Search Results (1,588)

Vietnam faces a hyperendemic burden of hepatitis B virus (HBV) infection, but the prevalence of occult HBV infection (OBI) and its underlying molecular mechanisms in healthy populations remain poorly understood. This study aimed to characterize the serological and molecular HBV profile of a healthy Vietnamese adult cohort in Southern Vietnam. We assessed the prevalence of occult HBV infection (OBI) and HBsAg-positivity (serving as a proxy for probable chronic infection). In this cross-sectional study, 397 healthy adults from Southern Vietnam underwent serological screening for HBsAg, anti-HBs, and anti-HBc. All participants were screened for HBV DNA using a high-sensitivity PCR assay (LOD ≥ 5 IU/mL). For all viremic cases, the full Pre-S/S region was sequenced to determine genotype and characterize escape mutations. We uncovered a high prevalence of both HBsAg-positivity (17.6%) and OBI (9.3% HBsAg-negative, HBV DNA-positive). Serological analysis revealed a massive, age-dependent reservoir of past exposure (63.7% anti-HBc) characterized by a high and increasing prevalence of the anti-HBc only profile (31.5%), a key serological marker for OBI. This trend contrasted sharply with a steep age-related decline in protective anti-HBs. The viral landscape was dominated by genotypes B (73.8%) and C (26.2%), with sub-genotypes B4 and C1 being the most prevalent. Critically, individuals with OBI carried a significantly higher burden of S gene escape mutations compared to those with HBsAg-positivity (p < 0.001). Canonical escape variants, including sG145R (21.6%), sK141R/T/E/Q (24.3%), and sT116N/A/I/S (18.9%), were exclusively or highly enriched in the OBI group. A LASSO-logistic model based on this mutational profile successfully predicted occult infection with high accuracy (AUC = 0.83). A substantial hidden reservoir of occult HBV infection exists within the healthy adult population of Vietnam, driven by a high burden of S gene escape mutations. These findings highlight the significant limitations of conventional HBsAg-only screening. They also underscore the need for comprehensive molecular surveillance to address the true scope of HBV viremia, hopefully enabling a reduction in hidden transmission of clinically significant viral variants. Full article

Gammaretroviruses are ubiquitous pathogens, often associated with the induction of neoplasia, especially leukemia, lymphoma, and sarcoma, and with a propensity to target the germline. The latter trait has left extensive evidence of their infectious competence in vertebrate genomes, the human genome being no exception. Despite the continuing activity of gammaretroviruses in mammals, including Old World monkeys, apes, and gibbons, humans have apparently evaded novel infections by the virus class for the past 30 million years or so. Nevertheless, from the 1970s onward, cell culture studies repeatedly discovered gammaretroviral components and/or virus replication in human samples. The last novel ‘human’ gammaretrovirus, identified in prostate cancer tissue, culminated in the XMRV frenzy of the 2000s. In the end, that discovery was shown to be due to lab contamination with a murine gammaretrovirus. Contamination is also the likely source of the earlier findings. Complementation between genes of partially defective endogenous proviruses could have been another source of the virions observed. However, the capacity of many gammaretroviruses to replicate in human cell lines, as well as the presence of diverse infectious gammaretroviral species in our animal companions, for instance in mice, cats, pigs, monkeys, chickens, and bats, does not make a transmission to humans an improbable scenario. This review will summarize evidence for, or the lack of, gammaretrovirus infections in humans in the past, present, and near future. Aspects linked to the probabilities of novel gammaretrovirus infections in humans, regarding exposure risk in connection to modern lifestyle, geography, diet, and habitat, together with genetic and immune factors, will also be part of the review, as will be the estimated consequences of such novel infections. Full article

Modern 5G+grade low power wide area network (LPWAN) technologies such as Narrowband Internet-of-Things (NB-IoT) operate utilizing a multi-channel slotted ALOHA algorithm at the random access phase. As a result, the random access phase in such systems is characterized by relatively low throughput and is highly sensitive to traffic fluctuations that could lead the system outside of its stable operational regime. Although theoretical results specifying the optimal transmission probability that maximizes the successful preamble transmission probability are well known, the lack of knowledge about the current offered traffic load at the BS makes the problem of maintaining the optimal throughput a challenging task. In this paper, we propose and analyze a new reactive access-barring scheme for NB+IoT systems based on machine learning (ML) techniques. Specifically, we first demonstrate that knowing the number of user equipments (UE) experiencing a collision at the BS is sufficient to make conclusions about the current offered traffic load. Then, we show that through utilizing ML-based techniques, one can safely differentiate between events in the Physical Random Access Channel (PRACH) at the base station (BS) side based on only the signal-to-noise ratio (SNR). Finally, we mathematically characterize the delay experienced under the proposed reactive access-barring technique. In our numerical results, we show that by utilizing modern neural network approaches, such as the XGBoost classifier, one can precisely differentiate between events on the PRACH channel with accuracy reaching 0.98 and then associate it with the number of user equipment (UE) competing at the random access phase. Our simulation results show that the proposed approach can keep the successful preamble transmission probability constant at approximately 0.3 in overloaded conditions, when for conventional NB-IoT access, this value is less than 0.05. The proposed scheme achieves near-optimal throughput in multi-channel ALOHA by employing dynamic traffic awareness to adjust the non-unit transmission probability. This proactive congestion control ensures a controlled and bounded delay, preventing latency from exceeding the system’s maximum load capacity. Full article

Valley-dependent topological physics offers a promising avenue for designing nanoscale devices based on gapless single-layer graphene. To demonstrate this potential, we investigate an electrical bias-controlled topological discontinuity in valley polarization within a two-segment armchair nanoribbon of gapless single-layer graphene. This discontinuity is created at the interface by applying opposite in-plane, transverse electrical biases to the two segments. An efficient tight-binding theoretical formulation is developed to calculate electron states in the structure. In a reference configuration, we obtain energy eigenvalues and probability distributions that feature interface-confined electron eigenstates induced by the topological discontinuity. Moreover, to elucidate the implications of interface confinement for electron transport, a modified configuration is introduced to transform the eigenstates into transport-active, quasi-localized ones. We show that such states result in Fano “anti-resonances” in transmission spectra. The resilience of these quasi-localized states and their associated Fano fingerprints is examined with respect to fluctuations. Finally, a proof-of-concept band-stop electron energy filter is presented, highlighting the potential of this confinement mechanism and, more broadly, valley-dependent topological physics in designing nanoscale devices in gapless single-layer graphene. Full article

This paper investigates the planning problem of AC-integrated wind–photovoltaic–hydro–storage (WPHS) bundled transmission systems. To effectively capture the uncertainty and interdependence in renewable power outputs, a Copula-enhanced distributionally robust optimization (DRO) framework is developed, enabling a unified treatment of stochastic and correlated renewable generation within the system planning process. First, a location and capacity planning model based on DRO for WPHS generation bases is formulated, in which a composite-norm ambiguity set is constructed to describe the uncertainty of renewable resources. Second, the Copula function is employed to characterize the nonlinear dependence structure between wind and photovoltaic (PV) power outputs, providing representative scenarios and initial probability distribution (PD) support for the construction of a bivariate ambiguity set that embeds coupling information. The resulting optimization problem is solved using the column and constraint generation (C&CG) algorithm. In addition, an evaluation metric termed the transmission corridor utilization rate (TCUR) is proposed to quantitatively assess the efficiency of external AC transmission planning schemes, offering a new perspective for the evaluation of regional power transmission strategies. Finally, simulation results validate that the proposed model achieves superior performance in terms of system economic efficiency and TCUR. Full article

Brazilian spotted fever (BSF) is a tick-borne acute febrile disease that can be lethal to humans, caused by the bacterium Rickettsia rickettsii. In the State of São Paulo, transmission occurs mainly through two tick species: Amblyomma sculptum and Amblyomma aureolatum. We analyzed trends in BSF incidence and mortality in relation to the spatial distribution of these vector species in the State of São Paulo from 2007 to 2017 and evaluated clinical outcomes according to hospitalization location. In A. sculptum areas, incidence and mortality showed significant increasing trends between 2007 and 2015 (p-value < 0.05). In contrast, A. aureolatum areas exhibited a significant decrease in incidence (p-value < 0.05), while mortality remained stable throughout the study period. Lethality was substantially higher in cases associated with A. aureolatum than in those linked to A. sculptum (67.1% versus 55.0%, p-value = 0.037). Most patients received care in hospitals located near the probable site of infection. Incidence and mortality patterns differed sharply between vector-specific areas, with notably higher mortality in A. aureolatum-related cases. These findings highlight the importance of incorporating vector distribution into surveillance, prevention, and clinical management strategies to better address the distinct epidemiological contexts within the State of São Paulo. Full article

An experimental investigation was conducted to evaluate the statistical properties and scintillation mitigation performance of multi-aperture free-space optical transmission under real-measured atmospheric turbulence. Continuous monitoring of turbulence parameters over a 24 h period showed that the atmospheric coherence length ranged from 3 to 29 cm, indicating that the experimental link operated predominantly under weak-to-moderate turbulence conditions, while a limited number of measurement intervals exhibited relatively strong scintillation and were included for statistical modelling analysis. An 865 m four-channel receiving link was constructed under the measured turbulence conditions to acquire irradiance data for analysis. The results show that the multi-aperture reception significantly suppresses scintillation, reducing the scintillation index from 0.36 to 0.04 under moderate turbulence. The irradiance probability density functions were fitted using lognormal, Gamma–Gamma, exponentiated Weibull, and Málaga (M) distributions. The M distribution exhibited superior adaptability, with fitting accuracy improved by 18.75% under weak turbulence and 13.16% under moderate turbulence. Further analysis shows that the shape parameters of the M distribution vary systematically with turbulence strength, effectively capturing the turbulence-induced evolution of irradiance statistics and providing experimental support for turbulence channel modelling and the optimisation of FSO diversity reception architectures. Full article

Acoustic telemetry monitoring for tagged sharks in nearshore waters has become an important tool for beach safety management; however, detection performance can vary widely in shallow, high-energy nearshore environments where management decisions are often most time-sensitive. Real-time acoustic telemetry buoys offer the potential to deliver live detections and system diagnostics, but their performance relative to autonomous bottom-mounted receivers remains poorly evaluated under realistic coastal conditions. We compared the detection efficiency of real-time buoy-mounted acoustic receivers and autonomous bottom-mounted receivers across five nearshore sites in southern California. Using paired long-term reference tag deployments and short-term range tests, we quantified detection probability, effective detection range, and the influence of environmental conditions and receiver placement. Detection performance was evaluated in relation to wind speed, water temperature, receiver tilt, and signal-to-noise ratio. Both buoy-mounted and bottom-mounted receivers maintained high long-term detection efficiency, recovering 77–99% of expected transmissions at 82–250 m. Range tests indicated greater effective detection distances for buoy-mounted receivers, with 50% detection probabilities occurring at approximately 471 m compared to 282 m for bottom-mounted receivers. Receiver placement strongly influenced performance, with surface-mounted receivers outperforming bottom-mounted units regardless of receiver model. Environmental effects on detections were site-specific and variable. Detection probability varied predictably with environmental conditions. Higher SNR increased detection success, particularly for bottom/substrate mounted receivers, while warm water significantly reduced detection probability across placement configuration. These results demonstrate that real-time acoustic telemetry buoys provide reliable detection performance in dynamic nearshore environments while offering key operational advantages, including immediate data access and system diagnostics. The observed relationships demonstrate that receiver performance is dynamic rather than fixed, and that real-time buoy systems therefore represent a practical tool for coastal monitoring programs that require timely information to support adaptive management, public safety, and conservation decision making. Full article

Mobile Ad Hoc Networks (MANETs) exhibit diverse deployment forms, such as unmanned swarms, mobile wireless sensor networks (MWSNs), and Vehicular Ad Hoc Networks (VANETs). While providing significant social application value, MANETs also face the challenge of accurately and efficiently evaluating connectivity reliability. Building on existing studies—which mostly rely on the assumptions of imperfect nodes and perfect links—this paper comprehensively considers link communication quality and channel capacity, and extends the imperfect link assumption to analyze and evaluate the connectivity reliability of MANETs. The Couzin-leader model is used to characterize the ordered swarm movement of MANETs, while various probability models are employed to depict the multiple actual failure modes of network nodes. Additionally, the Free-Space-Two-Ray Ground (FS-TRG) model is introduced to quantify link quality and reliability, and the probability of successful routing path information transmission is derived under the condition that channel capacity follows a truncated normal distribution. Finally, a simulation-based algorithm for solving the connectivity reliability of MANETs is proposed, which comprehensively considers node characteristics and link states. Simulation experiments are conducted using MATLAB R2023b to verify the effectiveness and validity of the proposed algorithm. Furthermore, the distinct impacts of link communication quality and channel capacity on the connectivity reliability of MANETs are identified, particularly in terms of transmission quality and network lifetime. Full article

The explosive growth of optical interconnection service traffic urgently necessitates the evolution of optical transponders and fibers. The core-division-multiplexed (CDM) transmission technique using weakly coupled multi-core fibers (MCFs) and beyond-1T optical transport network (OTN) transponders has emerged as an attractive solution to meet the bandwidth demands of future networks. In this paper, we demonstrate an ultra-high-speed OTN system using C+L-band 1.2 Tb/s OTN transponders with a weakly coupled seven-core fiber. The OTN transponders support two configurable modulation rates of 135 Gbaud and 155 Gbaud, along with a probability constellation-shaping 64-ary quadrature amplitude modulation (PCS-64QAM) format. The MCF exhibits characteristics comparable to those of SMFs and negligible inter-core crosstalk, providing a superior physical channel for ultra-high-speed CDM transmission. Fiber length and low-noise EDFAs are also chosen to enhance the transmission distance under the limited optical signal-to-noise ratio (OSNR) budget when using 1.2 Tb/s OTN transponders. Benefiting from the high-performance OTN transponders and MCF, we achieve real-time 0.672 Pb/s and 0.571 Pb/s 4 × 89 km CDM transmissions using 135 Gbaud and 155 Gbaud modulation rates, respectively. The performance of the two modulation configurations is also compared and discussed. This work demonstrates the feasibility of implementing 1.2 Tb/s OTN transponders with weakly coupled MCFs to achieve ultra-high-speed metro–regional transmission, presenting a promising solution for next-generation inter-city data center interconnections. Full article

In recent years, the complexity and suddenness of infectious disease transmission have posed significant limitations for traditional time-series forecasting methods when dealing with the nonlinearity, non-stationarity, and multi-peak distributions of epidemic scale variations. To address this challenge, this paper proposes a forecasting framework based on diffusion models, called CNL-Diff, aimed at tackling the prediction challenges in complex dynamics, nonlinearity, and non-stationary distributions. Traditional epidemic forecasting models often rely on fixed linear assumptions, which limit their ability to accurately predict the incidence scale of infectious diseases. The CNL-Diff framework integrates a forward–backward consistent conditioning mechanism and nonlinear data transformations, enabling it to capture the intricate temporal and feature dependencies inherent in epidemic data. The results show that this method outperforms baseline models in metrics such as Mean Absolute Error (MAE), Continuous Ranked Probability Score (CRPS), and Prediction Interval Coverage Probability (PICP). This study demonstrates the potential of diffusion models in complex-distribution time-series modeling, providing a more reliable probabilistic forecasting tool for public health monitoring, epidemic early warning, and risk decision making. Full article

The Internet of Vehicles (IoV) can transmit the status information of vehicles and roads through single-hop or multi-hop broadcast communication, which is a key technology for building intelligent transportation systems and enhancing road safety. However, in dense traffic environments, broadcasting Emergency messages via vehicles can easily trigger massive forwarding redundancy, leading to channel resource selection conflicts between vehicles and affecting the reliability of inter-vehicle communication. This paper analyzes the forwarding near the single-hop transmission radius boundary of the sending node in a probability-based inter-vehicle multi-hop forwarding scheme, pointing out the existence of the boundary forwarding redundancy problem. To address this problem, this paper proposes two probability-based schemes with boundary optimization: (1) By optimizing the forwarding probability distribution outside the transmission radius boundary of the sending node, the forwarding nodes outside the boundary can be effectively utilized while effectively reducing the forwarding redundancy they bring. (2) Additional forwarding backoff timers are allocated to nodes outside the transmission radius boundary of the sending node based on the distance to further reduce the forwarding redundancy outside the boundary. Experimental results show that, compared with the reference schemes without boundary forwarding probability optimization, the proposed schemes significantly reduce forwarding redundancy of Emergency messages while maintaining good single-hop and multi-hop transmission performance. When the reference transmission radius is 300 m and the vehicle density is 0.18 veh/m, compared with the probability-based forwarding scheme without boundary optimization, the proposed schemes (1) and (2) improve the single-hop packet delivery ratio by an average of about 5.41% and 11.83% and reduce the multi-hop forwarding ratio by about 18.07% and 36.07%, respectively. Full article

Seasonal influenza continues to pose a substantial and recurrent public health challenge in Queensland, driven by annual variability in transmission and uncertainty in climatic, demographic, and behavioural determinants. Predictive modelling is constrained by data limitations and parameter uncertainty. In response, this study developed a Bayesian network (BN) model to estimate the probability of influenza epidemics in Queensland, Australia. The model integrated diverse inputs, including international and local influenza surveillance data, demographic health statistics, and expert and stakeholder insights to capture the complex multifactorial causal relationships underlying epidemic risk. Scenario-based simulations revealed that Southeast Asian viral origin, severe global influenza seasons, peak season timing, increasing international travel, absence of control measures, and low immunisation rates substantially elevate the likelihood of influenza epidemics. Southeast Queensland was identified as particularly vulnerable under high-risk conditions. Model evaluation demonstrated good discriminative performance (AUC = 0.6974, accuracy = 70%) with appropriate uncertainty quantification through credible intervals and sensitivity analysis. Its modular design and capacity for integrating various data sources make it a practical decision-making support tool for public health preparedness and responding to evolving climatic and epidemiological conditions. Full article

The integration of low Earth orbit (LEO) satellites and unmanned aerial vehicles (UAVs) into the maritime Internet of Things (MIoT) offers an effective solution to overcoming the limitations of connectivity and transmission reliability in conventional MIoT, thereby supporting marine data acquisition. However, the highly dynamic ocean environment necessitates a theoretical framework for system-level performance evaluation before practical deployment. In this article, we consider a LEO satellite and UAV-assisted MIoT (LSU-MIoT) network and develop an analytical framework to evaluate its transmission performance. Specifically, marine devices and relaying buoys are modeled as a Matérn cluster process on the sea surface, UAVs as a homogeneous Poisson point process, and LEO satellites as a spherical Poisson point process. Signal transmissions over marine, aerial, and space links are characterized by Nakagami-m, Rician, and shadowed Rician fading, respectively, with the two-ray path loss model applied to sea and air links for accurately capturing propagation characteristics. By leveraging stochastic geometry, we derive analytical expressions for transmission success probability and end-to-end delay of regular and emergency data under the time division multiple access and non-orthogonal multiple access schemes. Simulation results validate the accuracy of derived expressions and reveal the impact of key parameters on the performance of LSU-MIoT networks. Full article

This study examines the impact of extreme weather events on electrical transmission lines, with a particular focus on high-impact, low-probability (HILP) meteorological conditions. Investigating how these conditions affect transmission lines and the potential effects of power outages is crucial for the reliability and continuity of electrical grids. The study conducts a comprehensive review of the literature on the effects of extreme weather events on electrical grids. Specifically, it categorizes and analyzes faults occurring on transmission lines caused by high-impact, low-probability meteorological conditions such as storms, hurricanes, and ice storms. Identifying and classifying these faults is a fundamental step in enhancing the reliability of power systems. Another focus of the study is examining various strategies to prevent power outages, including probabilistic modeling and resilience enhancement technologies. Solutions such as the development of advanced warning systems, design modifications to enhance the physical resilience of transmission lines, and emergency response plans have the potential to increase the reliability of electrical grids. In conclusion, the findings of this study contribute significantly to understanding the impact of HILP meteorological conditions on electrical transmission lines and identifying measures to enhance the reliability of electrical grids. The results of this study can provide valuable guidance to planners, engineers, and decision-makers in the energy sector. Full article