Models to Simulate Effective Coverage of Fire Station Based on Real-Time Travel Times

In recent years,frequent fire disasters have led to enormous damage in China.Effective firefighting rescues can minimize the losses caused by fires.During the rescue processes,the travel time of fire trucks can be severely affected by traffic conditions,changing the effective coverage of fire stations.However,it is still challenging to determine the effective coverage of fire stations considering dynamic traffic conditions.This paper addresses this issue by combining the traveling time calculationmodelwith the effective coverage simulationmodel.In addition,it proposes a new index of total effective coverage area(TECA)based on the time-weighted average of the effective coverage area(ECA)to evaluate the urban fire services.It also selects China as the case study to validate the feasibility of the models,a fire station(FS-JX)in Changsha.FS-JX station and its surrounding 9,117 fire risk points are selected as the fire service supply and demand points,respectively.A total of 196 simulation scenarios throughout a consecutiveweek are analyzed.Eventually,1,933,815 sets of valid sample data are obtained.The results showed that the TECA of FS-JX is 3.27 km^(2),which is far below the standard requirement of 7.00 km^(2) due to the traffic conditions.The visualization results showed that three rivers around FS-JX interrupt the continuity of its effective coverage.The proposed method can provide data support to optimize the locations of fire stations by accurately and dynamically determining the effective coverage of fire stations.

Evaluation of a Wireless Solar Powered Personal Weather Station

We are evaluating dryland cotton production in Martin County, Texas, measuring cotton lint yield per unit of rainfall. Our goal is to collect rainfall data per 250 - 400 ha. Upon selection of a rainfall gauge, we realized that the cost of using, for example, a tipping bucket-type rain gauge would be too expensive and thus searched for an alternative method. We selected an all-in-one commercially available weather station;hereafter, referred to as a Personal Weather Station (PWS) that is both wireless and solar powered. Our objective was to evaluate average measurements of rainfall obtained with the PWS and to compare these to measurements obtained with an automatic weather station (AWS). For this purpose, we installed four PWS deployed within 20 m of the Plant Stress and Water Conservation Meteorological Tower that was used as our AWS, located at USDA-ARS Cropping Systems Research Laboratory, Lubbock, TX. In addition, we measured and compared hourly average values of short-wave irradiance (Rg), air temperature (Tair) and relative humidity (RH), and wind speed (WS), and calculated values of dewpoint temperature (Tdew). This comparison was done over a 242-day period (1 October 2022-31 May 2023) and results indicated that there was no statistical difference in measurements of rainfall between the PWS and AWS. Hourly average values of Rg measured with the PWS and AWS agreed on clear days, but PWS measurements were higher on cloudy days. There was no statistical difference between PWS and AWS hourly average measurements of Tair, RH, and calculated Tdew. Hourly average measurements of Rg and WS were more variable. We concluded that the PWS we selected will provide adequate values of rainfall and other weather variables to meet our goal of evaluating dryland cotton lint yield per unit rainfall.

Simulation of Underground Reservoir Stability of Pumped Storage Power Station Based on Fluid-Structure Coupling

Based on global initiatives such as the clean energy transition and the development of renewable energy,the pumped storage power station has become a new and significant way of energy storage and regulation,and its construction environment is more complex than that of a traditional reservoir.In particular,the stability of the rock strata in the underground reservoirs is affected by the seepage pressure and rock stress,which presents some challenges in achieving engineering safety and stability.Using the advantages of the numerical simulation method in dealing deal with nonlinear problems in engineering stability,in this study,the stability of the underground reservoir of the Shidangshan(SDS)pumped storage power station was numerically calculated and quantitatively analyzed based on fluid-structure coupling theory,providing an important reference for the safe operation and management of the underground reservoir.First,using the COMSOL software,a suitablemechanicalmodel was created in accordance with the geological structure and project characteristics of the underground reservoir.Next,the characteristics of the stress field,displacement field,and seepage field after excavation of the underground reservoir were simulated in light of the seepage effect of groundwater on the nearby rock of the underground reservoir.Finally,based on the construction specifications and Molar-Coulomb criterion,a thorough evaluation of the stability of the underground reservoir was performed through simulation of the filling and discharge conditions and anti-seepage strengthening measures.The findings demonstrate that the numerical simulation results have a certain level of reliability and are in accordance with the stress measured in the project area.The underground reservoir excavation resulted in a maximum displacement value of the rock mass around the caverns of 3.56 mm in a typical section,and the safety coefficient of the parts,as determined using the Molar-Coulomb criterion,was higher than 1,indicating that the project as a whole is in a stable state.

Strategic Placement of Charging Stations for Enhanced Electric Vehicle Adoption in San Diego, California

California mandated that 100% of vehicles sold must be electric by 2035. As electric vehicles (EVs) reach a higher penetration of the car sector, cities will need to provide publicly accessible charging stations to meet the charging demand of people who do not have access to a private charging spot like a personal garage. We have chosen to limit our scope to San Diego County due to its non-trivial size, well-defined shape, and dependence on personal vehicles;this project models 100% of current vehicles as electric, roughly 2.5 million. By planning for the future, our model becomes more useful as well as more equitable. We anticipate that our model will find locations that can service multiple population centers, while also maximizing distance to other stations. Sensitivity analysis and testing of our algorithms are conducted for Coronado Island, an island with 24,697 residents. Our formulation is then scaled to set the parameters for the whole county.

Research on Operation Optimization of Energy Storage Power Station and Integrated Energy Microgrid Alliance Based on Stackelberg Game

With the development of renewable energy technologies such as photovoltaics and wind power,it has become a research hotspot to improve the consumption rate of new energy and reduce energy costs through the deployment of energy storage.To solve the problem of the interests of different subjects in the operation of the energy storage power stations(ESS)and the integrated energy multi-microgrid alliance(IEMA),this paper proposes the optimization operation method of the energy storage power station and the IEMA based on the Stackelberg game.In the upper layer,ESS optimizes charging and discharging decisions through a dynamic pricing mechanism.In the lower layer,IEMA optimizes the output of various energy conversion coupled devices within the IEMA,as well as energy interaction and demand response(DR),based on the energy interaction prices provided by ESS.The results demonstrate that the optimization strategy proposed in this paper not only effectively balances the benefits of the IEMA and ESS but also enhances energy consumption rates and reduces IEMA energy costs.

Optimal Scheduling of Multiple Rail Cranes in Rail Stations with Interference Crane Areas

In this paper,we consider a multi-crane scheduling problem in rail stations because their operations directly influence the throughput of the rail stations.In particular,the job is not only assigned to cranes but also the job sequencing is implemented for each crane to minimize the makespan of cranes.A dual cycle of cranes is used to minimize the number of working cycles of cranes.The rail crane scheduling problems in this study are based on the movement of containers.We consider not only the gantry moves,but also the trolley moves as well as the rehandle cases are also included.A mathematical model of multi-crane scheduling is developed.The traditional and parallel simulated annealing(SA)are adapted to determine the optimal scheduling solutions.Numerical examples are conducted to evaluate the applicability of the proposed algorithms.Verification of the proposed parallel SA is done by comparing it to existing previous works.Results of numerical computation highlighted that the parallel SA algorithm outperformed the SA and gave better solutions than other considered algorithms.

Study on site selection planning of urban electric vehicle charging station

The large-scale development of electric vehicles(EVs)requires numerous charging stations to serve them,and the charging stations should be reasonably laid out and planned according to the charging demand of electric vehicles.Considering the costs of both operators and users,a site selection model for optimal layout planning of charging stations is constructed,and a queuing theory approach is used to determine the charging pile configuration to meet the charging demand in the planning area.To solve the difficulties of particle swarm global optimization search,the improved random drift particle swarm optimization(IRDPSO)and Voronoi diagram are used to jointly solve for the optimal layout of electric vehicles.The final arithmetic analysis verifies the feasibility and practicality of the model and algorithm,and the results show that the total social cost is minimized when the charging station is 9,the location of the charging station is close to the center of gravity and the layout is reasonable.

Estimation of Daily Global Solar Radiation with Different Sunshine-Based Models for Some Burundian Stations

Sunshine duration (S) based empirical equations have been employed in this study to estimate the daily global solar radiation on a horizontal surface (G) for six meteorological stations in Burundi. Those equations include the Ångström-Prescott linear model and four amongst its derivatives, i.e. logarithmic, exponential, power and quadratic functions. Monthly mean values of daily global solar radiation and sunshine duration data for a period of 20 to 23 years, from the Geographical Institute of Burundi (IGEBU), have been used. For any of the six stations, ten single or double linear regressions have been developed from the above-said five functions, to relate in terms of monthly mean values, the daily clearness index () to each of the next two kinds of relative sunshine duration (RSD): and . In those ratios, G0, S0 and stand for the extraterrestrial daily solar radiation on a horizontal surface, the day length and the modified day length taking into account the natural site’s horizon, respectively. According to the calculated mean values of the clearness index and the RSD, each station experiences a high number of fairly clear (or partially cloudy) days. Estimated values of the dependent variable (y) in each developed linear regression, have been compared to measured values in terms of the coefficients of correlation (R) and of determination (R2), the mean bias error (MBE), the root mean square error (RMSE) and the t-statistics. Mean values of these statistical indicators have been used to rank, according to decreasing performance level, firstly the ten developed equations per station on account of the overall six stations, secondly the six stations on account of the overall ten equations. Nevertheless, the obtained values of those indicators lay in the next ranges for all the developed sixty equations:;;;, with . These results lead to assert that any of the sixty developed linear regressions (and thus equations in terms of and ), fits very adequately measured data, and should be used to estimate monthly average daily global solar radiation with sunshine duration for the relevant station. It is also found that using as RSD, is slightly more advantageous than using for estimating the monthly average daily clearness index, . Moreover, values of statistical indicators of this study match adequately data from other works on the same kinds of empirical equations.

Discussion on Dust Removal(Suppression)System of Crushing Station in Open-Pit Coal Mine

This paper introduces the method of coal dust treatment in crushing station and the present situation of dust removal system in typical open-pit coal mine crushing station in China,and expounds the research idea of determining comprehensive dust removal(suppression)system in crushing station inspired by the working principle of"range hood".Based on the design example and link optimization of the crush-ing station of open-pit coal mine I of Thar coalfield,this paper finally draws some conclusions on the key technologies of dust removal(suppression)system of open-pit coal mine crushing station.This study has certain reference value for the technical innovation of dust removal(suppression)system in crushing station,the realization of green mining in"crushing link",and the reduction and avoidance of ecological environment pollution in mining area.

Functional Characteristics of Beijing Subway Stations Based on POI Data:Taking Beijing Metro Line 6 as An Example

Urban subway station is a key node related to urban social,political,economic and cultural activities.There are some differences in the location,function orientation,land use and flow characteristics of different types of stations in the city.This paper mainly used Tyson’s edge,kernel density analysis,chart analysis and other methods to classify the functional types of 412,393 POI data of 26 stations along Metro he results showed that the spatial distribution of Beijing Metro Line 6 was mainly divided into 3 categories,subway stations were divided into 4 categories.Among them,type A sites were divided into composite and single types,and the distribution characteristics of the 6 types of sites were quite different.Based on the qualitative and quantitative analysis of POI point data,this method can quickly classify and analyze the characteristics of stations along Line 6 in Beijing,which also has theoretical and practical value for the planning of urban subway lines.

Space physics and astronomy research from Chinese polar stations: current and future directions

Space weather has a remarkable effect on modern human activities,e.g.,communication,navigation,space exploration etc.Space physics study from polar stations is as an important part of the entire solar-terrestrial space,and conducts quantitative research from the perspective of overall space plasma behavior.One of the most important issues is to identify the dominant processes that transfer plasma and momentum from the solar wind to Earth’s magnetosphere.Thus,it is necessary to carry out research for combination the observations from polar ground stations and spacecraft observations in the space.Observations at polar regions can be as a window to the space for satellite traffic controls.The operation of the observation chain―Zhongshan-Taishan-Kunlun Station could monitor polar space debris in a large area with high temporal and spatial resolution.Also,night-time measurements of astronomical seeing at Dome A in Antarctica make it less challenging to locate a telescope above it,thereby giving greater access to the free atmosphere because of a thinner boundary layer.

A practical framework for performance-based reliability analysis of subway stations based on a faultestructure combined system

It is necessary to pay particular attention to the uncertainties that exist in an engineering problem to reduce the risk of seismic damage of infrastructures against natural hazards.Moreover,certain structural performance levels should be satisfied during strong earthquakes.However,these performance levels have been only well described for aboveground structures.This study investigates the main uncertainties involved in the performance-based seismic analysis of a multi-story subway station.More than 100 pulse-like and no pulse-like ground motions have been selected.In this regard,an effective framework is presented,based on a set of nonlinear static and dynamic analyses performed by OpenSees code.The probabilistic seismic demand models for computing the free-field shear strain of soil and racking ratio of structure are proposed.These models result in less variability compared with existing relations,and make it possible to evaluate a wider range of uncertainties through reliability analysis in Rtx software using the Monte Carlo sampling method.This work is performed for three different structural performance levels(denoted as PL1ePL3).It is demonstrated that the error terms related to the magnitude and location of earthquake excitations and also the corresponding attenuation relationships have been the most important parameters.Therefore,using a faultestructure model would be inevitable for the reliability analysis of subway stations.It is found that the higher performance level(i.e.PL3)has more sensitivity to random variables than the others.In this condition,the pulse-like ground motions have a major contribution to the vulnerability of subway stations.

Energy conversion materials for the space solar power station

Since it was first proposed,the space solar power station(SSPS)has attracted great attention all over the world;it is a huge space system and provides energy for Earth.Although several schemes and abundant studies on the SSPS have been proposed and conducted,it is still not realized.The reason why SSPS is still an idea is not only because it is a giant and complex project,but also due to the requirement for various excellent space materials.Among the diverse required materials,we believe energy materials are the most important.Herein,we review the space energy conversion materials for the SSPS.

Metagenomic Analysis of Environmental Samples from Wildlife Rescue Station at Poyang Lake, China

Objective To improve the understanding of the virome and bacterial microbiome in the wildlife rescue station of Poyang Lake,China.Methods Ten smear samples were collected in March 2019.Metagenomic sequencing was performed to delineate bacterial and viral diversity.Taxonomic analysis was performed using the Kraken2 and Bracken methods.A maximum-likelihood tree was constructed based on the RNA-dependent RNA polymerase(RdRp)region of picornavirus.Results We identified 363 bacterial and 6 viral families.A significant difference in microbial and viral abundance was found between samples S01–S09 and S10.In S01–S09,members of Flavobacteriia and Gammaproteobacteria were the most prevalent,while in S10,the most prevalent bacteria class was Actinomycetia.Among S01–S09,members of Myoviridae and Herelleviridae were the most prevalent,while the dominant virus family of S10 was Picornaviridae.The full genome of the pigeon mesivirus-like virus(NC-BM-233)was recovered from S10 and contained an open reading frame of 8,124 nt.It showed the best hit to the pigeon mesivirus 2 polyprotein,with 84.10%amino acid identity.Phylogenetic analysis showed that RdRp clustered into Megrivirus B.Conclusion This study provides an initial assessment of the bacteria and viruses in the cage-smeared samples,broadens our knowledge of viral and bacterial diversity,and is a way to discover potential pathogens in wild birds .

Muti-Fusion Swarm Intelligence Optimization Algorithm in Base Station Coverage Optimization Problems

As millimeter waves will be widely used in the Internet of Things(IoT)and Telematics to provide high bandwidth communication and mass connectivity,the coverage optimization of base stations can effectively improve the quality of communication services.How to optimize the convergence speed of the base station coverage solution is crucial for IoT service providers.This paper proposes the Muti-Fusion Sparrow Search Algorithm(MFSSA)optimize the situation to address the problem of discrete coverage maximization and rapid convergence.Firstly,the initial swarm diversity is enriched using a sine chaotic map,and dynamic adaptive weighting is added to the discoverer location update strategy to improve the global search capability.Diverse swarms have a more remarkable ability to forage for food and avoid predation and are less likely to fall into a“precocious”state.Such a swarm is very suitable for solving NP-hard problems.Secondly,an elite opposition-based learning strategy is added to expand the search range of the algorithm,and a t-distribution-based one-fifth rule is introduced to reduce the probability of falling into a local optimum.This fusion mutation strategy can significantly optimize the adaptability and searchability of the algorithm.Finally,the experimental results show that the MFSSA algorithm can effectively improve the coverage of the deployment scheme in the base station coverage optimization problem,and the convergence speed is better than other algorithms.MFSSA is improved by more than 10%compared to the original algorithm.

Research on Short-Term Load Forecasting of Distribution Stations Based on the Clustering Improvement Fuzzy Time Series Algorithm

An improved fuzzy time series algorithmbased on clustering is designed in this paper.The algorithm is successfully applied to short-term load forecasting in the distribution stations.Firstly,the K-means clustering method is used to cluster the data,and the midpoint of two adjacent clustering centers is taken as the dividing point of domain division.On this basis,the data is fuzzed to form a fuzzy time series.Secondly,a high-order fuzzy relation with multiple antecedents is established according to the main measurement indexes of power load,which is used to predict the short-term trend change of load in the distribution stations.Matlab/Simulink simulation results show that the load forecasting errors of the typical fuzzy time series on the time scale of one day and one week are[−50,20]and[−50,30],while the load forecasting errors of the improved fuzzy time series on the time scale of one day and one week are[−20,15]and[−20,25].It shows that the fuzzy time series algorithm improved by clustering improves the prediction accuracy and can effectively predict the short-term load trend of distribution stations.

PSO-BP-Based Optimal Allocation Model for Complementary Generation Capacity of the Photovoltaic Power Station

To improve the operation efficiency of the photovoltaic power station complementary power generation system,an optimal allocation model of the photovoltaic power station complementary power generation capacity based on PSO-BP is proposed.Particle Swarm Optimization and BP neural network are used to establish the forecasting model,the Markov chain model is used to correct the forecasting error of the model,and the weighted fitting method is used to forecast the annual load curve,to complete the optimal allocation of complementary generating capacity of photovoltaic power stations.The experimental results show that thismethod reduces the average loss of photovoltaic output prediction,improves the prediction accuracy and recall rate of photovoltaic output prediction,and ensures the effective operation of the power system.

Identification Method for Users-Transformer Relationship in Station Area Based on Local Selective Combination in Parallel Outlier Ensembles Algorithm

In the power distribution system,the missing or incorrect file of users-transformer relationship(UTR)in lowvoltage station area(LVSA)will affect the leanmanagement of the LVSA,and the operation andmaintenance of the distribution network.To effectively improve the lean management of LVSA,the paper proposes an identification method for the UTR based on Local Selective Combination in ParallelOutlier Ensembles algorithm(LSCP).Firstly,the voltage data is reconstructed based on the information entropy to highlight the differences in between.Then,the LSCP algorithmcombines four base outlier detection algorithms,namely Isolation Forest(I-Forest),One-Class Support VectorMachine(OC-SVM),Copula-Based Outlier Detection(COPOD)and Local Outlier Factor(LOF),to construct the identification model of UTR.This model can accurately detect users’differences in voltage data,and identify users with wrong UTR.Meanwhile,the key input parameter of the LSCP algorithm is determined automatically through the line loss rate,and the influence of artificial settings on recognition accuracy can be reduced.Finally,thismethod is verified in the actual LVSA where the recall and precision rates are 100%compared with othermethods.Furthermore,the applicability to the LVSAs with difficult data acquisition and the voltage data error in transmission are analyzed.The proposed method adopts the ensemble learning framework and does not need to set the detection threshold manually.And it is applicable to the LVSAs with difficult data acquisition and high voltage similarity,which improves the stability and accuracy of UTR identification in LVSA.

Monte Carlo calculation of the exposure of Chinese female astronauts to earth’s trapped radiation on board the Chinese Space Station

With the development of China’s crewed space mission,the space radiation risk for astronauts is increasingly prominent.This paper describes a simulation of the radiation doses experienced by a Chinese female voxel phantom on board the Chinese Space Station(CSS)performed using the Monte Carlo N-Particle(MCNP)software.The absorbed dose,equivalent dose,and effective dose experienced by the voxel phantom and its critical organs are discussed for different levels of shielding of the Tianhe core module.The risk of space-radiation exposure is then assessed by comparing these doses with the current risk limits in China(the skin dose limit for short-term low-earth-orbit missions)and the NASA figures(National Council on Radiation Protection and Measurements Report No.98)for female astronauts.The results obtained can be used to guide and optimize the radiation protection provided for manned space missions.

Base Station Energy Management in 5G Networks Using Wide Range Control Optimization

The traffic activity offifth generation(5G)networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation(4G)network technologies that demand always for varied control and data signalling based on control base station(CBS)and data base station(DBS).Hence,this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network.As the new radio(NR)based 5G network is configured to transmit signal blocks for every 20 ms,the proposed algorithm implements withstanding capacity of on or off based energy switching,which in-turn operates in wide range control by carrying out reduced computational complexity.The proposed Wide range of control for base station in green cellular network using sleep mode for switch(WGCNS)algorithm toon and off the base station will work in heavy load with neighbouring base station.For reducing the overhead duration in air,heuristic versions of the algorithm are proposed at the base station.The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings.The proposed algorithm reduces 40%to 83%of residual energy based on the traffic pattern of the urban scenario.