Search Results (742)

Antimicrobial resistance is a global health issue, and the WHO has made significant efforts in the development of tools for its monitoring. However, such tools are underutilized, due to limited knowledge, technical capacity, and scarcity of economic resources. AMR surveillance can be conducted using WHOnet and R, two free-of-charge software tools widely adopted in both clinical practice and scientific research. WHOnet is designed for managing laboratory data and antimicrobial susceptibility test results, while R is a programming language dedicated to statistical computing and data visualization. The combined use of these tools enables a reproducible workflow for retrospective AMR trend analysis. This paper provides step-by-step instructions on how to perform such analysis and also provides the respective R code. The described code and software results are shown using real-world data from an Italian hospital as an example. The standardization of the analysis process and the rapid availability of data on antimicrobial resistance are critical for both clinicians and public health professionals. They would allow for empirical decisions on antimicrobial treatment based on the specific epidemiological characteristics of the hospital or community setting. Full article

Grid-based distribution networks represent an advanced form of smart grids that enable modular, region-specific optimization of power resource allocation. This paper presents a novel planning framework aimed at the coordinated deployment of distributed generation, electrical loads, and energy storage systems, including both dispatchable and non-dispatchable electric vehicles. A three-dimensional objective system is constructed, incorporating investment cost, reliability metrics, and network loss indicators, forming a comprehensive multi-objective optimization model. To solve this complex planning problem, an improved version of the NSGA-II is employed, integrating hybrid encoding, feasibility constraints, and fuzzy decision-making for enhanced solution quality. The proposed method is applied to the IEEE 33-bus distribution system to validate its practicality. Simulation results demonstrate that the framework effectively addresses key challenges in modern distribution networks, including renewable intermittency, dynamic load variation, resource coordination, and computational tractability. It significantly enhances system operational efficiency and electric vehicles charging flexibility under varying conditions. In the IEEE 33-bus test, the coordinated optimization (Scheme 4) reduced the expected load loss from 100 × 10⁻⁴ yuan to 51 × 10⁻⁴ yuan. Network losses also dropped from 2.7 × 10⁻⁴ yuan to 2.5 × 10⁻⁴ yuan. The findings highlight the model’s capability to balance economic investment and reliability, offering a robust solution for future intelligent distribution network planning and integrated energy resource management. Full article

The development of the Connected and Autonomous Vehicle (CAV) and Hybrid Electric Vehicle (HEV) provides a new effective means for the optimization of eco-driving strategies. However, the existing research has not effectively considered the cooperative speed optimization and power allocation problem of the Connected and Autonomous Plug-in Hybrid Electric Vehicle (CAPHEV) platoon. To this end, a hierarchical eco-driving strategy is proposed, which aims to enhance driving efficiency and fuel economy while ensuring the safety and comfort of the platoon. Firstly, an improved car-following model is proposed, which considers the motion states of multiple preceding vehicles. On this basis, a platoon cooperative car-following decision-making method based on model predictive control is designed. Secondly, a distributed energy management strategy is constructed, and a bionic optimization algorithm based on the behavior of nutcrackers is introduced to solve nonlinear problems, so as to solve the energy distribution and management problems of powertrain systems. Finally, the tests are conducted under the driving cycle of the Urban Dynamometer Driving Schedule (UDDS) and the Highway Fuel Economy Test (HWFET). The results show that the proposed strategy can ensure the driving safety of the CAPHEV platoon in different scenes, and has excellent tracking accuracy and driving comfort. Compared with the rule-based strategy, the equivalent energy consumption of UDDS and HWFET is reduced by 20.7% and 5.5% in the battery’s healthy charging range, respectively. Full article

To address the complex trade-offs among charging efficiency, battery lifespan, energy efficiency, and safety in high-power electric vehicle (EV) fast charging, this paper presents an intelligent control framework based on contrastive learning and manifold-constrained multi-objective optimization. A multi-physics coupled electro-thermal-chemical model is formulated as a Mixed-Integer Nonlinear Programming (MINLP) problem, incorporating both continuous and discrete decision variables—such as charging power and cooling modes—into a unified optimization framework. An environment-adaptive optimization strategy is also developed. To enhance learning efficiency and policy safety, a contrastive learning–enhanced policy gradient (CLPG) algorithm is proposed to distinguish between high-quality and unsafe charging trajectories. A manifold-aware action generation network (MAN) is further introduced to enforce dynamic safety constraints under varying environmental and battery conditions. Simulation results demonstrate that the proposed framework reduces charging time to 18.3 min—47.7% faster than the conventional CC–CV method—while achieving 96.2% energy efficiency, 99.7% capacity retention, and zero safety violations. The framework also exhibits strong adaptability across wide temperature (−20 °C to 45 °C) and aging (SOH down to 70%) conditions, with real-time inference speed (6.76 ms) satisfying deployment requirements. This study provides a safe, efficient, and adaptive solution for intelligent high-power EV fast-charging. Full article

Against the backdrop of global emissions reduction and transportation electrification, electric vehicles are gradually replacing traditional fuel vehicles for delivery. However, issues such as limited range and charging times often conflict with time window service requirements. To balance economic and environmental performance, mixed fleets and multi-method charging strategies have emerged as viable approaches. This study addresses the problem by developing a mixed-integer programming model that incorporates multiple charging methods and carbon emission accounting. An Improved Adaptive Large Neighborhood Search (IALNS) algorithm is proposed, featuring multiple Removal and Insertion operators tailored for customers and charging stations, along with two local optimization operators. The algorithm’s superiority and applicability are validated through simulation and comparative analysis on benchmark instances and real-world data from an urban courier network. Sensitivity analysis further demonstrates that the proposed algorithm effectively coordinates vehicle type and charging mode selection, reducing total costs and carbon emissions while ensuring service quality. This approach provides practical reference value for operational decision-making in mixed fleet delivery. Full article

In recent years, frequent typhoon-induced disasters have significantly increased the risk of power grid outages, posing severe challenges to the secure and stable operation of distribution grids with high penetration of distributed photovoltaic (PV) systems. Furthermore, during post-disaster recovery, the dual uncertainties of distributed PV output and the charging/discharging behavior of flexible resources such as electric vehicles (EVs) complicate the configuration and scheduling of mobile energy storage systems (MESS). To address these challenges, this paper proposes a two-stage robust optimization framework for dynamic recovery of distribution grids: Firstly, a multi-stage decision framework is developed, incorporating MESS site selection, network reconfiguration, and resource scheduling. Secondly, a spatiotemporal coupling model is designed to integrate the dynamic dispatch behavior of MESS with the temporal and spatial evolution of disaster scenarios, enabling dynamic path planning. Finally, a nested column-and-constraint generation (NC&CG) algorithm is employed to address the uncertainties in PV output intervals and EV demand fluctuations. Simulations on the IEEE 33-node system demonstrate that the proposed method improves grid resilience and economic efficiency while reducing operational risks. Full article

Three-wheelers (3Ws—autos or auto-rickshaws) occupy a unique yet salient and substantive position within the context of India’s urban mobility. They provide critical first-and-last-mile connectivity, fill public transit coverage gaps, boost local and urban agglomeration economies, and are a major income source for millions. Their value and utility are especially pronounced in rapidly emerging Tier-II cities such as Amritsar. The city’s 7500-strong diesel 3W (d3W) fleet is the backbone of its transportation network but also contributes to air pollution. Though Amritsar’s favorable policies to transition the d3W fleet to electric (e3W) have reduced purchase costs by 40–60%, barriers remain. This study investigates the influence of the e3W user experience through a first-of-a-kind three-day pilot trial for ~300 d3W drivers. By leveraging a pre- and post-intervention framework combining surveys and trip diaries, this study evaluated how direct exposure influences adoption intentions, perceptions, and the social dynamics underpinning decision-making. In total, ~6% of participants switched to e3Ws following the trial, and there was a 20% drop in “don’t know” answers regarding charging duration and range. The results show non-random and meaningful shifts in attitudes, a greater awareness of range and charging times, improved views on charging convenience and vehicle safety, and air quality benefits. Full article

In fresh produce cold chain last-mile delivery, the highly dispersed customer base leads to exorbitant delivery costs, posing the greatest challenge for cold chain enterprises. Achieving a symmetrical balance between cost-efficiency, environmental sustainability, and service quality is a fundamental pursuit in logistics system optimization. This paper proposes integrating the crowd-shipping logistics model—characterized by internet platform sharing and flexibility—into the delivery service. It incorporates and extends features such as cold chain delivery, mixed fleets using gasoline and diesel vehicles (GDVs), electric vehicles (EVs), partial charging strategies for EVs, and time-of-use electricity pricing into the crowd-shipping model. A joint delivery mode combining traditional professional delivery (using GDVs and EVs) with crowd-shipping is proposed, creating a symmetrical collaboration between centralized fleet management and distributed social resources. The challenges associated with utilizing occasional drivers (ODs) are analyzed, along with the corresponding compensation decisions and allocation-related constraints. A route optimization model is constructed with the objective of minimizing total cost. To solve this model, an Improved Whale Optimization Algorithm (IWOA) is proposed. To further enhance the algorithm’s performance, an adaptive variable neighborhood search is embedded within the proposed algorithm, and four local search operators are applied. Using a case study of 100 customer nodes, the joint delivery mode with OD participation reduces total delivery costs by an average of 24.94% compared to the traditional professional vehicle delivery mode, demonstrating a more symmetrical allocation of logistical resources. The experiments fully demonstrate the effectiveness of the joint delivery model and the proposed algorithm. Full article

Discrimination between normal (fresh/non-frozen) and frozen-thawed beef is crucial for ensuring food safety. This paper proposed a novel, non-destructive and real-time you only look once for normal and frozen-thawed beef discrimination (YOLO-NF) model using deep learning techniques. The simple, parameter-free attention module (SimAM) and the squeeze and excitation (SE) attention mechanism were introduced to enhance the model’s performance. A total of 1200 beef samples were used, with their images captured by a charge-coupled device (CCD) camera. In the model development, specifically, the training set comprised 3888 images after data augmentation, while the validation set and test set each included 216 original images. Experimental results on the test set showed that the YOLO-NF model achieved precision, recall, F1-Score and mean average precision (mAP) of 95.5%, 95.2%, 95.3% and 98.6%, respectively, significantly outperforming YOLOv7, YOLOv5 and YOLOv8 models. Additionally, gradient-weighted class activation mapping (Grad-CAM) was adopted to interpret the model’s decision basis. Moreover, the model was deployed on the web interface for user convenience, and the discrimination time on the local server was 0.94 s per image, satisfying the requirements for real-time processing. This study provides a promising technique for high-performance and rapid meat quality assessment in food safety monitoring systems. Full article

A thermodynamically consistent model framework to describe ion transport in nanopores is presented. The continuum model unifies electro-diffusion and selective ion transport and extends the classical Poisson–Nernst–Planck (PNP) system for an idealized incompressible mixture by including finite ion size and solvation effects. Special emphasis is placed on the consistent modeling of the selectivity filter within the pore. It is treated as an embedded domain in which the constituents can change their chemical properties and mobility. Using this framework, we achieve good agreement with an experimentally observed current–voltage (IV) characteristic for an L-type selective calcium ion channel for a range of ion concentrations. In particular, we show that the model captures the experimentally observed anomalous mole fraction effect (AMFE). As a result, we find that calcium and sodium currents depend on the surface charge in the selectivity filter, the mobility of ions and the available space in the channel. Our results show that negative charges within the pore have a decisive influence on the selectivity of divalent over monovalent ions, supporting the view that AMFE can emerge from competition and binding effects in a multi-ion environment. Furthermore, the flexibility of the model allows its application in a wide range of channel types and environmental conditions, including both biological ion channels and synthetic nanopores, such as engineered membrane systems with selective ion transport. Full article

The adoption of electric vehicles (EVs) has grown substantially in recent years, offering a cleaner and highly promising pathway toward the decarbonization of urban environments. However, this trend introduces new challenges in charging infrastructure and management. This paper proposes a synergistic integration of Business Intelligence (BI) and Artificial Intelligence (AI) techniques—including machine learning and data analytics—for solving the EV charging problem. We begin with an in-depth analysis of charging behaviors, leveraging extensive datasets from EVs, charging stations (CSs), and auxiliary sources. Based on this analysis, we introduce a BI framework utilizing advanced data mining methods to utilize large-scale data effectively. We then present a BI-based decision-making model that enables comprehensive analysis and optimized solutions for EV charge scheduling and the cooperation among different CS owners. The model is validated across multiple real-world scenarios and case studies, demonstrating significant improvements in charging efficiency, utilization, and reliability. By showcasing the practical applications of BI-driven analytics, our findings underscore the transformative impact of data-informed methodologies on EV charging operations. This paper concludes with a discussion of open research opportunities in AI- and BI-driven intelligent transportation—specifically in EV charging optimization, grid integration, and predictive analytics. Full article

Background: Gold nanoparticles (AuNPs) are a promising platform for vaccine antigen delivery due to their ability to stimulate both innate and adaptive immune responses. These effects depend strongly on physicochemical properties such as size, polydispersity, morphology, and surface charge, which are in turn determined by the synthesis method. While amino acids are often used as capping agents for AuNPs, their direct use as both reducing and stabilizing agents has been rarely investigated. Objectives: This study aimed to establish an ultrasound-assisted method for synthesizing AuNPs using amino compounds as both reducing and stabilizing agents, and assess their physicochemical characteristics, antigen-binding capacity, and immunogenicity. Methods: AuNPs were synthesized using L-cysteine, L-arginine, and cysteamine as dual reducing/stabilizing agents under ultrasonic conditions. The nanoparticles were combined with a recombinant receptor-binding domain (RBD) of SARS-CoV-2 and evaluated in mice for their ability to induce antibody responses. Results: The synthesized AuNPs exhibited hydrodynamic diameters ranging from 6.3 to 12.4 nm and zeta potentials from −40.5 to +36.5 mV, depending on the amino compound used. All formulations elicited robust anti-RBD IgG responses, but virus neutralization activity varied significantly. Notably, AuNP–arginine induced the strongest neutralizing response despite lower adsorption capacity and stability, suggesting that epitope preservation and antigen presentation quality were more decisive than antigen density. Conclusions: These findings underscore the importance of nanoparticle design in optimizing antigen presentation and highlight the potential of amino compound-synthesized AuNPs as effective antigen delivery vehicles for future vaccine development. Full article

The increasing demand for transportation has created economic, social, and environmental challenges that sustainable mobility solutions can help address. Electric vehicles (EVs) represent a promising alternative by lowering greenhouse gas emissions and improving energy efficiency. However, EV adoption remains limited due to barriers such as high costs, insufficient charging infrastructure, technological constraints, and low consumer awareness. This study aims to identify and classify the main barriers to EV adoption and propose a prioritization framework to guide decision-makers in resource allocation and policy design. A systematic literature review was conducted to identify barriers to EV adoption, which were grouped into six thematic categories: vehicle-related, battery-related, charging infrastructure, energy supply, personal and behavioral, and governance and policy. A degree of impact (DI) metric was developed to quantify each barrier’s influence, allowing hierarchical classification. The results highlight that inadequate charging infrastructure, high purchase and maintenance costs, limited public knowledge, and long charging times are the most critical issues. The proposed framework will help policymakers, industry leaders, and energy providers focus their efforts on the most impactful barriers. This research supports the global shift toward sustainable mobility and contributes to the literature by introducing a quantitative method for ranking barriers, addressing a gap in previous studies that lacked prioritization. Full article

The idea that gut microbes or a “bacillus of crime” might promote criminal behavior was popularized in the early 20th century. Today, advances in neuromicrobiology and related omics technologies are lending credibility to the idea. In recent cases of dismissal of driving while intoxicated charges, courts in the United States and Europe have acknowledged that gut microbes can manufacture significant amounts of systemically available ethanol, without a defendant’s awareness. Indeed, emergent research is raising difficult questions for criminal justice systems that depend on prescientific notions of free moral agency. Evidence demonstrates that gut microbes play a role in neurophysiology, influencing cognition and behaviors. This may lead to justice involvement via involuntary intoxication, aggression, anger, irritability, and antisocial behavior. Herein, we discuss these ‘auto-brewery syndrome’ court decisions, arguing that they portend a much larger incorporation of neuromicrobiology and multi-omics science within the criminal justice system. The legalome, which refers to the application of gut microbiome and omics sciences in the context of forensic psychiatry/psychology, will likely play an increasing role in 21st century criminal justice. The legalome concept is bolstered by epidemiology, mechanistic bench science, fecal transplant studies, multi-omics and polygenic research, Mendelian randomization work, microbiome signature research, and human intervention trials. However, a more robust body of microbiota–gut–brain axis research is needed, especially through the lens of prevention, intervention, and rehabilitation. With ethical guardrails in place, greater inclusion of at-risk or justice-involved persons in brain science and microbiome research has the potential to transform justice systems for the better. Full article

The global automotive industry’ s rapid transformation has led to electric vehicles (EVs) capturing a significant market share as a sustainable transportation option. To sustain this growth, it is crucial to not only attract new users but also retain existing ones through repurchases. This decision is shaped by both vehicle attributes and users’ prior experiences. This study examines the impact of five dimensions of expected utility and experienced utility gap (including cost utility, functional utility, emotional utility, environmental utility, and social utility) on the repurchase intentions of 863 Chinese EV users. Discrete choice experiments were used to analyze these factors, considering both vehicle and personal attributes. The results show that when emotional utility exceeds expectations, users are more likely to repurchase pure electric and plug-in hybrid electric vehicles. However, if environmental and social utilities fall short of expectations, users may be discouraged from choosing these two vehicle types. In contrast, decisions regarding gasoline vehicles are primarily driven by economic and habitual factors, with minimal influence from emotional, environmental, or social utilities. Additionally, EV users show a preference for medium-sized models that offer shorter charging times and longer driving ranges. These findings offer insights for enhancing consumer acceptance, accelerating EV market penetration, and supporting the automotive industry’s sustainable development, thereby contributing to the achievement of environmental sustainability goals. Full article