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ABSTRACT: Charging infrastructure supports the rapid development of China's new energy vehicle industry. It not only plays a decisive role in providing accessible and convenient services for electric vehicle (EV) users but also, in one of the seven new infrastructure areas, plays an important role in stabilizing growth and unleashing economic potential during the new coronavirus (COVID-19) pandemic, impacting China's economy. In this study, the system dynamics model was used to predict the development of the EV industry and the demand for charging infrastructure, while considering the influence of policy, increase in EV mileage, and consumer purchase intention index. Furthermore, using the matching of EVs and charging infrastructure in Beijing and policy-oriented sensitivity analysis, a simulation of the construction of battery swap taxis and power stations under three policy scenarios was conducted. This research shows that with policies implemented to support charging infrastructure and swapping compatible taxis, Beijing can achieve its goal of replacing all EVs with fast-swap batteries and fast-charging functions within three years.

With the extensive growth in technology, healthcare sector has benefitted a lot recently. Looking into the academic research and validation in the area of medical image processing and visualization, many platforms and the open-source resources are available. Insight toolkit (ITK) and visualization toolkit (VTK) are extensively used for medical image processing and 3D visualization respectively. Resources used to develop an application using ITK-VTK and same resources be used to deliver it to the users such as, clinicians, doctors etc. This can be achieved by using respective hardware and the infrastructure. In the proposed article, the infrastructure and resource used to build and deploy the application and remote access given to the users are elucidated.

Wind characteristics (e.g., mean wind speed, gust factor, turbulence intensity and integral scale, etc.) are quite scattered in different measurement conditions, especially during typhoon and/or hurricane processes, which results in the structural engineer ambiguously determining the wind parameters in wind-resistant design of buildings and structures in cyclone-prone regions. In tropical cyclones (including typhoons and hurricanes), the inconsistent wind characteristics may be in part ascribed to the complex flow structure with the coexistence of both mechanical and convective turbulence in the boundary layer of tropical cyclones. Another significant contribution to the scattered wind characteristics is due to various measurement conditions (e.g., terrain exposure and height) and data processing schemes (e.g., averaging time). The removal of the inconsistency in the field-measurement system may offer a more rational comparison of measured wind data from various observation platforms, and hence facilitates a better identification scheme of the wind characteristics to guide the urban planning design and wind-resistant design of buildings and structures. In this study, an analytical framework was firstly proposed to eliminate the potential observation-related effects in wind characteristics and then the wind characteristics of seven field measured tropical cyclones (four typhoons and three hurricanes) were comparatively investigated. Specifically, field measurements of wind characteristics were converted to a standard reference station with a roughness length of 0.03 m, observation duration of 10 min for mean wind and averaging time of 3 s for gusty wind at a 10 m height. The differences of the measured wind characteristics between the typhoons and hurricanes were highlighted. The standardized turbulent wind characteristics under the analytical framework for typhoons and hurricanes were compared with the corresponding recommendations in standard of American Society of Civil Engineers (ASCE 7-10) and Architectural Institute of Japan Recommendations for Loads on Buildings (AIJ-RLB-2004).

To research the flow-induced vibration characteristics of the natural gas loop, unsteady numerical simulation is carried out on the loop calculation model under different ambient temperatures and different flow rates, and the influence of different flow rates on the dynamic characteristics of the flow field and its induced vibration characteristics is obtained. The results show that the inherent frequency of the natural gas loop increases slightly under the action of heat–fluid–solid coupling. The maximum equivalent stress of the loop increases with the increase in ambient temperature under low flow conditions, but it is almost constant under high flow conditions. The smaller the cross-sectional area of the loop pipeline inlet, the greater the pressure, and the more significant the pressure gradient along the flow direction. The pressure pulsation of monitoring points in the pipeline under different flow rates presents different rules, and the pulsation amplitude of pipes with different diameters is different, among which the amplitude of the pipe with a diameter of 250 mm is the largest and that of the pipe with a diameter of 150 mm is the smallest. The pressure pulsation signals are concentrated in the low-frequency band of 0–10 Hz, and the range of the band decreases with the increase in the flow rate. The vibration frequency of the loop structure is close to the fluid pressure pulsation frequency and the inherent frequency under the action of heat–fluid–solid coupling, which causes a resonance of about 2 Hz.

In this study, we investigated the influence of overall financial development and its components on energy consumption using the panel data of 120 countries and the generalized method of moments (GMM). By dividing the sample into developed and developing countries, we further examined the national differences of the impact of financial development on energy consumption. The empirical results indicate that the overall financial development significantly positively impacts energy consumption from a worldwide perspective, and its two components (financial institution and the financial market) have the same effect. The analysis of national differences indicates that the financial development also positively impacts energy consumption in developing countries but with no obvious effect in developed countries. The results also suggest that financial development cannot be used to restrain the increase in energy consumption from the global perspective, and policymakers in developing countries must balance the relationship between the development of the financial sector and energy consumption.

Second-generation bioethanol can be produced from various lignocellulosic biomasses such as wood, agricultural or forest residues. Lignocellulosic biomass is inexpensive, renewable and abundant source for bioethanol production. The conversion of lignocellulosic biomass to bioethanol could be a promising technology though the process has several challenges and limitations such as biomass transport and handling, and efficient pretreatment methods for total delignification of lignocellulosics. Proper pretreatment methods can increase concentrations of fermentable sugars after enzymatic saccharification, thereby improving the efficiency of the whole process. Conversion of glucose as well as xylose to bioethanol needs some new fermentation technologies to make the whole process inexpensive. The main goal of pretreatment is to increase the digestibility of maximum available sugars. Each pretreatment process has a specific effect on the cellulose, hemicellulose and lignin fraction; thus, different pretreatment methods and conditions should be chosen according to the process configuration selected for the subsequent hydrolysis and fermentation steps. The cost of ethanol production from lignocellulosic biomass in current technologies is relatively high. Additionally, low yield still remains as one of the main challenges. This paper reviews the various technologies for maximum conversion of cellulose and hemicelluloses fraction to ethanol, and it point outs several key properties that should be targeted for low cost and maximum yield.

The study explores the enhancement of wind-solar hybrid microgrids via the use of Swarm Intelligence Algorithms (SIAs). It assesses the efficacy of these algorithms in efficiently managing renewable energy sources, load demands, and battery storage inside the microgrid system. An examination of actual data highlights the influence of environmental elements on the production of electricity, as seen by the diverse wind speeds resulting in power outputs that range from 15 kW at 4 m/s to 30 kW at 7 m/s. This underscores the clear and direct relationship between wind speed and the amount of power created. Likewise, solar irradiance levels demonstrate oscillations ranging from 500 W/m² to 800 W/m², therefore yielding power outputs that include a range of 15 kW to 24 kW, so illuminating the profound impact of solar irradiance on energy capture. The dynamic energy consumption patterns are exposed by the varying load demands, whereby the demand levels oscillate between 20 kW and 28 kW. This highlights the crucial significance of demand variability in determining energy needs. In addition, the data on battery storage reveals a range of charge levels, ranging from 25 kWh to 40 kWh, which underscores its pivotal function in the equilibrium of energy supply and consumption. When evaluating SIAs, it becomes evident that Particle Swarm Optimization (PSO) surpasses both Ant Colony Optimization (ACO) and Genetic Algorithms (GA) in obtaining an impressive 80% renewable energy penetration rate. PSO effectively reduces operating costs by 15%, demonstrating its exceptional proficiency in optimizing microgrid operations. This study provides valuable insights into the intricate interplay among environmental conditions, load demands, battery storage, and algorithmic optimization in wind-solar hybrid microgrids.

<abstract> <p>The rising proportion of inverter-based renewable energy sources in current power systems has reduced the rotational inertia of overall microgrid systems. This may cause high-frequency fluctuations in the system leading to system instability. Several initiatives have been suggested concerning inertia emulation based on other integrated external energy sources, such as energy storage systems, to combat the ever-declining issue of inertia. Hence, to deal with the aforementioned issue, we suggest the development of an optimal fractional sliding mode control (FSMC)-based frequency stabilization strategy for an industrial hybrid microgrid. An explicit state-space industrial microgrids model comprised of several coordinated energy sources along with loads, storage systems, photovoltaic and wind farms, is considered. In addition to this, the impact of electric vehicles and batteries with adequate control of the state of charge was investigated due to their short regulation times and this helps to balance the power supply and demand that in turn brings the minimization of the frequency deviations. The performance of the FSMC controller is enhanced by setting optimal parameters by employing the tuning strategy based on an iterative teaching-learning-based optimizer (ITLBO). To justify the efficacy of the proposed controller, the simulated results were obtained under several system conditions by using a vehicle simulator in a MATLAB/Simulink environment. The results reveal the enhanced performance of the ITLBO optimized fractional sliding mode control to effectively damp the frequency oscillations and retain the frequency stability with robustness, quick damping, and reliability under different system conditions.</p> </abstract>