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Covariant residues identified by computational algorithms have provided new insights into enzyme evolutionary routes. However, the reliability and accuracy of routine statistical coupling analysis (SCA) are unable to satisfy the needs of protein engineering because SCA depends only on sequence information. Here, we set up a new SCA algorithm, SCA.SIM, by integrating structure information and MD simulation data. The more reliable covariant residues with high‐quality scores are obtained from sequence alignment weighted by residual movement for eight related subfamilies, belonging to α/β hydrolase family, with Candida antarctica lipase B (CALB). The 38 predicted covariant residues are tested for function by high‐throughput quantitative evaluation in combination with activity and thermostability assays of a mutant library and deep sequencing. Based on the landscapes of both activity and thermostability, most mutants play key roles in catalysis, and some mutants gain 2.4‐ to 6‐fold increase in half‐life at 50°C and 9‐ to 12‐fold improvement in catalytic efficiency. The activity of double mutants for A225F/T103A is higher than those of A225F and T103A which means that SCA.SIM method might be useful for identifying the allosteric coupling. The SCA.SIM algorithm can be used for protein coevolution and enzyme engineering research. [ABSTRACT FROM AUTHOR]

The Chang'e-5 (CE-5) mission marks China's first lunar sample return endeavor, with its landing site (43.06°N, 51.92°W) situated in the Mons Rümker region of the northern Oceanus Procellarum on the Moon. This region hosts some of the youngest mare basalts of the Moon and contains a relatively youthful geologic unit characterized by crater's equilibrium diameters slightly over 100 m. By refining the Faster Region-based Convolutional Neural Network (Faster R-CNN) algorithm and leveraging high-resolution imagery to create training samples, accurate identification of lunar craters can be achieved. In this study, we enhance the algorithm in aspects such as anchor boxes and Region of Interest alignment. Additionally, we have utilized high-resolution images for training, and identify and statistics craters within the CE-5 landing area. Ultimately, our model attains a validation set Recall of 90%, Precision of 69%, and an Average Precision score of 0.83. Notably, in certain scales, such as for the crater larger than 400 m, recognition results reach Precision of 88% and Recall of 89%. The findings of this study are mapped into a crater catalog. Furthermore, we predict crater density and integrate it with geochronological functions to estimate the absolute model age of nine major geologic units within the CE-5 landing area. The results are generally in agreement with those of other studies who have used manual methods for crater counting, and verify the correctness of our automatic crater identification results. • Enhanced small crater recognition in deep learning: sub-kilometer scale precision. • Employed three strategies to improve Faster RCNN in identifying lunar craters. • Created a crater catalog and estimated model age for the Chang'E-5 area. [ABSTRACT FROM AUTHOR]

• An analytical accuracy framework is developed for the second-order-type time integration methods. • Three types of second-order-type linear multistep methods are revealed with second-order convergence and A-stability. • A single-step methods family is revealed and its displacement, velocity, and acceleration can achieve the same order of accuracy simultaneously. • A family of accuracy-improved single-step methods with algorithmic equivalence to the second-order-type linear multistep methods is disclosed. • An algorithm optimization strategy is proposed, which leads to an optimal single-step method with a single parameter to control the accuracy and numerical dissipation. This paper presents a comprehensive study of the second-order-type time integration methods family encompassing the linear multistep (LMS) methods, the single-step methods, and the LMS equivalent single-step methods for structural dynamics. An analytical accuracy framework for algorithm design, convergence, and optimization is developed, where the obstacles encountered in algorithms construction, convergence accuracy, algorithms equivalence, accuracy measurement, and accuracy optimization in traditional accuracy frameworks are well addressed. Rigorous accuracy analysis successively to the second-order-type LMS (LMS2) methods, the single-step methods, and the LMS2 equivalent single-step methods aims to reveal optimal algorithms with desirable numerical properties. First, independent implicit algorithms including the velocity type and the acceleration type LMS2 methods except the displacement type ones are newly revealed in the LMS2 methods family with second-order convergence and A-stability, and three error constants that can play essential roles in the accuracy measurement and algorithm optimization are developed. Second, a single-step methods family where the displacement, velocity, and acceleration can achieve the same order of accuracy simultaneously is revealed in a generalized 16-parameters methods family, which contains numerous conventional single-step single-solve methods as subfamily methods. It is found that the unit consistent and non-consistent representations show a significant difference in the local truncation error, stability, and convergence performances. Third, a family of accuracy-improved single-step methods with algorithmic equivalence, which is stricter than the traditional spectral equivalence, to the LMS2 methods is revealed. It is clarified that the difference between the methods with equivalence can significantly influence their convergence accuracy, and the error constants concept of the LMS2 methods can be extended to the single-step method with algorithmic equivalence. Last, an algorithm optimization strategy is developed based on the error constants and the ultimate spectral radius, and an optimal single-step method with a single parameter to control the accuracy and numerical dissipation is revealed analytically without empirical assumptions. The optimal method is second-order accurate, unconditionally stable, overshooting acceptable, and equivalent to an optimal route between the Houbolt method and the trapezoidal rule, and its numerical performance is verified by the spectral accuracy and two numerical examples. [ABSTRACT FROM AUTHOR]

Unmanned Surface Vehicle (USV) has a broad application prospect and autonomous path planning as its crucial technology has developed into a hot research direction in the field of USV research. This paper proposes an Improved Dueling Deep Double-Q Network Based on Prioritized Experience Replay (IPD3QN) to address the slow and unstable convergence of traditional Deep Q Network (DQN) algorithms in autonomous path planning of USV. Firstly, we use the deep double Q-Network to decouple the selection and calculation of the target Q value action to eliminate overestimation. The prioritized experience replay method is adopted to extract experience samples from the experience replay unit, increase the utilization rate of actual samples, and accelerate the training speed of the neural network. Then, the neural network is optimized by introducing a dueling network structure. Finally, the soft update method is used to improve the stability of the algorithm, and the dynamic ϵ-greedy method is used to find the optimal strategy. The experiments are first conducted in the Open AI Gym test platform to pre-validate the algorithm for two classical control problems: the Cart pole and Mountain Car problems. The impact of algorithm hyperparameters on the model performance is analyzed in detail. The algorithm is then validated in the Maze environment. The comparative analysis of simulation experiments shows that IPD3QN has a significant improvement in learning performance regarding convergence speed and convergence stability compared with DQN, D3QN, PD2QN, PDQN, PD3QN. Also, USV can plan the optimal path according to the actual navigation environment with the IPD3QN algorithm.

An air-source heat pump simulation model, accounting for evaporator and condenser pressure drop, has been developed. The model is capable of computing the heat pump's coefficient of performance (COP) under different ambient temperatures and relative humidities above frosting conditions. This research extends an existing iterative simulation method that relies on the equalization of logarithmic mean temperature differences (LMTDs) calculated through two different approaches by adding a pressure drop simulation. Frictional and acceleration pressure drop is considered, computed iteratively. Simulation results for three different refrigerants, R410A, R32 and R290, are compared. The model's accuracy is validated by comparing simulated COP values with measured COP values from the reference heat pump datasheet. The model closely replicates the measured COP values above frosting conditions, with only a slight underestimation of approximately 1.5%. Results show a substantial impact of ambient temperature on the COP. For instance, an ambient temperature of 20 ◦ C, compared to 7 ◦ C, results in a COP increase of up to 35%, while an ambient temperature of -10 ◦ C leads to a 26% reduction in COP. Relative humidity enhances the COP if air moisture condensation becomes possible. Higher condenser capacities negatively affect the COP. The study highlights the differences in pressure drop characteristics between the condenser and the evaporator for the modeled heat pump, with maximum pressure drops of 220 kPa and 50 kPa for the condenser and evaporator, respectively. Additionally, the choice of refrigerant significantly influences pressure drop, with R32 displaying the lowest pressure drop, R410A showing the highest condenser pressure drop, and R290 causing the highest evaporator pressure drop., Competing Interests: We declare that we have no financial and personal relationships with other people or organizations that can inappropriately influence our work, there is no professional or other personal interest of any nature or kind in any product, service and/or company that could be construed as influencing the position presented in, or the review of, the manuscript entitled. We understand that the Corresponding Author is the sole contact for the Editorial process (including Editorial Manager and direct communications with the office). He is responsible for communicating with the other authors about progress, submissions of revisions and final approval of proofs. We confirm that we have provided a current, correct email address, which is accessible by the Corresponding Author., (© 2024 The Author(s).)

Fish stock assessment is crucial for sustainable marine fisheries management in rangeland ecosystems. To address the challenges posed by the overfishing of offshore fish species and facilitate comprehensive deep-sea resource evaluation, this paper introduces an improved fish sonar image detection algorithm based on the you only look once algorithm, version 5 (YOLOv5). Sonar image noise often results in blurred targets and indistinct features, thereby reducing the precision of object detection. Thus, a C3N module is incorporated into the neck component, where depth-separable convolution and an inverse bottleneck layer structure are integrated to lessen feature information loss during downsampling and forward propagation. Furthermore, lowercase shallow feature layer is introduced in the network prediction layer to enhance feature extraction for pixels larger than $ 4 \times 4 $. Additionally, normalized weighted distance based on a Gaussian distribution is combined with Intersection over Union (IoU) during gradient descent to improve small target detection and mitigate the IoU's scale sensitivity. Finally, traditional non-maximum suppression (NMS) is replaced with soft-NMS, reducing missed detections due to occlusion and overlapping fish targets that are common in sonar datasets. Experiments show that the improved model surpasses the original model and YOLOv3 with gains in precision, recall and mean average precision of 2.3%, 4.7% and 2.7%, respectively, and 2.5%, 6.3% and 6.7%, respectively. These findings confirm the method's effectiveness in raising sonar image detection accuracy, which is consistent with model comparisons. Given Unmanned Underwater Vehicle advancements, this method holds the potential to support fish culture decision-making and facilitate fish stock resource assessment.

Copyright of Journal of Tiangong University is the property of Journal of Tianjin Polytechnic University and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

With the rapid development of the IoT, the traditional traffic scheduling optimization model is difficult to adapt to the development needs of emerging services, bringing new challenges and problems to data center management. In order to solve the problem of data traffic management in data center network, a clustering algorithm is constructed to analyze its key technologies. The algorithm divides the data to be observed into a certain number of "class clusters" by some predetermined features, so that the similarity of the data in the cluster is measured by a certain "distance function" within each "class cluster". By analyzing the RFID automatic radio frequency identification technology, a data classification model based on RFID automatic radio frequency identification technology is constructed. The original data of the unbalanced state is processed based on the hierarchical partitioning method, and the sampling data analysis result is obtained. The results of data training experiments on the model show that for the prediction of a few samples, the prediction of the unbalanced data set has been further improved, and the AUC value has reached 98.72%. Research has provided new ideas for the operation and management of data centers. [ABSTRACT FROM AUTHOR]

The emergence of resource conflicts and overload control problems during the playback of smart TV terminals has brought many obstacles to the operation of smart TV terminals, which has seriously affected the user experience of smart TV terminal users. In this regard, the adaptive media playback algorithm is optimized for smart TV terminals. This method performs dynamic priority preemptive scheduling on exclusive resources according to resource characteristics and application priorities to optimize resource allocation and improve media playback. The feedback control algorithm is used to perform QoS scheduling on shared resources until QoS proportional fairness is achieved, and QoS proportional compression is used to eliminate resource overload. Finally, a DASH server on Apache and implements an analog DASH client using Python are built. In order to verify the performance of the algorithm, the research results show that the adaptive media playback algorithm has the overload control capability, which only solves the resource conflict and improves the response performance under heavy load of the system, and the algorithm consumes only 4.5% of the overall system QoS. Compared with the existing methods, it is about 30% lower, which is more suitable for resource scheduling of smart TV terminals. The research in this paper shows that the adoption of QoS scheduling mechanism contributes to the optimization of media playback resources and allocation ratio, thus making the playback process of smart TV terminals, which provides a reference for the optimization of smart TV terminal playback. [ABSTRACT FROM AUTHOR]

To study the application of ZigBee protocol in the security algorithm of Internet of Things, the relevant development status at home and abroad is firstly summarized and ZigBee technology is introduced in detail. Then, the principle of AES-128 algorithm is analyzed, and the steps of decryption algorithm are adjusted, so that the decryption algorithm is symmetric in structure and encryption algorithm. In addition, the encryption and decryption algorithm of AES-128 is implemented by using C language. Based on the characteristics of simplicity and low cost of ZigBee technology, the two optimization algorithms are designed and implemented from the analysis of simplicity and efficiency based on the performances of security algorithm. The analysis and test results show that the two optimization algorithms are better than the UN optimized algorithm in terms of speed and complexity. To sum up, in the result comparison, it is found that the speed and complexity of the round operation optimization algorithm is better than that of the column obfuscation optimization algorithm. [ABSTRACT FROM AUTHOR]

Agent‐based models find wide application in all fields of science where large‐scale patterns emerge from properties of individuals. Due to increasing capacities of computing resources it was possible to improve the level of detail and structural realism of next‐generation models in recent years. However, this is at the expense of increased model complexity, which requires more efficient tools for model exploration, analysis and documentation that enable reproducibility, repeatability and parallelization. NetLogo is a widely used environment for agent‐based model development, but it does not provide sufficient built‐in tools for extensive model exploration, such as sensitivity analyses. One tool for controlling NetLogo externally is the r‐package RNetLogo. However, this package is not suited for efficient, reproducible research as it has stability and resource allocation issues, is not straightforward to be setup and used on high performance computing clusters and does not provide utilities, such as storing and exchanging metadata, in an easy way.We present the r‐package nlrx, which overcomes stability and resource allocation issues by running NetLogo simulations via dynamically created XML experiment files. Class objects make setting up experiments more convenient and helper functions provide many parameter exploration approaches, such as Latin Hypercube designs, Sobol sensitivity analyses or optimization approaches. Output is automatically collected in user‐friendly formats and can be post‐processed with provided utility functions. nlrx enables reproducibility by storing all relevant information and simulation output of experiments in one r object which can conveniently be archived and shared.We provide a detailed description of the nlrx package functions and the overall workflow. We also present a use case scenario using a NetLogo model, for which we performed a sensitivity analysis and a genetic algorithm optimization.The nlrx package is the first framework for documentation and application of reproducible NetLogo simulation model analysis. [ABSTRACT FROM AUTHOR]

The computing performance optimization of the Short-Lag Spatial Coherence (SLSC) method applied to ultrasound data processing is presented. The method is based on the theory that signals from adjacent receivers are correlated, drawing on a simplified conclusion of the van Cittert-Zernike theorem. It has been proven that it can be successfully used in ultrasound data reconstruction with despeckling. Former works have shown that the SLSC method in its original form has two main drawbacks: time-consuming processing and low contrast in the area near the transceivers. In this study, we introduce a method that allows to overcome both of these drawbacks. The presented approach removes the dependency on distance (the "lag" parameter value) between signals used to calculate correlations. The approach has been tested by comparing results obtained with the original SLSC algorithm on data acquired from tissue phantoms. The modified method proposed here leads to constant complexity, thus execution time is independent of the lag parameter value, instead of the linear complexity. The presented approach increases computation speed over 10 times in comparison to the base SLSC algorithm for a typical lag parameter value. The approach also improves the output image quality in shallow areas and does not decrease quality in deeper areas. [ABSTRACT FROM AUTHOR]

Copyright of Chinese Journal of Liquid Crystal & Displays is the property of Chinese Journal of Liquid Crystal & Displays and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Brute-force radio-frequency ray tracing scales well on multicore computing architectures as each ray can be traced independently. The discrete nature of rays with no thickness shows a weakness in the aggregation step where nearby rays to the observation point need to be detected and differentiated. The fact that angular defect cannot be distributed evenly for more than 12 rays in space leads to a double-counting (DC) phenomenon, i.e., the radius of a reception sphere cannot exclude all but 1 ray per wavefront. This either leads to significant signal errors or requires the use of space- and time-consuming wavefront differentiation. Bloom filters configured with marginal false-positive rate are proposed here as a replacement of the exact wavefront differentiation, effectively eliminating DC errors. Substantial space gains while computing channel impulse responses are reported. Furthermore, due to the importance of ray-launching template grids for DC avoidance, the analytical angular bounds for the two common icosahedral grids are presented. The frequently referenced approximate DC probability of 20.9% is shown to be highly underestimated when affected by the refraction- and diffraction-induced changes in ray spacing. [ABSTRACT FROM AUTHOR]

Designing a Zero Energy Building (ZEB) requires an optimal choice of the materials of a building envelope. Different material properties and window areas could be selected to generate a set of possibilities of the design of a building, being the demand defined by its thermal characteristics. The energy demand of a building could be produced with renewable systems such as photovoltaic. Moreover, the HVAC (Heating, Ventilation, and Air Conditioning) systems could be selected considering the system cost. The present methodology focuses on finding a balance between investment and low energy consumption for a building, based on an integrated optimization method. Such methodology applies a Tabu search algorithm and a simplified model to select the passive design. Afterwards, active elements of the design, as photovoltaic systems, are selected. Therefore, the methodology faces the problem of estimating the annual energy demand and the life cycle cost. The goal is the design of a building with a large amount of energy generated by renewable energy, to have a ZEB, and in the worst case, a nearly Zero Energy Building (nZEB). This methodology reduces investment, reduces the energy demand and selects the best construction materials, renewable energy, and air conditioning system. The present paper analyzes a set of case studies considering different climatic zones in Spain. The results conclude that the methodology could help builders in the design stage, to find a new design that allows a ZEB with the optimal life cycle cost. [ABSTRACT FROM AUTHOR]

Abstract In recent years, convolutional neural networks (CNNs) have shown great performance in various fields such as image classification, pattern recognition, and multi-media compression. Two of the feature properties, local connectivity and weight sharing, can reduce the number of parameters and increase processing speed during training and inference. However, as the dimension of data becomes higher and the CNN architecture becomes more complicated, the end-to-end approach or the combined manner of CNN is computationally intensive, which becomes limitation to CNN's further implementation. Therefore, it is necessary and urgent to implement CNN in a faster way. In this paper, we first summarize the acceleration methods that contribute to but not limited to CNN by reviewing a broad variety of research papers. We propose a taxonomy in terms of three levels, i.e. structure level, algorithm level, and implementation level, for acceleration methods. We also analyze the acceleration methods in terms of CNN architecture compression, algorithm optimization, and hardware-based improvement. At last, we give a discussion on different perspectives of these acceleration and optimization methods within each level. The discussion shows that the methods in each level still have large exploration space. By incorporating such a wide range of disciplines, we expect to provide a comprehensive reference for researchers who are interested in CNN acceleration. [ABSTRACT FROM AUTHOR]

The paper considers stages of the development of studies in computing optimization in Ukraine (in particular, at the V.M. Glushkov Institute of Cybernetics of the National Academy of Sciences of Ukraine) related to elements of error theory, computational algorithms that are optimal in accuracy and time, high-tech computer technologies for solving problems in computational and applied mathematics with given values of quality characteristics. [ABSTRACT FROM AUTHOR]

The basic principle of the satcom on the move antenna is to detect the error angle between the antenna beam pointing and the satellite direction according to the received satellite signal, and convert it into the corresponding error electrical signal. The controller uses the error electrical signal to control the antenna to make the antenna move in the direction where the error signal decreases. The tracking accuracy of traditional step tracking is not high, and the response speed is very slow, and the signal amplitude fluctuation affects the tracking accuracy. Since the gain graph of the antenna can be regarded as a non-linear stepped objective function, an improved step tracking algorithm based on the gradient method is proposed, which combines the step tracking with the optimization algorithm to achieve precise tracking. The algorithm optimizes the antenna gain pattern as the objective function to find the azimuth and elevation coordinates corresponding to the maximum point of the antenna gain function. The gain function maximization is transformed into the objective function minimization problem, and the minimum value of the objective function is found through the optimization algorithm, which corresponds to the direction of the antenna when the satellite is accurately aligned. The gradient method is selected for optimization, which is stable as a whole and simple iterative process. Theoretical analysis and simulation results show that the algorithm can obtain higher tracking accuracy while maintaining a lower program complexity.

Capital buffers can improve the stability of the banking system, but they come at a cost. This study explores the efficient macro-prudential regulation of systemic risk from the perspective of capital network reconstruction. A model of option pricing is proposed to determine the market value of sector credits. Consequently, systemic risk is measured by both credit and interbank contagion risks. Credit network reconstruction is then optimized to minimize systemic risks. Our analysis was based on data from China's banking systems between 2008 and 2020. In different stress tests, the credit network reconstruction generally optimizes systemic risk to the lowest level. At the same time, it may save about 20% to 140% of the cost of capital. The optimization mechanism analysis shows that large banks with stability advantages share more shocks than small banks by reconstructing their sector credit network. As a result, the contagion process of systemic risk is prevented, and bankruptcy cascades are eliminated. These results imply that credit network reconstruction holds great potential for preventing systemic risks. • Credit network reconstruction has more advantages of efficiency in preventing systemic risk than capital requirements. • Systemic risk optimization is optimized through increased common credits where big banks share more shocks from the other banks. • Stability of the banking system is promoted when big banks take more risks than their market shares. [ABSTRACT FROM AUTHOR]

• Fiber-based composites integrating porous, resonant and damping structures were prepared. • The composite realizes effective noise absorption through gradient impedance design. • Sound waves can be absorbed more easily by optimizing the composite structure. In order to achieve effective noise absorption by finite thickness fiber-based materials, fiber-based composites integrating porous, resonant and damping structures were prepared in this research. Here, the effects of gradient structure, number of constituent units, nonwovens thickness and micro-perforated membrane fabric (MPMF) arrangement on the noise reduction of the composites were simulated and measured. The results showed that the gradient fiber-based composite has excellent broadband sound absorption effect. Moreover, designing the arrangement of micro-perforated structures in the composite can further improve its noise reduction coefficient (NRC). The fiber-based composite (thickness of 1.34 cm) designed by the algorithm has an NRC of 0.51, which is much higher than other noise reduction products with the same thickness. The multi-layer fiber-based composites have the advantages of strong noise reduction capability, small thickness, simple processing and low manufacturing cost, which can be flexibly applied in various noise reduction fields. [ABSTRACT FROM AUTHOR]

In recent years,federated learning has been proposed and received widespread attention to overcome data isolated island challenge.Federated learning related researches were adopted in areas such as financial field,healthcare domain and smart city related application.Federated learning concept was introduced into three different layers.The first layer introduced the definition,architecture,classification of federated learning and compared the federated learning with traditional distributed learning.The second layer presented comparison and analysis of federated learning algorithms from machine learning and deep learning aspects.The third layer separated federated learning optimization algorithms into three aspects to optimize federated learning algorithm through reducing communication cost,selecting proper clients and different aggregation method.Finally,the current research status and three main challenges on communication,heterogeneity of system and data to be solved were concluded,and the future prospects in federated learning domain were proposed.

In this paper, a tracker based on mean shift and radial basis function neural networks called MS-RBF is addressed. As its name implies, two independent trackers have been combined and linked together. The mean shift algorithm estimates the target's location within only two iterations. The scale and orientation of target are computed by exploiting 2-D correlation coefficient between reference and target candidate histograms instead of using Bhattacharyya coefficient. A code optimization strategy, named multiply-add-accumulate (MAC), is proposed to remove useless memory occupation and programmatic operations. MAC implementation has reduced computational load and made overall tracking process faster. The second tracker "RBFNN" has an input feature vector that contains variables such as local contrast, color histogram, gradient, intensity, and spatial frequency. The neural network learns the color and texture features from the target and background. Then, this information is used to detect and track the object in other frames. The neural network employs Epanechnikov activation functions. The features extracted in any frame are clustered by Fuzzy C-Means clustering which produces the means and variances of the clusters. The experimental results show that the proposed tracker can resist to different types of occlusions, sudden movement, and shape deformations. [ABSTRACT FROM AUTHOR]

Convolutional neural networks (CNN) have become ubiquitous in computer vision as well as several other domains, but the sheer size of the modern CNNs means that for the majority of practical applications, a significant speed up and compression are often required. Speeding-up CNNs therefore have become a very active area of research with multiple diverse research directions pursued by many groups in academia and industry. In this short survey, we cover several research directions for speeding up CNNs that have become popular recently. Specifically, we cover approaches based on tensor decompositions, weight quantization, weight pruning, and teacher-student approaches. We also review CNN architectures designed for optimal speed and briefly consider automatic architecture search. [ABSTRACT FROM AUTHOR]

In the background of big data, network in continuing education has become a new educational model. Continuing education network breaks space and time constraints, and how to make use of current resources and technology to realize the information sharing of software resources in continuing education, so as to better serve the teaching, has become an urgent problem to be solved. Based on idea of genetic algorithm, using advantages of genetic algorithm in spatial search, the development of software resource system in continuing education based on big data is carried out. And the modular design for function requirements of resource system in continuing education is also in progress. Finally, system tests show that the system has a certain reliability and stability. [ABSTRACT FROM AUTHOR]

Global warming and environmental degradation are essential issues that endanger human survival. The conflict between rising carbon emissions and carbon neutrality goals has prompted an urgent need for China's energy sector to step up efforts to develop clean energy generation. As a significant hydropower country, hydropower generation is China's mainstay of clean energy generation. The work contributing to sustainable hydropower development requires reasonable forecasts of clean energy generation. This paper proposes a seasonal optimized multivariate grey model that optimizes background value and supplements dummy variables to explore hidden factors through an optimization algorithm to the related sequences. The novel model improves the fitting and prediction accuracy through the loop supplementation of dummy variables. The model tests the effect of China's hydropower generation prediction and compares results with other methods. The mean absolute percentage error of the model training and test groups is 3.87 % and 0.83 %. Finally, this paper predicts hydropower generation in China from 2022 to 2025 based on the power generation during China's 13th Five-Year Plan Period. • This paper proposes a novel grey model method based on dummy variable regulation. • This paper uses the Whale Optimization Algorithm to optimize the grey model's initial and background values. • This paper uses the proposed method to solve the grey model's problem of not fitting the fluctuation trend. • This paper uses the proposed model to predict the trend of hydropower generation during the 14th Five-Year Plan. [ABSTRACT FROM AUTHOR]

The amount of data required in enterprise performance evaluation is increasing, and the number and complexity of data indicators are also increasing. Traditional enterprise performance evaluation methods often need a lot of manpower, material and financial input, and the accuracy and reliability of the results are difficult to guarantee. How to use computer science and technology to realize the intelligence and precision of enterprise performance evaluation becomes an important research object. This paper takes enterprise performance evaluation data index simulation as the research object, aiming to explore a new method to realize the precision and intelligence of enterprise performance evaluation. The improved neural network algorithm and parameter optimization technology are used to simulate the relevant data indicators of enterprise performance evaluation, so as to realize the intelligent analysis and accurate prediction of enterprise performance evaluation. The results show that the improved neural network algorithm and parameter optimization technology proposed in this paper can effectively simulate and predict the data indicators of enterprise performance evaluation. [ABSTRACT FROM AUTHOR]

Designing a Zero Energy Building (ZEB) requires an optimal choice of the materials of a building envelope. Different material properties and window areas could be selected to generate a set of possibilities of the design of a building, being the demand defined by its thermal characteristics. The energy demand of a building could be produced with renewable systems such as photovoltaic. Moreover, the HVAC (Heating, Ventilation, and Air Conditioning) systems could be selected considering the system cost. The present methodology focuses on finding a balance between investment and low energy consumption for a building, based on an integrated optimization method. Such methodology applies a Tabu search algorithm and a simplified model to select the passive design. Afterwards, active elements of the design, as photovoltaic systems, are selected. Therefore, the methodology faces the problem of estimating the annual energy demand and the life cycle cost. The goal is the design of a building with a large amount of energy generated by renewable energy, to have a ZEB, and in the worst case, a nearly Zero Energy Building (nZEB). This methodology reduces investment, reduces the energy demand and selects the best construction materials, renewable energy, and air conditioning system. The present paper analyzes a set of case studies considering different climatic zones in Spain. The results conclude that the methodology could help builders in the design stage, to find a new design that allows a ZEB with the optimal life cycle cost.

B+tree is short in search efficiency and space utilization when processing mass data. This paper proposed an improved structure of B+tree. Firstly, the method reduced the depth of B+tree by adjusting the number of leaf nodes and non-leaf nodes. Then, it increased the utilization of B+tree by optimizing the original insertion algorithm, which taking balance process (BP) before splitting the non-leaf nodes. Through experiment, compared with the traditional B+tree, searching efficiency and space utilization of the improved B+tree got increased by an average of 10% and 6%. It shows that the improvement is feasible. [ABSTRACT FROM AUTHOR]

In the wireless sensor network, there is a consistent one-to-one match between the information collected by the node and the location of the node. Therefore, it attempts to determine the location of unknown nodes for wireless sensor networks. At present, there are many kinds of node localization methods. Because of the distance error, hardware level, application environment and application costs and other factors, the positioning accuracy of various node positioning methods is not in complete accord. The objective function is established and algorithm simulation experiments are carried out to make a mobile ronot node localization. The experimnettal results showed that the proposed algorithm can achieve higher localization precision in fewer nodes. In addition, the localization algorithm was compared with the classical localization algorithm. In conclusion, it is verified that the localization algorithm proposed in this paper has higher localization accuracy than the traditional classical localization algorithm when the number of nodes is larger than a certain number. [ABSTRACT FROM AUTHOR]

This document presents a control strategy for permanent magnet synchronous motor (PMSM) intended to maximize the machine efficiency. This strategy reduces the motor losses without disturbing its dynamic performance. The technique is based on a mathematical model aimed at estimating losses and calculating the current intensity necessary to produce the desired electromagnetic torque so as to reduce the energy employed in the process. Several simulations and experiments were performed in order to test the validity of the proposed strategy and the results are presented. The tests confirm that it is possible to reach an enhancement of efficiency up to 2.6% in comparison with traditional control strategies in nominal operating conditions. [ABSTRACT FROM AUTHOR]