Search Results (879)

Picornaviruses pose a significant threat to both human and animal health, causing many diseases in humans and swine. Porcine sapelovirus (PSV) is a globally reported enteric picornavirus commonly associated with subclinical or mild enteric infections in swine populations. Critically, unlike other neurotropic picornaviruses such as Enterovirus A71, no PSV strain has been reported to breach the blood–brain barrier (BBB). Since 2023, outbreaks of diarrhea with concurrent neurological signs like ataxia and lameness have been observed in weaned piglets across China, in particular, on a farm in Zhejiang province in 2025 with 100% morbidity and 20% mortality among the cases. Routine diagnostics ruled out common swine pathogens, but qPCR was positive for PSV. We successfully isolated three PSV strains (ZJ, FJ, SD) from affected piglets. Genetic analysis revealed that the PSV-ZJ is a novel recombinant between strains YC2011/2012 (China, 2011) and XTND/2019 (Vietnam, 2018). Pathogenicity assessment confirmed that the recombinant PSV-ZJ is highly pathogenic, causing severe diarrhea, growth retardation, and significant viral shedding via the respiratory and digestive tracts. qPCR and histopathology confirmed viral presence in intestinal and brain tissues, indicating that PSV-ZJ can cross the blood–brain barrier. This study presents the first quantitative viral load and histopathological data for a highly pathogenic recombinant PSV strain in China and emphasizes the critical role of recombination in driving viral virulence evolution, necessitating enhanced surveillance and the development of targeted preventive strategies. Full article

Andes virus (ANDV) is the only hantavirus with well-documented evidence of person-to-person transmission. However, key parameters related to transmission timing, viral shedding, exposure contexts, and public health management remain incompletely defined. We conducted a systematic review in accordance with PRISMA 2020. MEDLINE/PubMed, Scopus, and Web of Science were searched from database inception up to 14 May 2026. Eligible studies reported epidemiological, virological, clinical, or public health data relevant to ANDV infection, person-to-person transmission, viral shedding, and/or outbreak control. Thirty-three studies, including 17,204 individuals, 2221 laboratory-confirmed ANDV cases, and 135 documented secondary cases, were included. Person-to-person transmission was identified as a primary or co-occurring route in 20 papers. The median incubation period among ANDV cases was 20.8 days, and the median serial interval was 21.8 days (upper bounds near 40 days). Secondary attack rates were higher among sexual and other close contacts. ANDV RNA was consistently detected in blood and occasionally in saliva, respiratory secretions, urine, breast milk, and semen, although RNA detection alone does not necessarily imply infectious virus. Rare reports of culture-confirmed isolation of replication-competent virus support the biological plausibility of transmission via close mucosal or respiratory exposure. Unlike other hantaviruses, Andes virus can spread person to person through close contact, supporting prolonged monitoring and risk-stratified follow-up of high-risk contacts based on ANDV-specific epidemiological evidence. Possible recommendations, including post-discharge counselling regarding possible sexual transmission, remain provisional and require further evidence. Preparedness activities against outbreaks should also be implemented in non-endemic regions, while future research should prioritize prospective contact studies, standardized virological sampling, and genomic confirmation. Full article

The worldwide shift to electric mobility has intensified in recent years owing to heightened apprehensions over greenhouse gas emissions, energy security, and the necessity for sustainable transportation systems. Electric vehicles (EVs) are acknowledged as a viable alternative for diminishing reliance on fossil fuels and enhancing energy efficiency in the transportation sector. While affluent nations have achieved considerable advancements in electric vehicle adoption and charging infrastructure, numerous developing countries still encounter significant technical and infrastructural obstacles that hinder extensive EV integration. In South Africa, these difficulties are exacerbated by ongoing electrical supply limitations, deteriorating transmission and distribution facilities, and recurrent load shedding, which heighten worries about the dependability and stability of the national power grid. The rising adoption of electric vehicles adds extra electrical demands to power systems, especially at the distribution network level, where most of the charging takes place. Disorganized EV charging can substantially modify current load patterns, leading to heightened peak demand, voltage variations, transformer overload, and network congestion. The technical consequences are especially significant in South Africa, where the power grid functions with constricted generation capacity and minimal reserve margins. Various mitigating measures have been suggested to tackle these difficulties, including intelligent charging, demand-side management, time-of-use pricing, and vehicle-to-grid technologies. This paper establishes a basic theoretical framework through an extensive literature review to investigate the technological problems related to electric vehicle adoption in South Africa, while assessing the environmental and economic ramifications for sustainable urban transportation systems. Full article

Recurring load-shedding and persistent power system disruptions in South Africa have intensified the need for reliable data-driven assessment of electricity supply dynamics. Addressing this challenge requires comprehensive and well-structured datasets that capture the key operational characteristics of the electricity system. This paper presents a dataset on load-shedding and power system operations in South Africa, developed to support time series modelling and electricity reliability studies. The dataset comprises hourly observations obtained from the Electricity Supply Commission (Eskom) data portal covering the period from July 2018 to June 2023. It contains key electricity system variables, including load-shedding frequency, contracted demand, dispatchable generation, thermal generation, renewable energy generation, electricity imports, and planned and unplanned capability loss factors. The response variable, load-shedding, was pre-processed (discretised) to construct structured data suitable for count time series and machine learning to analyse temporal patterns, seasonality, and electricity supply disruptions. In addition, selected variables were combined to provide comprehensive measures of planned and unplanned capability reductions within the electricity system. The dataset provides a valuable resource for load-shedding analysis, reliability assessment, forecasting, energy planning, and policy development in South Africa. Full article

Against the background of China’s dual carbon goals, high-renewable-power systems suffer severe resilience threats from destructive ice disasters, and existing recovery approaches fail to fully exploit multi-type flexible resources with unsatisfying computational efficiency. Targeting this gap, this work establishes a resilience enhancement framework for ice-affected power grids. This model quantifies line failure probability considering time-varying ice thickness and wind load, generates representative fault scenarios via sequential Monte Carlo and K-means clustering, and innovatively incorporates mobile energy storage systems (MESSs) and low-temperature-corrected PEM electrolyzers into coordinated post-fault dispatch; an improved parrot optimization (PO) algorithm with Chebyshev chaos, random mutation and adaptive t-distribution is designed to boost solving efficiency. Tested on the IEEE 39-bus system, the proposed method reduces average load shedding to 3.7% and raises renewable accommodation to 95.6%, outperforming fixed energy storage and literature-based strategies by cutting load curtailment by 45.6% and 30.2% respectively, while multi-condition sensitivity analyses validate its stable applicability under varying disaster intensity and renewable penetration. This coordinated scheduling strategy supplies feasible technical support for practical anti-icing resilience promotion of new-type power grids. Full article

Loading fluctuations cause structural responses such as deformations and vibrations on the propeller. Structural response of propellers results in vibrations on the shaft system or even the hull. Considering the demand for structural safety, the correlation between structural response of propellers and inflow conditions is numerically studied in the present paper. The interaction between the propeller and turbulence structures and vortex shedding from upstream structures is considered. Loading fluctuations on the propeller blade are obtained by a turbulence model of improved delayed detached eddy simulations (IDDESs). The deformations and vibrations of propeller blades fixed at their roots are captured considering fluid–structure interaction. Results show that the loading fluctuations and vibrations on the propeller contain tonal components occurring at harmonics of shaft frequency and broadband components. Inhomogeneous inflow amplifies pressure fluctuations as a product of space frequency and shaft frequency (SF). Inhomogeneous inflow also results in more intense fluctuations of velocity in the tip vortex at SF and blade wake at blade passing frequency and encounter frequency. As a result of loading fluctuations, the vibration of the blade is a superposition of excited vibrations and natural vibrations. Inhomogeneous inflow amplifies the vibrations at the encounter frequency. Resonance of the blade can be observed when the excited frequency approaches the first natural frequency. Full article

Islanded renewable microgrids must balance power internally, so day-ahead dispatch is affected by wind and photovoltaic variability, battery state-of-charge (SOC) dynamics, demand-response (DR) participation, and emissions from dispatchable generation. This paper proposes a low-carbon economic dispatch model for an islanded photovoltaic–wind-turbine–battery-energy-storage–dispatchable-generator–demand-response (PV-WT-BESS-DG-DR) microgrid. The objective includes fuel, operation and maintenance, BESS degradation, renewable curtailment, load shedding, DR compensation, and carbon-emission costs. A repair-based constraint-handling strategy keeps the search space continuous while enforcing power balance, DG ramping, BESS operating and SOC limits, terminal SOC, and DR constraints. An improved whale optimization algorithm (WOA) is then developed with three modules: diversity enhancement, exploration–exploitation balancing, and local escape and refinement. The method is assessed through base-case dispatch, benchmark comparison, strategy comparison, ablation tests, and sensitivity analysis. In 30 independent runs, the proposed method achieves a mean cost of 2662.96 CNY/day, 4.07% lower than standard WOA, and reduces the standard deviation by 79.72%. Wilcoxon and Friedman tests confirm significant differences from the benchmark algorithms. Sensitivity tests show that higher BESS degradation coefficients and carbon prices increase the accounting cost but do not change the qualitative feasibility of the deterministic dispatch framework. Full article

Medium- and long-term dispatching of renewable energy bases is an important method for ensuring large-scale transmission and consumption. However, most existing medium- and long-term dispatching methods ignore the uncertainties of wind and photovoltaic power output, resulting in excessive maintenance-window margins and insufficient regulation reserves. However, relevant studies that consider such uncertainties are mostly limited to short-term scheduling and are therefore inadequate for medium- and long-term dispatching needs. To this end, a two-stage robust optimal dispatch method for renewable energy bases that considers the impacts of wind and photovoltaic output uncertainties and unit maintenance is proposed. Firstly, the first stage decision variables consist of the on/off and maintenance statuses of thermal power units. Next, the output of each power source is taken as the conventional decision variables in the second stage, while the curtailed wind/photovoltaic power and load shedding are taken as the unconventional decision variables when the balance cannot be achieved by adjusting the power source output under the given wind and solar power output scenarios. In the end, a polyhedron set based on an uncertainty budget was adopted to describe the fluctuations in wind and photovoltaic output, and the minimum scheduling cost in the worst scenarios was solved using the column and constraint algorithm. A renewable energy base in Northwest China was selected as a case to validate the proposed model’s effectiveness. The results show that the proposed model significantly reduces the operating cost in actual operation compared to deterministic optimization and pre-maintenance robust optimization. Full article

Protein phosphatase PPM1A plays a critical role in cellular signaling by dephosphorylating key regulatory proteins. According to experimental data, the enzyme requires either Mn²⁺ or Mg²⁺ bound in the active center(s), hence its catalytic activity strongly depends on the chelated metal ions. In this study, the metal ion selectivity of PPM1A is investigated using DFT calculations on active site constructs of bi- and trinuclear metal centers and protein ligands from the first and second metal coordination shells. Binuclear Mn-Mn and trinuclear Mn-Mn-Mn sites show poor resistance to substitution by biogenic Fe²⁺ and Zn²⁺, with Gibbs energies of the Mn²⁺ → Fe²⁺/Zn²⁺ exchange being consistently negative in both the gas phase and condensed media. In contrast, Mg-Mg and Mg-Mg-Mg centers are substantially more robust, with a thermodynamically unfavorable Mg²⁺ → Fe²⁺/Zn²⁺ substitution—except in the case of the Mg-Mg-Zn complex. The primary factors governing this metal competition in the modeled structures are the nature of the competing cation and the solvation properties of its aqua complexes, while solvent exposure of the binding site and the number of metal cations in the catalytic center exert a comparatively minor effect. Overall, these findings demonstrate that Mg²⁺-loaded active sites offer considerably greater protection against biogenic metal displacement than their Mn²⁺ counterparts, thus shedding light on the metalloprotein stability and enzyme fidelity of PPM1A. Full article

With the large-scale integration of electric vehicles (EVs), charging demand exhibits significant spatiotemporal variability, further intensified by climatic factors, which makes it difficult for existing uncertainty models to capture underlying dependency structures. To address this issue, this paper proposes a Copula–Wasserstein-based distributionally robust optimization (C-WDRO) framework for the coordinated operation of distribution networks and charging stations. A climate-sensitive physical mapping model of electric vehicle energy consumption is first developed to establish a coupled climate–energy–load mechanism. Copula functions are then used to characterize dependencies among temperature, precipitation, and charging demand, and are incorporated into a bi-level optimization formulation. The model is solved using Karush–Kuhn–Tucker (KKT) conditions and a column-and-constraint generation (C&CG) algorithm. Case studies on the IEEE 33-bus system show that the proposed method reduces total operating cost by 4.26% compared with robust optimization (RO), while maintaining economic efficiency, and reduces the load shedding rate by 0.14 percentage points compared with Wasserstein distributionally robust optimization (WDRO), while keeping voltage security. These results demonstrate that explicitly modeling dependency structures can enhance operational efficiency and support more sustainable and reliable power–transportation system operation under uncertainty. Full article

With the increasing penetration of distributed renewable energy, distribution networks face severe operational challenges during grid faults, where rapid power restoration and system stability are crucial. Traditional fault restoration strategies often rely on static dynamic zoning or simple power balancing, neglecting the critical electrical interactions among nodes. To address these limitations, this paper innovatively proposes a hierarchical coordinated control framework for distribution network fault recovery, combining dynamic zoning and coordinated load shedding. The core novelty of this research lies in integrating the node electrical correlation degree into the load grading process to assist in coordinating dynamic network dynamic zoning. By comprehensively evaluating real-time power flow, the regulation capabilities of distributed resources, and intra-region electrical correlations, the proposed framework adaptively optimizes both the zoning structure and the load shedding sequence. Simulation results demonstrate that, compared with conventional static or uncoordinated methods, the proposed approach significantly minimizes load loss while improving grid recovery efficiency and voltage stability. Ultimately, this coordinated control strategy effectively enhances the resilience and operational safety of high-penetration renewable distribution networks, providing robust support for distribution network operations under a high proportion of renewable energy integration. Full article

Brucellosis remains a significant public health and economic burden in many regions, primarily transmitted from livestock to humans through direct contact and environmental contamination. In this paper, we develop a novel cross-species epidemic model that couples the transmission dynamics of brucellosis among sheep, humans, and the environmental reservoir of Brucella. The sheep population is divided into susceptible, exposed, infectious, and vaccinated compartments, while the human population is stratified into susceptible and infected classes. Environmental brucella load is explicitly modeled, and distributed time delays are incorporated to account for incubation periods and delayed exposure risks in humans. We prove that all solutions are non-negative and ultimately bounded, ensuring biological consistency. The basic reproduction number ${\mathcal{R}}_0$ is derived using the next-generation matrix method. Using Lyapunov functionals and LaSalle’s invariance principle, we establish that the disease-free equilibrium is globally asymptotically stable when ${\mathcal{R}}_0\le 1$, whereas a unique endemic equilibrium exists and is globally asymptotically stable when ${\mathcal{R}}_0>1$. Sensitivity analysis identifies the environmental transmission rate, shedding rate, and disinfection as the most influential parameters. Treatment efficacy is shown to exhibit a critical threshold $p^{\mathrm{cr}}=1-1/{\mathcal{R}}_0$, above which eradication becomes feasible. Numerical simulations validate the theoretical findings and demonstrate that time delays affect outbreak timing but not asymptotic stability. These results provide quantitative guidance for brucellosis control strategies, emphasizing environmental sanitation, culling, and vaccination as key interventions. Full article

There are significant difficulties with power quality and stability as a result of active cooperation between renewable energy sources and load demand. To maintain power stability between renewable energy supplies and the microgrid/utility grid, novel solutions must be implemented. By using an artificial and computational intelligence controller to schedule power from multiple sources (photovoltaic, wind, grid, and battery) under a set of constraints, such as weather, load-shedding hours, and peak pricing hours, this paper introduces a novel approach for power management in grid-connected hybrid renewable systems with PV–wind and energy storage systems. The approach involves using an artificial neural network (ANN) to process all of the inputs and creating an ANN rule set from a modelled hybrid renewable system. A rule-based power scheduler is developed, and simulations are run for a full day. The suggested fuzzy control approach can detect ongoing variations in grid load-shedding patterns, PV–wind power generation, load demands, and battery state-of-charge to enable prompt and accurate decision-making. The proposed ANN rule-based scheduler can handle nonlinearity by integrating metaheuristics into computer-assisted decision-making and can function effectively with imprecise inputs, negating the need for an exact numerical model. The MATLAB/Simulink R2023a software was used for simulation, and the system operated as efficiently as possible. The simulation results suggested that an ANN offers a foundation for extension to handle numerous particular scenarios. Full article

Modern distribution networks face dual challenges: extremely asymmetric spatial power flows caused by the high-penetration integration of distributed renewables under normal operating conditions and asymmetric system faults triggered by extreme weather such as blizzards under extreme conditions. To address these imbalances, this paper integrates distributed energy storage (DES) and soft open points (SOPs) as flexible resources to propose a two-stage joint optimal planning method that balances operational economy and resilience enhancement. First, by incorporating the spatiotemporal evolution trajectory and distance attenuation effects of blizzards, a multi-dimensional scenario sets characterizing asymmetric faults and normal source-load fluctuations are constructed. Second, a joint optimal planning model minimizing the total lifecycle cost is established. The progressive hedging algorithm is then adopted to decouple cross-scenario variables for efficient parallel solving. Verified on both the IEEE 33-node and large-scale 123-node systems, the coordinated planning strategy effectively avoids redundant investment in a single type of device. By establishing a symmetrical balance of flexible resources, the proposed method significantly reduces network losses and renewable curtailment during normal operation, while minimizing the amount of system load shedding under extreme asymmetric faults. Full article

Background/Objectives: Cryptosporidium parvum (C. parvum), a waterborne intestinal parasite, causes severe, persistent infections in immunocompromised hosts and has been linked to the onset of ileocecal adenocarcinoma. However, the molecular pathways linking chronic infection to carcinogenesis remain unclear. Nitazoxanide (NTZ), the only FDA-approved drug for this infection, shows limited efficacy. In contrast, azithromycin (AZM) possesses both antiparasitic and anticancer activity, though conclusive evidence supporting its effectiveness against cryptosporidiosis is still lacking. This study aimed to investigate the therapeutic potential of AZM against chronic cryptosporidiosis and its associated tumorigenic sequelae. Methods: Immunosuppressed mice were infected with C. parvum and treated with NTZ or AZM. Parasite burden was assessed by quantifying fecal oocyst shedding. Ileocecal tissues were analyzed for histopathology, inflammation (IL-6 and TNF-α), autophagy markers (LC3II, Beclin-1, and Atg7), PI3K/AKT signaling, and apoptotic markers (Bcl2, Bax, cleaved caspase-3, DR4, and DR5) using ELISA, real-time PCR, and Western blot. Results: Chronic C. parvum infection induced Vienna 4.4 adenocarcinoma, activated autophagy and PI3K/AKT signaling, and suppressed intrinsic and TRAIL-mediated apoptosis. AZM significantly reduced the parasitic load by 87%, outperforming NTZ (62%). It also restored epithelial integrity, attenuated inflammation, and counteracted pro-tumorigenic effects by inhibiting autophagy, downregulating the PI3K/AKT pathway, and stimulating apoptosis. Conclusions: AZM counteracted parasite-driven tumorigenic mechanisms by disrupting survival pathways and promoting apoptosis in infected and transformed cells. These findings provide evidence that AZM exerts dual antiparasitic effects and counteracts pro-tumorigenic signaling in chronic cryptosporidiosis, highlighting its potential as a therapeutic agent to prevent infection-associated ileocecal carcinogenesis. Full article