Search Results (717)

The diverse application scenarios envisioned for sixth generation (6G) are characterized by the deep integration of sensing and ubiquitous connectivity, which imposes unprecedentedly stringent security requirements. However, due to the open nature of wireless channels, mobile communications systems always face severe information security threats such as falsification, spoofing, interception, and repudiation. Cryptography-based symmetric and asymmetric encryption techniques remain mainstream solutions for information protection. Symmetric encryption is efficient and secure for legitimate users but suffers from key-distribution difficulties over open wireless channels, whereas asymmetric encryption resolves this problem but faces increasing risks from quantum computing due to its reliance on structured mathematical hardness assumptions. In response to these limitations, physical layer security (PLS) has gained a great deal of research attention as a powerful security component that leverages the features of varying wireless channels. Existing PLS schemes can be broadly classified into two categories. The first one takes advantage of the legitimate link’s opportunistic channel-quality superiority over or different spatial-domain directions from the attacking link, which still faces many practical implementation difficulties. The second category is termed physical-layer key generation (PLKG). It extracts the unique features of the legitimate link’s channel variation, which is often reciprocal, as the source of secret key generation and therefore can naturally implement secure key distribution tasks. This advantage no doubt injects new vigor to symmetric encryption as a stronger protection approach. Following this trend, we in this paper concentrate on the PLKG techniques. Specifically, we present a comprehensive overview on existing PLKG schemes, discussing diverse secret key generation and reconciliation methods. We further investigate the model-driven and deep-learning-based approaches tailored for the scenario with imperfect channel reciprocity between the sender and receiver. After comprehensively reviewing major existing schemes, we further discuss a recently proposed PLKG design based on codeword reconstruction, which makes use of the strong error-correcting capability of the forward-error-correction (FEC) codes to effectively implement secure and consistent secret key generation between the legitimate sender and receiver. Finally, we share our opinions on the unsolved challenges and potential research directions dedicated to PLKG toward meeting the security requirements of 6G. Full article

The Internet of Things (IoT) market is currently undergoing a period of unprecedented, rapid evolution, leading to the enabling of novel and diverse applications spanning both the civilian and industrial sectors. A significant proportion of these emerging use cases, particularly those in domains such as maritime communications and forestry management, require service continuity and connectivity within geographically remote regions, where conventional terrestrial infrastructure is often absent or economically unfeasible. To bridge this coverage gap and achieve truly ubiquitous connectivity, the recent 3GPP initiative to extend 5G services into Non-Terrestrial Segments (NTNs) holds substantial promise. This expansion is crucial for ensuring that massive Machine-Type Communication (mMTC) services can be reliably provisioned globally. This paper aims to detail the progress in standardization and academic activities towards the design and deployment of NTN-based Narrowband IoT (NB-IoT) systems, which are the leading NTN mMTC enabler in the 3GPP portfolio. We will specify the challenges faced by these systems and outline the solutions proposed thus far. We conclude the paper with a discussion on already operational systems and lessons learned from their deployment and operation. Full article

Although many studies had been conducted on soil microbial communities in forest ecosystems before this one, how the diversity and network complexity of soil microbial communities evolved throughout the succession of karst forests remained unclear. We collected soil samples from three vegetation successional stages (grassland stage, shrub stage and arbor stage) in Maolan National Nature Reserve, Guizhou Province, China, and applied high-throughput sequencing technology to explore soil microbial diversity, community composition, co-occurrence network characteristics and their internal correlations. The results showed that the succession process significantly affected soil microbial diversity (p < 0.05). Bacterial diversity increased by 12.33% as succession proceeded, whereas fungal diversity declined by 37.50%. The dominant microbial communities exhibited obvious stage-specific characteristics. For bacteria, Bacillaceae served as a universally dominant taxon. Paenibacillaceae and Thermoactinomycetaceae were enriched in the grassland (CD) stage, and the relative abundance of Streptosporangiaceae reached its maximum in the arbor (QM) stage. For fungi, Trimorphomycetaceae and Hypocreaceae were ubiquitous dominant groups. Aspergillaceae possessed the highest relative abundance in the CD stage, while Clavicipitaceae peaked in relative abundance in the QM stage. The complexity of bacterial co-occurrence networks rose with succession, whereas fungal network complexity decreased. Both positive and negative linkages in bacterial networks increased over succession, while both types of connections in fungal networks declined. Soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP) and N/P ratio exerted the strongest influences on bacterial communities and bulk density; SOC and TN were the key drivers structuring fungal assemblages. Micromonosporaceae and Xanthobacteraceae showed significant positive correlations with TP and N/P ratio (p < 0.05), whereas Thermoactinomycetaceae and Oscillospiraceae presented significant negative correlations with these two indicators (p < 0.01). Clavicipitaceae had strong positive correlations with SOC and TN (p < 0.01), and Herpotrichiellaceae together with Aspergillaceae displayed significant negative correlations with SOC and TN (p < 0.05). This study clarified the regulatory effects of karst forest secondary succession on soil microbial communities and provided theoretical evidence for the ecological restoration of karst regions. Full article

Marine oil spills represent a critical environmental threat. Petroleum contamination systematically accumulates in the world’s oceans, driving severe and long-term damage to the biodiversity of vulnerable coastal ecosystems. As its primary objective, this study assesses the ocean’s intrinsic genetic capacity to degrade aliphatic and aromatic hydrocarbons. Using the Ocean Gene Atlas v2.0 (OGA2) database, bacterial metabolic pathways were profiled via a four-marker framework: PF00487 (AlkB) and PF03433 (LadA) for medium and long-chain alkanes, alongside PF00355 and PF00848 domains for aromatic ring activation. The analyses revealed that while salinity levels between 34–36 PSU sustain baseline abundances, temperature acts as a primary selective filter, segregating microbial communities into distinct thermal niches. Medium-chain aliphatic potential (PF00487) is ubiquitous, reaching maximum values in surface polar waters near 0 °C before declining with depth. Conversely, long-chain machinery (PF03433) is restricted to warm surface hotspots. Aromatic-degrading potential (PF00355/PF00848) displayed high thermal resilience, narrowing vertically except for a mesopelagic cluster in the Arabian Sea. Global taxonomic analysis confirmed the dominance of Pseudomonadota (59%), which was mainly represented by the class Gammaproteobacteria (15%), with Alcanivorax (10%) as the most abundant genus. On the other hand, aromatic degraders persist as a low-abundance seed bank. In conclusion, the mere presence of specific genes does not automatically imply metabolic expression; actual in situ biodegradation remains strictly governed by transcriptional triggers and environmental factors. Full article

Polypropylene microplastics (PP-MPs) are ubiquitous in our daily lives, but their toxicological effects on mammals remain poorly understood. This study investigated the toxicity effects of PP-MPs on C57BL/6 mice using 16S rRNA gene amplicon sequencing and transcriptome sequencing. Female and male mice were randomly classified into the control and PP-MPs-treated groups, respectively, and the experiment lasted for 5 weeks. We found that PP-MPs exposure did not affect the levels of immunoglobulin G (IgG), interleukin-4 (IL-4), and interferon gamma (IFN-γ), indicating that humoral immunity and inflammatory levels were not influenced by PP-MPs treatment. However, PP-MPs exposure reduced the PHA response in female mice, but not in male mice. It also did not alter the wet mass of testicles and ovaries, nor the levels of testosterone and estradiol. Exposure to PP-MPs altered the expression of the testicular genes. G protein-coupled receptor signaling pathways, olfactory receptor activity, and protein digestion and absorption were downregulated in the PP group. Collagen genes (Col9a3, Col11a2, Col27a1, Col26a1, Col7a1) play a significant role in downregulating protein digestion and absorption. In addition, PP-MPs exposure caused a change in beta diversity of gut microbiota, indicating the alteration of their community structure. PP-MPs exposure reduced the relative abundance of the probiotic Lactobacillus. Changes in the gut microbiota may be related to the expression levels of testicular genes. Overall, PP-MPs exposure altered both the community structure of the gut microbiota and the expression levels of testicular genes in mice, and collagen genes may serve as a critical factor influencing testicular function. Full article

Small non-coding RNAs (sRNAs) are ubiquitous post-transcriptional regulators that enable rapid bacterial adaptation to fluctuating environments. Previous reviews have largely focused on sRNA mechanisms in model organisms. This review integrates computational prediction, meta-omics-based discovery, and synthetic biology applications of small regulatory RNAs in marine and environmental prokaryotes, providing a multi-layered perspective from identification to functional and engineering applications. The current landscape of sRNA identification tools is critically evaluated, with emphasis on strategies to overcome challenges such as false-positive predictions. Recent advances in mapping the RNA interactome and emerging evidence of previously underappreciated roles of sRNAs in environmental adaptation are discussed. Additionally, metagenomic and metatranscriptomic studies revealing the diversity of environmental sRNAs in uncultured microbial communities are summarized, highlighting their ecological significance. Finally, a curated overview of synthetic sRNA applications in metabolic engineering, including target genes and enhanced product yields, is provided as a resource for strain engineering. Collectively, this review provides a holistic view of prokaryotic sRNA biology, distinguishing it from more narrowly focused studies. Overall, sRNAs are highlighted as key regulatory elements linking microbial environmental adaptation with emerging biotechnological applications through advances in meta-omics guided discovery and synthetic RNA engineering. Full article

Background: Wastewater systems provide an integrated One Health perspective on antimicrobial resistance but remain uneven globally, with limited data from rapidly urbanizing and highly connected regions such as the Arabian Gulf. Methods: An eight-month prospective study was conducted in Dubai, United Arab Emirates, with monthly sampling from nine community and two hospital nodes and two wastewater treatment plants (WWTP). Samples were analysed using high-throughput quantitative PCR (HT-qPCR; Resistomap, Finland) with a 72-target One Health gene panel. Results: Across the 120 samples analyzed, the number of detected gene targets ranged from 26 to 68 genes, with the highest diversity in hospital wastewater and the lowest in WWTP effluent. Pathogen-associated markers were detected in all sources, with enterococci, Escherichia coli, and Klebsiella pneumoniae predominant. Hospital wastewater showed broader pathogen-associated gene markers, including those linked to Acinetobacter baumannii and Pseudomonas aeruginosa. Antibiotic resistance genes (ARGs) associated with macrolide–lincosamide–streptogramin B, tetracycline, and aminoglycoside resistance were widespread. Community and influent samples were dominated by msrE, tet(M), and aminoglycoside resistance genes, whereas hospital wastewater showed the highest ARG burden, including enrichment of aac(6′)-Ib, qnrS2, bla_GES, bla_TEM, bla_KPC-2, and bla_IMP-1. Several ARGs, including mcr-1, persisted in WWTP effluent. Mobile genetic elements (MGEs) were ubiquitous, with integron-associated markers prominent in WWTP effluent. ARG–MGE network analysis demonstrated extensive co-occurrence, with MGEs as central hubs linking multiple ARGs. Conclusions: Wastewater captures distinct resistome profiles across urban compartments, supporting its role for AMR surveillance. The persistence of ARGs and MGEs in WWTP effluent highlights the potential for environmental dissemination, through reuse of treated wastewater. Full article

The internet of things (IoT) has rapidly evolved into a ubiquitous communication paradigm for enabling the deployment of autonomous wireless networks across diverse application domains. However, the limited energy storage capacity and computational resources of IoT devices (IoTDs) pose a serious concern to their long-term sustainability and the expected quality of service delivery. Moreover, in the foreseeable era of the internet of everything, centralised network resource management is likely to constrain network scalability. To tackle these challenges in the current and next-generation communication networks, the adoption of adaptive and lightweight computational frameworks coupled with energy-efficient transmission strategies is essential. To demonstrate this, we exploit the concept of cooperative communication and radio frequency-based energy-harvesting to improve the network throughput while maintaining power supply to the IoTDs. Furthermore, to intelligently and autonomously perform resource allocation, we employ the reinforcement learning frameworks, particularly state–action–reward–state–action (SARSA) and Q-learning. Based on key performance evaluation metrics, we compare our findings with the baseline methods, including the equal, random, and greedy power level selection schemes, with SARSA exhibiting the most favourable performance trade-offs. Full article

Potato (Solanum tuberosum L.) is a vital food security and cash crop in Eritrea, yet bacterial and fungal pathogens responsible for 15–30% yield losses remain molecularly uncharacterized across its production systems. Here, we present the first nationwide amplicon-based metagenomic survey of potato pathogen communities, sampling 81 farms across 14 sub-regions spanning four agroclimatic regions during July–August 2023. High-throughput amplicon sequencing targeting the bacterial 16S rRNA V3–V4 region and fungal ITS1–ITS2 loci revealed pronounced geographic heterogeneity in community composition and alpha diversity. Pseudomonas spp. were ubiquitous, with a peak relative abundance of 32.5% in Dekemhare. Dominant fungi included Alternaria spp. (14.3% in Berik), Fusarium spp. (highest diversity 53.8% in Adi Kuala), Botrytis cinerea (36.9% in Adi Keih), and Rhizoctonia solani (44.4% in Adi Tekeliezan). Bacterial Shannon diversity averaged 5.67; fungal, 4.70. Weighted UniFrac PCoA accounted for 56.5% of bacterial community variance along PC1, confirming distinct geographic clustering. Potential pathogen-associated taxa of Pseudomonas, Alternaria, Fusarium, and Colletotrichum were detected in every asymptomatic sample examined, demonstrating the inadequacy of visual-only disease surveillance. These findings establish the first molecular baseline for potato-associated taxon diversity in Eritrea, providing the empirical foundation for region-specific integrated disease management and evidence-based seed certification protocols. Full article

This study represents the first comprehensive characterization of fish biodiversity in the Sonche Canyon (northeastern Peru) using environmental DNA (eDNA), revealing a community of 19 taxa whose detection depended critically on a multi-marker approach. The analysis demonstrated that the use of both Metafish and Mifish-U PCR primer pairs generated a high complementarity index (0.53), allowing the capture of divergent biological signals; Metafish was essential for identifying characiformes; while Mifish-U revealed the presence of specific gymnotiformes and loricariids. The findings, supported by rarification curves confirming sampling saturation. Principal Component Analysis (PCA) revealed co-variation between the biodiversity “hotspot” in Tingorbamba and elevated nitrate concentration (6.1 mg/L), as well as the ubiquitous presence of the invasive species Oncorhynchus mykiss. In conclusion, the utility of the molecular markers and the observed co-variation between physicochemical variables and species distributions underscore the need for standardized protocols for the monitoring and conservation of fauna in the vulnerable lotic systems of the Peruvian Andes. Full article

Radio Frequency Identification (RFID) is a fundamental enabling technology for the Internet of Things (IoT), providing automatic identification and data retrieval capabilities for various applications. With the increasing prevalence of ubiquitous and mobile computing, serverless RFID systems have attracted significant attention due to their elimination of the need for continuous connection to a centralized backend database. However, most existing RFID authentication protocols either rely on backend server involvement or are unable to simultaneously withstand denial-of-service (DoS) attacks, tracking attacks, reader intrusion attacks, and desynchronization attacks. To overcome these limitations, this paper proposes a lightweight authentication protocol suitable for serverless RFID environments. This scheme utilizes dynamic key updates, random numbers, and one-way hash functions to achieve bidirectional authentication between RFID tags and portable readers without the need for a backend server during the authentication process. Furthermore, this paper introduces a dual-key synchronization mechanism to maintain consistency between communicating entities and effectively prevent desynchronization attacks caused by message interception, loss, or replay. Security analysis shows that the proposed protocol meets all major RFID security requirements, including resistance to eavesdropping, tag cloning, identity impersonation, tracking, privacy breaches, denial-of-service attacks, reader intrusion attacks, and desynchronization attacks. Compared to typical RFID authentication protocols, this scheme is the only one that can simultaneously support serverless RFID operation and resist DoS attacks and reader intrusion attacks. Furthermore, the protocol requires only lightweight hash calculations and three rounds of communication, significantly reducing communication overhead compared to traditional four-round RFID authentication protocols. Performance analysis shows that the scheme maintains low computational complexity, storage requirements, and communication costs, making it suitable for resource-constrained RFID tags. The results demonstrate that the proposed protocol achieves an effective balance between security, efficiency, and deployment flexibility, making it a practical solution for next-generation serverless RFID applications in the IoT environment. Full article

Introduction: Mediterranean coastal wetlands are highly dynamic ecosystems that support diverse fish communities and are often of high conservation value. The Ebro Delta is one of the most important coastal wetlands in the Western Mediterranean, and knowledge of fish assemblages is essential for its effective conservation and management. Environmental DNA (eDNA) metabarcoding provides a non-invasive approach that can potentially complement conventional surveys for fish biodiversity monitoring. Objective: This study aimed to evaluate the potential of eDNA metabarcoding as a complementary tool to conventional fyke net surveys for fish biodiversity monitoring in the Mediterranean coastal wetlands. Methodology: In 2022, fish assemblages were surveyed across 12 areas of the Ebro Delta using eDNA metabarcoding (12S MiFish) and conventional fyke net sampling. Results were compared with a 22-year historical dataset. Results: A total of 27 fish taxa were detected, 13 of which were exclusive to eDNA, 11 were shared between methods, and three were recorded only by fyke nets. The reliability of eDNA metabarcoding was supported by the detection of endangered species, such as Anguilla anguilla and Apricaphanius iberus; ubiquitous taxa, such as Atherina boyeri and Pomatoschistus microps; and invasive species, such as Gambusia holbrooki and Cyprinus carpio. Detection of invasive species was maximized using eDNA. While eDNA revealed higher species richness than fyke nets, community composition differed significantly between methods, reflecting distinct detection patterns. eDNA preferentially detected non-benthic species, whereas fyke nets were more robust for benthic taxa detection. eDNA recovered most historically recorded species but failed to detect some taxa, such as Misgurnus anguillicaudatus and Sardina pilchardus. Despite richness differences, the two methods provided complementary views of fish assemblages, highlighting method-specific detection limitations and opportunities. Conclusions: eDNA does not fully replace conventional surveys and their combined use improves the detection of threatened and invasive species, better supporting conservation and management. Full article

Polyethylene microplastics (PE-MPs) are ubiquitous constituents of waste activated sludge (WAS), acting as a major land-based source threatening coastal environmental integrity. However, how particle size and dose govern the methanogenic outcome during WAS digestion remains poorly defined. This study evaluated two particle sizes (50 vs. 300 µm) and doses (100 vs. 200 particles/gTS) to elucidate the differential effects of PE-MPs on methane yield and the underlying biological mechanisms. The results show that, while low-dose treatments either slightly inhibited methane yield (RS1) or had no significant effect (RL1), high-dose treatments (RS2 and RL2) achieved a net positive effect, with significant increases of 10.2% (p < 0.05) and 9.0% (p < 0.05) relative to the control, respectively. Nevertheless, RS2 and RL2 achieved methanogenic enhancement via distinctly different biological pathways. RS2 harnessed the stress of reactive oxygen species (ROS) (110.5% of the control) to drive community restructuring and biomass accrual (positive correlation between ROS intensity and total VS, Pearson’s r = 0.99). Key syntrophic and electrogenic taxa (e.g., Syntrophales, Bacteroidetes vadinHA17) exhibited a fully interconnected, decentralized network, thereby achieving tight coupling between hydrolysis and methanogenesis. RL2 leveraged the physical carrier effect to promote granulation and biomass growth, enriching Syntrophobacter to enhance propionate degradation. This culminated in a highly modular, sparse network characterized by localized competitive interactions. Together, dosage governs the net methanogenic effect of PE MPs, whereas particle size dictates the mechanistic routes of action. This work offers a mechanistic framework to optimize energy recovery from PE-MP-contaminated sludge while mitigating secondary environmental risks, providing a science-based strategy for the sustainable management of plastic-laden sludge that reconciles renewable energy recovery with pollution control. Full article

With the rapid proliferation in communication devices and the expansion of applications, future sixth-generation (6G) networks are expected to enable a truly connected world. They will allow large-scale use cases, such as the Internet of Things (IoT) and unmanned aerial vehicles (UAVs), providing significantly faster and more innovative services ubiquitously. However, challenges remain, particularly in security. The growing number of devices and increased connectivity may lead to a larger attack surface. Many emerging technologies are actively addressing these security and privacy concerns, ensuring that we can benefit from the advantages of 6G networks and applications without falling victim to malicious attacks. In this paper, we conduct a comprehensive literature review of emerging technologies for secure communication in 6G networks, including artificial intelligence (AI) and machine learning (ML), blockchain technology, quantum-safe communication, and physical-layer security. First, we discuss the architecture of 6G networks from a security perspective. Second, we review existing surveys on 6G security issues and provide a quantitative analysis to identify research gaps, including technology-driven silos and domain fragmentation. Third, we develop a hierarchical taxonomy of security challenges and attacks in 6G networks, covering physical-layer attacks, network-level threats, device vulnerabilities, data privacy concerns, and emerging application-specific risks. We then examine the roles of key enabling technologies and present a mapping between security threats and corresponding technological solutions, along with a unified evaluation framework to facilitate cross-technology comparison. Furthermore, we propose an integrated multi-technology security framework and discuss practical deployment challenges by bridging the gap between simulation-based studies and real-world implementations. Finally, we outline concrete future research directions for advancing secure 6G communication systems. Full article

Sarcopenia is a progressive, age-related skeletal muscle disorder that serves as a driver of frailty, falls, and mortality in older adults. Despite the recent paradigm shift introduced by the latest sarcopenia consensus, which emphasizes early, proactive detection, sarcopenia cases frequently evade traditional screening due to inherent diagnostic bottlenecks and resource limitations. Artificial intelligence has emerged as a transformative solution to dismantle these barriers across the entire continuum of sarcopenia care. This review explores the rapid evolution of artificial intelligence, beginning with automated opportunistic screening that extracts prognostic musculoskeletal data from routine imaging and electronic health records, advancing toward high-precision multimodal assessment architectures. Beyond initial assessment, artificial intelligence is actively restructuring longitudinal care by the integration of ubiquitous wearables, Large Language Models, and computer vision, enabling dynamic exercise prescriptions and real-time kinematic postural correction for sarcopenia rehabilitation. Realizing this potential requires the medical community to confront urgent clinical barriers, including multi-center validation, semantic interoperability, health economic justification, and the strict preservation of human-centric ethics. By addressing these challenges, this review provides a definitive roadmap for embedding artificial intelligence across the entire sarcopenia care pathway, transforming isolated instances of opportunistic screening into a unified ecosystem for intelligent, proactive rehabilitation. Full article