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One of the most widely used measures of scientific impact is the number of citations. However, due to its heavy-tailed distribution, citations are fundamentally difficult to predict but can be improved. This study was aimed at investigating the factors and parts influencing the citation number of a scientific paper in the otology field. Therefore, this work proposes a new solution that utilizes machine learning and natural language processing to process English text and provides a paper citation as the predicted results. Different algorithms are implemented in this solution, such as linear regression, boosted decision tree, decision forest, and neural networks. The application of neural network regression revealed that papers' abstracts have more influence on the citation numbers of otological articles. This new solution has been developed in visual programming using Microsoft Azure machine learning at the back end and Programming Without Coding Technology at the front end. We recommend using machine learning models to improve the abstracts of research articles to get more citations. [ABSTRACT FROM AUTHOR]

In this paper, the reentry phase of the Aerospaceplane is taken as the research object, and the performance parameters of the reusable rocket of a private company are analyzed. Aiming at the guidance and control scheme of the spacecraft returning to the reentry trajectory in the real environment, the original natural algorithm is optimized by considering various reentry flight constraints, and the improved original natural algorithm is used to optimize the reentry trajectory of the Aerospaceplane. We obtained two types of reentry trajectories in the presence of large flight-restricted areas, the "S-type" trajectory and the "spiral-type" trajectory, and obtained data on various influencing factors. The results showed that the basic state parameters of the spiral trajectory optimized using the improved original natural algorithm after adding constraints met the constraint conditions. The aerodynamic heating rate and overload of the spiral reentry trajectory were to some extent greater than those of the S-type trajectory. Under the increasingly stringent requirements of the aerospace environment, new requirements were put forward for the thermal protection system to meet the wider environmental situation. This paper uses the improved original natural algorithm for the first time and applies it to the field of aerospace reentry and entry and adds more constraints to this algorithm for computation. Besides, for the first time, the macroscopic nature of trajectory types is used as a comparative element for parameter comparison, providing a reference basis for selecting trajectory optimization directions from the macroscopic perspective of trajectory types. [ABSTRACT FROM AUTHOR]

Transverse synchronization of vertical displacements of all jacking-up points is an important monitoring indicator to replace bearings in assembled multigirder bridges during the jacking phase. Currently, using target paper to identify the 3D coordinates of control points reduces the complexity of monitoring operations and improves the stability of data precision. However, the existing planar target locating methods have low accuracy, inefficiency, and subjectivity, which seriously hinders the construction process of bearing replacement. Accurately obtaining the center coordinates of multiple targets from a point cloud in a short monitoring period remains a challenge. This study proposes a high-precision automated algorithm to extract target center points in low-density point clouds to quickly calculate real target center points. First, we construct a standard point cloud model of the target papers for scanning, including color and geometric features. Then, we extract the measured point cloud of the typical jacking operation phase based on the reflection intensity and size information. Next, we map the intensity values of the measured point cloud into the color channel and register the measured point cloud with its standard point cloud model using the normal vector estimation and colored ICP algorithms. Finally, we extract the center point of the measured targets. Numerical experiments and engineering test results show that the proposed method converges quickly with high precision and good robustness, which saves 91.4% of the time compared with the traditional method. In general, this research can provide effective technical support for 3D laser scanning in monitoring the operation phase of bridge jacking. [ABSTRACT FROM AUTHOR]

This paper extends our previous method for COVID-19 diagnosis, proposing an enhanced solution for detecting COVID-19 from computed tomography (CT) images using a lean transfer learning-based model. To decrease model misclassifications, two key steps of image processing were employed. Firstly, the uppermost and lowermost slices were removed, preserving sixty percent of each patient's slices. Secondly, all slices underwent manual cropping to emphasize the lung areas. Subsequently, resized CT scans (224 × 224) were input into an Xception transfer learning model with a modified output. Both Xception's architecture and pretrained weights were leveraged in the method. A big and rigorously annotated database of CT images was used to verify the method. The number of patients/subjects in the dataset is more than 5000, and the number and shape of the slices in each CT scan varies greatly. Verification was made both on the validation partition and on the test partition of unseen images. Results on the COV19-CT database showcased not only improvement from our previous solution and the baseline but also comparable performance to the highest-achieving methods on the same dataset. Further validation studies could explore the scalability and adaptability of the developed methodologies across diverse healthcare settings and patient populations. Additionally, investigating the integration of advanced image processing techniques, such as automated region of interest detection and segmentation algorithms, could enhance the efficiency and accuracy of COVID-19 diagnosis. [ABSTRACT FROM AUTHOR]

In this paper, we develop the theoretical provisions of symmetric cryptographic algorithms based on the polynomial residue number system for the first time. The main feature of the proposed approach is that when reconstructing the polynomial based on the method of undetermined coefficients, multiplication is performed not on the found base numbers but on arbitrarily selected polynomials. The latter, together with pairwise coprime residues of the residue class system, serve as the keys of the cryptographic algorithm. Schemes and examples of the implementation of the developed polynomial symmetric encryption algorithm are presented. The analytical expressions of the cryptographic strength estimation are constructed, and their graphical dependence on the number of modules and polynomial powers is presented. Our studies show that the cryptanalysis of the proposed algorithm requires combinatorial complexity, which leads to an NP-complete problem. [ABSTRACT FROM AUTHOR]

Wireless Sensor Network (WSN) has inexpensive, small, and less energy sensor nodes, which are allocated in random ways in particular areas for measuring the phenomenon or events in that field. In recent days, WSN has played a vital role in various applications, like industrial monitoring, medical treatments, agricultural monitoring, and military operations. However, the security challenges and network lifetime are the main issues in the existing methods. In order to overcome these issues, the Taylor-Spotted Cat Optimization (Taylor-SCO) approach is devised in this paper. Here, the Cluster Heads (CHs) are selected based on the developed optimization method, named Taylor CSO. Moreover, the delay, distance, and energy parameters are considered for effective Cluster Head Selection (CHS). Here, route maintenance is also done for increasing network lifetime and reducing complexities. In addition, the Modified K-Vertex Disjoint Paths Routing (KVDPR) model is established for routing. The modification of KVDPR is carried out using several factors, such as link reliability, throughput, and various trust factors. Moreover, the developed Taylor-SCO algorithm is developed by combining the Spotted Hyena Optimizer (SHO), Cat Swarm Optimization (CSO) algorithm, and Taylor series. The Taylor-SCO achieved better performance with energy consumption, trust, and throughput of 0.00037 J, 0.51, and 793160 kbps. [ABSTRACT FROM AUTHOR]

Splicing the shredded paper means the technology that, according the paper, which has been shredded to design a particular Algorithm to splice and recover the original paper. This paper introduced the algorithm of splicing the shredded paper based on the matching to texture feature. By means of this algorithm, we modeled for the problem of splicing the shredded paper and solved it. And, we used the algorithm to splice both pieces of English shredded paper and Chinese shredded paper. The recovered paper had proved the accuracy of the algorithm. [ABSTRACT FROM AUTHOR]

The goods collection and delivery process often relates to distribution logistics problems. The task is to deliver goods from warehouses to customers under specific circumstances. Efforts to optimize the process are largely aimed at reducing overall costs of goods transportation. Among the prominent algorithms for solving the basic type of the delivery (or collection) problem, which includes a single depot and a homogeneous vehicle fleet, is the algorithm developed by Clarke and Wright in 1964. This algorithm minimizes transportation costs by maximizing the savings achieved through merging multiple routes into one. This paper primarily aims to solve the pickup and delivery problem where the goods must be delivered and empty packaging collected in a single process. The request of a customer can be routed from the depot or from another customer. Similarly, the destination of the request may be the depot or another customer. Unlike the original version of the Clarke and Wright algorithm, the initial routes are created to satisfy delivery orders, and therefore, the same customer can occur in multiple routes. Consequently, a situation may arise in which two routes containing one or more common vertices must be combined during the calculation. Furthermore, these vertices need not be the outermost vertices of the routes. This situation cannot be addressed by using the original version of the Clarke and Wright algorithm, and that is why we propose its modification. Merging routes through inner vertices means that the cost savings depend on the configurations of the routes, and therefore, they cannot be calculated a priori. Instead, the dynamic savings matrix must be used. [ABSTRACT FROM AUTHOR]

For the characteristics of channel instability in wireless sensor networks, this paper proposes an intrusion detection algorithm based on FedAvg (federated averaging) and XGBoost (extreme gradient boosting) wireless sensor networks using fog computing architecture. First, the network edge is extended by introducing fog computing nodes to reduce the communication delay. It reduces the transmission bandwidth and privacy leakage risk while improving the accuracy of jointly learned global and local models. Then, the histogram-based approximation calculation method is improved to adapt to the unbalanced data characteristics of wireless sensor networks. Finally, by introducing TOP-K gradient selection, the number of model parameter uploads is minimized, and the efficiency of model parameter interaction is improved. The experimental results show that this algorithm has superior detection performance and low energy consumption. It is also compared with other algorithms to demonstrate the high detection rate and low computational complexity of this algorithm. [ABSTRACT FROM AUTHOR]

Cables are important components of long-span bridge structures, whose operation is significantly affected by cable force changes. Nowadays, cable force testing is performed by physical methods; that is, sensors are installed on the cable structure to monitor its force changes. Obviously, this strategy requires an extensive amount of time to achieve cable force calculation, which makes it impossible to monitor the force of the cable structure in real time. Meanwhile, smartphones have attracted extensive attention in the field of structural health monitoring (SHM) because of their higher cost-effectiveness than accelerometers, which include price and lifespan. Besides, many people own a smartphone, which leads to the possibility of a wider range of applications. Therefore, this paper presents a framework for the rapid estimation of the cable force of long-span bridges based on smartphones-captured video and a template matching algorithm. First, the empirical mode decomposition (EMD) method with wavelet decomposition (WD) method, that is, the EMDWD model, is constructed to extract the vibration signal of the bridge cable by eliminating the effects of smartphone vibration and environmental noise on the measured dynamic displacement, thus effectively improving the accuracy of data processing. In addition, the vibration identification model of bridge cable based on a template matching algorithm is established, and the deformation curve of cable is obtained. Finally, the frequency of bridge suspender is calculated by the Fourier transform method (FFT), and the cable force is estimated based on the smartphone-captured video. [ABSTRACT FROM AUTHOR]

The rapid development of mobile Internet has promoted the rapid rise of cloud computing technology. Mobile terminal devices have greatly expanded the service capacity of mobile terminals by migrating complex computing tasks to run in the cloud. However, in the process of data exchange between mobile terminals and cloud computing centers, on the one hand, it consumes the limited power of mobile terminals, and on the other hand, it results in longer communication time, which negatively affects user QoE. Mobile cloud can effectively improve user QoE by shortening the data transmission distance, reducing the power consumption, and shortening the communication time at the same time. In this paper, we utilize the property that genetic algorithm can perform global search seeking the global optimal solution and construct a dynamic task scheduling model by combining the device-cloud link. The task scheduling model based on genetic algorithm and random scheduling algorithm is compared through comparison experiments, which show that the assignment time of the task scheduling model based on genetic algorithm is shortened by 11.82% to 48.51% and the energy consumption is reduced by 22.28% to 47.52% under different load conditions. [ABSTRACT FROM AUTHOR]

In recent years, solar photovoltaic (PV) energy, as a clean energy source, has received widespread attention and experienced rapid growth worldwide. However, the rapid growth of PV power deployment also brings important challenges to the maintenance of PV panels, and in order to solve this problem, this paper proposes an innovative algorithm based on PA-YOLO. First, we propose to use PA-YOLO's asymptotic feature pyramid network (AFPN) instead of YOLOv7's backbone network to support direct interactions of nonadjacent layers and avoid large semantic gaps between nonadjacent layers. For the occlusion problem of dense targets in the dataset, we introduce a repulsive loss function, which successfully reduces the occurrence of false detection situations. Finally, we propose a customized convolutional block equipped with an EMA mechanism to enhance the perceptual and expressive capabilities of the model. Experimental results on the dataset show that our proposed model achieves excellent performance with an average accuracy (mAP) of 94.5%, which is 6.8% higher than YOLOv7. In addition, our algorithm also succeeds in drastically reducing the model size from 71.3 MB to 48.4 MB, which well demonstrates the effectiveness of the model. [ABSTRACT FROM AUTHOR]

Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms. [ABSTRACT FROM AUTHOR]

Meta-heuristic algorithms have the advantages of resilience, global optimization capacity, and coding flexibility, making them helpful in tackling difficult optimization issues. The enhanced wind-driven optimization (CHTWDO) that was proposed in this paper coupled the chaotic map approach and the hyperbolic tangent with the T-distribution mutation method. The initial air particles are evenly distributed in the system space through a tent mapping strategy. Meanwhile, the variation probability of the hyperbolic tangent model and the T-distribution variation method are used to improve the comprehensive performance of the algorithm. In this way, the global search accuracy and the ability of avoiding the extreme value of the algorithm can be taken into account. Combining the three strategies, CHTWDO had higher global search accuracy and a stronger ability to jump out of local extremum. Comparing with the eight meta-heuristic algorithms (including WDO) and the single strategy improved WDO on 24 test functions, the experimental results show that CHTWDO with two improved strategies has better convergence precision and faster convergence speed. Statistical tests such as Friedman's and Wilcoxon's rank-sum tests are used to determine significant differences between these comparison algorithms. Finally, CHTWDO also obtains the best results on four classical optimization problems in engineering applications, which verifies its practicality and effectiveness. [ABSTRACT FROM AUTHOR]

This paper proposes a hybrid metaheuristic approach to optimize a duty cycle framework based on Seagull and Mayfly Optimization (HSMO-DC) Algorithm. This approach becomes crucial as current clustering protocols are unable to efficiently tune the clustering parameters in accordance to the diversification of varying WSNs. The proposed HSMO-DC primarily has two parts, where the first part takes care of the online cluster head selection and network communication using the seagull algorithm while the second part performs parameter optimization using the mayfly algorithm. The seagull is aimed at improving the energy distribution in the network through an effective bandwidth allocation procedure while reducing the total energy dissipation. Comparatively, with other clustering protocols, our proposed methods reveal an enhanced network lifetime with an improved network throughput and adaptability based on selected standard metric of performance measurement. [ABSTRACT FROM AUTHOR]

The quality of collision detection algorithm directly affects the performance of the whole simulation system. To address the low efficiency and low accuracy in detecting the collisions of flexible cloths in virtual environments, this paper proposes an oriented bounding box (OBB) algorithm with a simplified model, tree structure for a root-node double bounding box, and continuous collision detection algorithm incorporating an OpenNN-based neural network optimization. First, for objects interacting with the cloths with more complex modeling, the model is simplified with a surface simplification algorithm based on the quadric error metrics, and the simplified model is used to construct an OBB. Second, a bounding box technique commonly used for collision detection is improved, and a root-node double bounding box algorithm is proposed to reduce the construction time for the bounding box. Finally, neural networks are used to optimize the continuous collision detection algorithm, as neural networks can efficiently process large amounts of data and remove disjoint collision pairs. An experiment shows that the construction of an OBB using the simplified model is almost identical to that of the original model, but the taken to construct the OBB is reduced by a factor of approximately 2.7. For the same cloth, it takes 5.51%–11.32% less time to run the root-node double bounding box algorithm than the traditional-hybrid bounding box algorithm. With an average removal rate nearly identical to that of the traditional filtering method, the elapsed time is reduced by 7%–11% by using the continuous collision detection algorithm based on an OpenNN neural network optimization. The simulation results are realistic and in line with the requirements for real-time cloth simulations. [ABSTRACT FROM AUTHOR]

With the emergence and vigorous development of 5G technology, there is a significant surge in network usage and traffic, resulting in heightened complexity within network and IT environments. This exponential increase in activity produces a plethora of events, making conventional systems inadequate for the efficient management of 5G networks. In comparison to 4G technology, 5G technology brings forth a host of new features, one of which is the network data analytics function (NWDAF). This function grants network operators the flexibility to either employ their own data analytics methodologies based on machine learning (ML) and deep learning (DL) into their networks. In this paper, we present a dataset named "NWDAF-NFPP" for network function performance time series prediction, collected from a laboratory at China Telecom. The dataset is carefully anonymized to ensure maximum realism and comprehensiveness, while safeguarding sensitive information. It encompasses eight categories of network functions, with data collected at five-minute intervals. The availability of this dataset provides valuable resources for researchers to conduct time series prediction research on network element performance. Following data collection, a total of six models were employed for network element performance prediction, encompassing both machine learning and deep learning approaches. This diverse set of models was carefully chosen to ensure comprehensive coverage of different techniques and algorithms. Through the comparison and analysis of these models, we aim to evaluate their predictive capabilities and identify the most effective approach for network element performance prediction. This comparative analysis will provide valuable insights into the strengths and limitations of each model, aiding in informed decision-making for network optimization and management strategies in the future. [ABSTRACT FROM AUTHOR]

In this paper, a variable-structure multimodel (VSMM) filtering algorithm based on the long short-term memory (LSTM) regression-deep Q network (L-DQN) is proposed to accurately track strong maneuvering targets. The algorithm can map the selection of the model set to the selection of the action label and realize the purpose of a deep reinforcement-learning agent to replace the model switching in the traditional VSMM algorithm by reasonably designing a reward function, state space, and network structure. At the same time, the algorithm introduces a LSTM algorithm, which can compensate the error of tracking results based on model history information. The simulation results show that compared with the traditional VSMM algorithm, the proposed algorithm can quickly capture the maneuvering of the target, the response time is short, the calculation accuracy is significantly improved, and the range of adaptation is wider. Precise tracking of maneuvering targets was achieved. [ABSTRACT FROM AUTHOR]

This paper is concerned with the design of a dual-loop control system for permanent magnet synchronous motor (PMSM). An improved linear extended state observer (LESO) with excellent estimation capability is employed to develop an improved linear active disturbance rejection control (LADRC) suitable for PMSM speed regulation, achieving outstanding disturbance suppression in PMSM speed control. The use of an internal model control scheme to initialize the parameters of the proportional-integral- (PI-) based current controller simplifies the search space of the control system parameter optimization. An improved particle swarm optimization (PSO) algorithm is applied to optimize the controller parameters, thereby enhancing the overall system performance. Finally, through a series of simulations and experiments, we validate that our proposed controller exhibits superior performance compared to some other control methods. [ABSTRACT FROM AUTHOR]

The Pareto dominance-based evolutionary algorithms can effectively address multiobjective optimization problems (MOPs). However, when dealing with many-objective optimization problems with more than three objectives (MaOPs), the Pareto dominance relationships cannot effectively distinguish the nondominated solutions in high-dimensional spaces. With the increase of the number of objectives, the proportion of dominance-resistant solutions (DRSs) in the population rapidly increases, which leads to insufficient selection pressure. In this paper, to address the challenges on MaOPs, a knee point-driven many-objective evolutionary algorithm with adaptive switching mechanism (KPEA) is proposed. In KPEA, the knee points determined by an adaptive strategy are introduced for not only mating selection but also environmental selection, which increases the probability of generating excellent offspring. In addition, to remove dominance-resistant solutions (DRSs) in the population, an interquartile range method is adopted, which enhances the selection pressure. Moreover, a novel adaptive switching mechanism between angle-based selection and penalty for selecting solutions is proposed, which is aimed at achieving a balance between convergence and diversity. To validate the performance of KPEA, it is compared with five state-of-the-art many-objective evolutionary algorithms. All algorithms are evaluated on 20 benchmark problems, i.e., WFG1-9, MaF1, and MaF4-13 with 3, 5, 8, and 10 objectives. The experimental results demonstrate that KPEA outperforms the compared algorithms in terms of HV and IGD in most of the test instances. [ABSTRACT FROM AUTHOR]

With the rapid development of 5G communication and wireless Internet of Things technology, the application of intelligent wearable devices based on wireless data transmission technology is becoming more and more popular. However, due to the bandwidth of wireless data transmission nodes and the power consumption of the device system, the use time and data storage of wearable devices are severely restricted. The compressed sensing (CS) technology has become an effective method to tackle this problem. CS technology includes data compressive sensing at the transmission end and data reconstruction at the receiving end. In this paper, we consider the reconstruction of nonnegative sparse vectors, an important problem in the area of CS for wireless data transmission. It is known that the interval-passing (IP) algorithm is a low-complexity message-passing type method for the problem. However, the reconstruction performance of the IP algorithm is inferior to that of the other state-of-the-art CS reconstruction algorithms such as the orthogonal matching pursuit (OMP) algorithm. In order to address the problem, we propose a two-stage reconstruction algorithm in this paper. The proposed algorithm applies the IP algorithm for the first stage of reconstruction. If the reconstruction fails, the OMP algorithm is then used on the basis of the results in the first stage. The proposed algorithm is evaluated and compared with other state-of-the-art algorithms by the probability of perfect reconstruction under the given sparsity order value. Simulation results suggest that the proposed two-stage algorithm can greatly improve the reconstruction performance of the IP algorithm and can even outperform the OMP algorithm. In addition, the low-complexity advantages of the IP algorithm are maintained in the proposed algorithm. [ABSTRACT FROM AUTHOR]

To establish a suitable pure electric bus arrival time prediction model, this paper takes pure electric bus as the research object. Based on the analysis of the influencing factors of the arrival time of the pure electric bus, the BP neural network arrival time prediction model optimized by the firefly algorithm (FA-BP prediction model) is established by selecting vehicle type, SOC value, battery age, and time as input conditions. The model is trained and tested by using bus operation data. The root mean square error of the Kalman filter model is 0.351, of the BP neural network model is 0.059, and of the FA-BP prediction model is 0.04. The results show that the model in this paper effectively improves the prediction accuracy and has good reliability and feasibility. It can provide some theoretical references for pure electric bus operation and managers and provide some basis for improving bus reliability. [ABSTRACT FROM AUTHOR]

Mechanical properties are important indicators for evaluating the quality of strips. This paper proposes a mechanical performance prediction model based on the Gray Wolf Optimization (GWO) algorithm and the Extreme Learning Machine (ELM) algorithm. In the modeling process, GWO is used to determine the optimal weights and deviations of ELM and experiments are used to determine the model's key parameters. The model effectively avoids manual intervention and significantly improves aluminum alloy strips' mechanical property prediction accuracy. This paper uses processed data from the aluminum alloy production plant of Shandong Nanshan Aluminum Co., Ltd. as experimental data. When the prediction deviation is controlled within ±10%, the GWO-ELM model can achieve a correct rate of 100% for tensile strength, 97.5% for yield strength, and 77.5% for elongation on the test set. The RMSE of the tensile strength, yield strength, and elongation of the GWO-ELM model was 5.365, 11.881, and 1.268, respectively. The experimental results show that the GWO-ELM model has higher accuracy and stability in predicting aluminum alloy strips' tensile strength, yield strength, and elongation. The GWO-ELM model effectively avoids the defects of the traditional model. It has a special guiding significance for producing aluminum alloy strips. [ABSTRACT FROM AUTHOR]

SUMMARY The present study deals with a comprehensive thermodynamic modeling of a combined heat and power (CHP) system in a paper mill, which provides 50 MW of electric power and 100 ton h−1 saturated steam at 13 bars. This CHP plant is composed of air compressor, combustion chamber (CC), Air Preheater, Gas Turbine (GT) and a Heat Recovery Heat Exchanger. The design parameters of this cycle are compressor pressure ratio ( rAC), compressor isentropic efficiency ( ηAC), GT isentropic efficiency ( ηGT), CC inlet temperature ( T3), and turbine inlet temperature ( T4). In the multi-objective optimization three objective functions, including CHP exergy efficiency, total cost rate of the system products, and CO2 emission of the whole plant, are considered. The exergoenvironmental objective function is minimized whereas power plant exergy efficiency is maximized using a Genetic algorithm. To have a good insight into this study, a sensitivity analysis of the results to the interest rate as well as fuel cost is performed. The results show that at the lower exergetic efficiency, in which the weight of exergoenvironmental objective is higher, the sensitivity of the optimal solutions to the fuel cost is much higher than the location of the Pareto Frontier with the lower weight of exergoenvironmental objective. In addition, with increasing exergy efficiency, the purchase cost of equipment in the plant is increased as the cost rate of the plant increases. Copyright © 2010 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) scholars and mediciners have reported AI systems that accurately detect medical imaging and COVID-19 in chest images. However, the robustness of these models remains unclear for the segmentation of images with nonuniform density distribution or the multiphase target. The most representative one is the Chan-Vese (CV) image segmentation model. In this paper, we demonstrate that the recent level set (LV) model has excellent performance on the detection of target characteristics from medical imaging relying on the filtering variational method based on the global medical pathology facture. We observe that the capability of the filtering variational method to obtain image feature quality is better than other LV models. This research reveals a far-reaching problem in medical-imaging AI knowledge detection. In addition, from the analysis of experimental results, the algorithm proposed in this paper has a good effect on detecting the lung region feature information of COVID-19 images and also proves that the algorithm has good adaptability in processing different images. These findings demonstrate that the proposed LV method should be seen as an effective clinically adjunctive method using machine-learning healthcare models. [ABSTRACT FROM AUTHOR]

Purpose. As a scientific field, bioinformatics has drawn remarkable attention from various fields, such as information technology, mathematics, and modern biological sciences, in recent years. The topic models originating from the field of natural language processing have become the focus of attention with the rapid accumulation of biological datasets. Thus, this research is aimed at modeling the topic content of the bioinformatics literature presented by Iranian researchers in the Scopus Citation Database. Methodology. This research was a descriptive-exploratory study, and the studied population included 3899 papers indexed in the Scopus database, which had been indexed in this database until March 9, 2022. The topic modeling was then performed on the abstracts and titles of the papers. A combination of LDA and TF-IDF was utilized for topic modeling. Findings. The data analysis with topic modeling resulted in identifying seven main topics "Molecular Modeling," "Gene Expression," "Biomarker," "Coronavirus," "Immunoinformatics," "Cancer Bioinformatics," and "Systems Biology." Moreover, "Systems Biology" and "Coronavirus" had the largest and smallest clusters, respectively. Conclusion. The present investigation demonstrated an acceptable performance for the LDA algorithm in classifying the topics included in this field. The extracted topic clusters indicated excellent consistency and topic connection with each other. [ABSTRACT FROM AUTHOR]

This paper proposes an interactive two-archive method to solve many-objective optimization problems. Two updating strategies based on the aggregation-based framework are presented and incorporated into a two-archive framework. In addition, we further extend this method by introducing an interactive mechanism in which evolutionary information is passed from the diversity archive to the convergence archive. Given the requirement to balance convergence and diversity, a mating selection method is proposed to regulate the evolutionary speed of these two archives collaboratively. The proposed algorithm has been tested extensively on several problems with different peer algorithms to validate its effectiveness. The results show that the proposed method can outperform several state-of-the-art evolutionary algorithms for handling many-objective optimization. [ABSTRACT FROM AUTHOR]

Existing research combines acupuncture theory with network science and proposes a new paradigm for the study of acupoint selection patterns—a key acupoint mining algorithm based on acupoint networks. However, the basic idea of this study for finding key acupoints is based on binary acupoint synergy relationships, which ignores the higher-order synergy among multiple acupoints and does not truly reflect the implicit patterns of each acupoint among meridian systems. Moreover, the mining results assessment method, which this new paradigm involves, does not have wide applicability and universality. In this paper, with the introduction of higher-order interactions between multiple acupoints, a high-specificity key acupoint mining algorithm based on 3-node motif is proposed in the acupoint-disease network (ADN). In response to the narrow applicability of the new research paradigm involving the evaluation of algorithms' measures, new and widely applicable and universal evaluation criteria are introduced in terms of resolution, network loss, and accuracy, respectively. Based on the principles of acupoint selection involved in acupuncture clinics in Chinese medicine, the acupoints involved in the data were divided into a total of 19 regions according to their distribution characteristics. From these 19 regions, we selected the key acupoints that have a large impact on the global network. Finally, we compared this algorithm with five other acupoint importance assessment algorithms in terms of resolution, network loss, and accuracy, respectively. The comprehensive results show that the algorithm identifies key acupoints with an accuracy of 63%, which is 14% to 21% higher than other existing methods. The key acupoints identified by the algorithm have a significant disruptive effect on the connectivity of the network, indicating that the key acupoints are at the core of the acupoint-disease network topology. They have a significant propagation influence on other acupoints, which means that the key acupoints have high-synergistic cooperation with other acupoints. Meanwhile, the stability and specificity of the algorithm ensure the reliability of the key acupoints. We believe that the key acupoints identified by the algorithm can be used as core acupoints from the perspective of network topology and high synergy of other acupoints, respectively, and help researchers explore targeted and high-impact combinations of acupoints to optimize existing acupuncture prescriptions under condition constraints. [ABSTRACT FROM AUTHOR]

Hom–Lie algebras are generalizations of Lie algebras that arise naturally in the study of nonassociative algebraic structures. In this paper, the concepts of solvable and nilpotent Hom–Lie algebras are studied further. In the theory of groups, investigations of the properties of the solvable and nilpotent groups are well-developed. We establish a theory of the solvable and nilpotent Hom–Lie algebras analogous to that of the solvable and nilpotent groups. We also provide examples to illustrate our results and discuss possible directions for further research. Dedicated to Al Farouk School & Kinder garten-Irbid-Jordan [ABSTRACT FROM AUTHOR]

Automatic production system scheduling problem under a just-in-time environment is researched in this paper. The automatic production system is composed of many tanks and one robotic, the tank of the researched problem is responsible for processing the job, and the robotic moves the job from one tank to the other tank. The difference between the researched problem and the classic shop scheduling problem is that the former must consider job scheduling and the robotic move sequence, but the latter considers only job scheduling. For optimizing simultaneously job scheduling and robotic move sequence in the proposed problem and minimizing total earliness/tardiness, an improved NEH (Nawaz-Enscore-Ham) and variable search (INEH-VNS) algorithm are developed. In the proposed method, firstly, to obtain initial solution, an improved NEH is shown. Secondly, for computing value of the objective function, the double procedure method is constructed. Thirdly, according to the properties of the proposed problem, three neighborhood structures, adjacent exchange, random insertion, and job exchange, are investigated. To test the performance of the INEH-VNS, 100 instances are randomly generated. When the run time is the same, compared with CPLEX 12.5, the INEH-VNS algorithm can find high-quality approximate optimal solution, a special big scale. Compared with the G-VNS algorithm, the average improvement rate of the approximate optimal solution is 45.9%, and the average stability rate of the INEH-VNS algorithm enhances 75.04%. That is to say, the INEH-VNS algorithm is outstanding and more effective. [ABSTRACT FROM AUTHOR]

This paper proposes a blockchain-based identity authentication (BA) scheme for IoT devices to solve the authentication security problem of IoT devices. The BA scheme uses hashing and digital signature algorithms to achieve integrity and nonrepudiation of authentication messages. Blockchain technology is used to achieve decentralised and distributed storage and management of authentication data. Besides, the BA scheme uses the idea of trust domains and trust credentials to establish a master-slave connection between IoT devices. The BA scheme is then compared with the existing four schemes and analysed from six perspectives to show that the BA scheme has better security. Also, the results show that the BA scheme has reasonable computational and storage overhead. Finally, the advantages of the BA scheme over traditional centralised and existing blockchain-based authentication schemes are compared and analysed. The results show that it can perfectly solve the problem of overreliance on trusted third parties in traditional authentication schemes. [ABSTRACT FROM AUTHOR]

Image restoration is an interesting ill-posed problem. It plays a critical role in the concept of image processing. We are looking for an image that is as near to the original as possible among images that have been skewed by Gaussian and additive noise. Image deconstruction is a technique for restoring a noisy image after it has been captured. The numerical results achieved by the prox-penalty method and the split Bregman algorithm for anisotropic and isotropic TV denoising problems in terms of image quality, convergence, and signal noise rate (SNR) are compared in this paper. It should be mentioned that isotropic TV denoising is faster than anisotropic. Experimental results indicate that the prox algorithm produces the best high-quality output (clean, not smooth, and textures are preserved). In particular, we obtained (21.4, 21) the SNR of the denoising image by the prox for sigma 0.08 and 0.501, such as we obtained (10.0884, 10.1155) the SNR of the denoising image by the anisotropic TV and the isotropic TV for sigma 0.08 and (-1.4635, -1.4733) for sigma 0.501. [ABSTRACT FROM AUTHOR]

The minimum spanning tree (MST) is widely used in planning the most economical network. The algorithm for finding the MST of a connected graph is essential. In this paper, a new algorithm for finding MST is proposed based on a deduced theorem of MST. The algorithm first sorts the edges in the graph according to their weight, from large to small. Next, on the premise of ensuring the connectivity of the graph, it deletes the edges in this order until the number of edges of the graph is equal to the number of vertexes minus 1. Finally, the remaining graph is an MST. This algorithm is equivalent to an inverse Kruskal algorithm. For sparse graphs, the proposed algorithm has higher efficiency than the Kruskal algorithm. Experiments and analysis verify the effectiveness of the algorithm proposed in this paper. The algorithm provides a better choice for finding the MST of the sparse graph. [ABSTRACT FROM AUTHOR]

In recent years, with changing conditions in global markets and competition between supply chains, organizations have realized that in order to grow and survive in today's turbulent environment, they must make every effort to create and maintain a sustainable competitive advantage. Therefore, in this research, a multiobjective, multilevel, multiperiod, and multicommodity mathematical model is presented. The proposed model has five levels in the direct direction and three levels in the reverse direction. This research is one of the few studies that has considered the sustainable closed-loop supply chain of cellulosic-based products (paper and corrugated board) in the model and has considered it at all levels of the chain. Also, the quality of returned products in the reverse supply chain and its impact on the collection and recycling of products and returning to the supply chain are other contributions of this research. The results show, by increasing the transportation costs, the number of established distribution centers increases to a certain extent and finally remains constant. As it is shown, the reduction of transportation costs to the point of establishing four centers in the recession scenario and five centers in the boom scenario becomes saturated and is no longer able to minimize the number of established centers. [ABSTRACT FROM AUTHOR]

This paper deals with the optimal design of different VLSI circuits, namely, the CMOS voltage reference circuit and the CMOS ring oscillator (RO). The optimization technique used here is the multiobjective differential evolution algorithm (MDEA). All the circuits are designed for 90 nm technology. The main objective of the CMOS voltage reference circuit is to minimize the voltage variation at the output. The targeted value of the reference voltage is 550 mV. A CMOS ring oscillator (RO) is designed depending on the performance parameters such as power consumption and phase noise. The optimal transistor sizing of each circuit is obtained from MDEA. Each circuit is implemented in SPICE by taking the optimal dimensions of the transistors, and the performance parameters are achieved. The designed voltage reference circuit achieves a reference voltage of 550 mV with 600 nW power dissipation. The reference voltage variation of 8.18% is observed due to temperature variation from −40°C to + 125°C. The MDEA-based optimal design of RO oscillates at 2.001 GHz frequency, has a phase noise of −87 dBc/Hz at 1 MHz offset frequency, and consumes 71 μW power. This work mainly aims to optimize the MOS transistors' sizes using MDEA for better circuit performance parameters. SPICE simulation has been carried out by using the optimal values of MOS transistor sizes to exhibit the performance parameters of the circuit. Simulation results establish that design specifications are closely met. SPICE results show that MDEA is a better technique for the optimal design of the above-mentioned VLSI circuits. [ABSTRACT FROM AUTHOR]

Recently, the same authors provided a switching algorithm for the preferred clock skew estimator appropriate for the precision time protocol (PTP) scenario. The algorithm chooses the one-way delay (OWD) clock skew estimator for the Forward path or the Reverse path or the two-way delay (TWD) clock skew estimator that has the best performance in the mean square error (MSE) perspective. However, the switching algorithm applies only to the Gaussian scenario. In a real system, the packet delay variation (PDV) can be characterized as an fractional Gaussian noise (fGn) process where the Hurst exponent parameter can also have values higher than 0.5. Thus, the Gaussian case-switching algorithm may not apply in a real system where after a small set of PTP measurements, the switching algorithm should be able to switch effectively to the preferred clock skew estimator with the best performance in the MSE perspective. In this paper, the PDV is characterized as an fGn process where the Hurst exponent (H) is in the range of 0.5 ≤ H < 1. We estimate the unknown PDV variances and the Hurst exponent parameters of the Forward and Reverse paths. These estimated parameters are used for switching to the preferred clock skew estimator from the MSE perspective, even in the presence of asymmetry in the PDV or in the Hurst exponent parameters. We also detect and alarm for the unexpected load, cyber-attack, or clock skew deviation that can occur in a real system. [ABSTRACT FROM AUTHOR]

Accurate determination of photovoltaic (PV) parameters holds immense significance for ensuring the reliability of solar system operations, uninterrupted power supply for load consumption, and efficient control and management of energy sources. I-V curves transform parameter extraction into a nonlinear optimization problem supported by the I-V data points in the PV model to characterize the PV model macroscopically. Therefore, this paper proposes a novel parameter extraction model using the Q -learning-based multistrategy improved shuffled frog leading algorithm (CRNSFLA). During the evaluation process of the proposed algorithm, the colony predation algorithm (CPA) is utilized to expand the search range of the worst individual, which is no longer confined to the line segment range between the current and best values. In the later stage of evaluation, the optimal individual serves as the starting point and is applied to the Nelder-Mead simplex (NMS) for forming a simplex to mine higher-quality solutions. Besides, the simplest reinforcement Q -learning allows for a reasonable switch between these two mechanisms. A reasonable balance between exploration and exploitation trends is ensured while making full use of the advantages of both according to the reward and punishment mechanisms. The comprehensive test results under various optimization functions, different PV models, and environmental conditions demonstrate that the proposed algorithm is more advantageous than existing algorithms for parameter extraction problems. Specifically, CRNSFLA had RMSEs of 9.8602E-04, 9.8248E-04, and 9.8248E-04 in the single-diode model (SDM), double-diode model (DDM), and three-diode model (TDM), respectively. Moreover, compared with the original shuffled frog leading algorithm, the CRNSFLA showed significant improvements in 62% of the optimization functions. Therefore, CRNSFLA can be considered an effective tool for solar cell parameter extractions. [ABSTRACT FROM AUTHOR]

In recent decades, with the large-scale construction and rapid development of half-through arch bridges, as well as the increase of bridge service time, the suspender damage of arch bridge has become increasingly prominent. Therefore, real-time monitoring and regular detection of the health of arch bridge suspenders and timely detection and accurate judgment of the damage location and extent of suspenders are of great engineering significance for evaluating the reliability and residual life of arch bridge structures. By analyzing the main difficulties and existing problems of suspender damage identification, this paper takes the change rate of modal curvature as the damage index, introduces fireworks algorithm into the neural network model, optimizes the optimization process of neural network weight and threshold, and proposes a prediction model based on improved BP neural network by fireworks algorithm. According to the measured data of the damage degree of a long-span arch bridge in daily monitoring and on-site inspection, the proposed prediction method is applied to verify the effectiveness and accuracy in engineering health detection. On this basis, the improved BP neural network by fireworks algorithm is used to predict the suspender damage of a certain long-span half-through arch bridge, which provides an important basis for the actual bridge safety assessment. [ABSTRACT FROM AUTHOR]

In this paper, a novel UAV path planning algorithm based on improved cellular ant colony algorithm and dynamic window algorithm (ICACO-IDWA) is proposed to solve the problem of dynamically changing threat during actual flight. The main innovations of this paper are as follows. (a) The hexagon grid method is proposed to model the UAV flight space, which solves the problem of inconsistent simulation time step. (b) A novel ICACO-IDWA algorithm is proposed. In the first stage, the optimal path is obtained by the improved cellular ant colony algorithm (ICACO). In the second stage, the improved dynamic window algorithm (IDWA) is used to optimize the optimal path considering dynamic threat. Through the algorithm, the UAV path planning with dynamic threat change is realized. Finally, simulation results verify the effectiveness of the proposed model and algorithm. [ABSTRACT FROM AUTHOR]

The identification of disruptive technologies is intended to focus on training and incubation in advance and is an important means to accelerate the upgrading of industrial structure and the transformation of developmental mode and seize the commanding heights of future development. Based on summarizing the existing major identification methods of disruptive technologies, this paper concludes the rule that "disruptive technologies are always at the root node of a certain classification in the deeply classified technology development network". It also proposes a new algorithm to use term frequency-inverse document frequency technology and a patented Subject-Action-Object structure to extract technical features, develop networks based on similarity matrix generation technology, and identify subversive technologies based on the depth classification model. Using patent data, it is found that the technology development network generated by the algorithm proposed in this paper can effectively show the trajectory of technology development by fitting the patent citation relationship. Through this algorithm, we have successfully identified technologies that have had disruptive effects in the field and verified the effectiveness of this algorithm. [ABSTRACT FROM AUTHOR]

The incidence of liver cancer is increasing year by year, and how to effectively diagnose early-stage liver cancer and improve the survival rate of liver cancer patients has become one of the current research topics of concern. Aiming at this problem, it is of great significance for the diagnosis of early liver cancer. With the in-depth research on the diagnosis of early-stage liver cancer, the research on growth differentiation factor 15 is gradually carried out, and its performance advantages are of great significance to solve the problem of detection and diagnosis of early-stage liver cancer. This study can improve the accuracy of early diagnosis of liver cancer. The purpose of this paper is to study the application of data mining in the study of clinical value of growth and differentiation factor 15 detection and diagnosis of early liver cancer. In this paper, the data mining algorithm is analyzed, the performance of the algorithm is experimentally analyzed, and the relevant theoretical formulas are used to explain. The results showed that the expression level of GDF-15 was significant in early primary liver cancer (tumor diameter <2.5 cm). Different from normal liver tissue (P < 0.01), there was a significant difference (P < 0.01) compared with adjacent tissue (P < 0.01). Serum GDF-15 can be used as a tumor marker for predicting early stage liver cancer. The high expression of GDF-15 in early stage liver cancer is an independent risk factor affecting the prognosis of liver cancer patients. [ABSTRACT FROM AUTHOR]

For traditional predictor-corrector guidance algorithm for reentry glide vehicle, it cost a lot of time to obtain predicted flight range with a slow speed to iterate. In this paper, according to residual network (ResNet)'s block and dynamic model of vehicle, through analyzing the characteristics of predicted flight range with constraints, the flight range prediction block and flight range prediction neural network are designed, which can obtain the predicted range accurately and quickly; then aiming at the separation between guidance logic and no-fly zone avoidance logic, which may lead to guidance failure and increasing of the sign variation number of the bank angle, the no-fly zone crossrange and the no-fly zone mapping crossrange are proposed in this paper. According the repulsion force of artificial potential field, an adaptive crossrange corridor combining guidance logic and no-fly zone avoidance logic is proposed, and the convergence of the corridor is analyzed theoretically. Through simulation, the block number of flight range prediction network is determined firstly. By this method, the efficiency of lateral guidance can be improved. Then, through the simulation with the different no-fly zones under different disturbed conditions, the stability and validity of the guidance method are verified. Finally, compared with other predictor-corrector algorithms, the proposed method can realize guidance with less sign variation number of bank angle and better avoidance for no-fly zones. [ABSTRACT FROM AUTHOR]

In the face of the batch, dynamic access data, or the flow of data that continuous changes over time, the traditional support vector machine algorithm cannot dynamically adjust the previous classification model. To overcome this shortcoming, the incremental support vector machine (ISVM) algorithm is proposed. However, many incremental support vector algorithms still have shortcomings such as low efficiency, memory limitation, and poor generalization. This paper puts forward the new ISVM algorithm, HDFC-ISVM ∗ algorithm, based on the high-dimensional distance and forgetting characteristics. This paper firstly proposes the original HDFC-ISVM algorithm that first learns the distribution characteristics of the samples according to the distance between the samples and the normative hyperplane. Then, it introduces the forgetting factor. In the incremental learning process, the classifier gradually accumulates the spatial distribution knowledge of samples, eliminates the samples that have no contributions to the classifier, and selectively forgets some useless samples according to the forgetting factor, which overcomes the shortcomings such as low efficiency and poor accuracy of some algorithms. But, the original HDFC-ISVM algorithm is sensitive to parameters, and different settings of the parameters have a great impact on the final classification accuracy of the algorithm. Therefore, on the basis of the original algorithm, an improved algorithm HDFC-ISVM ∗ based on the adjustments to the initialization strategy and updating rules of the forgetting factor is proposed. The initialization strategy and updating rules of the forgetting factor are adjusted to adapt datasets with different distributions in this improved algorithm. The rationality of the improved strategy about the forgetting factor is discussed theoretically. At the same time, the proposed algorithm has better classification accuracy, classification efficiency, and better generalization ability than other algorithms, which is verified by experiments. [ABSTRACT FROM AUTHOR]

Small samples are prone to overfitting in the neural network training process. This paper proposes an optimization approach based on L2 and dropout regularization called a hybrid improved neural network algorithm to overcome this issue. The proposed model was evaluated based on the Modified National Institute of Standards and Technology (MNIST, grayscale-28 × 28 × 1) and Canadian Institute for Advanced Research 10 (CIFAR10, RGB - 32 × 32 × 3) as the training data sets and data applied to the LeNet-5 and Autoencoder neural network architectures. The evaluation is conducted based on cross-validation; the result of the model prediction is used as the final measure to evaluate the quality of the model. The results show that the proposed hybrid algorithm can perform more effectively, avoid overfitting, improve the accuracy of network model prediction in classification tasks, and reduce the reconstruction error in the unsupervised domain. In addition, employing the proposed algorithm without increasing the time complexity can reduce the effect of noisy data and bias and improve the training time of neural network models. Quantitative and qualitative experimental results show that the accuracy of using the proposed algorithm in this paper with the MNIST test set has an improvement of 2.3% and 0.9% compared to L2 regularization and dropout regularization, respectively, and based on the CIFAR10 data set, the accuracy improvement of 0.92% compared with L2 regularization and 1.31% concerning dropout regularization. The reconstruction error of using the proposed algorithm in this paper with the MNIST data set has an improvement of 0.00174 and 0.00398 compared to L2 regularization and dropout regularization, respectively, and based on the CIFAR10 data set, the accuracy improvement of 0.00078 compared with L2 regularization and 0.00174 concerning dropout regularization. [ABSTRACT FROM AUTHOR]

In this paper, we consider the problem of motion intention recognition for cluster targets with splitting behaviour and lack of motion prior information. This is a challenge to the classical Bayesian inference based intention recognition algorithms because they rely heavily on a priori knowledge. In order to solve these problems, a joint algorithm of deep echo state network optimized by adaptive genetic algorithm (AGADESN) and DBSCAN clustering algorithm is proposed in this paper. We use improved Olfati-Saber model with direction noise to generate cluster motion and use the cluster motion data to drive AGADESN algorithm to predict cluster destination, which achieves higher destination prediction accuracy than DESN algorithm. We innovatively design the motion similarity distance (MSD) and take the destination prediction output as one of the distance inputs, alleviating the lack of differentiation among different cluster targets caused by only relying on speed and position distance at the early stage of cluster motion. Based on the MSD, DBSCAN clustering algorithm is used to identify clusters in the field of view to determine whether splitting behaviour occurs. Simulation results demonstrate the effectiveness of the proposed algorithm in cluster target motion intention recognition and its superiority over DESN algorithm and DBSCAN algorithm only based on speed and position distance. [ABSTRACT FROM AUTHOR]

Traditional Chinese medicine (TCM) is a summary of the diagnosis and treatment experience formed by the working people in the long-term struggle against diseases, so it is very important to protect the intangible cultural heritage of TCM. How to extract valuable knowledge accurately and conveniently from the massive medical records of TCM is one of the important issues in the current research on the development of TCM. Due to the large amount of data of TCM medical records, many feature attributes, and diverse patterns, the existing classification technology has high computational complexity, low mining efficiency, and poor universality. Therefore, this paper proposed to quantify the medical records of TCM and obtained the main symptoms according to the improved hierarchical clustering feature selection algorithm. This paper also proposed a support vector machine (SVM) classification method using improved particle swarm algorithm to classify TCM information, which not only improves the efficiency and accuracy of TCM information classification but also discovers the potential dialectical and symptom patterns in diagnosis and treatment, so that the intangible cultural heritage protection of TCM can be developed sustainably. This paper showed that the information acquisition accuracy of the improved algorithm was very high. Before the improved algorithm was used, the accuracy of information mining for TCM was 67.90% at the highest and 65.53% at the lowest, but after using the improved algorithm, the accuracy rate of information mining for TCM was 88.02% at the highest and 82.45% at the lowest. It can be seen that using the improved algorithm to mine TCM information can quickly process effective information. [ABSTRACT FROM AUTHOR]

In this paper, the existing dynamic adaptive recommendation methods are studied, which combine the practical application scheme of transforming the actual dynamic adaptive recommendation problem into user microblog information. After that, a dynamic adaptive weight fusion method is proposed and based on experimental verification, a real-time dynamic adaptive recommendation system is finally designed. The speech recognition of the Internet of Things takes natural language problems as the research object for a long time and takes the sound signal as the research topic. This paper analyzes the application of dynamic adaptive recommendation and Internet of Things speech recognition in mass sports data monitoring. The simulation results show that the system in this paper is convenient for users to monitor the exercise indicators in real time through the mobile client, and at the same time query the exercise historical data and compare the exercise data through the network terminal, thereby improving the exercise method and exercise load. Users can access the motion monitoring module and see the past floating state of motion parameters more intuitively than graphs, contains queries for metrics such as heart rate, body temperature, kinetic energy, pulse, and weight. Due to the diversity and complexity of people's differences, personal characteristics and business environments, sports data monitoring systems also need to be designed according to the scope of use. This paper analyzes the requirements for a motion data monitoring system and provides the system architecture design and basic data for producing detailed information for the system. [ABSTRACT FROM AUTHOR]

The existing interpretation models for the time spectrum of impulse neutron oxygen activation require interpreters to select the peak range or background range manually from the time spectrum curve, and there is no adaptive interpretation model that can determine the peak range or background range. In this paper, an adaptive selection rule for background segment is proposed, and a semiautomatic interpretation model is constructed by combining background segment interpretation model. Firstly, the interpretation operator selects the time spectrum curve, then the algorithm program adaptively determines the background segment according to the rules, and then calculates and displays the transit time and volume flow according to the background segment interpretation model. The processing results of the measured data show that the interpretation model in this paper not only retains the interpretation precision of the background interpretation model, but also reduces the labor intensity of the interpretation operator, realizing the semiautomatic interpretation of the time spectrum. [ABSTRACT FROM AUTHOR]

With the continuous improvement of the network hardware environment, people turn the demand target to the network application environment and the construction of information resources. How to build a network teaching platform for general undergraduate teaching to ensure the stability of the system and high-quality services during operation especially large-scale concurrent access will inevitably lead to the increase in the business volume of each core part of the network, the number of visits, and data traffic. With the growth of the network, the corresponding processing power and computing intensity also increase rapidly, which causes problems such as overloading of core network equipment, network bottlenecks, and network congestion. Simply pursuing high-performance hardware to solve problems will undoubtedly result in high cost investment; moreover, equipment with excellent performance cannot meet the needs of the current rapidly growing business volume. According to the design goal of the dance online teaching platform, to meet the online teaching load requirements of many people at the same time, the pressure of the web server cluster must be great. Because many people in online at the same time put too much pressure on the web server, this part of the network cannot be processed in time, which leads to the phenomenon that the performance of this part and even the whole network is degraded. In severe cases, it will even cause network communication services to come to a standstill, that is, the so-called deadlock phenomenon. If the protocol software cannot detect congestion and reduce the packet sending rate, the network will be paralyzed due to congestion. This situation will cause the problem of movement delay for online dance teaching, which will seriously affect the quality of teaching. Therefore, the dance online course system should be continuously improved, the quality of online courses should be continuously improved, and the study of the practical application of load balancing technology in the network teaching environment has become an important means to solve the relationship between supply and demand of network teaching. According to the experimental analysis, when the number of Worker' actuators is fixed, the execution time span of MakeSpan increases with the increase of tasks, while the time required by the optimized load balancing algorithm proposed in this paper increases by 1.32 s on average with the increase of tasks, and the time required by heuristic algorithm and bee colony algorithm increases by 3.68 s and 3.45 s on average with the increase of tasks. On the whole, the optimized load balancing algorithm proposed in this paper has obvious advantages. [ABSTRACT FROM AUTHOR]

With the development of The Times, social events are increasing, and emergency management has gradually become the main helper to solve the crisis in the public domain. By observing the current situation of many countries and regions, we can find that various types of public crises often occur in many countries and regions in the world, which have severely affected people's daily life, lives, and property. Through long-term research and analysis, it can be known that the emergency management mechanism currently established in China has certain shortcomings. The communication problem of emergency information is likely to cause the emergency work to not proceed smoothly. In addition, problems in the communication channels of emergency information are likely to cause problems in the cooperation of various departments when people carry out emergency management work, and the efficiency of the government in dealing with problems will also be reduced in real scenarios. In order to improve the efficiency of emergency information management, this paper aims at the various problems existing and facing in the construction of emergency management system. On this basis, the integration of various relevant emergency information management plan models is analyzed and sorted out, and based on the research and integration of the development of artificial intelligence algorithms. The main research results of emergency information management at home and abroad are comprehensively studied and evaluated. Finally, a QG algorithm based on more model fusion is developed. In the process of analysis, this article uses artificial intelligence algorithms to build a prediction model of multiple modes and collects the data needed to build the model by random extraction. Through the analysis of different data sets, it is used as the basic training data for prediction. Through comprehensive analysis, the model constructed in this paper can promote the sharing of emergency information among departments to a certain extent. [ABSTRACT FROM AUTHOR]