Typical Scenarios Research Articles

An improved identity-based public audit protocol for cloud storage

With the rapid development of informatization, a vast amount of data is continuously generated and accumulated, leading to the emergence of cloud storage services. However, data stored in the cloud is beyond the control of users, posing various security risks. Cloud data auditing technology enables the inspection of data integrity in the cloud without the necessity of data downloading. Among these, public auditing schemes have experienced rapid development due to their ability to avoid additional user auditing expenses. However, malicious third-party auditors can compromise data privacy. This paper proposes an improved identity-based cloud auditing scheme that can resist malicious auditors. This scheme is also constructed on an identity-based public auditing scheme using blockchain to prevent malicious auditing. We found the scheme is not secure because a malicious cloud server can forge authentication tags for outsourced data blocks, while our scheme has not these security flaws. Through security proofs and performance analysis, we further demonstrate that our scheme is secure and efficient. Additionally, our scheme has typical application scenarios.

Future scenario generation and reduction methods for solar photovoltaic electricity production analysis

Abstract A morphing-clustering-based method of generation and reduction future scenario for solar photovoltaic electricity production analysis is studied. Present historical annual weather data are transformed into future scenarios and timeframes by a morphing method with a general circulation model. The Copula methodology is applied to model the forthcoming decade’s annual meteorological data and generate abundant intra-day hourly scenario samples. The comparative analysis of the photovoltaic power output variations between future and present in reduction typical scenarios is conducted by the K-means clustering method with appropriate clustering parameter optimization. The simulation results show that the annual mean variations in global horizontal radiation, dry bulb temperature, and wind speed are projected to increase by approximately from 20.1 to 36.5 W/m2, 1.6 to 2.3°C, and 0.1 to 0.6 m/s, respectively in the forthcoming scenario of shared socioeconomic pathways 2-4.5. It is seen that solar photovoltaic electricity production anticipates a positive increase from 2.5% to 17.9%.

Multi-objective stochastic-robust based selection-allocation-operation cooperative optimization of rural integrated energy systems considering supply-demand multiple uncertainties

The carbon neutrality goal places greater demands on integrated energy system (IES) in rural areas. In line with this trend, a multi-objective stochastic-robust based selection-allocation-operation cooperative optimization model is established for planning of rural IES in this paper. Firstly, this paper construct three typical IESs at three levels (user, township and user-township interaction) and make economic selection. Secondly, in order to coordinate the objectives of technology-economy-environment and solve the multiple uncertainties existing in the system, a multi-objective stochastic-robust planning model is proposed. In the model, the objective function introduces indicators such as total cost, primary energy resource efficiency and renewable energy consumption rate. Then constructing stochastic hierarchical scenarios to characterize the stochastic uncertainty of parameters in the time dimension. And introducing typical scenarios into the box sets which characterize the supply-demand sides uncertainty. Finally, a sensitivity analysis is performed to discuss the influence of robustness parameters and biogas prices. The annual total cost of the user-township interaction system is 53.8 % and 8.4 % lower than that of the other two systems, respectively. And from the allocation results of selected system, primary energy resource efficiency is 0.57, total cost is 1.81 × 106 USD and renewable energy consumption rate is 99.46 %. In addition, from the operation results, it is analyzed that the output of the equipment is perfectly balanced with the user's load. Finally, it is obtained by sensitivity analysis that uncertainty budget and biogas price have an impact on the selection of discrete equipment, the capacity of continuous equipment and the total cost. And the optimal capacity of the system under different situations is given.

Accessing Lexicon Versus Time Pressure: Testing Non-native Speakers of English to Perform in English Language

Storing words in memory is an interesting topic for a great number of researchers working in the fields of linguistics, applied linguistics and psycholinguistics around the globe. It is worth mentioning that the words we know are stored in a very complex way in our memory this can be pictured as the World Wide Web. Researchers have always been curious about the way this store is accessed, which in psycholinguistics is known as mental lexicon. This paper aims to explore how this network-like-store is accessed when a person is under pressure. In this study, twenty-four participants are tested in two runs of the experiment, which is used to conduct the study. On the first attempt, participants are placed in a typical scenario where there is no time constraint or any pressure to produce a certain number of words within a given amount of time. Later, they will be asked to write again in a shorter period of time and then the time is further decreased to produce written words in the third run. The effects of time constraints on the capacity to use the mental lexicon during written language production will be examined in this study. At the conclusion of the paper, the improvement of accessing mental lexicon is shown when the time pressure is increased.

Application Analysis of Artificial Intelligence Virtual Reality Technology in Fitness Training Teaching

The application of artificial intelligence virtual reality (VR) technology in fitness teaching and training is becoming increasingly widespread, and its significant advantages provide athletes with a more personalized, efficient and safe training experience. By simulating various fitness scenes, virtual reality technology enables athletes to train immersively, not only improving the fun of training, but also enhancing the training effect. At the same time, the introduction of artificial intelligence technology enables real-time monitoring and analysis of athlete performance during the training process, providing accurate data support for coaches and helping them develop more scientific training plans. This paper explores the application of virtual reality technology in physical education teaching and training, introduces the basic principles and characteristics of virtual reality and proposes several typical virtual reality application scenarios for the needs of physical education teaching and training. In football training, through virtual reality technology, athletes can conduct various technical and tactical exercises in simulated stadiums. At the same time, artificial intelligence systems can analyze the actions and decisions of athletes in real time, and provide feedback and suggestions. In track and field training, virtual reality technology can help athletes simulate competition scenes and improve their psychological adaptability during competitions. Through in-depth research, we have found that this technology provides athletes with a more personalized, efficient, and safe training experience. In football training, virtual reality technology can simulate real game scenes, allowing athletes to engage in various technical and tactical exercises in simulated sports stadiums. At the same time, artificial intelligence systems can analyze the actions and decisions of athletes in real time, provide accurate data support for coaches, and help them develop more targeted training plans.

Investigation on the restrain effect of air curtain to the spilled flame and its heat flux of a compartment-facade fire

This paper investigates the restrain effect of air curtain to the spilled flame and its heat flux of a compartment-facade fire. A compartment model of 1.2 m × 0.9 m × 0.9 m is established in this study, and an air curtain is facilized at the compartment opening for fire protection. The air curtain jet velocity, the heat release rate as well as the opening dimension are changed as representative to typical fire scenarios. Flame behavior and heat flux profile of spill plume are captured by CCD cameras and water-cooled heat flux gauge for discussion. Results show that the air curtain jet could have a substantial impact on the flame behavior of spilled plumes from a compartment-facade fire. Compared to that of free condition without air curtain, the flame height becomes 20 % lower as the jet velocity to be 2 m/s, while it becomes 60 % lower as the jet velocity to be 5 m/s. At the meantime, the spilled plume is noticeably influenced by the “restricting effect” of the air curtain and thus flame trajectory is significantly flattened by the inertia force of the air curtain but also pushed farther from the facade wall, resulting in an increasing of flame depth. The decreasing in flame height plus the increasing flame depth would result in the reduction of heat flux intensity upon the facade wall, which is helpful for the fire protection of the facade wall. Then, a normalized factor of Υ is further proposed to the new correlation of flame height under various air curtain jet conditions at the opening. Regarding to different ejected flame behavior at the presence of air curtain jet, the heat flux upon the facade wall is quantitative analyzed to evaluate the thermal impact of spilled plume from the compartment fire. Finally, new correlations of the vertical heat flux upon the facade wall could be well achieved by a function of normalized height factor Z/Zf, v.

Research on Information Fusion Method for Human Computer Interaction Interface of Command and Control System in MR Environment

In response to the challenge of high cognitive pressure and difficulty for commanders in cross-layer operations during the transition from traditional two-dimensional command and control system interfaces to mixed reality human-computer interaction interfaces, this paper conducts research on the information fusion display method for the human-computer interaction interface of the mixed reality command and control system. First, based on the findings of previous research, we developed a five-layer human-computer interaction interface for a mixed reality command and control system in typical combat scenarios. An evaluation experiment was designed and the resulting data analyzed. This analysis revealed that there are problems with low work efficiency and high cognitive load in the information acquisition process in cross-layer interaction operations. Consequently, we conducted research into the methods of integrating and displaying interactive interface information, integrating and displaying interactive and display interface information, and integrating and displaying complex situation interface information. The research findings indicate that: (1) The optimal display ratio for mixed reality 2D interface integration is 3:2. (2) The optimal display scheme for mixed reality 2D interface fusion is a combination of the main and secondary interfaces, with the main interface table displaying global information and the secondary interface displaying accurate information. (3) The optimal display scheme for mixed reality 2D and 3D interface fusion is the display of overall class information in 2D and the accurate identification of class information in 3D. The findings of this research can be applied to the design of human-computer interaction interfaces for mixed reality command and control systems in complex battlefield environments.

Research on a Distributed Cooperative Guidance Law for Obstacle Avoidance and Synchronized Arrival in UAV Swarms

In response to the issue where the original synchronization time becomes inapplicable for UAV swarms after temporal consistency convergence due to obstacle avoidance, a new distributed consultative temporal consistency guidance law that takes into account threat avoidance has been proposed. Firstly, a six-degree-of-freedom dynamic model and a guidance control model for unmanned aerial vehicles (UAVs) are established, and the guidance commands are decomposed into control signals for the pitch and yaw planes. Secondly, based on the theory of dynamic inversion control, a temporal consistency guidance law for a single UAV is constructed. On the other hand, an improved artificial potential field theory is used and integrated with a predictive correction network to generate guidance commands for threat avoidance. A threshold smoothing method is employed to integrate the two guidance systems, and a cluster consultation mechanism is introduced to design a two-layer temporal synchronization architecture, which negotiates to change the synchronization time of the swarm to achieve the convergence of consistency once again. Finally, in typical application scenarios, simulation verification demonstrates the effectiveness of the control method proposed in this paper. The proposed control method achieves the guidance of UAV formations to synchronize their arrival at the target location under complex threat conditions.

Mechanistic Insights into Micelle-Enhanced Nanofiltration for Heavy Metal Removal: Transformation of Ion Transport and Fouling Phenomena.

Toxic heavy metals are widely present in typical scenarios, such as mines and electroplating wastewater, presenting significant risks to biological and environmental safety. Membrane processes encounter a challenge in effectively intercepting heavy metals due to their small hydration radius. This research showcases the high efficiency of micelle-enhanced nanofiltration (MENF) in removing heavy metals. At the critical micelle concentration, sodium dodecyl sulfate demonstrated a high removal of Cu2+, Ni2+, Zn2+, and Cd2+ while maintaining substantial potential for complexation of heavy metals. The formation of micelles and the bonding of heavy metals with surfactants bolstered the resistance of heavy metal ions to transmembrane transport. The presence of heavy metals in ionic form in wastewater facilitated their complexation with surfactants or micelles. Notably, the valence state and concentration of interfering ions in the environment could slightly influence the removal of heavy metals by MENF. Additionally, MENF displayed remarkable antifouling properties. The loose gel layer created by surfactant molecules and the micelle enhanced the membrane permeability and reduced the scaling tendency of heavy metals. This study contributes to an improved understanding of the mechanisms involved in heavy metal rejection by using MENF.

A fast numerical method for calculating SPND space charge effect at steady state

To address the issue of low efficiency and long computation times in calculating the space charge effect in Self-Powered Neutron Detectors (SPNDs), we developed a fast and efficient numerical method to calculate the steady-state electric fields in SPND insulators. Previous calculations have shown that even when the electric field in the SPND insulator reaches 1 GV/m, its effect on electron transport is negligible. Therefore, we assume that the electric field has a negligible effect on electron transport. Based on this assumption, we developed a one-step numerical method for calculating the steady-state electric field in the insulator. This fast method is based on the charge conservation equation in equilibrium, thus requiring only a single charge deposition result without an electric field to obtain the distribution of the electric field under steady-state conditions. We validated our method by comparing it with precise iterative calculation results and experiments. The computational results indicate that, for three typical experimental scenarios, the fast method only takes up to one percent, or even less, of the time required by the iterative method. At the same time, the Mean Relative Error (MRE) in the electric field distribution between the fast method and iterative calculations are 1.48%, 0.31%, and 5.12%, respectively; whereas the relative errors in sensitivity calculations based on the electric field compared to iterative calculations are all less than 2%. Therefore, the fast method can significantly reduce computation time and ensure sufficient accuracy of steady state electric field distribution when the electric field is less than 1 GV/m. The introduction of this method has made it possible to conduct space charge effect assessments and precise sensitivity calculations for the placement of detectors throughout the entire reactor core.

Feasibility of Induced Magnetic Gradient Surveying for Seepage Detection in Earth-filled Dams: Insights from Synthetic and Field Studies

Seepage is a common hydrogeological hazard in engineering. Determining the seepage paths is vital for de-risking the instability of embankment structures. With the improvement of the acquisition accuracy of magnetic sensors, the magnetometric resistivity method has become an emerging technology for detecting seepage paths through earth-filled dams. This technique is non-destructive and gives prominent signals. However, the resulting magnetic data have seen ambiguity in fully determining the targets. We propose an induced magnetic gradient surveying approach to monitor seepage paths in earth-filled dams. First, we briefly review the electromagnetic theory for the magnetic gradient tensor based on Maxwell’s equations. To match against the measurements, we present an accurate modeling framework using the third-order finite element method and a novel compact difference scheme. We verify our approach on both semi-analytical 1D and 3D models. Systematic modeling studies are then carried out to investigate the spatial distribution characteristics and sensitivities of the induced magnetic gradient to the seepage in typical dam scenarios. In addition, we conducted two field experiments in the Zhongmou experimental base and Xixiayuan Reservoir in Henan Province, China,respectively. The induced magnetic field vector and its gradient components were both acquired. Cross-validation with a-priori geological information shows that the seepage path can be spatially identified by the induced magnetic gradient components Byy, Byz, Bzy, and Bzz while the field components failed to locate the seepage pathways. This successful application indicates that the proposed approach could be a promising solution for seepage path discrimination in earth-filled dams with high resolution.

Energy retrofitting of hospital buildings considering climate change: An approach integrating automated machine learning with NSGA-III for multi-objective optimization

In the context of sustainable development, energy-saving renovation of existing buildings can not only effectively reduce energy consumption but also improvement the residential comfort and office environments. This study proposes an optimization method for building energy-saving renovation based on automated machine learning and the NSGA-III algorithm, aiming at efficiently and accurately identifying the best energy-saving renovation schemes. The results showed that automated machine learning could provide a predictive model for multi-objective optimization analysis in a relatively short training time, with an average prediction accuracy of over 95%. And the Stacked Ensemble algorithm and Gradient Boosting Machine algorithm perform the best among all the algorithms considered in this study. Additionally, climate change significantly affects the energy-saving benefits and thermal comfort performance of renovation measures, thereby altering the choice of optimal schemes. The final renovation schemes obtained through the NSGA-III and entropy weight TOPSIS method can achieve an energy saving rate of 22.06% to 26.48% under typical climate scenarios, while also improving thermal comfort time by 31.87% to 43.73%. In future climate scenarios, an energy saving rate of 32.73% to 33.02% and a thermal comfort time of 40.20% to 42.29% can be achieved. Combining these two technologies can quickly and effectively find the best balance among energy consumption, whole life cycle cost, and thermal comfort time. The method proposed in this paper offers new perspectives and tools for researchers in the field of building energy conservation, demonstrating the application potential of automated machine learning and the NSGA-III algorithm in the energy-saving renovation of hospital buildings.

Experimental COP optimization procedure in an air-based reverse Brayton cycle for cryogenic applications

Sustainable and efficient refrigerants are essential due to increasing regulatory constraints on traditional high-GWP refrigerants. This study investigates the potential of natural refrigerants, specifically air, in Reverse Brayton Cycles (RBC) for low-temperature applications. Unlike carbon dioxide and ammonia, which pose limitations and safety concerns below -40°C, air offers a safer and more versatile solution due to its excellent thermodynamic properties and availability. An experimental RBC was constructed using automotive components like centrifugal compressors, a radial turbine, and inter-coolers. These cost-effective and accessible components shift the refrigeration paradigm. The RBC was tested to optimize the Coefficient of Performance (COP) at -100°C while dissipating 2 kW, a typical scenario for whole-body cryotherapy (WBC). Key control parameters included the pressure ratio of the centrifugal compressors and the position of the stator vanes in the variable geometry turbine (VGT). The optimization process resulted in a COP increase of up to 28%. Additionally, a 1D gas-dynamic model validated these results, suggesting that different component selections could enhance performance by 17% compared to the experimental optimum point. Air-based RBC systems using automotive components can effectively achieve temperatures below -40°C, offering a viable, eco-friendly alternative to traditional refrigerants. This advancement addresses regulatory challenges and contributes to the scientific community by providing a sustainable refrigeration solution using commercially available components and demonstrating improvements through experimental data.

KWH Cost Analysis of Energy Storage Power Station Based on Changing Trend of Battery Cost

Energy storage plays a vital role in enhancing the resilience of the power grid. Utilizing typical capacity and power energy storage application scenarios, coupled with industry research data and technical analysis of energy storage, this study calculates the cost of energy storage per kilowatt-hour and the associated mileage cost. The findings indicate that the current cost per kilowatt-hour of electrochemical energy storage ranges from approximately 0.6 to 0.9 yuan/(kW⋅h), revealing a considerable gap between the target cost for widespread application and the range of 0.3 to 0.4 yuan/(kW⋅h). Therefore, the development of energy storage technologies (EST) should prioritize achieving “low cost, long life, high safety, and easy recycling,” taking into account a comprehensive assessment of system manufacturing, system lifespan, system safety, and recycling. This paper delves into the changing trend of battery costs and their impact on kilowatt-hours, presenting strategic suggestions to reduce the kilowatt-hour cost of ESP stations. The research underscores that a continuous reduction in battery costs will contribute to enhancing the economic benefits of ESP stations and provide robust support for the future development of the energy storage industry.

Stochastic optimisation of district integrated energy systems based on a hybrid probability forecasting model

Day-ahead scheduling optimisation plays a significant role in solving stable operation problems of district integrated energy systems (DIESs) by carrying out scheduling optimisation of key equipment of the system based on forecasting data. However, the stable operation of DIESs is affected by multiple uncertainties from source-load. Day-ahead scheduling optimisation method is designed in this study for DIESs based on “prediction-optimisation” to reduce the impact of source-load uncertainty on the stable operation of system. Considering multidimensional source-load data, hybrid probability forecasting model based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN)-Long Short-Term Memory (LSTM)-Gaussian Process Quantile Regression (GPQR) is introduced to quantify the uncertainty using prediction interval. K-means method is used to generate typical scenarios in optimisation step and these scenarios are fed into stochastic optimisation to obtain day-ahead scheduling optimisation results. The experimental results in case study show that hybrid probability prediction model exhibits good prediction performance, while the day-ahead scheduling optimisation based on stochastic optimisation for system's key equipment has the smallest error in typical summer and winter days compared with the real situation, which is 0.018 and 0.0176, respectively. The “prediction-optimisation” approach proposed in this paper shows significant advantages for improving the performance of day-ahead scheduling optimisation of DIESs.

Novel Frequency Regulation Scenarios Generation Method Serving for Battery Energy Storage System Participating in PJM Market

As one of the largest frequency regulation markets, the Pennsylvania-New Jersey-Maryland Interconnection (PJM) market allows extensive access of Battery Energy Storage Systems (BESSs). The designed signal regulation D (RegD) is friendly for use with BESSs with a fast ramp rate but limited energy. Designing operating strategies and optimizing the sizing of BESSs in this market are significantly influenced by the regulation signal. To represent the inherent randomness of the RegD signal and reduce the computational burden, typical frequency regulation scenarios with lower resolution are often generated. However, due to the rapid changes and energy neutrality of the RegD signal, generating accurate and representative scenarios presents challenges for the methods based on shape similarity. This paper proposes a novel probability-based method for generating typical regulation scenarios. The method relies on the joint probability distribution of two features with a 15-min resolution, extracted from the RegD signal with a 2 s resolution. The two features can effectively portray the characteristic of RegD signal and its influence on BESS operation. Multiple regulation scenarios are generated based on the joint probability distributions of these features at first, with the final typical scenarios chosen based on their probability distribution similarity to the actual distribution. Utilizing regulation data from the PJM market in 2020, this paper validates and analyzes the performance of the generated typical scenarios in comparison to existing methods, specifically K-means clustering and the forward scenarios reduction method.

Evaluation of the EUROSEM on soil erosion simulation for loess plateau, northern China

AbstractThe Loess Plateau region features fragmented landforms, sparse vegetation, and short but intense summer rainstorms that can easily cause severe soil erosion. To evaluate its applicability to a typical Loess Plateau in northern China, an indoor laboratory experimental study was conducted for events with different slope lengths (3, 4, and 5 m) and rainfall intensities (50, 60, 70, 80, 90, 100, 110, and 120 mm/h). EUROSEM (European Soil Erosion Model) was used to simulate the soil erosion process and predict both total runoff and sediment yield. The EUROSEM modeling results were compared with the observed experimental values and analysed to determine the performance of EUROSEM. The results and analysis showed that EUROSEM could effectively simulate the runoff processes. It produced better predictions for the total runoff yield than for the sediment yield, with Nash–Sutcliffe efficiencies of 0.9003 and 0.1820, respectively. The qualified rates of the RE (relative error) between simulated and observed values of the total runoff and sediment yield were 87.5% and 37.5%, respectively. Compared to the sediment yield under rainfall intensities of 50–80 mm/h, those under 90–110 mm/h had lower RE and higher NSE, which increased from −3.9 to 0.5. However, the modeling results deviated more from the experimental data at a rainfall intensity of 120 mm/h. According to this study, EUROSEM is a good fit for simulating the runoff and partial sediment yield processes of the Loess Plateau region's slopes. However, the accurate prediction of sediment yield still requires more research to achieve complete effectiveness using EUROSEM for typical and wide scenarios in China.

Property tax and housing wealth inequality: Evidence from China

This paper empirically investigates the impact of the property tax on housing wealth inequality using the property tax pilot program in China as a quasi-natural experiment. Our findings uncover a significant decrease in the Gini coefficients of the housing wealth in the pilot cities. The main channels at work include evolutionary housing demand from home buyers and the convergence of housing prices for different sizes. Two typical tax-exemption scenarios, based on per capita housing area or total number of housing units, present significant differences in the magnitude of the policy effectiveness as well as the effective channels.

A Systematic Approach to Map and Evaluate the Wildfire Behavior at a Territorial Scale in the Northwestern Iberian Peninsula

In the current context of extreme wildfires, understanding fire behavior at a territorial level has proven crucial for territory planning. This type of analysis is usually conducted by analyzing past wildfire statistics. In this study, we forego the past information related to wildfires and analyze, instead, the behavior of the entire territory in the face of wildfires. This allows for the distribution of ignition points to be systematized and for typical and atypical weather scenarios to be considered. This analysis relies on the use of wildfire simulation software. Ignition points used for the simulations were distributed using a systematic 1 × 1 km grid throughout the whole study area. Wildfires were simulated for each ignition point using eight different weather scenarios representing both typical and atypical weather conditions. The fire behavior on the territory was analyzed using rate of spread and intensity parameters for each simulated wildfire. It was observed that this territory is extremely prone to large wildfires both in typical and atypical weather conditions and that there is a tendency for extreme behaviors to develop. Some features were identified as prevention issues that ought to be addressed. This study develops a strategy to evaluate, in a systematic manner, the response of the territory to the threat of wildfires.

Research on energy storage capacity optimization of rural household photovoltaic system considering energy storage sharing.

With the promotion of the photovoltaic (PV) industry throughout the county, the scale of rural household PV continues to expand. However, due to the randomness of PV power generation, large-scale household PV grid connection has a serious impact on the safe and stable operation of the distribution network. Based on this background, this paper considers three typical scenarios, including household PV without energy storage, household PV with distributed energy storage, and household PV with centralized energy storage. Then, a calculation model for PV local consumption rate and annual net cost under different scenarios is constructed. Combined with a natural village in Shandong Province, the PV local consumption rate and annual net cost under three scenarios are compared and analyzed, and the potential of energy storage sharing in reducing storage capacity and improving PV local consumption is explored. The results show that configuring energy storage for household PV can significantly improve the power self-balancing capability. When meeting the same PV local consumption, household PV centralized energy storage can achieve smaller energy storage configuration and lower cost compared to household PV distributed energy storage. Finally, suggestions are proposed to further promote the development of household PV energy storage system. The research results can provide reference for improving the local consumption of rural household PV and accelerating the application of household PV energy storage system.