Search

As processor performance advances, the cache has become an essential component of computer architecture. Moreover, the rapid digital transformation of daily life has resulted in electronic devices storing greater amounts of sensitive information. Thus, device users are becoming more concerned about the security of their personal information, so improving processor performance is no longer the sole priority. Hardware vulnerabilities are generally more difficult to detect and address compared to software viruses and related threats. A common technique for exploiting hardware vulnerabilities is through side-channel attacks. They can bypass software security to extract personal information directly from hardware components like the cache or registers. This paper introduces a novel architecture for the arithmetic logic unit (ALU) and associated memory controller (MC) based on the RISC-V microarchitecture to mitigate side-channel attacks. The proposed approach employs hardware-generated random numbers and has minimal design costs, negligible impact on the original system structure, seamless integration, and easy modification of internal components. Results are presented that show it is effective against side-channel attacks.

Summary: Synergetic management of waste activated sludge, heavy metals (HMs) and CO2 for their valorization and cyclic utilization is rarely reported. Herein, we employed sludge-derived extracellular polymeric substances (EPS) and HMs in wastewater to fabricate a gas diffusion electrode (GDE) for electrochemical CO2 reduction. This approach atomically dispersed Ni at each nanofiber of the GDE. Abundant N element in the EPS proved to play a key role in the formation of Nx-Ni (mixture of N3-Ni and N4-Ni) sites for highly efficient CO2 to CO conversion. The atomical Ni3+ shows high catalytic activity. Direct gaseous CO2 reduction in a membrane electrode assembly generated a current density up to 50 mA·cm−2 with CO:H2 ratio of ∼100 and ∼75% FECO under 2.69 cell voltage. This strategy takes advantage of all waste streams generated on site and consolidates traditionally separated treatment processes to save costs, produces value-added products and generates carbon benefits during wastewater treatment.

As an indispensable component of coal-fired power plants, boilers play a crucial role in converting water into high-pressure steam. The oxygen content in the flue gas is a crucial indicator, which indicates the state of combustion within the boiler. The oxygen content not only affects the thermal efficiency of the boiler and the energy utilization of the generator unit, but also has adverse impacts on the environment. Therefore, accurate measurement of the flue gas’s oxygen content is of paramount importance in enhancing the energy utilization efficiency of coal-fired power plants and reducing the emissions of waste gas and pollutants. This study proposes a prediction model for the oxygen content in the flue gas that combines the whale optimization algorithm (WOA) and long short-term memory (LSTM) networks. Among them, the whale optimization algorithm (WOA) was used to optimize the learning rate, the number of hidden layers, and the regularization coefficients of the long short-term memory (LSTM). The data used in this study were obtained from a 350 MW power generation unit in a coal-fired power plant to validate the practicality and effectiveness of the proposed hybrid model. The simulation results demonstrated that the whale optimization algorithm–long short-term memory (WOA-LSTM) model achieved an MAE of 0.16493, an RMSE of 0.12712, an MAPE of 2.2254%, and an R2 value of 0.98664. The whale optimization algorithm–long short-term memory (WOA-LSTM) model demonstrated enhancements in accuracy compared with the least squares support vector machine (LSSVM), long short-term memory (LSTM), particle swarm optimization–least squares support vector machine (PSO-LSSVM), and particle swarm optimization–long short-term memory (PSO-LSTM), with improvements of 4.93%, 4.03%, 1.35%, and 0.49%, respectively. These results indicated that the proposed soft sensor model exhibited more accurate performance, which can meet practical requirements of coal-fired power plants.

As an important component of the air-breathing combined precooling cycle engine of the hypersonic aircraft, the compact heat exchanger plays an important role in precooling the incoming air. However, the compact heat exchanger is easy to frosting during the precooling operation, which leads to an extremely unfavorable result, that is, the specific impulse and specific thrust of the engine significantly decrease. Therefore, in order to maintain the performance of the engine, some measures should be taken to defrosting the compact heat exchanger of the engine. In order to carried out the frosting and defrosting experiments on the compact heat exchanger that operating at relatively low altitudes, the frosting and defrosting performance of the compact heat exchanger are experimentally studied in the wind tunnel under the conditions of moderate airflow humidity value (6.4 g/kg), as well as the flow velocity and the temperature of the mainstream are 10 m/s and 50 °C, respectively. In the defrosting experiments, anhydrous methanol and anhydrous ethanol are sprayed into the mainstream as the defrosting solvents, respectively, and the mass ratios of anhydrous methanol to water and anhydrous ethanol to water are 0.75, 1.0 and 1.25. The experimental results indicate that for the frosting experiment, after coolant flows through the inside of the heat exchanger tube bundles, the frost layer quickly condenses on the outside of tube bundles. However, in the defrosting experiments, once a certain mass ratio of anhydrous methanol or anhydrous ethanol is sprayed into the main flow, the wall temperature of the heat exchanger tube bundles increases significantly, and the wall temperature is close to or higher than the freezing point of water. Furthermore, the defrosting effect and the heat transfer rate are obviously improved, and the pressure loss coefficient drops sharply. Through the analyzation of the defrosting experimental results, it is found that the defrosting performance of anhydrous methanol and anhydrous ethanol are obvious different, and the defrosting effect of anhydrous methanol is significantly higher than that of anhydrous ethanol with the same mass ratio. Within the range of experimental study parameters, for anhydrous methanol, the best defrosting performance can be obtained when the mass ratio is 1.0; and for anhydrous ethanol, the best defrosting effect can be realized when the mass ratio is 1.25. However, the defrosting performance of anhydrous ethanol with the mass ratio of 1.25 is still slightly lower than that of anhydrous methanol with the mass ratio of 0.75. Moreover, in order to achieve the best defrosting performance of the compact heat exchanger, the optimal mass ratio of anhydrous methanol to water may be in the range of between 1.0 and 1.25; and the optimal mass ratio of anhydrous ethanol to water should be greater than 1.25.

In modern society, intelligent spatial analysis services have been playing an important role in building smart cities. Especially, people’s spatial information needs have changed with the technological progress of times, from traditional two-dimensional appearance to three-dimensional interior. In this case, the construction of street networks is a key part of spatial information analysis research. Establishing an integrated indoor-outdoor street network data model with accurate information and complete attributes can provide strong technical support for indoor-outdoor navigation, emergency evacuation, and other application services. However, most of the current studies on internal and external road networks are independent of each other. This means that indoor road networks are mainly based on floor plans, which contain little information and do not represent the spatial relationships between floors. Indoor and outdoor links often suffer from poor connectivity and data loss. Heterogeneous robotics is a high degree of integration of intelligent control and platform control technologies. In a sense, it is a technology platform for connecting heterogeneous robotic populations. By developing effective collaboration and control strategies, tasks can be performed with the highest success rate and the lowest risk while respecting spatial and temporal constraints. As a result, the study of the coordination and management of heterogeneous robotic systems for road network construction, management, and maintenance is a topic of a great theoretical value and significance. Therefore, this paper proposes an intelligent road network construction method that incorporates BIM and GIS technologies. This study uses the building information model to enrich the information of indoor building components and combines the strong spatial analysis capability of outdoor areas with GIS road network technology to build an indoor road network and design a connection scheme for indoor and outdoor areas. After that, the construction of an integrated indoor-outdoor street network is completed, and the interaction between indoor as well as outdoor spatial data is very smooth.

The internal temperature of the pigsty has a great impact on the pigs. Keeping the temperature in the pigsty within a certain range is a pressing problem in environmental control. The current pigsty temperature regulation method is based mainly on manual and simple automatic control. There is rarely intelligent control, and such direct methods have problems such as low control accuracy, high energy consumption and untimeliness, which can easily lead to the occurrence of heat stress conditions. Therefore, this paper proposed an improved sparrow search algorithm (ISSA) based on a multi-strategy improvement to optimize the least squares support vector machine (LSSVM) to form a pigsty temperature prediction model. In the optimization process of the sparrow search algorithm (SSA), the initial position of the sparrow population was first generated by using the reverse good point set; secondly, the population number update formula was proposed to automatically adjust the number of discoverers and followers based on the number of iterations to improve the search ability of the algorithm; finally, the adaptive t-distribution was applied to the discoverer position variation to refine the discoverer population and further improve the search ability of the algorithm. Tests were conducted using 23 benchmark functions, and the results showed that ISSA outperformed SSA. By comparing it with the LSSVM models optimized by four standard algorithms, the prediction effect of the ISSA-LSSVM model was tested. In the end, the ISSA-LSSVM temperature prediction model had MSE of 0.0766, MAE of 0.2105, and R2 of 0.9818. The results showed that the proposed prediction model had the best prediction performance and prediction accuracy, and can provide accurate data support for the prediction and control of the internal temperature of the pigsty.

While single atom catalysis combines heterogeneous materials with molecular understanding, the role of the single atoms remains vague. Here, authors examine single Ni on MoS2 via in situ X-ray absorption spectroscopy to reveal the intermediate and catalytically active species.

To describe and predict the creep deformation of Changsha red loam (including sandy soil and silty clay) in China, an empirical creep model was proposed based on a laboratory consolidation compression test. Two classical soil layers were sampled from the deep foundation pit site and fourteen samples were designed for tests under different loading conditions. Results show that the deformation process illustrates deceleration and stabilization creep with its vertical load lower than 500 kPa, while it may illustrate acceleration with its vertical load higher than 500 kPa. By analyzing the experimental results, the empirical creep model of the red loam was established. Adopting the model to predict the deformation of red loam shows the prediction curves match the actual situation, proving that the model plays a significant role in predicting the creep deformation of Changsha red loam.

Macro Fiber Composites (MFCs) exhibit significant potential in active control applications. These include vibration control for unmanned aerial vehicle wings and helicopter rotors. However, the high-voltage drive requirements of MFCs present challenges. The miniaturization of the controller is a mandatory condition in order not to affect the overall space utilization. Thus, this paper presents a specialized miniaturized high-voltage control system designed specifically for MFC actuators. The proposed system employs a mixed analog-digital modulation method (ADM). This method precisely regulates a discontinuous conduction mode flyback switch-mode power supply operating in current mode. The system achieves an adjustable high-voltage output range of -500 V to 1500 V. The mixed control system consists of several components. These include a switching power supply, a voltage divider circuit, a bleeder circuit, and a digital controller. Additionally, this high-voltage control system integrates with a Simulink software environment. The system is compact and lightweight. It also features high load capacity, high power, and excellent dynamic response. Moreover, it offers real-time control capabilities. Experimental validation on a high-aspect-ratio wing demonstrates that this control system achieves a vibration reduction effect of 65%. The miniaturized control system provides a valuable research base for vibration control studies.