Search Results (4,116)

Satellites supporting IoT connectivity may need to serve a large population of LoRa terminals, where collisions among packets using the same spreading factor (SF) can severely degrade uplink reliability. The ALOHA-based access used in LEO-IoT leads to frequent collisions under massive terminal access, which limits system capacity. Conventional signal separation methods that rely on the capture effect typically require a sufficiently large power difference between colliding signals. However, due to the channel characteristics of LEO links, this condition is often difficult to satisfy. We propose ConvLoRa, a collision demodulation method for co-SF LoRa uplink signals in LEO-IoT based on a fully convolutional neural network (FCN). To improve robustness to synchronization deviations, ConvLoRa uses an up-chirp in the preamble as a reference for feature matching, and employs data augmentation to emulate synchronization deviations during training. In addition, a multi-task design is adopted to estimate the payload length with minimal introduction of extra network parameters. Experiments show that ConvLoRa achieves lower demodulation bit error rate (BER) under collision conditions compared with baselines, including CoRa and SIC-based receivers. Under the condition of a two-signal collision with SNR = −9 dB and SF = 8, the BER of the proposed method is 21% that of CoRa and 28% that of the SIC-based method. Full article

In the early stages of the COVID-19 pandemic, uncertainty around the extent of SARS-CoV-2 spread hampered policymakers’ understanding of the epidemic’s extent. Mathematical models, which proved vital for aiding decision-making, relied primarily on reported cases that were unreliable due to significant underdetection and underreporting. While serological data was used to improve understanding of the epidemiology, it can be costly and difficult to implement without bias. To counter these issues, we integrated serological data from 7229 remnant serum samples collected in 15 Maryland emergency departments (EDs) in Maryland between August and December 2020 into a Bayesian modeling approach to derive an estimate of the incidence of infection and the case fatality rate during the pandemic’s initial wave. We estimated that 5.2% (95% CI, 3.7–7.2%) of the population of Maryland had been infected by late fall 2020. The inferred reporting rate that was estimated started low (<10% in March 2020) and increased to 32% (95% HDI = 26–41%) by the fall, while the estimated infection fatality rate was likely initially higher but fell to 0.51% (95% HDI = 0.43–0.68%) after 1 September 2020. These results demonstrate how existing ED infrastructure can be leveraged to generate less biased, more accurate estimates of the true prevalence of a disease, improving the ability to make decisions and allocate resources under uncertainty. Full article

This study examines partial differential equation models with delay on both unbounded and bounded domains. The mathematical model is reformulated as a system of nonlinear integral equations. The primary objective is to investigate the long-time behavior of solutions to nonlinear integral equations and to compare these findings with those from the partial differential equation model under varying growth rates. The theory of asymptotic spreading speed is employed to achieve this objective. The existence and uniqueness of solutions to the nonlinear integral equations are demonstrated. Minimal wave speeds are calculated for the model with bounded and unbounded domains, considering different growth rates and time delays. Numerical experiments are conducted to validate the theoretical results. Full article

Oropouche fever (OF), caused by Oropouche virus (OROV), has expanded beyond its Amazonian range into Minas Gerais (MG), Brazil, raising concern about transmission in extra-Amazonian Atlantic Forest landscapes. Critical gaps persist regarding Culicoides vector communities, anthropophily, and climate-sensitive transmission risk in these newly affected regions. We conducted targeted entomological surveys outbreak-driven by human OF cases, standardized across five MG communities using CDC light traps and Protected Human Attraction (PHA) to characterize Culicoides composition. Females of Culicoides underwent RT-qPCR for OROV (n = 819) and physiological assessment (n = 312). We developed an entomological alert framework that integrates blood-fed abundance, minimum infection rate (MIR) upper confidence bounds, and environmental drivers (i.e., mean temperature, relative humidity and precipitation) via generalized additive mixed models, which explained 68% of the variability in Culicoides abundance and the alert index across communities. We collected 1171 Culicoides individuals representing five species (C. leopoldoi, C. paraensis, C. pusillus, C. foxi, and C. limai). C. leopoldoi (79.1%) and C. paraensis (20.3%) were the predominant species; notably, C. paraensis is recognized as the primary vector of OROV in the Americas. C. paraensis was documented for the first time in all five outbreak areas and dominated PHA captures (90%), suggesting anthropophily. Although no specimens tested OROV-positive (consistent with expected field infection rates of 0.01–1%), MIR upper bounds reached 132/1000 in low-sample settings and humidity and temperature strongly modulated abundance. This operational baseline and alert index transform virologically negative, sparse surveillance data into prioritized targets for intensified sampling and vector control during early, low-prevalence phases, when containment of OROV’s extra-Amazonian spread is still achievable. Full article

Cellular automata-based wildfire simulation models are widely used to support fire management, risk assessment, and operational decision-making, due to their efficiency and computational advantages. However, the accuracy of these models heavily depends on the quality of input data provided by the user, including the composition and geospatial extend of forest fuels, current meteorological conditions and terrain information. This publication examines how quantitative and spatial input data uncertainties affect the estimates of the impacted areas. Using a series of simulation experiments, inaccurate data are introduced to specific input variables (such as the vegetation type and the fuel moisture content) to reflect realistic levels of uncertainty commonly observed in operational scenarios, where users with different cognitive backgrounds fail to properly identify key characteristics of a fire. Model outputs are then compared using spatial and temporal performance metrics, including the rate of spread and burned area extent. The results demonstrate that uncertainties in fuel models and meteorological inputs exert a dominant influence on simulated fire behavior. Our findings highlight the sensitivity of wildfire simulations to compounded input uncertainties and stress the need for improved in-field data acquisition strategies. Full article

The adoption of electric vehicles (EVs) has posed new challenges to fire safety, especially when multiple EVs are transported on electric trailers, as limited studies exist on heavy electric vehicle transportation and little research has been conducted on fire development during EV tunnel transport. The aim of this study is to investigate the temperature, smoke, and tenability conditions produced by an electric trailer transporting eight EVs, where a fire initiates and spreads to all eight EVs, under two scenarios: natural ventilation and longitudinal tunnel ventilation. The Fire Dynamics Simulator (FDS) was used, and the combined peak heat release rate (HRR) of the vehicles was found to exceed 76 MW. Air temperatures around the fire source exceeded 1100 °C, while temperatures above 950 °C were recorded at the tunnel ceiling. The simulations captured thermal behaviour, smoke propagation, and the accumulation of carbon dioxide (CO₂) and carbon monoxide (CO). Longitudinal ventilation was shown to reduce upstream smoke spread and help maintain tenable conditions for evacuation and emergency response. These findings raise critical safety concerns regarding EV transportation in tunnels and support improved decision-making for tunnel infrastructure design and emergency responders. Full article

Electrochemiluminescence immunoassay (ECLIA) is widely used in clinical diagnostics owing to its high sensitivity, broad dynamic range, and excellent analytical stability. However, the influence of magnetic bead deposition behavior on electrochemiluminescence (ECL) signal performance remains insufficiently characterized. In this study, a quantitative evaluation method for magnetic bead distribution uniformity on the electrode surface was established and applied to optimize fluidic parameters in an ECLIA measurement system. By combining microscopic imaging with image analysis, magnetic bead spreading behavior under different flow conditions was systematically characterized and correlated with luminescence signal intensity. Optimization of the flow rate (18.46 µL·s⁻¹) improved bead distribution uniformity and resulted in a 26.32% increase in luminescence intensity without altering bead coverage or assay chemistry. The optimized system was further validated using thyroid-stimulating hormone (TSH) detection, showing a linear response over 0.016–120 µIU·mL⁻¹ (R² > 0.996) and high consistency with a commercial analyzer (R² = 0.998) from Roche. These results demonstrate that quantitative control of magnetic bead distribution provides an effective strategy for improving ECLIA performance and offers a general optimization framework for bead-based electrochemiluminescence systems. Full article

An experimental study was conducted to quantitatively assess the separate effects of smoke-layer height and temperature on fire spread along a cable tray in a compartment. Smoke-layer height was controlled by varying the opening height (h) using side-wall configurations (SW0%, SW25%, and SW50%), while smoke-layer temperature was adjusted by changing the heat release rate (HRR) of an LPG burner (10, 14, and 18 kW). Fire spread was quantified using flame imaging and measurements of HRR, fire growth and spread rates, incident heat flux at tray height, and gas temperature and O₂ concentration above and below the tray. At 10 kW, self-extinction occurred before the flame reached the tray end for all side-wall configurations. At 14 and 18 kW, fire spread to the tray end occurred under SW25% and SW50%. For a given HRR, SW50% produced higher heat flux and temperature near the tray but lower oxygen concentration, especially below the tray. These findings indicate that cable tray fire spread is governed by the combined effects of smoke-layer height and temperature through thermal feedback and local oxygen availability. Fire spread was promoted by stronger thermal feedback, but could be limited under a deeper smoke layer when oxygen availability near the tray was reduced. Full article

Background/Objectives: Proton therapy has emerged as an advanced radiotherapy modality due to its unique physical dose distribution and its distinct radiobiological properties. The finite range of protons in tissue enables highly conformal dose delivery with minimal exit dose, significantly reducing irradiation of surrounding normal tissues compared to photon-based radiotherapy. Beyond these physical advantages, proton beams exhibit a spatially varying linear energy transfer that increases toward the distal edge of the spread-out Bragg peak, leading to clustered and complex DNA damage that is more difficult for cancer cells to repair. Methods: This review integrates experimental, computational, and clinical evidence to examine how proton-induced DNA damage, relative biological effectiveness, oxygen effects, and non-targeted responses contribute to tumor control and normal tissue sparing. Results: Comparative analyses with photon intensity-modulated radiotherapy demonstrate consistent reductions in acute and late toxicities across multiple tumor sites, particularly in pediatric patients and in tumors located near critical organs. The review also discusses emerging technologies, including pencil beam scanning, image-guided and adaptive proton therapy, compact accelerator systems, and ultra-high dose rate FLASH proton therapy, which collectively aim to enhance treatment precision, biological effectiveness, and accessibility. Conclusions: Together, these developments support proton therapy as a rapidly evolving modality with significant potential to improve therapeutic outcomes in modern oncology. Full article

Recently, numerous countries have experienced devastating wildfires, leading to significant destruction and loss of life. These catastrophic events highlight the shortcomings in current building regulations and testing methods. There is a pressing need for a more profound understanding of the characteristics and behaviour of large outdoor fires to address these inadequacies effectively. Wildfires can spread to structures located at the wildland–urban interface, leading to further fire propagation from one building to another. In this study, the Fire Dynamics Simulator (FDS) model was validated using experimental data from the National Institute of Standards and Technology (NIST). The experiment consisted of a target wall and a small wooden shed containing six wooden cribs as fuel, with a separation distance of 3 m. Both FDS and the experiment proved that 3 m is the safe separation distance. Different shed materials, such as steel, were used, which reduced the total heat release rate by 40% and the flame height by 20%. The effects of wind speed and direction were investigated using two wooden sheds in FDS to observe fire spread between them. The safe separation distance was 3 m for both wind speeds (2 and 5 m/s) in all directions, where the critical temperature was not reached to cause self-ignition of the second shed, except in the north direction (inward) at a speed of 5 m/s. When the separation distance increased to 3.5 m, the average heat flux at the other shed reduced to 3.18 kW/m², which did not cause self-ignition. Therefore, the safe separation distance between two structures for a wind speed of 5 m/s should be 3.5 m to mitigate the spread of fire based on the shed dimensions and the fire source load. Full article

Carbapenem-resistant Enterobacter cloacae Complex (CRECC) has emerged as an important multidrug-resistant pathogen in healthcare settings, although it has historically received less attention than carbapenem-resistant Klebsiella pneumoniae and other major carbapenem-resistant Enterobacterales (CRE). Recent epidemiological reports from several regions indicate increasing detection rates of CRECC in tertiary hospitals, where it is associated with bloodstream infections, pneumonia, urinary tract infections, and prolonged hospitalization. The dissemination of carbapenemase genes, particularly bla_NDM, bla_KPC, and bla_OXA-48-like, carried predominantly on conjugative plasmids (e.g., IncFII, IncX3, IncL), represents the primary resistance mechanism, often accompanied by porin loss and efflux pump overexpression. High-risk clones such as ST171 and ST78 contribute to nosocomial persistence and outbreak potential. Beyond clinical settings, CRECC and related resistance determinants have been reported in companion animals, livestock, food products, wastewater systems, and natural aquatic environments. Although most available studies examine these sectors separately, the recurring detection of genetically related resistance genes and plasmid types suggests potential epidemiological links that warrant integrated surveillance. Environmental reservoirs, particularly hospital effluents and wastewater treatment systems, may facilitate the maintenance and dissemination of resistance genes. This review synthesizes current evidence on the epidemiology, resistance mechanisms, and evolutionary dynamics of CRECC in human, animal, and environmental contexts under a One Health framework. A better understanding of its ecological distribution and genetic plasticity is essential to inform coordinated surveillance strategies and mitigate the public health risks associated with the continued spread of carbapenem resistance. Full article

Background/Objective: Acinetobacter baumannii is an opportunistic pathogen responsible for various healthcare-associated infections, particularly in critically ill patients. The emergence and rapid spread of multidrug- and extensively drug-resistant strains, notably carbapenem-resistant A. baumannii (CRAB), threaten global health. We aimed to investigate the clonal distribution, antimicrobial resistance profiles, and resistance determinants of CRAB bloodstream isolates in Korean hospitals to identify emerging high-risk clones and their potential clinical impact. Methods: Sequence types (STs) were determined using the Oxford multilocus sequence typing scheme, and antimicrobial susceptibility profiles and resistance determinants were evaluated. Results: We analyzed 812 CRAB bloodstream isolates collected from nine South Korean tertiary hospitals between 2016 and 2020. The isolates were classified into 39 STs, with ST191 (n = 245) and ST369 (n = 192) being the most prevalent. Between 2016 and 2020, ST369 increased from 2.6% to 37.9%, while ST195, first detected in 2018 (0.5%), increased to 19.0%; however, ST191 declined from 45.2% to 19.0%. Most CRAB infections were hospital-acquired (91.6%, 744 of 812), predominantly affecting men aged ≥51 years, particularly the 71–80-year-olds. Resistance rates were ≥80% for ampicillin-sulbactam and ciprofloxacin. bla_OXA-23 was detected in 807 isolates, confirming its central role in carbapenem resistance. ST195 exhibited higher resistance to minocycline (29.4%) than did the other STs. Conclusions: Dynamic clonal shifts and high antimicrobial resistance exist among CRAB isolates in Korean hospitals, with the rapid emergence of ST195 and ST369 increasing clinical challenges. Continuous epidemiological surveillance and targeted infection control measures are essential to control the spread of high-risk CRAB clones. Full article

Cassava mosaic begomoviruses are a major threat to cassava cultivation in Africa. The virulent Ugandan variant of the East African cassava mosaic virus (EACMV-Ug), which caused substantial damage to cassava production in Uganda in the 1990s and which was previously confined to East and Central Africa, was recently found to be well established in Guinea and Sierra Leone in West Africa. Molecular analysis of cassava leaf samples from a nationwide cassava fields survey conducted in Côte d’Ivoire in 2022 suggested the absence of EACMV-Ug in the country in 2022. Given the proximity of some confirmed EACMV-Ug infected locations in Guinea to Côte d’Ivoire, we conducted another survey in 2025 along the entire western border of Côte d’Ivoire, bordering Guinea and Liberia, to update the status of EACMV-Ug in the country. Molecular analysis of the leaf samples collected confirmed the presence of EACMV-Ug in Côte d’Ivoire for the first time, along with other begomoviruses. The infection rate of EACMV-Ug along the Liberian border was higher (28.85%) than the 17.07% observed along the Guinean border. African cassava mosaic virus (ACMV) and East African cassava mosaic Cameroon virus (EACMCMV) were detected both as a single infection and in double co-infections (ACMV+EACMCMV) in some plants, whereas EACMV-Ug was found as a double co-infection (EACMCMV+EACMV-Ug) and as a triple co-infection (ACMV+EACMCMV+EACMV-Ug). Our results also show that all the cassava varieties grown in the surveyed locations were susceptible to EACMV-Ug. Epidemiological assessment of cassava fields revealed that the incidence and severity of cassava mosaic disease (CMD) were significantly higher along the Liberian border compared to the Guinean border. However, whitefly populations were relatively low across the entire area surveyed. Furthermore, we found that the spread of CMD in the survey area was mainly through the use of infected cassava cuttings for the establishment of new farms. Based on these results, it is imperative to conduct an urgent nationwide cassava fields survey to assess the extent of EACMV-Ug spread in Côte d’Ivoire and implement containment measures to stop further spread. Full article

In this paper, high-strength high-nitrogen steel Cr₁₈Mn₁₅ was fabricated using centrifugal casting. High-temperature tensile tests were subsequently performed on the centrifugally cast material. Based on the dynamic material model (DMM), power dissipation and instability maps were constructed for the steel. The results revealed that the optimal processing conditions for Cr₁₈Mn₁₅ are within a temperature range of 940 °C to 980 °C and a strain rate range of 0.001 s⁻¹ to 0.01 s⁻¹. Flow instability was observed primarily under high strain rate conditions (1 s⁻¹) at a lower temperature of 900 °C. Four constitutive equation models were established based on the experimental results, and the prediction accuracy was assessed by calculating their average absolute relative errors (AAREs) and correlation coefficients (r). It was found that the Modified-JC constitutive model could simultaneously take care of both accuracy and simulation convergence with an AARE of 17.823 and Pearson’s correlation coefficient (PCC) of 0.968. For the practical application of Cr₁₈Mn₁₅ high-nitrogen steel, a three-layer composite tube forming and a continuous rolling equipment were developed. The rolling and spreading process was simulated using finite elements, and the stress field, strain field, and temperature field in the spreading process were analyzed to determine the following optimum process parameters of the alloy: a temperature of 950 °C, a processing line speed of 1 m/s, and a preheating temperature of 200 °C. Full article

Integrating Joint Source–Channel Coding (JSCC) with the LoRa Chirp Spread Spectrum (CSS) physical layer (PHY) presents a significant challenge due to the complexity of joint optimization, which remains underexplored despite the known advantages of JSCC. Traditional LoRa systems rely on decoupled source and channel coding, resulting in redundant overhead and limited adaptability under dynamic Wireless Body Area Network (WBAN) conditions. To address these limitations, we propose a novel LoRa–JSCC framework: a fully learned, end-to-end differentiable architecture that jointly optimizes source compression and channel redundancy. The proposed system integrates a Denoising Autoencoder (DAE) for non-linear source compression with learned neural channel encoder and decoder modules, trained via backpropagation to minimize reconstruction distortion under noisy channel conditions. Rigorous Monte Carlo simulations conducted under unified and reproducible channel conditions demonstrate consistent performance improvements across LoRa configurations. The proposed approach achieves an average 25–30% improvement in goodput across moderate-to-high SNR regimes, with gains exceeding 100% under noise-limited conditions. It further reduces Time on Air (ToA) by approximately 30–35%, enhancing spectral efficiency and lowering effective energy cost per delivered bit. In the transitional Bit Error Rate (BER) region, the proposed LoRa–JSCC framework exhibits an effective SNR gain of approximately 18–20 dB relative to conventional LoRa, corresponding to multiple orders-of-magnitude reduction in BER. These results indicate substantial improvements in reliability, coverage robustness, and energy efficiency for WBAN and IoT deployments. Full article