Search Results (642)

Smart cities are technologically advanced urban environments where interconnected systems and data-driven technologies enhance public service delivery and quality of life. These cities rely on information and communication technologies, the Internet of Things, big data, cloud computing, and other Industry 4.0 tools to support efficient city management and foster citizen engagement. Often referred to as digital cities, they integrate intelligent infrastructures and real-time data analytics to improve mobility, security, and sustainability. Ubiquitous sensors, paired with Artificial Intelligence, enable cities to monitor infrastructure, respond to residents’ needs, and optimize urban conditions dynamically. Given the increasing significance of Industry 4.0 in urban development, this study adopts a bibliometric approach to systematically review the application of these technologies within smart cities. Utilizing major academic databases such as Scopus and Web of Science the research aims to identify the primary Industry 4.0 technologies implemented in smart cities, assess their impact on infrastructure, economic systems, and urban communities, and explore the challenges and benefits associated with their integration. The bibliometric analysis included publications from 2016 to 2023, since the emergence of urban researchers’ interest in the technologies of the new industrial revolution. The task is to contribute to a deeper understanding of how smart cities evolve through the adoption of advanced technological frameworks. Research indicates that IoT and AI are the most commonly used tools in urban spaces, particularly in smart mobility and smart environments. Full article

The spotted longbarbel catfish, Hemibagrus guttatus, a nationally protected Class II species in China, faces increasing threats from habitat degradation. Recently, the spotted longbarbel catfish has gained attention as a promising aquaculture species, not only for its premium flesh quality but also for its potential role in conservation through sustainable captive breeding programs. Ammonia nitrogen (ammonia-N) is a ubiquitous byproduct of intensive farming and serves as the primary environmental stressor confronting aquatic species. Elucidating the ammonia-N tolerance of spotted longbarbel catfish constitutes a critical prerequisite for its successful domestication, which is the aim of this study. We demonstrate that ammonia-N stress significantly decreases the survival rate of spotted longbarbel catfish and induces tissue damage, including gill lamella proliferation, hepatocyte blurring, and renal necrosis. Transcriptomic analysis revealed that ammonia-N stress promotes the expression of genes related to endoplasmic reticulum stress, heat-shock proteins, immune response, and apoptosis, while inhibiting antioxidant-related genes and Wnt-related genes. Enzymatic assays indicate that ammonia-N stress inhibits the activities of multiple antioxidant enzymes, including SOD, CAT, GSH, GSH-Px, and T-AOC. Microbiome analysis showed that ammonia-N stress altered the intestinal microbial community by increasing harmful bacteria (e.g., Vibrio and Aeromonas) and suppressing beneficial bacteria (e.g., Cetobacterium and Lactococcus). These findings highlight the comprehensive negative impacts of ammonia-N on the health of the spotted longbarbel catfish and provide a theoretical basis for optimizing aquaculture conditions to support the sustainable protection and domestication of the spotted longbarbel catfish. Full article

Advancements in Internet of Things (IoT) technologies have had a profound impact on interconnected devices, leading to exponentially growing networks of billions of intelligent devices. However, this growth has exposed Internet of Things (IoT) systems to cybersecurity vulnerabilities. These vulnerabilities are primarily caused by the inherent limitations of these devices, such as finite battery resources and the requirement for ubiquitous connectivity. The rapid evolution of deep learning (DL) technologies has led to their widespread use in critical application domains, thereby highlighting the need to integrate DL methodologies to improve IoT security systems beyond the basic secure communication protocols. This is essential for creating intelligent security frameworks that can effectively address the increasingly complex cybersecurity threats faced by IoT networks. This study proposes a hybrid methodology that combines fractional discrete Tchebichef moment analysis with deep learning for the prevention of IoT attacks. The effectiveness of our proposed technique for detecting IoT threats was evaluated using the UNSW-NB15 and Bot-IoT datasets, featuring illustrative cases of common IoT attack scenarios, such as DDoS, identity spoofing, network reconnaissance, and unauthorized data access. The empirical results validate the superior classification capabilities of the proposed methodology in IoT cybersecurity threat assessments compared with existing solutions. This study leveraged the synergistic integration of discrete Tchebichef moments and deep convolutional networks to facilitate comprehensive attack detection and prevention in IoT ecosystems. Full article

In recent years, tires have become a prominent concern for researchers and environmentalists in regard to their potential threat of tire-derived pollutants (TDPs) to human health. Among these pollutants, N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6PPD) and its oxidized form, 6PPD-quinone (6PPD-Q), have been of primary interest due their ubiquity in urban environments, and their potential negative effects on human health. This review provides a summary of human health implications of TDPs, including 6PPD and 6PPD-Q. For the methodology, datasets were collected from the literature sources, including sources, formations and ecological effects of these pollutants, and pathways of human exposure and public health significance. Urban soils are key for services including carbon storage, water filtration, and nutrient cycling, underpinning urban ecosystem resilience. Soil degradation through compaction, sealing, and pollution, particularly by pollutants from tire wear, destroys these functions, however. These pollutants disturb the soil microbial communities, leading to a loss of diversity, an increase in pathogenic species, and changes in metabolism, which in turn can impact human health by increasing disease transmission and diseases of the respiratory systems. Incorporating green-infrastructure practices can enhance the ecosystem service potentials of urban soils and contribute to sustainable, climate-resilient urban city development. These findings underscore the pressing need for a coordinated international campaign to study chronic health effects and science informed policy frameworks to address this ubiquitous environmental health concern—an issue that crosses urban water quality, environmental justice, and global management of tire pollution. Full article

The capabilities of 6G networks surpass those of existing networks, aiming to enable seamless connectivity between all entities and users at any given time. A critical aspect of achieving enhanced and ubiquitous mobile broadband, as promised by 6G networks, is merging satellite networks with land-based networks, which offers significant potential in terms of coverage area. Advanced routing techniques in next-generation network technologies, particularly when incorporating terrestrial and non-terrestrial networks, are essential for optimizing network efficiency and delivering promised services. However, the dynamic nature of the network, the heterogeneity and complexity of next-generation networks, and the relative distance and mobility of satellite networks all present challenges that traditional routing protocols struggle to address. This paper provides an in-depth analysis of 6G networks, addressing key enablers, technologies, commitments, satellite networks, and routing techniques in the context of 6G and satellite network integration. To ensure 6G fulfills its promises, the paper emphasizes necessary scenarios and investigates potential bottlenecks in routing techniques. Additionally, it explores satellite networks and identifies routing challenges within these systems. The paper highlights routing issues that may arise in the integration of 6G and satellite networks and offers a comprehensive examination of essential approaches, technologies, and visions required for future advancements in this area. 6G and satellite networks are associated with technical terms such as AI/ML, quantum computing, THz communication, beamforming, MIMO technology, ultra-wide band and multi-band antennas, hybrid channel models, and quantum encryption methods. These technologies will be utilized to enhance the performance, security, and sustainability of future networks. Full article

Membrane tubes, a class of soft biological confinement for ubiquitous transport intermediates, are essential for cell trafficking and intercellular communication. However, the confinement interaction and directional migration of diffusive nanoparticles (NPs) are widely dismissed as improbable due to the surrounding environment compressive force. Here, combined with the mechanics analysis of nanoparticles (such as extracellular vesicles, EVs) to study their interaction in confinement, we perform dissipative particle dynamics (DPD) simulations to construct a model that is as large as possible to clarify the submissive behavior of NPs. Both molecular simulations and mechanical analysis revealed that the interactions between NPs are controlled by confinement deformation and the centroid distance of the NPs. When the centroid distance exceeds a threshold value, the degree of crowding variation becomes invalid for NPs motion. The above conclusions are further supported by the observed dynamics of multiple NPs under confinement. These findings provide new insights into the physical mechanism, revealing that the confinement squeeze generated by asymmetric deformation serves as the key factor governing the directional movement of the NPs. Therefore, the constraints acting on NPs differ between rigid confinement and soft confinement environments, with NPs maintaining relative stillness in rigid confinement. Full article

Bacteriophages (phages) are one of the critical biotic drivers of prokaryotic community dynamics, functions, and evolution. Despite their importance in aquatic ecosystems, very few phages have been isolated from freshwater lakes, hampering our understanding of their ecological importance and usage in a variety of biotechnological applications. Limnohabitans, with a ubiquitous distribution, is a metabolically versatile, fast-growing, morphologically diverse freshwater lake bacterial genera. It is especially abundant in pH-neutral and alkaline aquatic habitats, where it represents an average of 12% of freshwater bacterioplankton and plays an important role in funneling carbon from primary producers to higher trophic levels. However, no phages infecting Limnohabitans have been reported to date. Here, we describe, for the first time, three phages infecting Limnohabitans, DC31, DC33, and YIMV22061, isolated from two freshwater lakes in China and characterized using genome content analysis and comparative genomics. DC31 and DC33, recovered from the eutrophic Dianchi Lake, with auxiliary metabolic genes (AMGs), associated with nucleotide metabolism, whereas YIMV22061, isolated from the oligotrophic Fuxian Lake, carried AMGs involved in antibiotic resistance. The AMGs they carried highlight their impacts on Limnohabitans in different environments. Comparative genomic analyses indicate that DC31, DC33, and YIMV22061 represent three novel species in the Caudoviricetes class. IMG/VR database alignment further reveal that these phages are widely distributed across diverse aquatic and terrestrial ecosystems globally, suggesting their ecological significance. This study provides a basis for better understanding Limnohabitans–phage interactions. Full article

Hopanoids are a series of important lipid biomarkers in the bacterial cellular membranes that are found ubiquitously in different spatial and temporal environments. Squalene-hopane cyclase, a key and prerequisite molecular component of the hopanoid biosynthesis pathway, is encoded by the sqhC gene. To investigate the composition, niche, and distribution of microbial sqhC-containing communities, we analyzed hopanoid producer data and environmental parameters across different ecosystems on the basis of sequencing reads of peat samples from increasing gradient depths across peatland profile C in the Dajiuhu Peatland, as well as data collected from available published papers. The results indicated that the acidic Dajiuhu Peatland harbored mainly Acidobacteria (59.16%) among its sqhC-containing groups. The main composition of hopanoid producers in the peatland was different from that in other ecosystems, with Alphaproteobacteria found in soil (37.78%), cave (48.21%), hypersaline lagoon (34.04%), and marine (32.59%) ecosystems; Betaproteobacteria, Gammaproteobacteria, and Deltaproteobacteria found in reef (100%), acid mine drainage (55.00%), and estuary, mangrove, and harbor (39.66%) ecosystems; and an unknown cluster found in freshwater (29.43%) and hot spring (89.58%) ecosystems. Compared with other phyla or sub-phyla, Alphaproteobacteria, Betaproteobacteria, and Gammaproteobacteria were the most widespread, occurring in eight ecosystems. Peatland was significantly separated from the other nine ecosystem modules in the occurrence network, and the marine ecosystem had the greatest impact on the eco-network of sqhC microbes. An RDA indicated that pH, DO, salinity, and TOC had significant impacts on sqhC-containing microbial communities across the different ecosystems. Our results will be helpful to understanding the diversity, composition, and distribution of the sqhC community and its response to multiple environmental factors across different ecosystems. Full article

Visible light communication (VLC) has become one important candidate technology for beyond 5G and even 6G wireless networks, mainly thanks to its abundant unregulated light spectrum resource and the ubiquitous deployment of light-emitting diodes (LED)-based illumination infrastructures. Due to the high directivity of VLC channel propagation, relay-based cooperative techniques have been introduced and explored to enhance the transmission performance of VLC links. Nevertheless, almost all current works are limited to scenarios adopting well-known Lambertian transmitter and relay, which fail to characterize the scenarios with distinctive non-Lambertian transmitter or relay. For filling this gap, in this article, relay-assisted VLC employing diverse non-Lambertian optical beam configurations is proposed. Unlike the conventional Lambertian transmitter and relay-based research paradigm, the presented scheme employs the commercially available non-Lambertian transmitter and relay to configure the cooperative VLC links. Numerical results illustrate that up to 40.63 dB SNR could be provided by the proposed non-Lambertian relay-assisted VLC scheme, compared with about a 34.22 dB signal-to-noise ratio (SNR) of the benchmark Lambertian configuration. Full article

Increasing reported works identify that drones could and should be sufficiently utilized to work as aerial base stations in the upcoming 6G aerial vehicular networks, for providing emergency communication and flexible coverage. Objectively, light-emitting diode (LED) based lighting devices are ubiquitously integrated into these commercially available drone platforms for the general purposes of illumination and indication. Impresively, for further enhancing and diversifying the wireless air interface capability of the above 6G aerial vehicular networks, the solid-state light emitter, especially LED-based visible light communication (VLC) technologies, is increasingly introduced and explored in the rapidly developing drone communications. However, the emerging investigation dimension of spatial light beam is still waiting for essential research attention for the LED-based drone VLC. Up to now, to the best of our knowledge, almost all LED-based drone VLC schemes are still limited to conventional Lambertian LED beam configuration and objectively reject these technical possibilities and potential value of drone VLC schemes with distinct non-Lambertian LED beam configurations. The core contribution of the study is overcoming the existing limitation of the current rigid Lambertian beam use, and comparatively investigating the performance of drone VLC with non-Lambertian LED beam configurations for future 6G aerial vehicular networks. Objectively, this work opens a novel research dimension and provides a series of valuable research opportunities for the community of drone VLC. Numerical results demonstrate that, for a typical drone VLC scenario, compared with about 6.40 Bits/J/Hz energy efficiency of drone VLC based on the baseline Lambertian LED beam configuration with the same emitted power, up to about 15.64 Bits/J/Hz energy efficiency could be provided by the studied drone VLC with a distinct non-Lambertian LED beam configuration. These results show that the spatial LED beam dimension should be further elaborately explored and utilized to derive more performance improvement of the 6G aerial vehicular networks oriented drone VLC. Full article

Extracellular vesicles (EVs) are lipid bilayer-enclosed particles secreted by cells and ubiquitously present in various biofluids. They not only mediate intercellular communication but also serve as promising drug carriers that are capable of delivering therapeutic agents to target cells through their inherent physicochemical properties. In this review, we summarized the recent advances in EV isolation techniques and innovative drug-loading strategies. Furthermore, we emphasized the distinct advantages and therapeutic applications of EVs derived from different cellular sources in cancer treatment. Finally, we critically evaluated the ongoing clinical trials utilizing EVs for drug delivery and systematically assessed both the opportunities and challenges associated with implementing EV-based drug delivery systems in cancer therapy. Full article

Cognitive sensors are embedded in home appliances and other surrounding devices to create a connected, intelligent environment for providing pervasive and ubiquitous services. These sensors frequently create massive amounts of data with many redundant and repeating bit values. Cognitive sensors are always restricted in resources, and if careful strategy is not applied at the time of deployment, the sensors become disconnected, degrading the system’s performance in terms of energy, reconfiguration, delay, latency, and packet loss. To address these challenges and to establish a connected network, there is always a need for a system to evaluate the contents of detected data values and dynamically switch sensor states based on their function. Here in this article, we propose a reinforcement learning-based mechanism called “Adaptive Scheduling in Cognitive IoT Sensors for Optimizing Network Performance using Reinforcement Learning (ASC-RL)”. For reinforcement learning, the proposed scheme uses three types of parameters: internal parameters (states), environmental parameters (sensing values), and history parameters (energy levels, roles, number of switching states) and derives a function for the state-changing policy. Based on this policy, sensors adjust and adapt to different energy states. These states minimize extensive sensing, reduce costly processing, and lessen frequent communication. The proposed scheme reduces network traffic and optimizes network performance in terms of network energy. The main factors evaluated are joint Gaussian distributions and event correlations, with derived results of signal strengths, noise, prediction accuracy, and energy efficiency with a combined reward score. Through comparative analysis, ASC-RL enhances the overall system’s performance by 3.5% in detection and transition probabilities. The false alarm probabilities are reduced to 25.7%, the transmission success rate is increased by 6.25%, and the energy efficiency and reliability threshold are increased by 35%. Full article

Zooplankton are ubiquitous in aquatic ecosystems and play crucial roles in material cycling and energy flow. However, the mechanisms governing zooplankton community assembly, particularly habitat-specific differences, remain poorly understood. In this two-year study, we monitored zooplankton communities across reservoir and river habitats within the Chayuan watershed, a representative karst region in southwest China. Our findings revealed significant spatial divergence in water-quality variables (including water temperature, pH, total nitrogen, total phosphorus, permanganate index, dissolved oxygen, chlorophyll-a, and ammonia nitrogen) between habitats. Twenty-nine dominant zooplankton species were identified in reservoir and river communities, with only eight shared between the two habitats. The mechanisms underlying the corresponding zooplankton community structures showed distinct segregation between habitats, with deterministic processes predominating in reservoir communities (explaining 25.1% of the variation) and stochastic processes predominating in river communities (3.4% of the variation explained). Environmental drivers differed substantially between habitats: reservoir communities were primarily influenced by total nitrogen, dissolved oxygen, and chlorophyll-a concentrations, whereas river communities responded predominantly to ammonia nitrogen levels. This study provides novel insights into the divergent mechanisms governing zooplankton community assembly in lentic versus lotic systems within a shared karst watershed, offering theoretical foundations for ecosystem-specific management strategies in fragile karst environments. Future research should focus on key climatic variables (e.g., extreme precipitation) and hydrological dynamics (such as flow velocity and water residence time) to further elucidate the mechanisms behind zooplankton community assembly, providing deeper insights to facilitate effective ecosystem management in karst environments. Full article

Listeria species are ubiquitous microorganisms that can be present all over the food chain. They can survive under adverse conditions and are frequently found in food-processing plants. In this study, 19 Listeria innocua and 19 Listeria welshimeri strains were isolated from meat product manufacturing companies in Spain, and biofilm formation capabilities were analyzed. In addition, 37 Listeria monocytogenes strains were also isolated, and their genetic diversity, biofilm formation capabilities, and antibiotic resistance were analyzed too. The species distribution was similar between two food-processing plants in the Basque Country, while it demonstrated significant variation when compared to three other plants from the Valencian Community, Catalonia, and Andalusia. Biofilm formation was significant at both 25 °C and 37 °C, with L. monocytogenes showing strong biofilm formation capabilities. Biofilms enhance the ability of bacteria to persist on surfaces and equipment. Listeria monocytogenes serogroup analysis indicated significant differences between Basque Country strains and those from the other regions, with most strains belonging to serogroups commonly associated with human listeriosis cases. Antibiotic multi-resistance was a common feature among L. monocytogenes strains. The presence of different antibiotic multi-resistance profiles and strong biofilm-forming capabilities highlights the importance of stringent hygiene and monitoring practices to prevent the spread of L. monocytogenes in the food chain and avoid food-safety threats and public-health issues. Full article

Global navigation satellite systems (GNSSs), with their ubiquitous coverage, have become a cornerstone of modern position, navigation, and timing (PNT) services. While their spread spectrum communication offers inherent, albeit partial, resilience against interference, GNSSs remain a prime target for malicious actors seeking to disrupt or degrade precise location and time synchronization. Jamming mitigation has been an active research area for over three decades. Despite diverse research efforts, a key weakness in the literature is the absence of rigorous, methodologically sound testing of proposed mitigation techniques in a controlled laboratory environment. This work addresses this deficiency by exploring the challenges of evaluating GNSS receiver performance and response under interference and by proposing a more robust methodological framework for result interpretation. We present a custom simulation environment that enables repeated, controlled measurements of GNSS receiver behavior under various jamming attacks, revealing discrepancies between expected performance and real-world observations. Using three low-cost receivers as a case study, we demonstrate the inherent uncertainty in the results, the unpredictable behavior of the receivers’ embedded software, and appropriate statistical analysis practices. A key contribution of this work is a publicly available dataset of extensive GNSS receiver response measurements acquired under controlled interference conditions using an advanced signal generation and a comprehensive satellite constellation simulator. Full article