Search Results (463)

The increasing demand for electricity in multi-tenanted residential areas has placed unforeseen strain on sub-transformers, particularly in dense urban environments. This strain compromises overall grid performance and challenges utilities with shifting and rising peak demand periods. This study presents a novel approach to enhance the operation of a virtual power plant (VPP) comprising a microgrid (MG) integrated with renewable energy sources (RESs) and energy storage systems (ESSs). By employing an advanced monitoring and control system, the proposed topology enables efficient energy management and demand-side control within apartment complexes. The system supports controlled electricity distribution, reducing the likelihood of unpredictable demand spikes and alleviating stress on local infrastructure during peak periods. Additionally, the model capitalizes on the large number of tenancies to distribute electricity effectively, leveraging locally available RESs and ESSs behind the sub-transformer. The proposed research provides a systematic framework for managing electricity demand and optimizing resource utilization, contributing to grid reliability and a transition toward a more sustainable, decentralized energy system. Full article

The mesoscopic-scale discrete element method (DEM) modeling approach demonstrated high compatibility with macroporous recycled concrete (MRC). However, existing DEM models failed to adequately balance modeling accuracy and computational efficiency for recycled aggregate (RA), replicate the three distinct interfacial transition zone (ITZ) types and pore structure of MRC, or establish a systematic calibration methodology. In this study, PFC 3D was employed to establish a randomly polyhedral RA composite model and an MRC model. A systematic methodology for parameter testing and calibration was proposed, and compressive test simulations were conducted on the MRC model. The model incorporated all components of MRC, including three types of ITZs, achieving an aggregate volume fraction of 57.7%. Errors in simulating compressive strength and elastic modulus were 3.8% and 18.2%, respectively. Compared to conventional concrete, MRC exhibits larger strain and a steeper post-peak descending portion in stress–strain curves. At peak stress, stress is concentrated in the central region and the surrounding arc-shaped zones. After peak stress, significant localized residual stress persists within specimens; both toughness and toughness retention capacity increase with rising porosity and declining compressive strength. Failure of MRC is dominated by tension rather than shear, with critical bonds determining strength accounting for only 1.4% of the total. The influence ranking of components on compressive strength is as follows: ITZ (new paste–old paste) > ITZ (new paste–natural aggregates) > new paste > old paste > ITZ (old paste–natural aggregates). The Poisson’s ratio of MRC (0.12–0.17) demonstrates a negative correlation with porosity. Predictive formulas for peak strain and elastic modulus of MRC were established, with errors of 2.6% and 3.9%, respectively. Full article

This study investigates the fire dynamics and structural response of steel-framed warehouse racking systems under various fire scenarios, emphasizing the critical importance of fire safety measures in mitigating structural damage. Through advanced computational simulations (Fire Dynamics Simulator) and thermomechanical analysis, this research reveals that fire intensity and progression are highly influenced by the ignition point and the stored material types, with maximum recorded temperatures reaching 720 °C and 970 °C in different scenarios. The results highlight the localization of significant strain and drift ratios in structural elements near the ignition zone, underscoring their vulnerability. This study demonstrates the rapid loss of load-bearing capacity in steel elements at elevated temperatures, leading to severe deformations and increased collapse risks. Key findings emphasize the necessity of strategically positioned sprinkler systems and the integration of passive fire protection measures, such as fire-resistant coatings, to enhance structural resilience. Performance-based fire design approaches, aligning with FEMA-356 criteria, offer realistic frameworks for improving the fire safety of warehouse structures. Full article

Plasmodium vivax malaria continues to pose a significant and enduring public health challenge across the Americas. Transmission-blocking vaccines (TBVs), which target gametocyte surface antigens such as Pvs47 and Pvs48/45, are being investigated as promising tools to interrupt transmission and advance toward disease elimination. To investigate the genetic diversity and phylogeographic structure of the pvs47 and pvs48/45 genes in P. vivax, we conducted molecular analyses on samples collected from seven malaria-endemic regions of Honduras using PCR-based sequencing, population genetics, and phylogenetic approaches. This study presents the first complete characterization of the pvs47 gene and expands the available data on pvs48/45 in P. vivax from Honduras. We observed a low level of genetic diversity with no evidence of geographic structuring within the country. At a global scale, Honduran sequences shared variants with other Latin American strains and exhibited region-specific amino acid signatures. These findings suggest that local selective pressures, possibly driven by mosquito vector compatibility, are shaping the evolution of these TBV candidate genes. Our results underscore the importance of regional surveillance to inform the development and deployment of effective transmission-blocking strategies. Full article

The degradation of edible fungi can lead to a decrease in cultivation yield and economic losses. In this study, a nondestructive detection method for strain degradation based on the fusion of hyperspectral technology and image texture features is presented. Hyperspectral and microscopic image data were acquired from Pleurotus geesteranus strains exhibiting varying degrees of degradation, followed by preprocessing using Savitzky–Golay smoothing (SG), multivariate scattering correction (MSC), and standard normal variate transformation (SNV). Spectral features were extracted by the successive projections algorithm (SPA), competitive adaptive reweighted sampling (CARS), and principal component analysis (PCA), while the texture features were derived using gray-level co-occurrence matrix (GLCM) and local binary pattern (LBP) models. The spectral and texture features were then fused and used to construct a classification model based on convolutional neural networks (CNN). The results showed that combining hyperspectral and image texture features significantly improved the classification accuracy. Among the tested models, the CARS + LBP-CNN configuration achieved the best performance, with an overall accuracy of 95.6% and a kappa coefficient of 0.96. This approach provides a new technical solution for the nondestructive detection of strain degradation in Pleurotus geesteranus. Full article

Hepatitis E virus (HEV) is a pathogen that has caused various epidemics and sporadic localized cases. It is considered to be a public health problem worldwide. HEV is a small RNA virus with a significant genetic diversity, a broad host range, and a heterogeneous geographical distribution. HEV is mainly transmitted via the faecal–oral route. However, some animals are considered to be natural or potential reservoirs of HEV, thus elucidating the zoonotic route of transmission via the environment through contact with these animals or consumption of their by-products. Other routes of human-to-human transmission are not negligible. The various human–animal–environment entities, taken under one health approach, show the circulation and involvement of the different species (mainly Paslahepevirus balayani and Rocahepevirus ratti) and genotypes in the spreading of HEV infection. Regarding P. balayani, eight genotypes have been described, of which five genotypes (HEV-1 to 4 and HEV-7) are known to infect humans, while six have been reported to infect animals (HEV-3 to HEV-8). Furthermore, the C1 genotype of the rat HEV strain (HEV-C1) is known to be more frequently involved in human infections than the HEV-C2 genotype, which is known to infect mainly ferrets and minks. Contamination can occur during run-off, flooding, and poor sanitation, resulting in all of these genotypes being disseminated in the environment, contaminating both humans and animals. This systematic review followed the PRISMA guidelines and was registered in PROSPERO 2025 CRD420251071192. This research highlights the importance of investigating the transmission routes and major circulating HEV genotypes in order to adopt a holistic approach for controlling its emergence and preventing future outbreaks. In addition, this article outlines the knowledge of HEV in Africa, underlining the absence of large-scale studies at the environmental, human, and animal levels, which could improve HEV surveillance on the continent. Full article

The excessive use of antimicrobials drives the emergence of multidrug resistance (MDR) in bacterial strains, which harbor resistance genes to survive under diverse drug pressures. Such resistance can result in life-threatening infections. The predominance of MDR Pseudomonas spp. poses significant challenges to public health and environmental sustainability, particularly in ecosystems affected by human activities. Characterizing MDR Pseudomonas spp. is crucial for developing effective diagnostic tools and biosecurity protocols, with broader implications for managing other pathogenic bacteria. Strains were diagnosed through 16S rRNA PCR and sequencing, complemented by phylogenetic analysis to evaluate local and global evolutionary connections. Antibiotic susceptibility tests revealed extensive resistance across multiple classes, with MIC values surpassing clinical breakpoints. This study examined the genetic diversity, resistance potential, and phylogenetic relationships among Pseudomonas aeruginosa strain DG2 and Pseudomonas fluorescens strain FM3, which were isolated from solid waste dump sites (n = 30) and dairy farms (n = 22) in West Bengal, India. Phylogenetic analysis reveals distinct clusters that highlight significant geographic linkages and genetic variability among the strains. Significant biofilm production under antibiotic exposure markedly increased resistance levels. RAPD-PCR profiling revealed substantial genetic diversity among the isolates, indicating variations in their genetic makeup. In contrast, SDS-PAGE analysis provided insights into the protein expression patterns that are activated by stress, which are closely linked to MDR. This dual approach offers a clearer perspective on their adaptive responses to environmental stressors. This study underscores the need for vigilant monitoring of MDR Pseudomonas spp. in anthropogenically impacted environments to mitigate risks to human and animal health. Surveillance strategies combining phenotypic and molecular approaches are essential to assess the risks posed by resilient pathogens. Solid waste and dairy farm ecosystems emerge as critical reservoirs for the evolution and dissemination of MDR Pseudomonas spp. Full article

A novel approach to estimating the absorbed energy (toughness) in a uniaxial tensile test with only knowledge of the microstructure is presented. The flow behavior of each Dual-Phase (DP) steel grade is predicted using idealized Representative Volume Elements (RVEs) up to uniform elongation. To estimate the flow behavior beyond uniform elongation, the stress-modified fracture strain in a non-local damage model was implemented in Abaqus. Damage parameters were calibrated using Finite Element (FE) simulations of purely ferritic tensile specimens. The damage parameters remained unchanged, except for the coefficient of triaxiality. This coefficient was adjusted based on the average triaxiality of ferrite elements at the instability point of the uniaxially loaded RVEs for each DP steel grade. The proposed approach comprises two steps: micron-sized RVEs to predict the flow behavior up to the point of uniform elongation and the average triaxiality and full-scale tensile-test simulations to predict the rest of the curves. The results show that the damage parameters calibrated for high-strain ferrite effectively estimate the absorbed energy during failure in tension tests. This approach is also geometry-independent; varying the geometry of the tensile specimen, including miniature or notched specimens, still yields predicted absorbed energies that are in good agreement with the experimental results. Full article

Flexible strain sensors, fabricated from high-elongation polymers and conductive filler particles, are proving an essential tool in the study of biomechanics using wearable technology. It has been previously shown that the resistive response of such composites, relative to the amount of conductive filler material, can be reasonably modeled using a standard percolation-type model. Once a certain critical fraction of filler material is reached, a conductive network across the sample is established and resistance rapidly decreases. However, modeling the more subtle resistance changes that occur while deforming the sensors during operation is more nuanced. Conductivity across the network of particles is dominated by tunneling mechanisms at the interfaces between the filler materials. Small changes in strain at these interfaces lead to relatively large, but nevertheless continuous, changes in local resistance. By assigning some arbitrary value of resistance as a dividing line between ‘low’ and ‘high’ resistance, one might model the piezoresistive behavior using a standard percolation model. But such an assumption is likely to lead to low accuracy. Our alternative approach is to divide the range of potential resistance values into several bins (rather than the usual two bins) and apply a relatively novel multi-state percolation theory. The performance of the multi-state percolation model is assessed using a random resistor model that is assumed to provide the ground truth. The model is applied to predict resistance response with both changes in relative amount of conductive filler (i.e., to help design the initial unstrained sensor) and with applied strain (for an operating sensor). We find that a multi-state percolation model captures the behavior of the simulated composite sensor in both cases. The multicomponent percolation theory becomes more accurate with more divisions/bins of the resistance distribution, and we found good agreement with the simulation using between 10 and 20 divisions. Full article

Odontogenic abscesses are a frequent and challenging clinical issue in pet rabbits, often requiring a comprehensive diagnostic and therapeutic approach. This review collates current evidence on the etiology, diagnosis, and treatment of rabbit odontogenic abscesses, with a focus on imaging advances, microbial diversity, and local antimicrobial therapies. Predisposing factors include congenital conformation, inappropriate diet (insufficient abrasiveness, calcium or Vit D deficiencies, etc.), trauma, and neoplasia. Imaging techniques such as CT and cone-beam CT (CBCT) enable early detection and surgical planning, while traditional radiography remains useful in general practice. Treatment includes systemic antibiotics, surgical curettage, and the use of localized delivery systems such as antibiotic-impregnated polymethyl methacrylate (AIPMMA) beads. Adjunctive therapies like Manuka honey are also discussed. Two original heatmaps summarize bacterial prevalence and antimicrobial resistance from six peer-reviewed studies. These visualizations highlight the polymicrobial nature of these infections and the emergence of multidrug-resistant strains. Preventive strategies focus on optimal diet, regular dental checks, and owner education. The review also identifies key gaps in the literature, including the underreporting of anaerobes and lack of standardized treatment protocols. This article aims to support veterinary professionals in delivering evidence-based, individualized care to improve outcomes in rabbits with odontogenic abscesses. Full article

This study addresses the lack of detailed geodetic assessments of crustal strain accumulation along the central Yangsan Fault in southeastern Korea, an area of recognized but insufficiently characterized seismic potential. To tackle this, we applied elastic strain tensor analysis to GNSS data from 33 stations, forming 49 triangular elements across the fault zone. From this, we quantified areal strain (Δ), maximum shear strain (γ_max), and principal stress orientations (θ_p, θ_γ_max) to map spatial deformation heterogeneity. The results identify several high-strain zones, notably Triangle 10 (2.984 µstrain/yr), Triangle 16 (2.325), and Triangle 31 (2.452), with Triangle 16—located at the Yangsan–Ulsan Fault intersection—exhibiting pronounced shear strain and a sharp angular deviation in stress orientation. These findings reveal localized stress reorganization likely caused by fault–fault interaction. Our analysis highlights the capability of GNSS-based strain tensor modeling to detect subtle intraplate deformation. The proposed methodology offers a practical framework for pinpointing structurally sensitive fault segments with elevated seismic risk in otherwise stable continental interiors, supporting more targeted seismic hazard assessment in Korea and other intraplate regions worldwide. Full article

In this contribution, the classical Cauchy first-gradient elastic theory is used to solve the equilibrium problem of a bidimensional (2D) reinforced elastic structure under small displacements and strains. Such a 2D first-gradient continuum is embedded with a reinforcement, which is modeled as a zero-thickness interface endowed with the elastic properties of an extensional Euler–Bernoulli 1D beam. Modeling the reinforcement as an interface eliminates the need for a full geometric representation of the reinforcing bar with finite thickness in the 2D model, and the associated mesh discretization for numerical analysis. Thus, the effects of the 1D beam-like reinforcements are described through proper and generalized boundary conditions prescribed to contiguous continuum regions, deduced from a standard variational approach. The novelty of this work lies in the formulation of an interface model coupling 1D and 2D continua, based on weak formulation and variational derivation, capable of accurately capturing stress distributions without requiring full geometric resolution of the reinforcement. The proposed framework is therefore illustrated by computing, with finite element simulations, the response of the reinforced structural element under uniform bending. Numerical results reveal the presence of jumps for some stress components in the vicinity of the reinforcement tips and demonstrate convergence under mesh refinement. Although the reinforcement beams possess only axial stiffness, they significantly influence the equilibrium configuration by causing a redistribution of stress and enhancing stress transfer throughout the structure. These findings offer a new perspective on the effective modeling of fiber-reinforced structures, which are of significant interest in engineering applications such as micropiles in foundations, fiber-reinforced concrete, and advanced composite materials. In these systems, stress localization and stability play a critical role. Full article

The Asian tiger mosquito Aedes albopictus is a highly invasive species capable of transmitting human pathogens. For population management, the sterile insect technique (SIT) is considered an effective and sustainable alternative to conventional methods, such as insecticides and reducing or eliminating breeding sites. The use of symbiotic bacteria to improve the application of SIT or design combined SIT/incompatible insect technique (IIT) approaches is currently considered. In this context, exploring the microbiota of local mosquito populations is crucial for identifying interesting components. This study employed 16S rRNA sequencing and microbiological methods to characterize the diversity of laboratory and wild Ae. albopictus in Greece. Differences were recorded between wild and lab-reared mosquitoes, with laboratory samples exhibiting higher diversity. Laboratory treatment, sex, and developmental stage also resulted in variations between communities. Populations reared in the same facility developed mostly similar bacterial profiles. Two geographically distant wild populations displayed similar bacterial profiles, characterized by seasonal changes in the relative abundance of Pantoea and Zymobacter. Wolbachia was dominant in most groups (63.7% relative abundance), especially in field-caught mosquitoes. It was identified with two strains, wAlbA (21.5%) and wAlbB (42.2%). Other frequent taxa included Elizabethkingia, Asaia, and Serratia. Blood feeding favored an increase in Serratia abundance. Various Enterobacter, Klebsiella, Aeromonas, and Acinetobacter strains were isolated from larval and adult mosquito extracts and could be further characterized as diet supplements. These findings suggest that the microbiota of local populations is highly variable due to multiple factors. However, they retain core elements shared across populations that may exhibit valuable nutritional or functional roles and could be exploited to improve SIT processes. Full article

The structural health monitoring (SHM) of safety relevant composite components is becoming increasingly relevant as it enables in-service diagnosis and data acquisition capabilities, contributing to the optimization and efficient operation of the overall system and ultimately saving costs and resources. In this field, machine learning (ML) techniques are attracting growing attention due to their capability to recognize complex patterns, making them very suitable for the identification of damages in operating mechanical structures. However, the acquisition of sufficiently large amounts of labeled and representative data from both pristine and damaged structures is very costly. To address this, a ML-based SHM approach is proposed that identifies structural damage using only physics-based synthetic strain data generated from the structure’s numerical finite element model. It employs a semi-supervised anomaly detection approach, trained solely on synthetic pristine data, to identify deviations in experimental data indicating damage. The method is validated on an aircraft spoiler demonstrator made of a composite sandwich panel, instrumented with a strain gauge grid on its surface layer. The results show that the proposed SHM approach accurately classifies damaged and undamaged experimental data, independent of the prevailing load case, solely based on synthetic pristine strain data. It is also able to localize these damages in the form of a confidence area with respect to the sensor grid. This demonstrates the feasibility of using only synthetic pristine data for data-driven SHM of composite aerospace structures. Full article

As an important part of national transportation infrastructure, the operation status of bridges is directly related to transportation safety and social stability. Structural deflection, which reflects the deformation behavior of bridge systems, serves as a key indicator for identifying stiffness degradation and the progression of localized damage. The accurate modeling and forecasting of deflection are thus essential for effective bridge health monitoring and intelligent maintenance. To address the limitations of traditional methods in handling multi-source data fusion and nonlinear temporal dependencies, this study proposes an enhanced iTransformer-based prediction model, termed LDAiT (LSTM Differential Attention iTransformer), which integrates Long Short-Term Memory (LSTM) networks and a differential attention mechanism for high-fidelity deflection prediction under complex working conditions. Firstly, a multi-source heterogeneous time series dataset is constructed based on wireless sensor network (WSN) technology, enabling the real-time acquisition and fusion of key structural response parameters such as deflection, strain, and temperature across critical bridge sections. Secondly, LDAiT enhances the modeling capability of long-term dependence through the introduction of LSTM and combines with the differential attention mechanism to improve the precision of response to the local dynamic changes in disturbance. Finally, experimental validation is carried out based on the measured data of Xintian Yellow River Bridge, and the results show that LDAiT outperforms the existing mainstream models in the indexes of R², RMSE, MAE, and MAPE and has good accuracy, stability and generalization ability. The proposed approach offers a novel and effective framework for deflection forecasting in complex bridge systems and holds significant potential for practical deployment in structural health monitoring and intelligent decision-making applications. Full article