There are many uncertainties and risks in residential electricity consumption during the economic development. Knowledge of the relationship between residential electricity consumption and its key determinant—income—are important to the sustainable development of electric power industry. Using panel data from 30 provinces for the 1995-2012 period, this study investigates how residential electricity consumption changes as incomes increase in China. Previous studies typically used linear or quadratic double-logarithmic models imposing ex ante restrictions on the indistinct relationship between residential electricity consumption and income. Contrary to those models, we employed a reduced piecewise linear model that is self-adaptive and highly flexible and circumvents the problem of “prior restrictions.” Robust tests of different segment specifications and regression methods are performed to ensure the conservatism of the research. The results provide strong evidence that the income elasticity was approximately one, and it remained stable throughout the estimation period. The income threshold at which residential electricity consumption automatically remains stable or slows has not been reached. To ensure the sustainable development of the electric power industry, introducing higher energy efficiency standards for electrical appliances and improving income levels are vital. And government should emphasize electricity conservation in industrial sector rather than in residential sector.

Demand response flexible loads can provide fast regulation and ancillary services as reserve capacity in power systems. This paper proposes a joint optimization dispatch control strategy for source-load system with stochastic renewable power injection and flexible thermostatically controlled loads (TCLs) and plug-in electric vehicles (PEVs). Specifically, the optimization model is characterized by a chance constraint look-ahead programming to maximal the social welfare of both units and load agents. By solving the chance constraint optimization with sample average approximation (SAA) method, the optimal power scheduling for units and TCL/PEV agents can be obtained. Secondly, two demand response control algorithms for TCLs and PEVs are proposed respectively based on the aggregate control models of the load agents. The TCLs are controlled by its temperature setpoints and PEVs are controlled by its charging power such that the DR control objective can be fulfilled. The effectiveness of the proposed dispatch and control algorithm has been demonstrated by the simulation studies on a modified IEEE 39 bus system with a wind farm, a photovoltaic power station, two TCL agents and two PEV agents.

This paper proposes the impact of plug-in electric vehicles integrated into power distribution system based on voltage dependent control. The plug-in electric vehicles was modeled as the static load model in power distribution systems under balanced load condition. The power flow analysis is determined by using the basic parameters of the electrical network. The main point of this study are compare with voltage magnitude profiles, load voltage deviation, and total power losses of the electrical power system. There are investigating the affected from constant power load, constant current load, constant impedance load and plug-in electric vehicles load, respectively. The IEEE 33 bus test system is used to test the proposed method by assigning each load type to a balanced load in steady state and applied the solving methodology based on the bus injection to branch injection matric, branch current to bus voltage matrix, and current injection matrix to solve the power flow problem. The simulation results showed that the plug-in electric vehicles load had the lowest impact compared to other loads. The lowest plug-in values for the electric vehicle loads were 0.062, 119.67 kW and 79.31 kVar for the load voltage deviation, total active power loss and total reactive power loss, respectively. Therefore, this study can be verified that the plug-in electric vehicles load were affected to the lowest of the electrical power system in condition to same sizing and position. So that, in condition to the plug-in electric vehicles load added into the electrical power system with the conventional load type or complex load type could be considered that the affected from the plug-in electric vehicles load in next study.

Based on the dynamic strength variation mechanism of concrete under complex loading conditions, two approaches were devised: numerical testing and CT scanning, guided by the principle of minimizing energy dissipation. A methodology for calculating concrete's failure area and measuring its strength was developed. The analysis delves into the mechanisms behind failure patterns and variations in concrete specimen strength, focusing on crack initiation, development, and the resultant failure surface formed by crack propagation, along with the stress distribution in concrete specimens subjected to diverse loading conditions. Through evaluating the strength and computed failure area of concrete specimens, it is evident that the loading method influences both the strength and failure area of concrete specimens. In general, a larger failure area correlates with a higher measured strength.

Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.

Reasonable allocation of production plans and saving the overall electricity cost are crucial for large manufacturing conglomerates, which is an effective way for conglomerates to realize open-source and cost-saving. This paper develops an optimization model of off-site industrial production scheduling for enterprises in response to the problems of high electricity costs due to the irrational allocation of production schedules on the demand side of China's electric power and the difficulty of promoting industrial and commercial distributed photovoltaic (PV) projects in China. The model makes full use of the conditions of different PV resources and different electricity prices in different places to optimize the scheduling of industrial production in different places. The model is embedded with two sub-models of the electricity price prediction model and distributed photovoltaic kWh cost model to complete the model parameters, in which the electricity price prediction model utilizes the LSTM neural network. The integrated model is then solved by the particle swarm algorithm. To verify the effectiveness of the model in solving the problem of off-site production scheduling on the demand side of electricity, the research team researched two off-site pharmaceutical factories belonging to the same large pharmaceutical company. Finally, the research data were substituted into the model for solving, and it was concluded that the optimization model could significantly reduce the cost of electricity consumption of the enterprise by about 7.9 %.

Electromobility has gained significant attention in the auto- motive industry, with car manufacturers striving to improve the charging experience for their users. In this study, we compare multiple realistic charging strategies in order to quantify the benefits arising from better planning of charging sessions. We base our evaluation on electricity price data from three regions: Sweden (SE3 region), Germany (DE region), and Denmark (DK2 region). This data is obtained from the ENTSO-E Transparency Platform, covering the two full years 2021 and 2022. We enhance this dataset with commercial price forecasts from the company StormGeo and benchmark it against a naive forecasting method. The primary aim of our work is to compare how the strategies of varying levels of complexity perform across the three different countries in terms of minimizing energy costs for electric car users.

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

The work aims to improve the battery performance in hybrid electric vehicles (HEVs) by reducing the vehicle speed. We develop a specific protocol for managing battery use and optimizing the energy consumption rate to achieve this goal. The protocol automatically controls the driving operation, avoiding incompatible driving patterns with energy-saving mode and performance improvement. The protocol is submitted to a simulation process to predict the energy rate lowering and battery performance enhancement. The proposed protocol applies to any hybrid electric vehicle type and route conditions since it uses vehicle mass, drag and rolling coefficients, and road slope as variable parameters to determine the minimum energy consumption rate. We run experimental tests to validate the simulation data and the proposed protocol. Furthermore, the protocol applies to variable starting vehicle speeds, from 10 to 50 km/h, corresponding to the current driving patterns, sport, normal, and eco, set up by car manufacturers. A reduction of 10% in vehicle speed in urban and peripheral routes achieves a minimum energy rate, enhancing battery management. Current vehicle speed shows a deviation from optimum management of 18% while applying the vehicle speed reduction limits the deviation to 0.2%. Experimental results show a good agreement with simulation data with 94% accuracy. We tested the protocol for urban and peripheral routes with maximum vehicle speed limits of 60 and 90 km/h.

Efficient processing of end-of-life lithium-ion batteries of electric vehicles is important and a pressing challenge for a circular economy. Regardless of whether the processing strategy is recycling, repurposing or remanufacturing, the first processing step would usually involve disassembly. As battery disassembly is a dangerous task, efforts have been made to robotise it. In this paper, a robotic disassembly platform using four industrial robots is proposed to automate the non-destructive disassembly of a plug-in hybrid electric vehicle battery pack into modules. The work was conducted as a case study to demonstrate the concept of autonomous disassembly of an electric vehicle battery pack. A two-step object localisation method based on visual information is used to overcome positional uncertainties from different sources and is validated by experiments. Also, the unscrewing system is highlighted, and its functions, such as handling untightened fasteners, loosening jammed screws, and changing the nutrunner adapters with square drives, are detailed. Furthermore, the time required for each operation is compared with that taken by human operators. Finally, the limitations of the platform are reported and future research directions are suggested.

This paper analyzes and compares models for predicting average magnet losses in interior permanent-magnet motors with fractional-slot concentrated windings due to harmonics in the armature reaction (assuming sinusoidal phase currents). Particularly, loss models adopting different formulations and solutions to the Helmholtz equation to solve for the eddy currents are compared to a simpler model relying on an assumed eddy-current distribution. Boundaries in terms of magnet dimensions and angular frequency are identified (numerically and using an identified approximate analytical expression) to aid the machine designer whether the more simple loss model is applicable or not. The assumption of a uniform flux-density variation (used in the loss models) is also investigated for the case of V-shaped and straight interior permanent magnets. Finally, predicted volumetric loss densities are exemplified for combinations of slot and pole numbers common in automotive applications.

An indisputable fact about our planet is that its atmospheric temperature has risen dramatically during the past century. Combustion of fossil fuels and their subsequent greenhouse gas emissions are thought to be the main contributors to recent changes within the Earth’s ecosystem. The transportation sector and electricity generating power plants are each responsible for approximately one-third of these emissions. Shifting towards a cleaner and renewable resources to generate electricity is believed to omit a big portion of polluting substances. Improvements in vehicles’ fuel efficiency and the introduction of alternative fuels besides strategic plans to control travel demand are among the most promising approaches to alleviate emissions from the transportation sector. Recent technology advancements, however, drew much attention to the production and manufacturing of alternative fuel vehicles, electric vehicles in particular. Since these vehicles use electricity as part of or all their powertrain, assessing the amount of emissions they produce is closely tied to the cleanliness of the electricity source. In order for a valid comparison to be made between internal combustion and electric vehicles, hence, a life cycle assessment procedure needs to be followed from production stages to terminal life of vehicles. Involvement of numerous affecting factors during the lifetime of a vehicle on one hand, and the ambiguity in the exact source of electricity used to charge electric vehicles on the other hand bring about more complexities. The latter case is more commonly known as the marginal grid problem, which deals with how a combination of sources used to generate electricity can influence the life cycle emissions. There are also other concerns regarding the growth in fuel-efficient and electric vehicles. Transportation planners argue that new developments in the vehicle industry may attract more people to owning and driving cars. This phenomenon which is better known as a rebound effect not only will result in increased traffic congestion, but it can also outpace the environmental benefits from utilizing electric vehicles. Moreover, since fuel taxes comprise the majority of Highway Trust Funds, alternative ways to compensate for state and federal revenues should be devised. This paper is an attempt to review the existing literature to better elaborate on the role of the transportation sector in controlling climate change threats. More specifically, issues around the use of electric vehicles and how they can contribute to more environmentally friendly communities are discussed.

Open source software such as OpenDSS has given a lot of help to distribution network researchers and educators. With high penetration of distributed renewable energy resources into distribution network, tradition distribution steady state analysis software such as OpenDSS is faced with difficulty in handling distributed generators. Three-phase distributed generators are often modeled in sequence frame while unbalanced distribution network are usually modeled in phase frame. So a load flow in sequence-phase coupled frame is proposed to handle models described in both frames. Voltage controlled DGs which are difficult to cope with in OpenDSS are handled in proposed program. The steady state analysis platform is programmed with open source Modelica language and the main aim of this paper is to introduce an open source platform for active distribution network steady analysis include load flow and short circuit analysis which can be easily adopted and improved by other educators and researchers.

Transportation electrification has been identified as a crucial shift to be made for achieving energy security and emission reduction targets. Electric Vehicles (EVs) play a significant role in achieving these targets; therefore, there is a growing trend to develop new EV technologies and associated infrastructure. Along with these developments, EVs' Quality of Service (QoS) aspects should be enhanced to increase the number of EVs on the road. Various QoS factors are defined to assess the QoS of EVs, and one of the two aims of this paper is to provide a comprehensive review of these factors. The other aim is to review recent developments of associated communication technologies which are essential for exchanging information between EVs and charging stations. This paper serves as a comprehensive review and useful reference for researchers working on QoS aspects of EVs and associated communication systems aiming to enhance the QoS.

Coal gasification with carbon dioxide is a process for generating clean gaseous fuels and relieving greenhouse effect. Zhundong coal has high alkali and alkali earth metals (AAEMs) content, medium volatile and low ash in nature. Isothermal CO₂ gasification of char derived from Zhundong coal (R-char) and char from acid washing R-char (AR-char) are performed in thermo-gravimetric analyzer (TGA). The effect of AAEMs is investigated on the gasification behavior in the range of temperatures 1073 K to 1273 K. The carbon conversion increases rapidly with increasing reaction temperature and CO₂ concentration. R-char has high gasification rate and carbon conversion compared with AR-char. The accuracy of the free-model approach for calculating activation energy at different conversions is validated by compared with different kinetic models (volume reaction model, distributed activation energy model). Moreover, R-char gasification with CO₂ shows a compensation effect as the Arrhenius parameters (EA and k0) increase or decrease simultaneously.

The evolution of transportation has been inextricably tied to the progress of civilization. Through innovation, the automobile business has been working to improve safety, quality, and compliance with environmental regulations. Electric vehicles have made significant strides in this area, but optimizing their efficiency requires a special focus because they expend energy that can be recovered in a variety of ways. Energy harvesting, a cutting-edge technology that captures wasted energy from vehicles, has recently received a lot of attention as it constitutes a means to improve the efficiency of electric vehicles. Dissipated energy can be converted into electricity using regenerative energy recovery systems and put to various uses. This study tenders a thorough examination into energy recovery technologies which could be applied to the various types of energy dissipated in electric vehicles. Firstly, the paper investigates the possible sources of energy recoverable from an electric vehicle, as well as the various types of energy dissipated. Secondly, the article examines the energy recovery technologies most frequently used in vehicles, categorizing them according to the type of energy and application. Finally, it determines that with further research and development, energy harvesting holds considerable potential for improving the energy efficiency of electric vehicles. New and innovative methods for capturing and utilizing wasted energy in electric vehicles can be established. The potential benefits of applying energy recovery systems in electric vehicles is a vital issue for the automobile industry to focus on due to the potential benefits involved. The ongoing progress currently being made in this field is expected to play a significant role in shaping the future of transportation.

Abstract: High step-up voltage gain nonisolated DC-DC converter have attracted much attention in photovoltaic, fuel cells and other renewable energy system applications. In this paper, by combining input current ripple-free boost cell with coupled-inductor voltage-doubler cell, an input current ripple-free high voltage gain nonisolated converter is proposed. In addition, passive lossless clamp circuit is adopted to recycle the leakage inductor energy and to reduce the voltage spike across the power switch. By utilizing voltage-doubler cell consisting of diode and capacitor, the voltage stress of switch is further reduced and the resonance between the leakage inductor and the stray capacitor of the output diode is eliminated. A low switch-on-resistance low-voltage-rated MOSFET can therefore be employed to reduce the conduction loss and cost. The reverse recovery loss of output diode is reduced, and the efficiency of converter can be improved. Furthermore, the proposed converter can achieve nearly zero input current-ripple and make the design of electromagnetic interference (EMI) filter easy. Steady state analysis and operation mode of the converter is performed. Finally, experimental results are presented to verify the analysis results of the proposed converter.

Abstract: As the policies and regulations currently in place concentrate on environmental protection and greenhouse gas reduction, we are steadily witnessing a shift in the transportation industry towards electromobility. There are, though, several issues that need to be addressed to encourage the adoption of EVs at a larger scale. To this end, we propose a solution capable of addressing multiple EV charging scheduling issues, such as congestion management, scheduling a charging station in advance, and allowing EV drivers to plan optimized long trips using their EVs. The smart charging scheduling system we propose considers a variety of factors such as battery charge level, trip distance, nearby charging stations, other appointments, and average speed. Given the scarcity of data sets required to train the Reinforcement Learning algorithms, the novelty of the recommended solution lies in the scenario simulator, which generates the labelled datasets needed to train the algorithm. Based on the generated scenarios, we created and trained a neural network that uses a history of previous situations to identify the optimal charging station and time interval for recharging. The results are promising and for future work we are planning to train the DQN model using real-world data.

Recently, design of electric scooters (ESs) has commonly adopted brushless DC motors (BLDCMs) in place of brushed DC motors. This invention develops a new anti-lock braking system (ABS), based on a slip-ratio estimator, for ES utilizing the braking force generated by the BLDCM when electrical energy releases to the load yielding an analogous effect of ABS control in gas-engine vehicles. Comparing to mechanical ABS, the design possesses the advantages of rapid torque responses due to fast actuating response. The electrical ABS is realized by associating with kinematic and Short-circuit braking. A current controller is used to adjust the braking force, while the sliding mode control strategy is adopted to regulate the slip ratio for best road adhesion while braking. Real-world experiments have been conducted for functional and performance verification.

The electric vehicle (EV) industry is experiencing rapid growth, accompanied by continuous advancements in charging infrastructure to satisfy the rising need for fast and reliable charging. Particularly, the focus has shifted towards EV fast charging (EVFC) and its impact on the power grid. This paper presents a comprehensive analysis and survey of scholarly literature and projects conducted in this field over the past decade. The findings highlight the rising demand for EVFC and the need to address its potential adverse effects on the power grid. However, there is a noticeable lack of clear research guidance regarding charging time. To tackle these challenges and pave the way for future solutions, extensive research on DC fast charging, ultra-fast charging, and the integration of vehicle-to-grid (V2G) systems is required. This review paper thoroughly investigates the development of fast charging technology for electric vehicles (EVs), including its advantages and comparative analyses from various perspectives. Furthermore, it delves into the advancements in DC fast charging infrastructure, emphasizing charging standards and control modes. The study also investigates different categories of battery chargers for both on-board and off-board charging. Additionally, it explores the classification of DC-DC converters in the context of DC fast charging stations, discusses control strategies for EV systems, identifies existing challenges, and outlines future trends in EV fast charging.

The thermal degradation of brakes has a significant impact on the stability of pure electric commercial vehicles during long downhill braking. Based on road slope and using fuzzy control method, design strategies for motor regenerative braking force and friction braking force distribution, with the aim of reducing friction braking force, improving braking stability, and recovering braking energy. By establishing driving conditions on roads with different slopes and using numerical analysis methods to verify the proposed control strategy. the results show that under the 6% constant slope driving condition, the vehicle maintains a constant speed of 30km/h downhill driving, and the braking energy recovery rate reaches 50.93%; Under the condition of 3% -6% gradient driving, the vehicle speed fluctuates slightly, but it can still be maintained at a constant speed of 30km/h for downhill driving through the adjustment of the motor's reverse drag characteristic, with a braking energy recovery rate of 49.97%; During the process of driving downhill at a constant speed, the frictional braking force does not participate in braking; The braking energy recovery rate is determined by the slope and braking deceleration.

The advancement of technology has led to a shift in the automotive industry from gasoline vehicles to electric vehicles. One of the key factors in modern vehicles is the communication between control devices and commands. This thesis presents a prototype of communication in the form of CAN BUS Protocol with CAN STANDARD SAE.J1939 for All-Terrain Vehicle (ATV) using MRS Developers Studio software to control the CAN Bus system. This thesis discusses the operation of communication from the assembly of the ATV, the packing of a 72 V 25Ah battery as a power source for the vehicle, the layout of the wiring system for receiving/sending communication signals for the 3 CAN Bus control boxes, and programming with MRS Developers Studio software used in communication. The communication of the vehicle in the ATV consists of receiving/sending signals of switches, headlights, left turn signal, right turn signal, brake light, emergency light, horn, forward-reverse gear, and accelerator. The results of the operation of the CAN Bus Protocol found that the CAN Bus control box has only 6 input and output pins, making the communication that has both receiving and sending signals insufficient. Therefore, starting from the CAN Bus1 control box acts as a receiver of signals from the forward-reverse gear switch sent to the output of the CAN Bus3 control box, the CAN Bus1 control box acts as a receiver of all light signals sent to the output of the CAN Bus2 control box. The signal transmission between devices will send signals through the CAN HUB line to ensure stability and minimal interference. In addition, the communication between devices from testing the operation of various switches on and off up to 1000 times found that this communication does not cause errors, making this communication reliable and stable.

The transportation sector is seeing an increased adoption rate of electric vehicles (EV), which comprise battery electric vehicles (BEV), hybrid electric vehicles (HEV) and plug-in hybrid electric vehicles (PHEV). This mode of transportation is likely to displace automobiles powered by internal combustion engines (ICE) in the nearest future, as indicated by the current trend. Each of the key EC components is home to a variety of technologies that are either currently in use or will be in the near future. Electric vehicles have the potential to have a significant impact on a variety of fields, including the economy, the energy infrastructure, and the natural environment. As a result of the production of carbon dioxide gas from the combustion of fossil fuels, transportation is the sector that is responsible for the second-highest amount of greenhouse gas emissions. Electric vehicles, often known as EVs, are getting a lot of attention as a potentially game-changing solution to this issue. It is possible for electric vehicles to produce fewer emissions of carbon dioxide (CO2) than conventional automobiles due to the fact that an electric motor serves as the vehicle's propeller rather than an internal combustion engine. EVs have the potential to become zero-emission vehicles if they are paired with renewable energy sources. This document presents an overview of the numerous types of EV drive circuits, covering their design as well as the benefits and drawbacks associated with each type. The current state of battery technology, particularly that pertaining to the batteries utilized in electronic vehicles, is discussed in this paper. This article also discusses electric motor efficiency, power density, fault tolerance, dependability, cost, and the best electric motor for EVs. Then, a thorough study of future EV implementation's obstacles and prospects is done. Charging times and battery performance are examples of technological challenges, but government regulation of EVs continues to be a substantial non-technical hurdle.

Energy storage systems play a crucial role in the overall performance of hybrid electric vehicles. Therefore, the state of the art in energy storage systems for hybrid electric vehicles has been discussed in this paper along with appropriate background information for facilitating future research in this domain. Specifically, we have compared the key parameters such as cost, power density, energy density, cycle life, and response time for various energy storage systems. For the energy storage systems employing ultra capacitors, we have presented the characteristics such as cell voltage, cycle life, power density and energy density. Furthermore, we have discussed and evaluated the interconnection topologies for the existing energy storage systems. We have also discussed the hybrid battery-flywheel energy storage system as well as the mathematical modeling of the battery-ultracapacitor energy storage system. Toward the end, we have discussed energy efficient powertrain for hybrid electric vehicles.

The results shown in this paper extend our research group's previous work, which presents the theoretically achievable hydrogen engine-out NOx (H2-NOx) Pareto front of a hydrogen hybrid electric vehicle (H2-HEV). While the Pareto front is calculated offline, which requires significant computing power and time, this work presents an online-capable algorithm to tackle the energy management of a H2-HEV with explicit consideration of the H2-NOx trade-off. Through the inclusion of realistic predictive data on the upcoming driving mission, a model predictive control algorithm (MPC) is utilized to effectively tackle the conflicting goal of achieving low hydrogen consumption while simultaneously minimizing the NOx. In a case study it is shown that the MPC is able to satisfy user-defined NOx limits over the course of various driving missions. Moreover, a comparison to the optimal Pareto front highlights the MPC's ability to achieve close-to-optimal fuel-performance for any desired cumulated NOx target on four realistic routes for passenger cars.

Electric mobility is one of the ways to contain greenhouse gas and local pollutants emissions in urban areas. Nevertheless, the massive introduction of battery-powered electric vehicles (EVs) brings some concerns related to their energy demand. Modelling vehicle usage and charging behavior is essential for charge demand forecasting and energy consumption estimation. Therefore, it is crucial to understand how the charging decisions of EV owners are influenced by different factors, ranging from the charging infrastructure characteristics to the users’ profiles. This review intends to investigate the approaches used to investigate on charging behavior and highlight trends and differences between the results, remarking on any gaps worthy of further investigation.

This paper provides a comprehensive overview of recent advancements in autonomous electric vehicle (AEV) within the specified region. It elaborates on the progress and comparative analysis of diverse subsystems, including energy storage, cell balancing for battery systems, vehicle charger layouts, electric vehicle motor mechanisms, and braking systems. Furthermore, this paper showcases several prototype autonomous electric vehicles as conclusive study findings.

Three kinds of inorganic alkali are introduced into tetraethylenepentamine (TPEA) and polyethyleneimine (PEI) modified MCM-41 as the CO2 adsorbents. FT-IR and TGA are used to characterize the surface groups and the thermal stability of adsorbents. Chemical titration method is used to measure the alkali amounts of adsorbents. Thermo-gravimetric analysis with 10%CO2/N2 as the simulated flue gas is used to test the CO2 adsorption performance of adsorbents. The results show that all three kinds of inorganic alkali containing adsorbents exhibit promoted CO2 adsorption capability than traditional TPEA and PEI modified samples. Ca(OH)2 and PEI modified samples exhibit the highest adsorption capacity and stable regeneration property. The introduction of inorganic alkali changes the chemical adsorption mechanism between CO2 and increases the effective alkali amounts due to its hydroxyl groups. The CO2 adsorption capacities have a linear dependence relation with the alkali amounts of adsorbents, indicating that alkali amount is a critical factor for novel adsorbents exploring.

To fulfil a need of limitation automotive emissions the reduction of vehicle weight is widely recommended and achieved in many ways, both by the construction of individual elements of the vehicle, and by the selection of light materials including Al alloys. Connecting these elements with each other and with elements made of iron alloys can be realized, inter alia, by welding or stir welding. However, the quality of the welds obtained varies widely and depends on many design, operational and environmental factors. The present study focused on a review of various welding technics used to joint both similar and dissimilar Al alloys utilized in automotive industry, the effect of various process parameters on welds quality, and phenomena appeared in such welds..

Brain aging causes a wide variety of changes at molecular and cellular levels, leading to the decline of cognitive functions and increased vulnerability to neurodegenerative disorders. The research aimed at understanding the aging of the brain has made much progress in recent decades. Technological innovations such as single-cell RNA-sequencing (scRNA-seq), proteomic analyses, and spatial transcriptomic analyses have facilitated the research on the dynamic changes occurring within neurons, glia, and other cells along with their impacts on intercellular communication during aging. In this review, we introduce recent trends of how neurons and glia change during aging and discuss the impact of the brain microenvironment such as the blood-brain barrier (BBB).

In this study, a novel two-layer control scheme aimed at enhancing the dynamic performance of a nonlinear vehicle through the utilization of an optimal control approach grounded in predictive control principles is presented. The first layer of our control system focuses on the computation of an optimized rotational torque that ensures lateral dynamic stability. This torque is subsequently translated into differential forces acting on the wheels, employing the inverse tire model to derive the desired longitudinal slips. These slip values are then transmitted to the second layer of our control system. In the second layer, the electric motors associated with the vehicle's wheels dynamically adjust the input torque to accurately track the desired slip, thereby ensuring the overall stability of the electric vehicle. In view of the paramount importance of energy consumption in electric vehicles, we adopt optimal control strategies to substantially minimize battery utilization. To this end, careful selection of appropriate weighting coefficients in the control laws enables us to maintain the electric motors within their permissible operational range while simultaneously minimizing battery energy consumption for desired slip tracking. Extensive simulation results validate the effectiveness of our proposed control system in proficiently managing nonlinear effects and safeguarding the vehicle's stability.

Concern about the planet's future has motivated governments around the world to adopt policies that promote more responsible energy consumption. Reducing emissions of the principal cause of climate change man-made greenhouse gases requires greater system efficiency and the utilization of renewable energy sources. The generation of electricity is the single largest source of hazardous pollutants, followed by the transportation sector. This is true due to the extreme reliance on petroleum and its derivatives that these industries have. Electric mobility is receiving more attention from businesses, governments, and academic institutions as a means to reduce the negative impacts of current energy consumption. Without measures to address common technical issues, electric vehicles would strain electrical infrastructure. In order to determine the best strategy for switching operations and feeder reconfiguration, this research presents a helpful hybrid genetic algorithm particle swarm optimization (HGAPSO) method. Meet transmission limits while decreasing real power losses and enhancing the system's bus voltage profile. The effects of HGAPSO on power losses and voltage distributions in the network are analyzed. Then, a report is produced contrasting GA and PSO. This cross-breeding of GA and PSO led to HGAPSO. This important problem can be solved more effectively with the use of new or improved algorithms. To find the optimal reconfiguration and properly rearrange transmission network interconnection, it employs a plethora of heuristic optimization algorithms. The objective is to limit bus voltage variations while maintaining the system's radial topology and minimizing power usage. The reliability and performance of the procedure were evaluated using MATLAB's IEEE 33-bus communication network. The outcomes demonstrate the effectiveness of the proposed technique, which cuts down on power waste during standalone runs and speeds up the processing time.

China planned to promote the large-scale adoption of Electric Vehicles (EVs) in the thirteenth five-year plan, however, this target faces many obstacles. This paper was trying to analyze the main barriers to widespread adoption of EVs through a survey in Shenzhen, which has the biggest EVs market share in China major cities. Based on previous scholarly findings, this paper conducted a new study which collected 406 approved questionnaires among 500 participants. The study proposed five hypotheses to examine the main barriers to widespread adoption of EVs. The analysis conducted by statistical methods: two-way frequency tables, chi-square test, and factor analysis. The results noted that perception of EVs advantages and recharging access remained the main barriers to EVs large-scale penetration. Besides, financial incentives drop would not cause a significant decline of EVs adoption in future. The study proposed suggestions to carmaker and government policy administrator on the analysis and discussion.

The greenhouse gases and air pollution generated by extensive energy use have exacerbated climate change. An electric-bus (e-bus) transportation system favors reducing pollution and carbon emissions. This study analyzed the minimization of construction costs for an all battery-swapping public e-bus transportation system. A simulation was conducted according to existing timetables and routes. Daytime charging was incorporated during the hours of operation; the two parameters of the daytime charging scheme were the residual battery capacity and battery-charging energy during various intervals of daytime peak electricity hours. The parameters were optimized using three algorithms: particle swarm optimization (PSO), a genetic algorithm (GA), and a PSO–GA. This study observed the effects of optimization on cost changes (e.g., number of e-buses, on-board battery capacity, number of extra batteries, charging facilities, and energy consumption) and compared the plug-in and battery-swapping e-bus systems. The results revealed that daytime charging can reduce the construction costs of both systems. In contrast to the other two algorithms, the PSO–GA yielded the most favorable optimization results for the charging scheme. Finally, according to the cases investigated and the parameters of this study, the construction cost of the plug-in e-bus system was lower than that of the battery-swapping e-bus system.

More and more works are using machine learning techniques while adopting supervisory control and data acquisition (SCADA) system for wind turbine anomaly or failure detection. While parameter selection is important for modelling a wind turbine’s health condition, only a few papers have been published focusing on this issue and in those papers interconnections among sub-components in a wind turbine are used to address this problem. However, merely the interconnections for decision making sometimes is too general to provide a parameter list considering the differences of each SCADA dataset. In this paper, a method is proposed to provide more detailed suggestions on parameter selection based on mutual information. Moreover, after proving that Copula, a multivariate probability distribution for which the marginal probability distribution of each variable is uniform is capable of simplifying the estimation of mutual information, an empirical copula based mutual information estimation method (ECMI) is introduced for an application. After that, a real SCADA dataset is adopted to test the method, and the results show the effectiveness of the ECMI in providing parameter selection suggestions when physical knowledge is not accurate enough.

For offshore wind power generation, accessibility is one of the main factors that has great impact on operation and maintenance due to constraints on weather conditions for marine transportation. This paper presents a framework to explore the accessibility of an offshore site. At first, several maintenance types are defined and taken into account. Next, a data visualization procedure is introduced to provide an insight into the distribution of access periods over time. Then, a rigorous mathematical method based on finite state Markov chain is proposed to assess the accessibility of an offshore site from the maintenance perspective. A five-year weather data of a marine site is used to demonstrate the applicability and the outcomes of the proposed method. The main findings show that the proposed framework is effective in investigating the accessibility for different time scales and is able to catch the patterns of the distribution of the access periods. Moreover, based on the developed Markov chain, the average waiting time for a certain access period can be estimated. With more information on the maintenance of an offshore wind farm, the expected production loss due to time delay can be calculated.

The article studies and analyzes a hybrid power source system, lithium battery, and fuel cell assisted by flexible photovoltaic panels for hybrid electric vehicles (HEVs). The new configuration operates based on optimizing the performance of the dual power source to maximize the available energy and operating time. The operational mode simulates specific driving conditions in urban, peripheral, and intercity routes under variable driving patterns and traffic conditions. The simulation results show that the new configuration increases the hybrid electric vehicle performance, providing higher reliability despite the more complex design. The proposed system reduces the external power supply dependence and avoids unexpected sudden stops due to energy exhaustion. The new configuration also extends the vehicle driving range depending on driving mode and route conditions. The vehicle driving range extension may reach up to 276.3 km. The hybridization of fuel cells with lithium battery and PV panel assistance enhances battery management, reducing battery servicing and increasing lifespan. The proposed topology is cheaper than a single fuel cell power system by 17.2%, reducing the total cost related to the single battery power system if we consider the battery recharge for its entire lifespan. The cost saving is 2.6% for the American market and 14.8% for the European one.

This article provides a comprehensive exploration of the electric vehicle (EV) business, investigating its current state, market dynamics, and pivotal role in sustainable transportation. It delves into factors driving EV adoption, challenges faced by the industry, and the landscape of key players. The discussion extends to investments, infrastructure development, consumer trends, and the technological innovations shaping the future. The article concludes with insights into the anticipated growth and transformative impact of EVs, emphasizing their significance in fostering a cleaner, more sustainable future for global transportation.

This paper presents a new methodology to evaluate the performance of an electric vehicle hybrid power system consisting of a fuel cell and a supercapacitor. The study compares the results with those obtained for a battery-electric vehicle. The methodology extends to three driving modes, ECO, NORMAL, and SPORT, corresponding to conservative, moderate, and aggressive acceleration, and three driving conditions, low, medium, and high energy demand. We develop a simulation process to evaluate the energy consumption and the energy rate of a specific electric vehicle used as a prototype for the study. The methodology applies to a driving route that includes acceleration, deceleration, braking, and constant speed segments reproducing standard driving conditions in urban journeys. The proposed method considers combined driving modes, ECO, NORMAL, and SPORT, in each acceleration process, with variable fractions, 0% to 100%, for each mode. This methodology optimizes the simulation results as it fits the current driving way in urban environments. The simulation results show an average reduction in energy consumption of 37% and 27.1% in vehicle weight, contributing to lower energy use. The study concludes that using a hybrid power system, fuel cell/supercapacitor, instead of a battery in electric vehicles is beneficial, especially in journeys with frequent acceleration processes.

With the continuous improvement and observable benefits of electric vehicles (EVs), major logistic companies are introducing more EVs into their conventional fleets. This gives rise to a new type of vehicle routing problem with mixed vehicles, where heterogeneous internal combustion vehicles (ICVs) and electric vehicles are considered in route planning. In addition, certain deliveries that are not efficient on any type of vehicles, are outsourced to third-party common carriers. In this paper, we define this problem as a mixed vehicle routing problem with common carriers (MVRPC). The objective of such problems is to minimize the transportation costs by considering routes with ICVs and EVs, the possibility of visiting recharging stations, outsourcing options, and drivers’ layover regulations. This variant of the vehicle routing problems has many practical applications, particularly in the design of long-haul transportation and last-mile delivery services. Effective MVRPC solutions play a key role in promoting the going Green image and optimally allocating resources. The problem has received limited attention in the literature likely because addressing all the needed aspects is especially challenging. To solve the large-scale problem, we develop a branch-and-cut pricing framework that relies on strong cuts and customized labeling algorithms. Numerical experiments highlight the effectiveness of our algorithm. This success can be attributed to tailored critical resources, dynamically bounded bidirectional labeling procedures, strong dominance criteria, and implementation strategies.

Two new dimacrolide sesquiterpene pyridine alkaloids (DMSPAs), dimacroregelines A (1) and B (2), were isolated from the stems of Tripterygium regelii. The structures of both compounds were characterized by extensive 1D and 2D NMR spectroscopic analyses, as well as HRESIMS data. Compounds 1 and 2 are two rare DMSPAs possessing unique 2-(3′-carboxybutyl)-3-furanoic acid units forming the second macrocyclic ring, representing the first example of DMSPAs bearing an extra furan ring in their second macrocyclic ring system. Compound 2 showed inhibitory effects on the proliferation of human rheumatoid arthritis synovial fibroblast cell (MH7A) at a concentration of 20 μM.

Electric vehicles, being able to reduce pollutant and greenhouse gas emissions and shift the economy away from oil products, can play a major role in the transition towards low-carbon energy systems. However, the related increase in electricity demand inevitably affects the strategic planning of the overall energy system as well as the definition of the optimal power generation mix. With this respect, the impact of electric vehicles may vary significantly depending on the composition of both total primary energy supply and electricity generation. In this study, Italy and Germany are compared to highlight how a similarity in their renewable shares not necessarily leads to a CO₂ emissions reduction. Different energy scenarios are simulated with the help of EnergyPLAN software assuming a progressive increase in renewable energy sources capacity and electric vehicles penetration. Results show that, for the German case, the additional electricity required leads to a reduction in CO₂ emissions only if renewable capacity increases significantly, whereas the Italian energy system benefits from transport electrification even at low renewable capacity. Smart charging strategies are also found to foster renewable integration; however, power curtailments are still significant at high renewable capacity in the absence of large-scale energy storage systems.

Passive balancing is a traditional choice for lithium-ion battery packs although it is regarded as highly inefficient. One opportunity to improve traditional balancing in sub-medium size battery packs is to combine two balancing methods: passive and active. Statistical analysis have been performed to rationalize battery pack division in smaller packs and use of higher level selective charging method to decrease losses of passive balancing. An experimental balancing prototype have been designed and tested for efficiency. Results indicate energy performance degradation if cells of the pack are with low capacity variation. Proposed balancing approach could improve performance under high cell mismatch conditions.

The utilization of fuel cells (FC) in automotive technology has experienced significant growth in recent years. Fuel cell hybrid electric vehicles (FCHEVs) are powered by a combination of fuel cells, batteries, or/and ultracapacitors (UCs). By integrating power converters with these power sources, the FCHEV system can overcome the limitations of using them separately. The performance of an FCHEV is influenced by the efficiency of the power electronics converter controller, as well as the technical efficiency of the power sources. FCHEVs need intricate energy management systems (EMSs) to function effectively. Poor EMS can lead to low efficiency and accelerated fuel cell and battery degradation. The literature discussed various types of EMSs, such as equivalent consumption minimization strategy, classical PI controller, fuzzy logic controller, and Mutative Fuzzy Logic Controller (MFLC). However, comparing different EMSs can be challenging due to the varying vehicle and system parameters, which might lead to false claims being made regarding system performance. This research aims to categorize and discuss the various topologies of FCHEVs, highlighting their pros and cons, and comparing several EMSs based on performance metrics such as State of Charge (SOC) and FC deterioration.

A number of decision support tools facilitating the use of Electric Vehicles (EVs) have been recently developed. Due to the EVs’ limited autonomy, routing and path planning are the main challenges treated in such tools. Specifically, determining at which Charging Stations (CSs) to stop, and how much the EV should charge at them is complex. This complexity is further compounded by the fact that charging times depend on the CS technology, the EV characteristics, and follow a nonlinear function. Considering these factors, we propose a path planning methodology for EVs with user preferences, where charging is performed at public CSs. To achieve this, we introduce the Electric Vehicle Shortest Path Problem with time windows and user preferences (EVSPPWP) and propose an efficient solution algorithm for it. Given an origin and a destination, the algorithm prioritizes CSs close to Points of Interest (POIs) that match user inputted preferences, and user-defined time windows are considered for activities such as lunch and spending the night at hotels. The algorithm produces flexible solutions by considering clusters of charging points (CPs) as separate CSs. Furthermore, the algorithm yields resilient paths by ensuring that recommended paths have a minimum number of CSs in their vicinity. The main contributions of our methodology are: modeling user-defined time windows, including user-defined weights for different POI categories, creating CSs based on clusters of CPs with sufficient proximity, using resilient paths, and proposing an efficient algorithm for solving the EVSPPWP. To facilitate the use of our methodology, the algorithm was integrated into a web interface. We illustrate the use of the web interface, giving usage examples and comparing different settings.

Electric Vehicles (EVs) are the need of the hour due to growing climate change problems linked with the transportation sector. Battery Thermal Management System (BTMS), which is accountable for certifying safety and performance of lithium-ion batteries (LiB), is the most vital part of an EV. LiB has auspicious gravimetric energy density but the heat generation due to chemical reactions inside a LiB during charging and discharging causes temperature rise which has a direct effect on LiB performance and safety. This study specifically focuses on aircooled BTMS, defines different types of air-cooled BTMS (active and Passive), discusses limitations associated with air-cooled BTMS, and investigates different optimization techniques and parameters to improve performance of air-cooled BTMS. Maintaining temperature within optimum range and uniform temperature distribution between cells of a battery pack are the major design parameters for improving the performance and efficiency of air-cooled BTMS. Various optimization techniques including cell arrangement with a battery pack, air-flow channel optimization, and air inlet/outlet position variations are discussed and each technique is thoroughly reviewed. Finally, it’s noted that passive air-cooled BTMS is not that effective for long-distance vehicles so most researchers shifted their focus toward active air-cooled BTMS. Active air-cooled BTMS requires a lot of power for effective performance. Lastly, the most recent field of air-cooled BTMS technology which is Air-Hybrid BTMS is discussed and declared a very promising solution for overcoming major limitations associated with air-cooled BTMS.

This study evaluates eight-year ownership costs for battery electric vehicles (BEV) versus non-plugin hybrid vehicles using forecasting to estimate future electricity and conventional gasoline prices and incorporating these in a multiple design of experiments simulation. Results suggest that while electric vehicles are statistically dominant in terms of variable costs over an 8-year life-span, high-performance hybrid non-plugins achieve variable fuel costs nearly as good as low-performing electric vehicles (those attaining only 3 miles per kilowatt hour) and that these hybrid acquisition costs are (on average) lower yet the vehicles retain higher residual values. In general, the six smallest ownership costs are split evenly between hybrid and electric vehicles; however, inflation for conventional regular gasoline is estimated to outstrip inflation per kilowatt hour. Thus, non-plugin hybrid cars are likely to require considerably more advanced engineering to keep pace.

This paper presents a thermal runaway prognosis scheme based on the big-data platform and entropy method for battery systems in electric vehicles. It can simultaneously realize the diagnosis and prognosis of thermal runaway caused by the temperature fault through monitoring battery temperature during vehicular operations. A vast quantity of real-time voltage monitoring data was collected in the National Service and Management Center for Electric Vehicles (NSMC-EV) in Beijing to verify the effectiveness of the presented method. The results show that the proposed method can accurately forecast both the time and location of the temperature fault within battery packs. Furthermore, a temperature security management strategy for thermal runaway is proposed on the basis of the Z-score approach and the abnormity coefficient is set to make real-time precaution of temperature abnormity.

In this article, we describe a simple procedure for evaluating the sterility of nutrient media using a shear-horizontal surface acoustic wave (SH-SAW) biosensor. We used the SH-SAW biosensor platform to monitor the time-dependent phase responses of the material for the media that binds to a monolayer of 3-mercaptopropanyl-N-hydroxy succinimide on the biosensor surface. Increases in the phase shift less than 50% of the starting phase shift recorded 180 minutes after the sterilization indicate a successful sterility test of the media. Our devices were then applied to sterility testing using a viable Escherichia coli (E. coli) K-12 solution to confirm our hypothesis. Filtered LB Broth was the negative control, and a 24-hour culture of E. coli was the positive control. Before testing, the cell solution was pelleted, the media decanted and then replaced with a 1x phosphate-buffered saline solution (1x PBS). Since we know that the N-hydroxysuccinimide residues will bind to amine groups, only in cases where microbial species are present will the surface binding increase greater than 50%. We confirmed our hypothesis using SH-SAW biosensors and overnight E. coli cultures. The sensitivity for our 250 MHz quartz SH-SAW sensors was calculated based on the response from the E. coli K-12 solution over the linear dynamic range, which appears to be 2x104 – 2x107 CFU per ml and has a sensitivity of S = 1.655x105 o m2 Kg-1. The limit of detection (LOD) for these sensors was calculated to be approximately 2x10-9 grams (g) which is estimated to be approximately 2,000 Escherichia coli (E. coli) K-12 cells. However, the sensitivity and insertion loss remained approximately unchanged before and after coating the sensor with 3,3'-Dithiobis(succinimidyl) Propionate (DSP).

This study purposes the use of plug-in electric vehicles for demand side management (DSM) considering uncertainties in demand as well as uncertainties due to mobility of PEV to mitigate peak demand. The solution also seeks to reduce electric cost in addition to reducing the effects of greenhouse gases. In recent years DSM using distributed storage system such as battery energy management system (BESS) and plugged-in electric vehicles (PEV) have become very prevalent with most implementations resorting to deterministic load forecast. These methods do not consider the potential growth in demand making their solutions less robust. In this study we propose a real-time density demand forecast and stochastic optimization for robust operation of PEV for a building. This method accounts for demand uncertainties in addition to uncertainties in mobile energy storage as found in PEV, making the resulting solution robust as compared to the deterministic case. A case study on a real site in South Korea is used for verification and testing. The proposed study is verified and tested against existing algorithms. The result verifies the effectiveness of the proposed approach

In the modern power grid, characterized by the increased penetration of power electronics and extensive utilization of renewable energy, inverters-based power plants play a pivotal role as the principal interface of renewable energy sources (RESs) and the grid. Considering the stability characteristics of grid-following (GFL) inverters when the grid is relatively weak, the application of grid-forming (GFM) controls becomes imperative to enhance the stability of the entire power plant. Thus, there is an urgent need for suitable and effective models to study the interaction and stability of the paralleled inverters employing GFL and GFM controls. Thus, the small-signal modeling with full-order state space model and eigenvalues analysis is presented in this paper. First, the small-signal state-space model of the individual GFL and GFM inverters is obtained considering the control loop, interaction, reference frame, transmissions, and time delays. Then, the models of the individual inverter are extended to the hybrid inverters to study the effects of the GFM inverters on the small- signal stability of the entire system. And, the impacts of the inertia and damping are analyzed by the eigenvalues of the state-transition matrix. Case comprising three parallel GFL inverters and two GFL inverters with one GFM inverter, respectively, are studied to examine the effectiveness and accuracy of the model. Finally, the stability margin obtained from the eigenvalue analysis of the entire system is verified by time-domain simulations.

Water footprint has been widely used to illustrate the consumption of water in many situations; for instance, in products, processes or regions of interest. In this work, we analyzed – using a sensitivity analysis approach – the effect of some variables in the calculation of the water footprint in the viticulture in Brazilian Serra Gaúcha (the major producing region of Brazilian Wine).The classical Penman-Monteith model for evapotransporation was considered, with uncertainties in some parameters (dead mulch covering fraction in the vineyard, maximum temperatures for some months, altitudes and latitudes of the site). The sensitivity analysis was conducted using the SAFE toolbox under Octave framework. The results indicated that the the portion of the water footprint corresponding to the evapotranspiration is more sensitive to the values of mulch covering fraction and the altitude of the site in comparison with the latitude and the maximum temperatures.

This work aims to study and analyze a hybrid power system for electric vehicles consisting of a dual low and high-rate lithium battery block and a fuel cell. In this configuration, the high-rate lithium battery powers the electric vehicle in high power demand processes like acceleration mode or on an uphill road; the low-rate battery operates at a low output power range, servicing the auxiliary systems and low power loads and the fuel cell supplies energy at intermediate power demand conditions, normal driving mode, constant velocity, or flat and downhill terrain. The dual power system improves global efficiency since every power unit operates optimally depending on driving conditions. Power sharing optimizes the lithium battery performance and fuel cell capacity, minimizing the size and weight of each energy system and enlarging the driving range. A comparative study between different lithium battery configurations and fuel cell shows an efficiency improvement of 31.4% for the hybrid dual battery block and fuel cell operating in low, high, and intermediate output power ranges, respectively. The study bases on a simulation process recreating current driving conditions for electric cars in urban, peripheral, and intercity routes. An alternative solution consisting of a hybrid system, fuel cell and a high rate lithium battery produces a 29 % power gain.

Aiming at the current optimization problem of electric vehicle charging path planning, a charging path optimization strategy for electric vehicles under the "Traffic-Price-Distribution" mode is proposed. This strategy builds an electric vehicle charging and navigation system based on road traffic network model, real-time electricity price model and distribution network model. Based on Dijkstra shortest path algorithm and Monte Carlo time-space prediction method, the goal is to minimize the charging cost of electric vehicles. Optimal charging path. The simulation results of MATLAB and MATPOWER show that the electric vehicle charging path optimization strategy can better solve the local traffic congestion problem and improve the safety and stability of the distribution network on the basic of fully considering the convenience of electric vehicle charging.

The development of accurate dynamic battery pack models for electric vehicles (EVs) is critical for the ongoing electrification of the global automotive vehicle fleet, since these serve as a key element in the energy performance of an EV powertrain system. The Equivalent Circuit Model (ECM) technique at the cell level is commonly employed for this purpose, combining accuracy and efficiency in the representation of battery operation within the broader powertrain system. In this study, a second-order ECM model of a battery cell is being developed using Modelica, an equation-based modeling language. Parameter lookup tables at multiple levels of state of charge (SoC), extracted from Li-ion battery cells with 4 different commonly used cathode materials, have been utilized. This approach allows for a representation of the battery systems that are used in a wide range of commercial EV applications. To verify the model, an integrated EV model is developed and simulation results of the FTP75 driving cycle have been compared against an equivalent application in MATLAB Simulink. The findings demonstrate a close match between the results obtained from both software across different system points. Specifically, the maximum deviation of SoC is limited to 0.08% and the maximum value of relative voltage deviation is 1.4%. The verified model enables the exploration of multiple possible architecture configurations of EV powertrains with Modelica.

This work simulates the performance of lithium batteries for electric vehicles under different charge and discharge rates. The simulation is based on similarity factors for power, voltage and current, reproducing the real operation conditions of an electric vehicle at the model scale. Most current driving modes have been analysed corresponding to discharge rates from 0.1C to 0.37C. The simulation has also been applied to determine charging time using charge power in real conditions, from 6.1 kW to 18.3 kW (0.1 C to 0.3C). Driving conditions are obtained using equations for vehicle motion including all forces. Tests have been run under two configurations, continuous and alternate current circuits, to reproduce the two types of engines that electric vehicles use. The simulation shows very good agreement in charge and discharge processes, average deviation of 3% related to real conditions, and 1.6% between them, which proves the validity of the simulation process.

This article explores the crucial task of estimating State of Charge (SOC) in Hybrid Electric Vehicles (HEVs) and examines the applicability of various regression models for this purpose. We delve into the strengths and limitations of Linear Regression, Support Vector Regression (SVR), and Neural Network Regression (NNR) in the context of SOC estimation. Linear regression provides a simple and interpretable baseline, SVR extends this to nonlinear relationships, while NNR emerges as a powerful tool with adaptive capabilities. The choice of model depends on factors such as data characteristics, interpretability, and computational resources. As the field evolves, the article advocates for a nuanced approach, possibly incorporating hybrid models, to achieve robust and accurate SOC estimation, contributing to the ongoing enhancement of HEV efficiency and sustainability.

Battery/Supercapacitor(SC) current tracking control is a key issue for hybrid energy storage system (HESS) in electric vehicles. An innovative passivity-based L2-gain adaptive control (PBL2AC) based on port-controlled Hamiltonian model with dissipativity (PCHD) for reference current tracking and bus voltage stability in HESS is presented. The developed PCHD model has considered both parameter variations and external disturbances. By using L2-gain disturbance attenuation, the PBL2AC ensures robust reference current tracking and stable bus voltage. Moreover, adaptive mechanism is adopted to estimate the electrical parameters. To validate the proposed control scheme for HESS, simulations and experiments were done and compared with traditional PID and sliding mode control under several typical driving cycles, and results show that the effectiveness of the proposed controller can be confirmed.

Probiotic feed additives have attracted considerable research interest in recent years because the effectiveness of probiotics can differ across microbial strains and the supplemented macroorganisms. The present study was conducted on 16 lambs divided into two groups (C - control and E - experimental). The examined lambs were aged 11 days at the beginning of the experiment and 40 days at the end of the experiment. The diet of group E lambs was supplemented with a multi-strain probiotic formulation (Lactobacillus plantarum AMT14, Lactobacillus plantarum AMT4, Lactobacillus rhamnosus AMT15, and Bifidobacterium animalis AMT30), whereas group C lambs did not receive the probiotic additive. At the beginning of the experiment (day 0) and on experimental days 15 and 30, blood was sampled from the jugular vein to determine and compare: phagocytic activity (PHAGOTEST) and oxidative metabolism (PHAGOBURST) of peripheral blood granulocytes and monocytes by flow cytometry. An analysis of the phagocytic activity of granulocytes and monocytes revealed significantly higher levels of phagocytic activity (expressed as the percentage of phagocytic cells and mean fluorescence intensity) in lambs administered the multi-strain probiotic formulation than in the control group. The probiotic feed additive also exerted a positive effect on the oxidative metabolism of both granulocytes and monocytes (expressed as the percentage of oxidative metabolism and mean fluorescence intensity) after stimulation with Escherichia coli bacteria and with PMA (4-phorbol-12-β-myristate-13-acetate).

Facing the charging difficulties of free-floating shared electric vehicle and the high cost of mobile charging for single demand, this article proposes a collaborative charging planning method based on the complementary advantages of fixed charging stations and mobile charging vehicles, which can charge shared electric vehicles more efficiently and reduce the charging cost at the same time. A bi-level programming model for fixed and mobile cooperative charging is constructed. The upper level of the model is the minimization model of system charging total cost, seeking the optimal charging scheme and the number of mobile charging vehicles. The lower model is a fixed and mobile cooperative charging path planning model, which calculates the optimal routes for mobile charging vehicles and the shared electric vehicles that need to be transferred to the fixed charging station. The example results show that the cost of the proposed fixed-mobile cooperative charging scheme is reduced by 12.6% compared with the pure fixed charging scheme and 14.9% compared with the pure mobile charging scheme

- Power grids of the future will likely incorporate more renewable energy distributed generation (REDG), also known as alternative energy systems. REDG units are increasingly being used in electrical transmission networks because to the overall positive effects they have on power networks. REDG systems are the backbone of smart electric networks and are essential to the operation of the smart grid. These REDG systems can additionally improve system reliability by providing some customers with a backup generator in the event of power interruptions. The report offers a thorough evaluation of the existing body of information on the topic of electric vehicles' (EVs') future interactions with the smart grid. The combination of the EVs' potential deployment and the smart grid's conceptual goal presents challenges in electric grid infra-structure, communication, and control. The proposal for connecting EVs to the grid is based on research into cutting-edge smart metering and communication systems. In the context of the vehicle-to-grid (V2G) phenomenon, the possibilities, benefits, and limitations of various EV smart charging systems are also fully examined. There is a fast growing percentage of distributed energy that is wind and solar photovoltaic. Their variable power output, however, introduces fresh challenges for those responsible for organizing, operating, and controlling the power grid. While fluctuations in the electric grid are problematic, they may be mitigated by the entry of EVs into the energy market. As such, we do a comprehensive literature search to learn more about the exciting research gap that needs to be filled and the most recent possible answer that involves EVs. Additionally, we take a close look at the practicality of the sophisticated V2G technology. The smart grid is a developing concept for the world's energy infrastructure, and this study analyzes in depth how EVs interact with it.

As the emission regulations get more and more stringent in the different fields of energy and environmental systems, the electric and fuel cell vehicles (FCV) have attracted growing attention by automakers, governments, and customers. Research and development efforts have been focused on devising novel concepts, low-cost systems, and reliable electric/fuel cell powertrain. In fact, electric and fuel cell vehicles coupled with low-carbon electricity sources offer the potential for reducing greenhouse gas emissions and exposure to tailpipe emissions from personal transportation. In particular, Pedal Assisted Bicycles (PAB) popularity is rising in urban areas due to their low energy consumption and environmental impact. In fact, when electrically moved, they are zero emission vehicles with very low noise emissions, as well. These positive characteristics could be even improved by coupling a PAB with a fuel cell based power generation system, thus increasing the vehicle autonomy without influencing their emissions and consumption performances. In this paper, four types of vehicles are compared from an environmental and accessibility point of view: conventional car, bus, electric PAB and hydrogen fuel cell PAB; for such vehicles, the respective utilization stages are accounted for, i.e. without considering the manufacturing process. The analysis has been carried out comparing different vehicles performance along different routes of an Italian middle-size city, Viterbo, which represents a very good pilot case as its Municipality is adopting many solutions suggested by European Union (EU) through the planning tool called Sustainable Energy Action Plan (SEAP). The comparison is based on an ad-hoc developed mathematical procedure, which includes environmental (greenhouse gas and air pollution emissions), health (pollutants toxicity levels) and accessibility time (waiting times) indicators. According to this analysis, electric and fuel cell PAB exhibit interesting advantages over the other vehicles. However, the global economic efficiency of electric or fuel cell apparatus depends substantially on the exploited source of electrical energy.

This study conducts a comprehensive review of the integration of Hybrid Electric Turbochargers in mass-produced road vehicles. The primary objective is to inform the readers about the potential advantages and innovative applications of HETs. Employing a SWOT analysis, the research compares the performance of HETs on thermal engines versus traditional turbochargers and HETs on thermal engines versus HETs on hybrid engines based on a synthesis of multiple research findings. The study elucidates the fundamental mechanics of turbochargers, emphasizing their inherent benefits. Additionally, it introduces a novel approach with the integration of the hy-brid-electric turbocharger, demonstrating its capacity, supported by practical examples. The in-corporation of turbochargers with energy recovery mechanisms, including HETs, is shown to significantly contribute to emissions reduction. Moreover, the electric drive facilitates the utili-zation of hybrid turbines, particularly on low-displacement engines, resulting in enhanced effi-ciency, torque, and liter power. In conclusion, the research underscores the potential of HETs to substantially mitigate the environmental impact of the transportation sector by reducing emis-sions and conserving energy. Their ability to optimize the fuel efficiency and power output of internal combustion engines, coupled with the feasibility of downsized engines, positions HETs as an attractive option for both manufacturers and consumers. However, it is acknowledged that the implementation of HETs is in its early stages, necessitating further research to comprehensively understand their environmental impact. Despite this, the promising positive impact on the en-vironment designates HETs as a technology deserving close attention in the forthcoming years.

Members of the genus Armillaria are widespread forest pathogens against which effective protection has not yet been developed. Due to their longevity and the creation of large-scale cloning of Armillaria individuals, the use of mycoviruses as biocontrol agents (BCAs) against these pathogens could be an effective alternative. This work describes the screening of double-stranded RNA banding patterns as a first approach for potential virus discovery and the use of stranded total RNA-seq for viral detection and characterization in Armillaria spp. collected in the Czech Republic. A total of five single-stranded RNA viruses were detected in Armillaria ostoyae and A. cepistipes, including the first virus of the family Tymoviridae found infecting a member the genus Armillaria and four viruses belonging to the recently described virus group "ambiviruses" with circular ambisense genome organization. Both hammerhead (HHRz) and hairpin (HpRz) ribozymes were detected in all the ambiviricot sequences. Armillaria viruses were compared by phylogenetic analysis and confirmed their specific host by direct RT-PCR, which showed the occurrence of interspecies transmission.

Nowadays, there are several electric vehicle (EV) on the market, due to the innovation of technology that promotes the new components such as battery, transmission, electric motors. This paper proposes a design approach for the configuration synthesis and simulation of the in-wheel-hub motor transmissions with the six-link mechanisms. The synthesis process shows 6 mechanisms with six members and eight joints, 15 new clutchless motor transmissions and 16 new clutched motor transmissions. A novel motor transmission in the feasibility of the synthesized configurations is selected as an example to analyze the working principle with operation modes and power flow paths. And, this design is modeled for the simulation process that generates the results of operation mode transition and energy regulation.

In Brazil, Brazilian borreliosis (BB), characterized molecularly, exhibits symptoms akin to Lyme disease (LD) but presents unique epidemiological, clinical, and morphological features. This study explores the incidence, diagnostic challenges, and awareness initiatives regarding this disease in Brazil. Employing a narrative review methodology, data were sourced from Pubmed, Ebsco, Google Scholar, and Web of Science without temporal constraints. Results reveal diagnostic complexities in Brazil due to the low sensitivity of conventional tests and the use of North American B. burgdorferi antigens. Despite challenges, epidemiological studies indicate an upswing in cases in humans and animals. Therefore, there is an urgent need to define new biomarkers, molecular strategies, or new antigenic targets to advance the development of vaccines and diagnostics. Early and accurate pathogen identification is crucial for active surveillance to comprehend and manage this zoonosis.

Existence of hybrid electric vehicles nowadays is one of the emerging technologies worldwide. Different countries are using this technology on their transportation, and nowadays, a lot of other countries are gradually adapting on using this kind of innovation in their transport system. Philippines is among these countries which aims to have a sustainable mobility by embracing the use of hybrid - electric vehicles but still lots of consumers are lacking awareness on this technology. Lack of information on the social, environmental, and economic potential benefits are among the main issues of Philippines consumers that really affect its uptake. The goal of this study was to evaluate the consumer’s perception and purchase intention on the transition of hybrid - electric vehicles for sustainable mobility and transportation here in the Philippines. It aimed to assess consumer awareness with hybrid - electric vehicles and its potential benefits to human beings in terms of social, environmental, and economic aspects. The study also gauged the impact of this perception on consumer’s purchase intention on hybrid - electric vehicles. These objectives were attained using the Descriptive Statistics and Correlation Analysis. Using these methods, the results revealed that majority of the consumers believed on the positive benefits of transitioning to hybrid - electric vehicles in terms of social, environmental, and economic aspects. This study also showed that these benefits have high impact on the consumer’s intention of buying or having a hybrid – electric vehicle. Electric vehicle is one of the most effective measure of decarbonizing the transport system. Furthermore, government and automotive businesses must strengthen their drive in spreading awareness of hybrid – electric vehicle’s benefits to consumers to further support country’s effort for development.

In most studies reported in literature, a flat, constant coefficient is used to convert the amount of optical power required at the input of an optical link into the effective power consumption. This constant coefficient approach is very simple to apply but not accurate, since laser efficiencies are dependent on the optical output power in addition to the operating temperature. In this paper the relationship between laser wall plug efficiency and optical power output is investigated and the effect of this variance on optical links and on-chip laser source electric power consumption is presented

The massive amount of human biological, imaging and clinical data produced by multiple and diverse sources, necessitates integrative modeling approaches able to summarize all this information into answers to specific clinical questions. In this paper, we present a hypermodeling scheme able to combine models of diverse cancer aspects regardless of their underlying method or scale. Describing tissue-scale cancer cell proliferation, biomechanical tumor growth, nutrient transport, genomic-scale aberrant cancer cell metabolism, and cell signaling pathways that regulate the cellular response to therapy, the hypermodel integrates mutation, miRNA expression, imaging, and clinical data. The constituting hypomodels as well as their orchestration and links are described. Two specific cancer types, Wilms tumor (nephroblastoma) and non-small cell lung cancer, are addressed as proof of concept study cases. Personalized simulations over the actual anatomy of a patient have been carried out. The hypermodel has also applied to predict tumor control after radiotherapy and the relationship between tumor proliferative activity and response to neoadjuvant chemotherapy. Our innovative hypermodel holds promise as a digital twin based clinical decision support system and the core of future in silico trial platforms, although additional retrospective adaptation and validation is necessary.

This research paper presents an enhanced economic load dispatch (ELD) approach using the Teaching-Learning-Based Optimization (TLBO) algorithm for 10 thermal units, examining the impact of Plug-in Electric Vehicles (PEVs) in different charging scenarios. The TLBO algorithm is utilized to optimize the ELD problem, considering the complexities associated with thermal units. The integration of PEVs in the load dispatch optimization is investigated, and different charging profiles and probability distributions are defined for PEVs in various scenarios, including overall charging profile, off-peak charging, peak charging, and stochastic charging. These tables allow for the modeling and analysis of PEV charging behavior and power requirements within the power system. By incorporating PEVs, additional controllable resources are introduced, enabling more effective load management and grid stability. The comparative analysis showcases the advantages of the TLBO-based ELD model with PEVs, demonstrating the potential of coordinated dispatch strategies leveraging PEV storage and controllability. This paper emphasizes the importance of integrating PEVs into the load dispatch optimization process, utilizing the TLBO algorithm, to achieve economic and reliable power system operation while considering different PEV charging scenarios.

There is currently an exponential growth in the electric vehicle market, which will require an increase in the electrical grid capacity to meet the demand of charging. If, on the one hand, the introduction of energy generation from renewable sources can be used to meet that requirement, the intermittent nature of some of these sources poses challenges to the required real time equilibrium between generation and consumption. The impossibility of controlling generation from some of these sources leads to the attempt of controlling and manage the consumption of electricity, according with the levels of generation. In this context, the emergence of smart grids has introduced mechanisms that guarantee a balance between consumption and generation of electricity. One of these mechanisms is the smart charging of electric vehicles. Effective smart charging relies on communication between the supply equipment and the electric vehicle, en-abling the adjustment of the energy transfer according with the generation levels. Thus, there is a necessity for a standardised system that guarantees this communication. In this context, the ISO 15118 standard, allows high level communication mechanisms, much beyond the basic control solutions offered by the IEC 61851-1 specification. In this context, this paper presents the development of a charge emulation system, based on the ISO 15118 communication protocol and discusses its application for demand response purposes.

Electric vehicles (EVs) are being endorsed as the uppermost successor to fuel-powered cars, with timetables for banning the sale of petrol-fueled vehicles announced in many countries. However, the range and charging times of EVs are still considerable concerns. Fast charging could be a solution to consumers' range anxiety and the acceptance of EVs. Nevertheless, it is a complicated and systematized challenge to realize the fast charging of EVs because it includes the coordinated development of battery cells, including electrode materials, EV battery power systems, charging piles, electric grids, etc. This paper aims to serve as an analysis for the development of fast-charging technology, with a discussion of the current situation, constraints and development direction of EV fast-charging technologies from the macroscale and microscale perspectives of fast-charging challenges. It is emphasized that to essentially solve the problem of fast charging, the development of new battery materials, especially anode materials with improved lithium ion diffusion coefficients, is the key. It is highlighted that red phosphorus is the most promising anode that can simultaneously satisfy the double standards of high-energy density and fast-charging performance to a maximum degree.

With the development of higher voltage power grid, the high and low voltage parallel loops are emerging, which lead to energy losses, even threaten the security and stability of power system. The multi-infeed HVDC configurations widely appearing in AC/DC interconnected power system make this situation even worse. Aimed at energy saving and system security, a decision optimization method for power grid operating condition with high and low voltage parallel loops is proposed in this paper. Firstly, considering hub substation distribution and power grid structure, parallel loop opening schemes are generated with GN algorithm. Then, candidate opening schemes are preliminarily selected from all these generated schemes based on a filtering index. Finally, with the influence on power system security, stability and operation economy in consideration, an evaluation model for candidate opening schemes is founded based on analytic hierarchy process (AHP). And a fuzzy evaluation algorithm is used to find the optimal scheme. Simulation results of New England 39-bus system and an actual power system validate the effectiveness and superiority of this proposed method.

Populational aging is marked by chronic non-communicable diseases, such as metabolic syndrome (MetS). IL-10 and IL-1β are pleiotropic cytokines with multiple biological effects linked to metabolic disorders. This cross-sectional study assessed 193 participants' IL-10 and IL-1β serum levels regarding their role in developing MetS, clinical characteristics, and their IL1B rs1143627 and IL10 rs1800890 variants' genotype frequencies in a population over 60. IL-10 levels correlated weakly with HDL levels and fat mass and inversely with triglycerides, glucose, glycated hemoglobin, and estimated average blood glucose levels. IL-10 levels were also indirectly influenced by the patient's T2DM duration, lean mass amount, and bone mineral content. Participants with altered HDL, elevated serum glucose, raised HbA1c levels, or those over 80 had reduced serum IL-10 levels compared to those with normal levels or other age groups, respectively. Women also had higher serum IL-10 levels than men. Dissimilarity, IL-1β levels correlated directly only with the number of total leukocytes and segmented neutrophils, showing only significant variations with self-reported alcohol consumption. Our study also found that those with the IL10 AA genotype (lower IL-10 levels) had a significantly higher risk of developing MetS. These findings may help direct future research and more targeted therapeutic approaches in older adults.

With the continuous development of Artificial Intelligence technology and Internet of Things engineering, more and more driver-less vehicles have been developed and put into the industrial production. The birth of driver-less vehicles undoubtedly brings new vitality to a large amount of industries, particularly in transportation. For the mining industry, transportation is undoubtedly an extremely important link in the whole production process. If the driver-less vehicles can be applied to the underground mines, it can not only improve the production and transportation capacity of the whole mine, but also can reduce the occurrence of many mine safety accidents. ZigBee WSN technology can play a greater role in the narrow environment like underground mines according to the relevant literature, this concept paper just like a engineering project plan mainly tries to integrate the ZigBee WSN technology and the communication-based train control (CBTC) system to explore the possibility of the driver-less vehicles to be used in the underground mines, which aims to solve practical engineering problems for the engineering projects. As the mining engineers, we put forward the concept of this integrated system in this concept paper, but we need to continue to work hard for the future of the underground mines. This concept paper serves just as a guide to the Tossing out a brick to get a jade gem, has a few implications for the development of underground mine transportation.

Precision agriculture is a farm management technology that involves sensing and then responding to the observed variability in the field. Remote sensing is one of the tools of precision agriculture. The emergence of small unmanned aerial vehicles (sUAV) have paved the way to accessible remote sensing tools for farmers. This paper describes the comparison of two popular off-the-shelf sUAVs: 3DR Iris and DJI Phantom 2. Both units are equipped with a camera gimbal attached with a GoPro camera. The comparison of the two sUAV involves a hovering test and a rectilinear motion test. In the hovering test, the sUAV was allowed to hover over a known object and images were taken every second for two minutes. The position of the object in the images was measured and this was used to assess the stability of the sUAV while hovering. In the rectilinear test, the sUAV was allowed to follow a straight path and images of a lined track were acquired. The lines on the images were then measured on how accurate the sUAV followed the path. Results showed that both sUAV performed well in both the hovering test and the rectilinear motion test. This demonstrates that both sUAVs can be used for agricultural monitoring.

Cancer treatment is a significant focus in medicine owing to the increasing global incidence. Patients with advanced cancers that do not respond to conventional therapies have limited options and an unfavorable prognosis. Consequently, researchers are investigating complementary approaches to conventional treatments. One such approach is alkalization therapy, which aims to neutralize the acidic tumor microenvironment (TME) by increasing its pH level. The acidic TME promotes inflammation, tumor progression, and drug resistance. Alkalization therapy has been demonstrated to be effective for various cancers. In addition, natural products, such as triterpenoids, parthenolides, fulvic acid, Taxus yunnanensis, and apple pectin have the potential to alleviate symptoms, maintain physical fitness, and improve treatment outcomes of cancer patients through their anti-inflammatory, antioxidant, and anticancer properties. In this review, we focus on the effects of alkalization therapy and natural products on cancer. Furthermore, we present a case series of advanced cancer patients who received alkalization therapy and natural products alongside standard treatments, resulting in long-term survival. We posit that alkalization therapy together with the supplementation of natural products may confer benefits to cancer patients by mitigating the side effects of chemotherapy and complementing standard treatments. However, further research is warranted to validate these clinical findings.

Microgrid is one of the important ways to accommodate distributed PV generation. However, the optimal planning scheme of microgrids is closely related to solar resources, load characteristics, and the key components of microgrid systems. This paper uses HOMER software to conduct collaborative planning for grid-connected PV-storage microgrids with electric vehicles in multiple scenarios. Specifically, we construct a multi-scenario capacity optimization model for PV generation, energy storage, and converters, considering both the cleanliness and economic performance of microgrids. The performance is quantified using indices such as net present cost, levelized cost of electricity, and carbon dioxide emission under multiple scenarios. Finally, we conduct extensive case studies on a business park in Wuhan, China, to compare and discuss the planning performance under multiple scenarios, as well as sensitivity analysis with specific cases.

Polymer inclusion membranes (PIMs), which contained cellulose tri-acetate used as a polymer base, a plasticizer in the form of o-NPOE, and meso-tetra methyl tetrakis-[methyl-2-(4-acetlphenoxy)] calix[4]pyrrole (KP) used as a carrier, were prepared and tested for the effective purification of aqueous solutions of methylene blue dye. The scanning electron microscope (SEM), thermogravimetric analysis (TGA), and attenuated total reflection Fourier transform infrared (ATR-FTIR) were used to define the microstructure and surface of PIMs. Experimental results showed that with the increased concentration of methylene blue in an aqueous solution, the removal percentage also increased. Further observation also showed that the flux increased with the rise of the source phase pH values from 3 to 10. The carrier and plasticizer content in the membrane significantly influenced the membrane’s transport properties. The optimal composition of the membrane in per cent by weight was for KP: 74% plasticizer; 18% support; 8% carrier. It was shown that the membrane with optimal composition showed good reusability and enabled the easy and spontaneous separation of methylene blue from aqueous solutions.

IgE-mediated wheat allergy is a gluten-related disorder. Wheat is one of the five most common food allergens in children. However, the natural history of IgE-mediated wheat allergy has seldom been described in the research literature. This study presents the current state of knowledge about the IgE-mediated wheat allergy in children.

The importance of conflict analysis and safety considerations during work zone intervals at signalized intersections cannot be overstated, as these periods pose unique challenges and safety risks for both motorists and pedestrians. Work zones introduce temporary changes to traffic patterns, lane configurations, and signal timings, which can disrupt the flow of traffic and increase the likelihood of conflicts between vehicles and pedestrians. By conducting comprehensive conflict analysis, it is possible to identify potential conflict points, such as merging areas, pedestrian crossings, and lane transitions, and implement targeted safety measures to mitigate risks. Additionally, considering safety considerations during work zone intervals is paramount to safeguarding the well-being of road users and minimizing the occurrence of conflicts or collisions. Measures such as enhanced signage, temporary traffic control devices, and increased enforcement efforts can help promote compliance with traffic regulations and ensure the safe passage of vehicles and pedestrians through work zones. This study investigates the dynamics of traffic behavior and safety implications within work zones at signalized intersections, aiming to address key challenges and enhance intersection management strategies. Through comprehensive analysis, varying frequencies of Vehicle-to-Vehicle (V2V) conflicts were observed across different movement directions, with significant occurrences identified in specific phases of the traffic signal cycle. Specifically, 69 V2V conflicts in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8 were recorded. Furthermore, the examination of Vehicle-to-Pedestrian (V2P) conflicts underscored the importance of prioritizing pedestrian safety, with notable occurrences observed in phases associated with pedestrian crossing movements. Meanwhile, despite the closure of left-turn movements, instances of red light running persisted, with 69 red light running events in phase #1, 181 in phase #2, 31 in phase #3, 207 in phase #4, 1 in phase #5, 274 in phase #6, 93 in phase #7, and 186 in phase #8.To mitigate these safety concerns during work zone intervals, proactive measures are essential. Strategies include improving communication through clear signage and advanced warning systems, enhancing enforcement efforts with red light cameras and increased police presence, and investing in infrastructure improvements such as temporary pedestrian facilities. Integration of real-time data from sensors, particularly LiDAR technology, offers significant advantages in detecting conflicts and red light runners during work zone intervals. LiDAR's precise, real-time data on vehicle and pedestrian movements, combined with its 360-degree coverage and high spatial resolution, facilitates accurate detection and tracking of potential conflict scenarios, contributing to enhanced intersection safety and traffic management efficiency. This study endeavored to conduct a traffic safety analysis during work zone intervals at a signalized intersection in Baltimore City, known for its high rate of traffic crashes, utilizing LiDAR sensor technology.

Electric vehicles (EVs) will have a greater need for the amount of electricity needed to charge them as their popularity grows. It is anticipated that in order to accomplish this objective, it will be essential to implement a variety of solutions for grid transportation that are designed to complement one another and to make significant changes to the transmission infrastructure. It is possible to reduce the amount of energy that is lost on the power network through strategic planning and control, which may include economic models and methods to engage and reward users. This would eliminate the need for grid upgrades. Charging electric vehicles can also assist alleviate problems with transmission systems that are caused by the allocation of electric vehicles (EVs) using bidirectional charging method. The most significant problems that can occur with a transmission network are power loss and unstable voltage. Adding EV units to the transmission network is typically an effective method for resolving these challenges. As a result, EVs need to have the appropriate arrangement and dimensions. This research establishes where and how many electric vehicles (EVs) should be in a radial transmission network both before and after the adjustment is made. An artificially intelligent (AI) approach, known as a hybrid genetic algorithm particle swarm optimization (HGAIPSO), is used both before and after the radial network modification to find the optimal EV location and size. When electric vehicles are coordinated in an active transmission network, power losses are decreased, voltage profiles are raised, and system stability is increased. These benefits can be attributed to the greater use of electric vehicles. The simulation found that incorporating EVs into the testing system resulted in a considerable decrease in the quantity of power that was wasted. The minimal bus voltage of the system also undergoes similar kinds of enhancements. According to the findings of the comparative study, the proposed method mitigates both the voltage fluctuations and the power losses that occur in the transmission system. For type 1, type 2, and type 3 EV allocations, the IEEE-30 bus test system reduced real power loss by 40.70%, 36.24%, and 42.94%, respectively. IEEE-30 bus voltage reaches 1.01 pu.

Lake sediment records are of great importance for understanding the evolution of watershed environments. Various studies have been carried out to constrain the depositional ages of lake sediments and examine their physical, chemical, and biological characteristics, aiming to construct historical vegetation, environment and climate patterns in Chinese lake watersheds. In this study, we obtained relevant literature on lake sediment records by searching the key word 'age-depth' from the databases – Web of Science and China National Knowledge Infrastructure (CNKI, the largest Chinese academic database). We analyzed the literature based on its type (published in a Chinese/English journal or as a Master’s/PhD thesis), period of publication, journal (if published in a journal), key authors, study area, dating scale, and main aims. The results suggest that the lakes in the plateau regions are the most popular research topic typically covering 100–200 years (short-term) and 500–30,000 years (long-term). The literature focuses on a wide range of topics, from past environmental evolution in the watershed to lake ecology, and provides a solid foundation for better understanding regional climate change and the preservation of lake environments and ecosystems. In the future, resulting data from environmental reconstruction with lake sediments will be needed to integrate with emerging information processing technologies (e.g., artificial intelligence and meta-analysis), to disentangle the complex interplay between Earth surface processes and global climate change; furthermore, strengthening interdisciplinary collaboration will deepen our comprehension of man-land relationship and promote the sustainable management of lake ecosystems in the context of global climate change.

In this paper, we proposed the fusion of two projection based face recognition algorithms: local binary Patterns in DCT domain and singular value decomposition (SVD) characterized by its simplicity and efficiently. Standard databases ORL are used to test the experimental results which prove that proposed system achieves more accurate face recognition as compared to individual method.

With the ongoing amendment of the EU legislation on the treatment of urban wastewater, stricter requirements for the removal of pollutants are expected, which calls for the need for innovative wastewater treatment technologies. Biological systems are still the first choice. The survey of typical bioreactors applied in wastewater treatment is presented. Wastewater treatment objective, bio-chemical environment and microbial growth are selected as a main criterion for classification of these bioreactors. Hydraulic and kinetic aspects are considered, advantages and drawbacks of these bioreactors regarding the selection of appropriate type of the reactor as well as regarding operation of reactors are mentioned. The aim of this paper is to provide operators and designers with a brief overview of selected traditional and advanced processes, reactors, and technologies for nutrient removal from municipal wastewater. Another goal is to discuss the possibilities and limitations to comply with more strict effluent standards. From the evaluation of the published papers, it follows that the currently applied traditional methods of nutrients removal have the potential to convey also with the expected stricter limits.

In this study, we investigate the growth of EV ownership among Swedish households. Through detailed socio-economic data and the Swedish vehicle register, we are able compare characteristics of households that bought an EV in 2016 with those that became EV owners in 2020. We are thus able to describe and analyse the transition from the early EV owners to the early majority. Unlike many prior studies in the field, which are based on surveys or interviews, this study relies on registry data, thereby encompassing all car owners in Sweden. In this study, we have also scrutinized the significance of ownership mode by distinguishing between households that have chosen to purchase a car and those that have chosen to lease one. The results show that the demand for EVs has reached new consumer groups, e.g. both younger and older car buyers, and that the option to lease a car privately has broadened the market for new cars in general, and for EV’s in particular.

Despite the recognized influence of maximum or acceleration strength on combat techniques and countermovement jump (CMJ) performance, the relationship between body segment weights and combat techniques remains unexplored. This study aimed to examine the impact of body segment weights, including striking arm weight (SAW), kicking leg weight (KLW), and trunk weight (TW), on the dynamics of direct punches (DP), palm strikes (PS), elbow strikes (ES), front kicks (FK), and CMJ performance. Sixteen male military cadets (22.3±1.8 years, 181.4±7.0 cm, 83.0±8.1 kg) performed combat techniques, with performance measured by using a force plate and body segment weights assessed by Dual-energy X-ray absorptiometry. Spearman's correlation analysis was applied. Results indicated a strong correlation between KLW and FK impulse (r = 0.64, p ≤ 0.01) and CMJ impact force (r = 0.80, p ≤ 0.01). SAW showed moderate correlations with DP impulse (r = 0.53, p ≤ 0.05) and ES impulse (r = 0.54, p ≤ 0.05). Additionally, FK peak and impact forces were strongly correlated with CMJ jump height (r = 0.74, p ≤ 0.01; r = 0.77, p ≤ 0.01), and FK peak force moderately correlated with CMJ peak velocity (r = 0.63, p ≤ 0.05). These findings suggest targeted arm strength training could enhance striking abilities, while improvements in leg acceleration strength may directly boost kicking performance and vertical jump capabilities, which are crucial in combat scenarios.

Although a growing number of evidence emphasizes superior of VATS over conventional thoracotomy, still is little known about early postoperative diaphragm muscle function after lobectomy via these two approahces. To fill the gap in existing literature, we conducted comparative study between VATS and conventional thoracotomy in terms of postoperative diaphragm muscle function, assessing its contractility, strength, magnitude of effort and potential risk of dysfunction such as: atrophy and paralysis. A total of 59 patients (30 after VATS), who underwent anatomical pulmonary resection at our institution, were enrolled into this study. The control group was consisted of 28 health subjects without medical condition could contribute to diaphragm dysfunction. Diaphragm muscle was assessed before and after surgery using ultrasonography. We found that both surgical approaches were associated with postoperative impairment of diaphragm muscle function - compared to baseline data. Postoperative reduction of diaphragm contraction was demonstrated in all of 59 patients. In case of control group, the differences between measurements were no observed. We noted that lobectomy via thoracotomy was linked with greater percentage of patients with diaphragm paralysis or/and atrophy that VATS. Similar findings were observed in refer to diaphragm magnitude effort, as well as, diaphragm contraction strength, where minimal invasive surgery was associated with better diaphragm function parameters - in comparison to thoracotomy. Disturbance of diaphragm work was reported both at operated and non-operated side. Upper right and left lobectomy was connected with greater diaphragm function impairment than other segments. In conclusion, VATS technique seems to be less invasive than conventional thoracotomy providing a better postoperative function of a main respiratory muscle.

Hybrid electric vehicles (HEVs) are perceived as a first step towards the future of sustainable transport. Of course, battery electric vehicles (EVs) are currently ideal for what is wanted to stand for transport in the future. The lack of infrastructure for these vehicles makes many potential users choose hybrid vehicles in these conditions. This paper analyzes the influence of the engine and electric motor of hybrid vehicles on their performances. Three engines with slightly different power and close characteristics were considered for configured models. Also, two electric motors with very different power but having a very close maximum continuous torque have been used in models. One is an induction motor, and the other is a permanent magnet synchronous motor. ADVISOR software has been used for vehicles configuration and simulation. Series and parallel hybrid vehicles have been considered. The main dynamic performances and the fuel consumption in two driving cycles were compared for the configured models. Three conventional models with the same engines used in HEVs have also been simulated for reference. The results highlight that in the case of HEVs, the choice of combination engine/electric motor is crucial in obtaining the best report between dynamic performances and low fuel consumption, and implicitly a low negative impact on the environment.

This paper presented a method for calculating daily load curves by considering charging and discharging locations of electric vehicles to help to understand the impact of loads generated by charging and discharging of electric vehicles on the power grid. Based on the estimated PEVs’ share, the PEVs discharge power was calculated to reflect both the characteristics of the arriving vehicle in the morning and the SMP plan after establishing a assumption that the electric vehicle arrived at work in the morning and the electric vehicle arrived at home in the afternoon for each of the charging/discharging locations, that is, work and home, of electric vehicles in the city. After calculating the daily load curve for each charging/discharging power type for the PEVs charging strategy, which takes into account both the characteristics of the vehicle arriving at home in the afternoon and the TOU fare system and the characteristics of the vehicle arriving at work in the morning and the SMP fare system, it was analyzed by comparing the impact assessment on the grid by adding the existing load. The results of this paper provide an accurate understanding of the impact of PEVs charging and discharging loads on the power grid. The results should help to establish PEVs charging and discharging load management plans to prevent overloading the power grids with appropriate SMP and TOU tariffs while curbing the reinforcement and expansion of power grids as much as possible.

Pulmonary hypertension (PH) is a progressive and potentially fatal complication of sickle cell disease (SCD) affecting 6-10% of adult SCD patients. Various mechanisms and theories have been evaluated to explain the pathophysiology of the disease. However, questions remain, particularly regarding the clinical heterogeneity of the disease in terms of symptoms, complications, and survival. Beyond the classical mechanisms that have been thoroughly investigated and include hemolysis, nitric oxide availability, endothelial disorders, thrombosis, and left heart failure, attention is focused nowadays on the potential role of the genes involved in such processes. Potential candidate genes are investigated through next-generation sequencing, with the TGF-β pathway being the initial target. This field of research may also provide novel targets for pharmacologic agents in the future as is already the case with idiopathic PH. The collection and processing of data and samples from multiple centers can yield safe results that will allow a better understanding of SCD-related PH as a part of the disease’s clinical spectrum. The following review attempts to capture the most recent findings of studies on gene polymorphisms that have been associated with PH in SCD patients.

Livestock farming plays a crucial role in agriculture and is important in meeting people's needs for a better life in the new era. However, due to China's "double carbon" target, the development of animal husbandry has been affected by the level of carbon emissions. To study the spatial dis-tribution and dynamic evolution of livestock carbon emissions in Shandong Province, we used panel data from 16 prefecture-level cities in Shandong Province from 2001 to 2022 to determine the spatial differentiation of livestock carbon emissions in Shandong Province and their dynamic evolution by combining spatial and non-parametric estimation methods based on livestock carbon emissions and livestock emission intensity measured by city. The results of the study show that the distribution of carbon emissions from animal husbandry in Shandong Province is in a "dis-persed-aggregated" pattern, and the intensity of carbon emissions shows a gradual downward trend, with a ladder-like distribution characterized by high in the west and low in the east. Through the analysis of the dynamic evolution of nuclear density, we observe that the regional differences of animal husbandry carbon emissions in Shandong Province are expanding, showing the characteristics of polarization. The difference of carbon emissions in the peninsula is gradually narrowing, and the phenomenon of polarization is weakening. The carbon emissions of animal husbandry in the central and western regions of Shandong are polarized.

In Germany, the phloem sucking planthopper Pentastiridius leporinus (Hemiptera: Cixiidae) currently represents the epidemiological driver for the spread of the syndrome “Basses Richesses” in sugar beet, which results in a reduced sugar content and an economic loss for the farmers. This disease is associated with the γ-proteobacterium ‘Candidatus Arsenophonus phytopathogenicus’ and the Stolbur phytoplasma ‘Candidatus Phytoplasma solani’. Recently, P. leporinus was found in potato fields in Germany associated with Stolbur-like symptoms in this crop. In this study, we confirmed that the vector completes its lifecycle on sugar beet as well as on potato when reared under controlled conditions. Transmission experiments with adults of this vector combined with molecular analyses showed for the first time that both pathogens are transmitted by this vector to potato. For an accurate assessment of the Ca. P. solani and Ca. A. phytopathogenicus titers in the vector and host plants, gBlocks derived from the hsp20- and 16S rRNA gene were employed, respectively. For Ca. P. solani the limit of detection was determined in potato and sugar beet material. The results of this study will further research on the epidemiology of the syndrome “Basses Richesses” and “Stolbur” diseases and the response of host plants and vector insects to both pathogens.

Fingerprints are essential for human identification and are valuable tools in criminal investigations. The pursuit of new materials for digital printing is expanding, with increasing interest in natural compounds such as bixin, sourced from annatto seeds. Despite its traditional use as a natural dye with medicinal properties, the potential of bixin in papilloscopy remains largely untapped. In this study, we meticulously extracted bixin from annatto seeds and meticulously developed composites incorporating zinc carbonate (bixin/ZnCO3) and kaolinite (bixin/kaolinite). UV-visible spectroscopy was used for characterization, and the extracted bixin showed absorption peaks at 429, 453, and 481 nm, which were very similar to standard peaks at 429, 457, and 487 nm. The two samples also had the same retention times (7.07 min) according to further liquid chromatography analysis. Sweat pores were easier to detect thanks to the effectiveness of the bixin/ZnCO3 and bixin/kaolinite composites in creating high contrast sebaceous and natural latent fingerprints. These results highlight the composites’ potential as novel and fascinating instruments for papilloscopy applications, which might also improve forensic investigations.

The design and integration of intelligent energy management systems in hybrid Electric Vehicle (EV) charging stations, leveraging Industry 4.0 and renewable energy sources, is crucial for advancing sustainability, efficiency, and technological development. This paper introduces a pioneering hybrid EV charging station that employs hybrid power sources, combining photovoltaic panels, a battery, and a fuel cell, each operating at 240V and 14A. Utilizing wireless power transfer technology, the system comprises five integral blocks: a power source, a boost converter, a High-Frequency (HF) inverter, transfer coils, and EV batteries. The optimization process involves two key stages: i) maximum power point tracking optimization of the boost converter, ensuring maximum power generation by varying the duty cycle between 10% and 90%, and ii) the HF phase employing a class-ϕ2 inverter at 30 MHz, synchronized with the resonant frequency of wireless power transfer coils. Control is executed through a digital signal processor card utilizing zero-voltage switching for efficient operations. This design enhances sustainability in EV charging solutions by optimizing power transfer through the use of hybrid sources.

The paper outlines a methodology for developing a model to estimate energy consumption in electric vehicles (EVs). The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. Such models hold particular significance for microscale road analyses. The resultant model for test data in summer conditions demonstrates validation indicators with an R2 of 86% and an MSE of 1.4, while for winter conditions, the values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision-makers in city road management and infrastructure planners, aiding both cognitive understanding and better planning of charging infrastructure networks.

: Tunnel instability and lining integrity are intimately tied to the creep properties of the surrounding rock. The acquisition of rock mass creep parameters is critical in ascertaining the appropriate timing for lining construction. Nevertheless, creep tests on rock specimens conducted in a controlled setting cannot be straightforwardly extrapolated to rock mass creep analysis. This study performs laboratory-based creep tests on mudstone samples and establishes the corresponding creep constitutive model. Integration of a Mohr-Coulomb element with the Burgers model in series serves to characterise the yield creep behaviour of the rock mass. Additionally, the research outlines a series of 100 orthogonal experiments utilising randomised creep parameters, and formulates a GA-BP neural network inversion model. Employing long-term deformation measurements from the crown and sidewalls of the project site, this study deduces the creep parameters of the mudstone in the investigated tunnel section and examines the prolonged deformation traits of the surrounding rock. Drawing on the deformation traits of the surrounding rock and the forces impacting the primary support and lining structures, this paper evaluates the earliest and latest viable lining casting periods and pinpoints an optimal timing interval for lining implementation. The methodologies employed herein can serve as a benchmark for analogous endeavors internationally.

Latin America and the Caribbean offer the most favorable natural and social conditions for the emergence of cattle ranching that regenerate and maintains the ecosystem services on which life on Earth depends, that is energy efficient and that contributes to meeting fundamental human food needs. Unlike other major global producers, cattle farming in Latin America and the Caribbean is fundamentally based on pasture. Therein lies both its strength (due to the regenerative potential of well-managed pastures) and its vulnerability (due to the pervasive link between deforestation and pasture formation, especially in the Amazon, where 40% of the Brazilian herd is concentrated). The diversification of pastures (today marked by impressive monotony, with brachiaria accounting for 85% of the species in Brazil), the introduction of legumes and tree plantations within them, and the proper management of herds, open the way to the possibility of drastically reducing the area currently occupied by the activity (especially in the Amazon) while maintaining the supply of its products. Agricultural research has disseminated forms of moderate intensification of cattle ranching, which represent a low opportunity cost of animal feed and, at the same time, regeneration of ecosystem services that have so far been destroyed by the prevailing methods of farming.

Merkel cell carcinoma (MCC) is a rare, highly aggressive skin cancer characterized by two etiologies: the first one, observed in most cases, is attributed to Merkel cell polyomavirus (MCPyV), a DNA tumor virus, while the second one is due to ultraviolet (UV) radiation exposure. Currently, there are limited diagnostic, prognostic, and therapeutic biomarkers available for MCC. MicroRNAs (miRNAs) are small RNA molecules that do not code for proteins but regulate various cellular processes, including cell cycle progression, proliferation, differentiation, and apoptosis. The dysregulation of miRNAs has been associated with a wide range of cancers since they can act as either tumor suppressors or oncogenes. This paper aims to collect, summarize and re-organize the recent discoveries concerning miRNAs, whose dysregulation in the pathogenesis and progression of MCC is slowly emerging. Furthermore, the potential clinical applications of miRNAs as diagnostic biomarkers, possible differentiation between MCPyV-related and unrelated MCC, predictors of response to therapy and prognostics are also discussed. From this review, it emerges that miRNAs may have, in the near future, the potential to become clinically significant in improving the diagnostic accuracy, prognostic evaluation, and therapeutic options of MCCs. Finally, the new perspectives on the poorly explored field of artificial intelligence in MCC are provided.

This study aimed to learn disturbance information to effectively control multiple small unmanned surface vehicles (USVs) for marine research purposes at sea. The learned disturbance information was used to investigate the control of multiple USV swarms based on the leading–following formation technique. Numerous studies have already been conducted on swarm control using drones in the air; however, there has been a paucity of studies on swarm control in the sea. Contrary to swarm control in aviation, the fluid density of the sea is high, and the environment is constantly changing; thus, a robot system would be significantly affected by disturbances. This ultimately influences the maintenance of swarm formations and the accomplishment of missions [1,2]. Therefore, it is difficult to control numerous moving objects while maintaining a formation at sea by using the existing aerial drone swarm control technology. To solve this problem, we learned a long short-term memory (LSTM) model with time-series marine environmental data to predict maritime disturbances. Thereafter, the model was applied to a mission-based position control algorithm of each USV constituting the swarm to design a marine unmanned floating vessel swarm control system that can be applied to various marine environments. Specifically, to effectively control numerous small USVs for marine research purposes, ocean currents are learned using the LSTM algorithm, and the predicted ocean currents are used to generate a swarm USV control system for USV formations. In this study, a control system model of several small USVs equipped with two rear thrusters and a front lateral thruster was designed. The LSTM algorithm was learned using the previous ocean current data, and the velocity of the following ocean currents was predicted. The predictions were then used as a system disturbance to determine the thrust of the controller. To measure ocean currents in the sea when each USV moves, the velocity, azimuth, and position (latitude, longitude) data from the GPS mounted on the USV were used to measure the velocity and direction of the hull's movement. Further, the flow rate was measured using a flow rate sensor on a small USV. The USV movement and position were controlled using an artificial neural network-PID (ANN-PID) controller. This study entailed a comparative analysis of the designed USV model results and those generated by the simulator, including the behavior control rule of the USV swarm and the path of the actual USV swarm at sea. It was verified that the effectiveness of the USV mathematical model and behavior control rules. By comparing the movement path of the swarm USV with and without the disturbance learning algorithm and ANN-PID control algorithm applied to the designed simulator, the position error and maintenance performance of the swarm formation were analyzed, and the application results were compared.