Search Results (361)

Currently, the primary random access protocol for satellite communications is Irregular Repetition Slotted ALOHA (IRSA). This protocol leverages interference cancellation and burst repetition based on probabilistic distributions, achieving up to 80% channel utilization in practical use. However, it faces three significant issues: (1) low channel utilization with smaller frame sizes; (2) drastic performance degradation under heavy load, where channel utilization can be lower than that of traditional Slotted ALOHA; and (3) even under optimal load and frame sizes, up to 20% of the valuable satellite channel resources are still wasted despite reaching up to 80% channel utilization. In this paper, we propose the Separated Transmission and Control ALOHA (STCA) protocol, which introduces a space–air–ground layered network and separates the access control process from the satellite to an airborne platform, thus preventing collisions in satellite channels. Additionally, the airborne-platform estimates the load to ensure maximum access rates. Simulation results demonstrate that the STCA protocol significantly outperforms the IRSA protocol in terms of channel utilization. Full article

Blockchain technology’s increasing adoption across diverse sectors necessitates robust security measures to mitigate rising fraudulent activities. This paper presents a comprehensive bibliometric analysis of anomaly detection research in blockchain networks from 2017 to 2024, conducted under the PRISMA paradigm. Using CiteSpace 6.4.R1, we systematically map the knowledge domain based on 363 WoSCC-indexed articles. The analysis encompasses collaboration networks, co-citation patterns, citation bursts, and keyword trends to identify emerging research directions, influential contributors, and persistent challenges. The study reveals geographical concentrations of research activity, key institutional players, the evolution of theoretical frameworks, and shifts from basic security mechanisms to sophisticated machine learning and graph neural network approaches. This research summarizes the state of the field and highlights future directions essential for blockchain security. Full article

With the ongoing advancement of industrialization and rapid urbanization, the emission of volatile organic compounds (VOCs) has increased significantly. As key precursors of PM_2.5 and ozone formation, VOCs pose a growing threat to the health of ecosystems. Due to their complex and dynamic transformation processes across air, water, and soil media, the ecological risks associated with VOCs have attracted increasing attention from both the scientific community and policy-makers. This study systematically reviews the core literature on the ecological impacts of VOCs published between 2005 and 2024, based on data from the Web of Science and Google Scholar databases. Utilizing three bibliometric tools (CiteSpace, VOSviewer, and Bibliometrix), we conducted a comprehensive visual analysis, constructing knowledge maps from multiple perspectives, including research trends, international collaboration, keyword evolution, and author–institution co-occurrence networks. The results reveal a rapid growth in the ecological impact of VOCs (EIVOCs), with an average annual increase exceeding 11% since 2013. Key research themes include source apportionment of air pollutants, ecotoxicological effects, biological response mechanisms, and health risk assessment. China, the United States, and Germany have emerged as leading contributors in this field, with China showing a remarkable surge in research activity in recent years. Keyword co-occurrence and burst analyses highlight “air pollution”, “exposure”, “health”, and “source apportionment” as major research hotspots. However, challenges remain in areas such as ecosystem functional responses, the integration of multimedia pollution pathways, and interdisciplinary coordination mechanisms. There is an urgent need to enhance monitoring technology integration, develop robust ecological risk assessment frameworks, and improve predictive modeling capabilities under climate change scenarios. This study provides scientific insights and theoretical support for the development of future environmental protection policies and comprehensive VOCs management strategies. Full article

In this paper, we calibrate the luminosity relation of gamma−ray bursts (GRBs) by employing artificial neural networks (ANNs) to analyze the Pantheon+ sample of type Ia supernovae (SNe Ia) in a manner independent of cosmological assumptions. The A219 GRB dataset is used to calibrate the Amati relation ($E_{\mathrm{p}}$-$E_{\mathrm{iso}}$) at low redshift with the ANN framework, facilitating the construction of the Hubble diagram at higher redshifts. Cosmological models are constrained with GRBs at high redshift and the latest observational Hubble data (OHD) via the Markov chain Monte Carlo numerical approach. For the Chevallier−Polarski−Linder (CPL) model within a flat universe, we obtain ${\Omega }_{\mathrm{m}}={0.321}_{-0.069}^{+0.078}$, $h={0.654}_{-0.071}^{+0.053}$, $w_0={-1.02}_{-0.50}^{+0.67}$, and $w_a={-0.98}_{-0.58}^{+0.58}$ at the 1 −$\sigma$ confidence level, which indicates a preference for dark energy with potential redshift evolution ($w_a\ne 0$). These findings using ANNs align closely with those derived from GRBs calibrated using Gaussian processes (GPs). Full article

Leakages in water distribution networks pose a formidable challenge, often leading to substantial water wastage and escalating operational costs. Traditional methods for leak detection often fall short, particularly when dealing with complex or subtle data patterns. To address this, a comprehensive comparison of fourteen machine learning algorithms was conducted, with evaluation based on key performance metrics such as multi-class classification metrics, micro and macro averages, accuracy, precision, recall, and F1-score. The data, collected from an experimental site under leak, major leak, and no-leak scenarios, was used to perform multi-class classification. The results highlight the superiority of models such as Random Forest, K-Nearest Neighbours, and Decision Tree in detecting leaks with high accuracy and robustness. Multiple models effectively captured the nuances in the data and accurately predicted the presence of a leak, burst, or no leak, thus automating leak detection and contributing to water conservation efforts. This research demonstrates the practical benefits of applying machine learning models in water distribution systems, offering scalable solutions for real-time leak detection. Furthermore, it emphasises the role of machine learning in modernising infrastructure management, reducing water losses, and promoting the sustainability of water resources, while laying the groundwork for future advancements in predictive maintenance and resilience of water infrastructure. Full article

This study explores novel alginate–konjac glucomannan core–shell aerogel particles for drug delivery systems fabricated via air-assisted coaxial prilling. A systematic approach is needed for the optimization of this method due to the numerous processing variables involved. This study investigated the influence of six variables: alginate and konjac glucomannan concentrations, compressed airflow, liquid pump pressures, and nozzle configuration. A hybrid software using Artificial Neural Networks and genetic algorithms was used to model and optimize the hydrogel formation, achieving a 100% desirable solution. The optimal formulation identified resulted in particles displaying a log-normal size distribution (R² = 0.967) with an average diameter of 1.57 mm. Supercritical CO₂ drying yielded aerogels with macropores and mesopores and a high specific surface area (201 ± 10 m²/g). The loading of vancomycin hydrochloride (Van) or a dexamethasone base (DX) into the aerogel cores during the process was tested. The aerogels exhibited appropriate structural characteristics, and both drugs showed burst release profiles with ca. 80% release within 10 min for DX and medium-dependent release for Van. This study demonstrates the feasibility of producing konjac aerogel particles for delivery systems and the high potential of AI-driven optimization methods, highlighting the need for coating modifications to achieve the desired release profiles. Full article

Enhancing the seismic resilience of urban water distribution networks (WDNs) requires the improvement of both earthquake resistance and rapid recovery capabilities within the system. This paper proposes a multi-objective method to enhance the seismic resilience of the WDNs, focusing on system restoration capabilities while comprehensively considering the hydraulic recovery index, maintenance time, and maintenance cost. The method utilizes a random simulation approach to generate various damage scenarios for the WDN, considering pipe leakage, pipe bursts, and variations in node flow resulting from changes in water pressure. It characterizes the functions of the WDN through hydraulic service satisfaction and quantifies system resilience using a performance response function. Additionally, it determines the optimal dispatch strategy for emergency repair teams and the optimal emergency repair sequence for earthquake-damaged networks using a genetic algorithm. Furthermore, a comprehensive computational platform has been developed to systematically analyze and optimize seismic resilience strategies for WDNs. The feasibility of the proposed method is demonstrated through an example involving the WDN in Xi’an City. The results indicate that the single-objective seismic resilience improvement method based on the hydraulic recovery index is the most effective for enhancing the seismic resilience of the WDN. In contrast, the multi-objective method proposed in this article reduces repair time by 17.9% and repair costs by 3.4%, while only resulting in a 0.2% decrease in the seismic resilience of the WDN. This method demonstrates the most favorable comprehensive restoration effect, and the success of our method in achieving a symmetrically balanced restoration outcome demonstrates its value. The proposed methodology and software can provide both theoretical frameworks and technical support for urban WDN administrators. Full article

The continuous evolution of technology compels higher education leaders to adapt to VUCA (volatile, uncertain, complex, and ambiguous) and BANI (brittle, anxious, non-linear, and incomprehensible) environments through innovative strategies that ensure institutional relevance. While VUCA emphasizes the challenges posed by rapid change and uncertain decision-making, BANI underscores the fragility of systems, heightened anxiety, unpredictable causality, and the collapse of established patterns. Navigating these complexities requires agility, resilience, and visionary leadership to ensure that institutions remain adaptable and future ready. This study presents a bibliometric analysis of digital academic leadership in higher education transformation, examining empirical studies, reviews, book chapters, and proceeding papers published from 2014 to 2024 (11-year period) in the Web of Science—Science Citation Index Expanded (SCIE) and Social Science Citation Index (SSCI). Using CiteSpace software (version 6.3. R1-64 bit), we analyzed 5837 documents, identifying 24 key publications that formed a network of 90 nodes and 256 links. The reduction to 24 publications occurred as part of a structured bibliometric analysis using CiteSpace, which employs algorithmic thresholds to identify the most influential and structurally significant publications within a large corpus. These 24 documents form the core co-citation network, which serves as a conceptual backbone for further thematic interpretation. This was the result of a multi-step refinement process using CiteSpace’s default thresholds and clustering algorithms to detect the most influential nodes based on centrality, citation burst, and network clustering. Our findings reveal six primary research clusters: “Enhancing Academic Performance”, “Digital Leadership Scale Adaptation”, “Construction Industry”, “Innovative Work Behavior”, “Development Business Strategy”, and “Education.” The analysis demonstrates a significant increase in publications over the decade, with the highest concentration in 2024, reflecting growing scholarly interest in this field. Keywords analysis shows “digital leadership”, “digital transformation”, “performance”, and “innovation” as dominant terms, highlighting the field’s evolution from technology-focused approaches to holistic leadership frameworks. Geographical analysis reveals significant contributions from Pakistan, Ireland, and India, indicating valuable insights emerging from diverse global contexts. These findings suggest that effective digital academic leadership requires not only technical competencies but also transformational capabilities, communication skills, and innovation management to enhance student outcomes and institutional performance in an increasingly digitalized educational landscape. Full article

Citral, an organic compound found in lemongrass (Cymbopogon citratus) oil and Litsea cubeba essential oil, has been reported to exhibit notable antifungal activity against Magnaporthe oryzae (M. oryzae), the pathogen of rice blast, which causes significant economic losses in rice production. However, the role of citral in inducing oxidative stress related to antifungal ability and its underlying regulatory networks in M. oryzae remain unclear. In this study, we investigated the oxidative effects of citral on M. oryzae and conducted transcriptomic and widely targeted metabolomic (WTM) analyses on the mycelia. The results showed that citral induced superoxide dismutase (SOD), catalase (CAT), ascorbate peroxidase (APX) activities but reduced glutathione S-transferase (GST) activity with 25% maximal effective concentration (EC₂₅) and 75% maximal effective concentration (EC₇₅). Importantly, citral at EC₇₅ reduced the activities of mitochondrial respiratory chain complex I, complex III and ATP content, while increasing the activity of mitochondrial respiratory chain complex II. In addition, citral triggered a burst of reactive oxygen species (ROS) and a loss of mitochondrial membrane potential (MMP) through the observation of fluorescence. Furthermore, RNA-seq analysis and metabolomics analysis identified a total of 466 differentially expression genes (DEGs) and 32 differential metabolites (DAMs) after the mycelia were treated with citral. The following multi-omics analysis revealed that the metabolic pathways centered on AsA, GSH and melatonin were obviously suppressed by citral, indicating a disrupted redox equilibrium in the cell. These findings provide further evidences supporting the antifungal activity of citral and offer new insights into the response of M. oryzae under oxidative stress induced by citral. Full article

Background: The development of cerebral organoids created from human pluripotent stem cells in 3D culture may greatly improve the discovery of neuropsychiatric medicines. Methods: In the current study we differentiated neural organoids from a human pluripotent stem cell line in vitro, recorded the development of neurophysiological activity using multielectrode arrays (MEAs) and characterized the neuropharmacology of synaptic signaling over 8 months in vitro. In addition, we investigated the ability of these organoids to display epileptiform activity in response to a convulsant agent and the effects of antiseizure medicines to inhibit this abnormal activity. Results: Single and bursts of action potentials from individual neurons and network bursts were recorded on the MEA plates and significantly increased and became more complex from week 7 to week 30, consistent with neural network formation. Neural spiking was reduced by the Na channel blocker tetrodotoxin but increased by the inhibitor of K_V7 potassium channels XE991, confirming the involvement of voltage-gated sodium and potassium channels in action potential activity. The GABA antagonists bicuculline and picrotoxin each increased the spike rate, consistent with inhibitory synaptic signaling. In contrast, the glutamate receptor antagonist kynurenic acid inhibited the spike rate, consistent with excitatory synaptic transmission in the organoids. The convulsant 4-aminopyridine increased spiking, bursts and synchronized firing, consistent with epileptiform activity in vitro. The anticonvulsants carbamazepine, ethosuximide and diazepam each inhibited this epileptiform neural activity. Conclusions: Together, our data demonstrate that neural organoids form inhibitory and excitatory synaptic circuits, generate epileptiform activity in response to a convulsant agent and detect the antiseizure properties of diverse antiepileptic drugs, supporting their value in drug discovery. Full article

Randomness plays an important role in data communication as well as in cybersecurity. In the simulation of communication systems, randomized bit sequences are often used to model a digital source information stream. Cryptographic outputs should look more random than deterministic in order to provide an attacker with as little information as possible. Therefore, the investigation of randomness, especially in cybersecurity, has attracted a lot of attention and research activities. Common tests regarding randomness are hypothesis-based and focus on analyzing the distribution and independence of zero and non-zero elements in a given random sequence. In this work, a novel approach grounded in a gap-based burst analysis is presented and analyzed. Such approaches have been successfully implemented, e.g., in data communication systems and data networks. The focus of the current work is on detecting deviations from the ideal gap-density function describing randomized bit sequences. For testing and verification purposes, the well-researched post-quantum cryptographic CRYSTALS suite, including its Kyber and Dilithium schemes, is utilized. The proposed technique allows for quickly verifying the level of randomness in given cryptographic outputs. The results for different sequence-generation techniques are presented, thus validating the approach. The results show that key-encapsulation and key-exchange algorithms, such as CRYSTALS-Kyber, achieve a lower level of randomness compared to digital signature algorithms, such as CRYSTALS-Dilithium. Full article

Introduction: Combat sport athletes are exposed to repetitive head impacts yet also develop distinct performance-related brain adaptations. Electroencephalography (EEG) provides millisecond-level insight into both processes; however, findings are dispersed across decades of heterogeneous studies. This mechanistic review consolidates and interprets EEG evidence to elucidate how participation in combat sports shapes brain function and to identify research gaps that impede clinical translation. Methods: A structured search was conducted in March 2025 across PubMed/MEDLINE, Scopus, Cochrane Library, ResearchGate, Google Scholar, and related databases for English-language clinical studies published between January 1980 and March 2025. Eligible studies recorded raw resting or task-related EEG in athletes engaged in boxing, wrestling, judo, karate, taekwondo, kickboxing, or mixed martial arts. Titles, abstracts, and full texts were independently screened by two reviewers. Twenty-three studies, encompassing approximately 650 combat sport athletes and 430 controls, met the inclusion criteria and were included in the qualitative synthesis. Results: Early visual EEG and perfusion studies linked prolonged competitive exposure in professional boxers to focal hypoperfusion and low-frequency slowing. More recent quantitative studies refined these findings: across boxing, wrestling, and kickboxing cohorts, chronic participation was associated with reduced alpha and theta power, excess slow-wave activity, and disrupted small-world network topology—alterations that often preceded cognitive or structural impairments. In contrast, elite athletes in karate, fencing, and kickboxing consistently demonstrated neural efficiency patterns, including elevated resting alpha power, reduced task-related event-related desynchronization (ERD), and streamlined cortico-muscular coupling during cognitive and motor tasks. Acute bouts elicited transient increases in frontal–occipital delta and high beta power proportional to head impact count and cortisol elevation, while brief judo chokes triggered short-lived slow-wave bursts without lasting dysfunction. Methodological heterogeneity—including variations in channel count (1 to 64), reference schemes, and frequency band definitions—limited cross-study comparability. Conclusions: EEG effectively captures both the adverse effects of repetitive head trauma and the cortical adaptations associated with high-level combat sport training, underscoring its potential as a rapid, portable tool for brain monitoring. Standardizing acquisition protocols, integrating EEG into longitudinal multimodal studies, and establishing sex- and age-specific normative data are essential for translating these insights into practical applications in concussion management, performance monitoring, and regulatory policy. Full article

Composite protective coatings are critical for material durability but face challenges like fragmented knowledge and scalability issues. Existing research lacks the systematic integration of nanomaterial properties with macroscale performance and standardized evaluation protocols for hybrid systems. This study uses CiteSpace to analyze 18,363 publications (2015–2025) from Web of Science, visualizing collaborative networks, keyword clusters, and citation bursts. China leads global research output (8508 publications), with the USA and India following, while materials science, chemistry, and physics dominate disciplines. Key themes include nanocomposite coatings (e.g., graphene oxide, MXene), corrosion resistance mechanisms, and sustainable technologies, with citation bursts highlighting nanocomposites and surface functionalization. The study reveals interdisciplinary synergies in 2D nanomaterial-polymer systems, thereby improving barrier properties and enabling stimuli-responsive inhibitor release, yet it identifies gaps in lifecycle sustainability and industrial scalability. By constructing a holistic knowledge framework, this work bridges theory and application, quantifying interdisciplinary linkages and pinpointing frontiers like smart, multifunctional coatings. This study integrates data-driven insights to facilitate cross-sector collaboration. It delivers a strategic framework to tackle global challenges in material durability, sustainability, and practical application. Full article

Symmetries and symmetry-breaking play significant roles in data security. Digital watermarking is widely employed in information security fields such as copyright protection and traceability. With the continuous advancement of technology, the research into and application of digital watermarking face numerous challenges. To gain a comprehensive understanding of the current research status and trends in the development of digital watermarking, this paper conducts a bibliometric analysis using the CiteSpace software, focusing on 8621 publications related to digital watermarking (watermark/watermarking) from the Web of Science (WOS) Core Collection database, spanning from 2004 to 2024. This study explores the research landscape and future trends in digital watermarking from various perspectives, including annual publication volume, keyword co-occurrence and burst detection, leading authors, research institutions, and publishing countries or regions. The results reveal a regional concentration of research efforts, with early research being primarily dominated by the United States, Taiwan, and South Korea, while recent years have seen a rapid rise in research from China and India. However, global academic collaboration remains relatively fragmented and lacks a well-integrated international research network. Keyword analysis indicates that research hotspots have expanded from traditional copyright protection to data integrity verification, multimedia watermarking, and the incorporation of intelligent technologies. Notably, the introduction of deep learning has propelled watermarking algorithms toward greater sophistication and intelligence. Using CiteSpace, this study is the first to systematically illustrate the dynamic evolution of digital watermarking research over the past 20 years, focusing on thematic trends and regional distributions. Unlike previous reviews that rely mainly on qualitative analyses, this study offers a quantitative and visualized perspective. These findings provide concrete references for the future development of more targeted research efforts. Full article

In order to improve the accuracy of pipe burst detection in water distribution networks (WDNs), a novel small sample diagnosis method (SSDM) based on the head loss ratio (HLR) method and deep transfer learning (DTL) method has been proposed. In this paper, the burst state was quickly detected through the limited data analysis of pressure monitoring points. The HLR method was introduced to enhance data features. DTL was introduced to improve the accuracy of small sample burst detection. The simulated data and real data were enhanced by HLR. Then, the model was trained and obtained through the DTL. The performance of the model was evaluated in both simulated and real scenarios. The results indicate that the leaked features can be improved by 350% by the HLR. The accuracy of SSDM reaches 99.56%. The SSDM has been successfully applied to the detection of real WDNs. The proposed method provides potential application value for detecting pipe bursts. Full article