organisations following data breach incidents. Using qualitative analysis based on Benoit’s image restoration theory, this study shows that employees do more than relay official messages—they actively shape information governance after a cyberattack. Employees adapt Benoit’s response strategies (denial, evasion of responsibility, reducing offensiveness, corrective action, and mortification) based on how authentic they perceive the organisation’s response, their identification with the company, and their sense of fairness in crisis management. This investigation substantively extends extant crisis communication theory by showing how knowledge sovereignty is shaped through negotiation, as employees manage their dual role as breach victims and organisational representatives. The findings suggest that employees are key actors in post-breach information governance, and that their authentic engagement is critical to organisational recovery after cybersecurity incidents. Full article

The thalamus is an important sensory relay station. It integrates all somatic sensory pathways (excluding olfaction) and transmits information through thalamic relay neurons before projecting to the cerebral cortex via thalamocortical axons (TCAs). Emerging evidence has shown that FGF3, a member of the morphogen family, is an axon guidance molecule that repels TCAs away from the hypothalamus and into the internal capsule so that they subsequently reach different regions of the cortex. However, current studies on FGF-mediated axon guidance predominantly focus on phenomenological observations, with limited exploration of the underlying molecular mechanisms. To address this gap, we investigated both direct and indirect downstream signaling pathways mediating FGF3-dependent chemorepulsion of TCAs at later developmental stages. Firstly, we used pharmacological inhibitors to identify the signaling cascade(s) responsible for FGF3-triggered direct chemorepulsion of TCAs, in vitro and in vivo. Our results demonstrate that the PC-PLC pathway is required for FGF3 to directly stimulate the asymmetrical repellent growth of developing TCAs. Then, we found the FGF3-mediated repulsion can be indirectly induced by Slit1 because the addition of FGF3 in the culture media induced an increase in Slit1 expression in the diencephalon. Furthermore, by using downstream inhibitors, we found that the indirect repulsive effect of FGF3 is mediated through the PI3K downstream pathway of FGFR1. Full article

The stochastic variance-reduced gradient (SVRG) theory is particularly well-suited for addressing gradient variance in deep neural network (DNN) training; however, its direct application to DNN training is hindered by adaptation challenges. To tackle this issue, the present paper proposes a series of strategies focused on adaptive alternating learning rates to effectively adapt SVRG for DNN training. Firstly, within the outer loop of SVRG, both the full gradient and the learning rate specific to DNN training are computed. For two distinct formulas used for calculating the learning rate, an alternating strategy is introduced that employs them alternately across iterations. This approach allows for simultaneous provision of diverse guidance information regarding parameter change rates and gradient change rates during DNN weight updates. Additionally, a threshold method is utilized to correct the learning rate into an appropriate range, thereby accelerating convergence. Secondly, in the inner loop of SVRG, DNN weights are updated using mini-batch average gradient along with the proposed learning rate. Concurrently, mini-batch average gradients from each iteration within the inner loop are refined and aggregated into a single gradient exhibiting reduced variance through an inertia strategy. This refined gradient is then relayed back to the outer loop to recalculate the new learning rate. The efficacy of the proposed algorithm has been validated on models including LeNet, VGG11, ResNet34, and DenseNet121 while being compared against several classic and advanced optimizers. Experimental results demonstrate that the proposed algorithm exhibits remarkable training robustness across DNN models with diverse characteristics. In terms of training convergence, the proposed algorithm demonstrates competitiveness with state-of-the-art algorithms, such as Lion, developed by the Google Brain team. Full article

This paper investigates the error performance of cooperative decode-and-forward (DF) multiple-input multiple-output (MIMO) systems employing one-bit analog-to-digital converters (ADCs) over Rayleigh fading channels. In cooperative DF MIMO systems, detection errors at the relay may propagate to the destination, thereby degrading overall detection performance. Although joint maximum likelihood detection can efficiently mitigate error propagation by leveraging probabilistic information from a source-to-relay link, its computational complexity is impractical. To address this issue, an approximate maximum likelihood (AML) detection scheme is introduced, which significantly reduces complexity while maintaining reliable performance. However, its analysis under one-bit ADCs is challenging because of its nonlinearity. The main contributions of this paper are summarized as follows: (1) a tractable upper bound on the pairwise error probability (PEP) of the AML detector is derived using Jensen’s inequality and the Chernoff bound, (2) the asymptotic behavior of the PEP is analyzed to reveal the achievable diversity gain, (3) the analysis shows that full diversity is attained only when symbol pairs in the PEP satisfy a sign-inverted condition and the relay correctly decodes the source symbol, and (4) the simulation results verify the accuracy of the theoretical analysis and demonstrate the effectiveness of the proposed analysis. Full article

In this work, we investigate a new design strategy for the implementation of a deep neural network (DNN)-based limited-feedback relay system by using conventional filters to acquire training data in order to jointly solve the issues of quantization and feedback. We aim to maximize the effective channel gain to reduce the symbol error rate (SER). By harnessing binary feedback information from the implemented DNNs together with efficient beamforming vectors, a novel approach to the resulting problem is presented. We compare our proposed system to a Grassmannian codebook system to show that our system outperforms its benchmark in terms of SER. Full article

In the context of global climate governance and the low-carbon energy transition, virtual power plant (VPP), a key technology for integrating distributed energy resources, is urgently needed to solve the problem of decentralization and lack of synergy in electricity, carbon, and green certificate trading. Existing studies mostly focus on single energy or carbon trading scenarios and lack a multi-market coupling mechanism supported by blockchain technology, resulting in low transaction transparency and a high risk of information tampering. For this reason, this paper proposes a synergistic optimization strategy for electricity/carbon/green certificate virtual power plants based on blockchain cross-chain transactions. First, Latin Hypercubic Sampling (LHS) is used to generate new energy output and load scenarios, and the K-means clustering method with improved particle swarm optimization are combined to cut down the scenarios and improve the prediction accuracy; second, a relay chain cross-chain trading framework integrating quota system is constructed to realize organic synergy and credible data interaction among electricity, carbon, and green certificate markets; lastly, the multi-energy optimization model of the virtual power plant is designed to integrate carbon capture, Finally, a virtual power plant multi-energy optimization model is designed, integrating carbon capture, power-to-gas (P2G) and other technologies to balance the economy and low-carbon goals. The simulation results show that compared with the traditional model, the proposed strategy reduces the carbon emission intensity by 13.3% (1.43 tons/million CNY), increases the rate of new energy consumption to 98.75%, and partially offsets the cost through the carbon trading revenue, which verifies the Pareto improvement of environmental and economic benefits. This study provides theoretical support for the synergistic optimization of multi-energy markets and helps to build a low-carbon power system with a high proportion of renewable energy. Full article

This paper presents an analytical study of the bit error rate (BER) and signal-to-noise ratio (SNR) performance in simultaneous wireless information and power transfer (SWIPT) amplify-and-forward (AF) relaying systems over Rayleigh fading channels. A power-splitting (PS) protocol is employed at the energy-constrained relay to divide the received signal for concurrent energy harvesting and information processing. Closed-form and asymptotic BER expressions are derived based on exact and bounded moment-generating functions (MGFs), offering insights into how the SNR balance between the source–relay (SR) and relay–destination (RD) links influences system performance. An asymptotic BER expression further reveals that a SWIPT AF relay system can be interpreted as a generalized AF relaying model, sharing the same diversity order as conventional AF systems. Based on this interpretation, an optimization method for the PS factor is proposed, effectively reducing the BER by reinforcing the weaker link. Simulation results confirm the tightness of the derived expressions and the effectiveness of the optimization strategy. Moreover, the analytical framework is extended to multiple SWIPT relaying systems, where multiple relays operate with individually optimized PS ratios. For such configurations, approximations for the system BER, outage probability, and channel capacity are derived and validated. Results demonstrate that increasing the number of relays significantly improves system performance, and the proposed analysis accurately captures these performance gains under varying channel conditions. Full article

Plastic recycling is an important but complicated issue. Some plastics are currently readily recyclable with existing technologies, whereas others are not. However, the general public often does not appreciate the benefits and costs associated with hard-to-recycle (e.g., multi-material) plastics, potentially causing confusion and, in some cases, backlash. While some methods of relaying information to the public have been previously studied (e.g., infographics, descriptive labeling), educational videos have not. We created an educational video on the properties and functions of single- and multi-material plastic. Participants who viewed the educational video were more knowledgeable about multi-material plastic recycling and had higher intentions to use and be satisfied with a hypothetical recycling company that intends to begin recycling multi-material plastic. Our data suggest that education interventions have the potential to inform and empower the public while protecting common values. Full article

Since the early 14th century, the Mediterranean coasts of the Crown of Aragon had mechanisms in place to alert populations of incoming threats from the sea. In addition to maritime surveillance systems strategically positioned at elevated vantage points, any information reaching the coast that posed a threat to the safety of the population or trade was swiftly relayed along the shoreline, ensuring that coastal communities could prepare and defend themselves. This information, preserved in the correspondence of coastal city authorities, serves today as a primary source not only for reconstructing maritime threats in the late Middle Ages but also for assessing the role of urban leaders in managing defence. This article explores both aspects. By analysing maritime alerts either received in the city of Barcelona or disseminated from it during the first half of the 15th century, this study examines the main threats to the Catalan coastline while emphasizing the central role of cities in managing the alert system. Full article

This paper investigates data transmission in an Internet of Things (IoT) network, where multiple devices send environmental data to a remote base station through an unmanned aerial vehicle (UAV) relay. The UAV serves as an airborne intermediary that collects status information from distributed IoT devices (e.g., temperature readings in a real-time forest fire monitoring system) and forwards it to the base station. To capture the impact of data staleness, a novel Age of Information (AoI) and entropy-aware system loss is defined in terms of L-conditional cross-entropy, which quantifies the expected penalty caused by state misestimation. The scheduling problem, which aims to minimize the system loss defined by L-conditional cross-entropy, is formulated as a Restless Multi-Armed Bandit (RMAB) problem. By applying Lagrange relaxation, the objective function is decomposed into tractable sub-problems, enabling a low-complexity, gain-index-based scheduling strategy. Numerical simulations validate the effectiveness of the proposed algorithm in reducing the long-term average system loss. In particular, the gain-index-based policy achieves a significant reduction in average penalty compared to random, round-robin, periodic update, and MAX-AoI scheduling strategies, demonstrating its superior performance over these baselines. Full article

In this paper, a new short reference differential chaos shift keying (SR-DCSK) cooperative communication system based on modified code index modulation, referred to as the MCIM-SR-DCSK-CC system, is proposed. In the proposed MCIM-SR-DCSK-CC system, the information bits are transmitted to both the relay and the destination in the first time slot. On the other hand, in the second time slot, the relay not only forwards the information bits but also sends new information bits to the destination. Specifically, the relay adopts the modified code index modulation to transmit more information bits to the destination. The theoretical bit error rate (BER) expressions of the proposed MCIM-SR-DCSK-CC system are obtained over additive white Gaussian noise (AWGN) and the multipath Rayleigh fading channels. It is demonstrated that the simulation results of BER performance match the theoretical results. The energy efficiency (EE), the data rate, and transmission throughput are carefully analyzed. The performance of the proposed system is better than its competitors’. Full article

Maritime relay communication has emerged as a critical application scenario for non-terrestrial networks (NTNs), providing beyond-line-of-sight (BLOS) connectivity for offshore terminals. Unlike terrestrial environments, the complex marine propagation conditions lead to signal instability. To enhance the robustness of maritime two-way relay networks (TWRNs), we propose a novel physical-layer network coding (PNC) scheme based on block Markov superposition transmission (BMST). The proposed scheme introduces a novel co-design framework that achieves dual breakthroughs: (1) robust error correction via BMST’s spatially coupled coding architecture and (2) spectral efficiency maximization through PNC’s spatial-domain signal superposition. Moreover, we develop a decoding–computing (DC) algorithm that sequentially performs iterative decoding followed by computing. Compared to the computing–decoding (CD) algorithm, the proposed DC algorithm mitigates useful information loss at relay nodes, achieving a 2.9 dB coding gain at a bit error rate (BER) of ${10}^{-5}$. Owing to the DC algorithm’s dual-layer decoding architecture, we can further improve the overall system performance through targeted optimization of either the code rate or memory size for communication sides with poor channel conditions, yielding an extra 0.2 dB gain at a BER of ${10}^{-5}$ compared to non-optimized configurations. The simulation results demonstrate that the proposed scheme significantly enhances maritime relay communication performance under harsh oceanic channel conditions while providing actionable insights for optimizing next-generation maritime communication system designs. Full article

Flood incidents occur annually in Taman Negara Endau Rompin Selai Bekok due to continuous substantial rains during the rainy period. The absence of a structured flood tracking and detection system limits effective information dissemination regarding flooding to the public; currently, visitors are only informed at office counters. This inefficient conventional method should be upgraded to a real time flood monitoring and alert system utilizing Internet of things (IoT) technology. UTHM personnel have requested the development of an easily accessible flood detection system via tablets or smartphones to efficiently relay flood information to visitors. Consequently, a flood detection system called “Floodwatch” was developed. The prototype provides live water level readings, air temperature, humidity, and flood images to users. The prototype has undergone rigorous testing to ensure stability, consistency, and accuracy, enabling its effective utilization. The Floodwatch system aims to enhance safety and awareness during flood events in the Taman Negara Endau Rompin Selai Bekok area. Full article

Background and Objectives: Sport practice, performance satisfaction, and interoception influence physical and mental health. Motor imagery (MI) training improves sensorimotor and cognitive–emotional functions. This study aimed to (a) compare sedentary and artistic gymnastics-practicing young females and (b) evaluate the changes in interoception and performance satisfaction occurring in gymnastics-practicing participants after one month of motor imagery training. Materials and Methods: The difference in interoceptive accuracy (IA) and sensibility (IS) between young sedentary females (Control group, C, n = 27) and age-matched females practicing artistic gymnastics (Experimental group, E, n = 27) were studied using the Interoceptive Accuracy Scale (IAS), the Multisensory Assessment of Interoceptive Awareness (MAIA), and Body Perception Questionnaire (BPQ). The capacity for focusing one’s attention on specific tasks (absorption) was assessed by the Tellegen Absorption Scale (TAS). Groups were compared at T0 (before motor imagery training). In group E, the same variables and satisfaction with performance were rated before and after 1 month of motor imagery training. The years of practice and absorption were used as covariates in analyses. Results: (a) Group E exhibited significantly higher scores in the MAIA dimensions than group C and similar BPQ and IAS scores; (b) group E’s satisfaction with performance, MAIA, IAS, and BPQ scores increased significantly from T0 to T1. The increase in performance satisfaction became non-significant when using years of practice as the control. The improvement in MAIA dimensions became non-significant when using TAS as the control. Conclusions: Despite the limitations as a result of the absence of an objective evaluation of the performance and physiological correlations of mental imagery and interoceptive accuracy, the baseline differences between the two groups confirm that practicing artistic gymnastics improves interoception. The experience undergone by group E of better performance after training is associated with further improvement in interoceptive intermingled pathways and shared relay stations of sensorimotor and interoceptive information. The results are relevant to the setting up of personalized mental training to improve physical and mental health. Full article

This paper investigates the outage performance of simultaneous wireless information and power transfer (SWIPT)-assisted device-to-device (D2D)-based hybrid satellite–terrestrial networks (HSTNs). In the considered system, an energy-constrained terrestrial user terminal (UT) harvests energy from the radio frequency (RF) signal of a terrestrial amplify-and-forward (AF) relay and utilizes the harvested energy to cooperate with the shadowed terrestrial Internet of Things (IoT) devices in a D2D communication. Both power splitting (PS)-based and time switching (TS)-based SWIPT-D2D schemes are adopted by the energy-constrained UT to obtain sustainable energy for transmitting information to the shadowed IoT device. Considering shadowed Rician fading for satellite–terrestrial links and Nakagami-m fading for terrestrial links, we analyze the system performance by deriving the closed-form expressions for the outage probability (OP) of both the UT and the IoT device. Our theoretical analyses are validated via Monte Carlo simulations. Full article