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Variable (feature) selection plays an important role in data analysis and mathematical modeling. This paper aims to address the significant lack of formal evaluation benchmarks for feature selection algorithms (FSAs). To evaluate FSAs effectively, controlled environments are required, and the use of synthetic datasets offers significant advantages. We introduce a set of ten synthetically generated datasets with known relevance, redundancy, and irrelevance of features, derived from various mathematical, logical, and geometric sources. Additionally, eight FSAs are evaluated on these datasets based on their relevance and novelty. The paper first introduces the datasets and then provides a comprehensive experimental analysis of the performance of the selected FSAs on these datasets including testing the FSAs' resilience on two types of induced data noise. The analysis has guided the grouping of the generated datasets into four groups of data complexity. Lastly, we provide public access to the generated datasets to facilitate bench-marking of new feature selection algorithms in the field via our Github repository. The contributions of this paper aim to foster the development of novel feature selection algorithms and advance their study. [ABSTRACT FROM AUTHOR]

The problem of multiple DAGs sharing satellite constellation resources has gradually attracted widespread attention. Due to the limited computing resources and energy consumption of satellite networks, it is necessary to formulate a reasonable multi-DAG task scheduling scheme to ensure the fairness of each workflow under the premise of considering latency and energy consumption. Therefore, in this paper, we propose a multi-DAG satellite network task scheduling algorithm based on cache-composite priority under the Software-Defined Networking satellite network architecture. The basic idea of this algorithm lies in the DAG selection phase, where not only are task priorities computed but also the concept of fair scheduling is introduced, so as to prevent the excessively delayed scheduling of low-priority DAG tasks. In addition, the concept of public subtasks is introduced to reduce the system overhead caused by repetitive tasks. The experimental results show that the hybrid scheduling strategy proposed in this paper can meet the demand of DAG scheduling and improve the degree of task completion while effectively reducing the task latency and energy consumption. [ABSTRACT FROM AUTHOR]

Recently, artificial intelligence (AI)-based algorithms have revolutionized the medical image segmentation processes. Thus, the precise segmentation of organs and their lesions may contribute to an efficient diagnostics process and a more effective selection of targeted therapies, as well as increasing the effectiveness of the training process. In this context, AI may contribute to the automatization of the image scan segmentation process and increase the quality of the resulting 3D objects, which may lead to the generation of more realistic virtual objects. In this paper, we focus on the AI-based solutions applied in medical image scan segmentation and intelligent visual content generation, i.e., computer-generated three-dimensional (3D) images in the context of extended reality (XR). We consider different types of neural networks used with a special emphasis on the learning rules applied, taking into account algorithm accuracy and performance, as well as open data availability. This paper attempts to summarize the current development of AI-based segmentation methods in medical imaging and intelligent visual content generation that are applied in XR. It concludes with possible developments and open challenges in AI applications in extended reality-based solutions. Finally, future lines of research and development directions of artificial intelligence applications, both in medical image segmentation and extended reality-based medical solutions, are discussed. [ABSTRACT FROM AUTHOR]

This paper presents a multi-algorithm fusion model (StackingGroup) based on the Stacking ensemble learning framework to address the variable selection problem in high-dimensional group structure data. The proposed algorithm takes into account the differences in data observation and training principles of different algorithms. It leverages the strengths of each model and incorporates Stacking ensemble learning with multiple group structure regularization methods. The main approach involves dividing the data set into K parts on average, using more than 10 algorithms as basic learning models, and selecting the base learner based on low correlation, strong prediction ability, and small model error. Finally, we selected the grSubset + grLasso, grLasso, and grSCAD algorithms as the base learners for the Stacking algorithm. The Lasso algorithm was used as the meta-learner to create a comprehensive algorithm called StackingGroup. This algorithm is designed to handle high-dimensional group structure data. Simulation experiments showed that the proposed method outperformed other R2, RMSE, and MAE prediction methods. Lastly, we applied the proposed algorithm to investigate the risk factors of low birth weight in infants and young children. The final results demonstrate that the proposed method achieves a mean absolute error (MAE) of 0.508 and a root mean square error (RMSE) of 0.668. The obtained values are smaller compared to those obtained from a single model, indicating that the proposed method surpasses other algorithms in terms of prediction accuracy. [ABSTRACT FROM AUTHOR]

Wind power generation has aroused widespread concern worldwide. Accurate prediction of wind speed is very important for the safe and economic operation of the power grid. This paper presents a short-term wind speed prediction model which includes data decomposition, deep learning, intelligent algorithm optimization, and error correction modules. First, the robust local mean decomposition (RLMD) is applied to the original wind speed data to reduce the non-stationarity of the data. Then, the salp swarm algorithm (SSA) is used to determine the optimal parameter combination of the bidirectional gated recurrent unit (BiGRU) to ensure prediction quality. In order to eliminate the predictable components of the error further, a correction module based on the improved salp swarm algorithm (ISSA) and deep extreme learning machine (DELM) is constructed. The exploration and exploitation capability of the original SSA is enhanced by introducing a crazy operator and dynamic learning strategy, and the input weights and thresholds in the DELM are optimized by the ISSA to improve the generalization ability of the model. The actual data of wind farms are used to verify the advancement of the proposed model. Compared with other models, the results show that the proposed model has the best prediction performance. As a powerful tool, the developed forecasting system is expected to be further used in the energy system. [ABSTRACT FROM AUTHOR]

In recent years, UAV techniques are developing very fast, and UAVs are becoming more and more popular in both civilian and military fields. An important application of UAVs is rescue and disaster relief. In post-earthquake evaluation scenes where it is difficult or dangerous for human to reach, UAVs and sensors can form a wireless sensor network and collect environmental information. In such application scenarios, task allocation algorithms are important for UAVs to collect data efficiently. This paper firstly proposes an improved immune multi-agent algorithm for the offline task allocation stage. The proposed algorithm provides higher accuracy and convergence performance by improving the optimization operation. Then, this paper proposes an improved adaptive discrete cuckoo algorithm for the online task reallocation stage. By introducing adaptive step size transformation and appropriate local optimization operator, the speed of convergence is accelerated, making it suitable for real-time online task reallocation. Simulation results have proved the effectiveness of the proposed task allocation algorithms. [ABSTRACT FROM AUTHOR]

The normal operation of a integrated hub station is of great significance for the safe operation of the entire city's transportation network. Accurately monitoring the passenger flow operation status of the station is the fundamental basis for achieving scientific management and control of passenger flow. In response to the urgent need for accurate and real-time detection of passenger flow in station passageways, a Yolov7-based improved Deep-Sort algorithm is proposed to detect and track bi-directional passenger flow in the passageways of integrated hub stations. Based on the Yolov7 detection algorithm, the SimAM attention mechanism was introduced to improve the accuracy of detecting passenger flow in the passageways. On the basis of the Deep-Sort tracking algorithm, the Kalman Filter (KF) method was optimized to make the tracking box of the target more accurate. Meanwhile, the Fast-ReID method was used to improve the long-term tracking of targets, thereby improving the value of IDF1. This algorithm can help to achieve real-time and accurate detection and tracking of bi-directional passenger flow in station passageways. In the event of an abnormal situation, the station staff can react rapidly to improve the station's operational safety. [ABSTRACT FROM AUTHOR]

This paper presents a novel approach—an efficient closed-form approximation iterative fitting algorithm based on laser absorption spectra. Through this closed-form approximation iterative fitting, key parameters such as peak value, spectral line width, and normalized signal area serve as indicators for iteration completion, improving the speed without compromising accuracy. Furthermore, it employs the spectral signal of n cycles as a window for further processing, minimizing external interference. The results show that the proposed method averages 9.75 iterations, while the Levenberg–Marquardt fitting method averages 60.17 iterations. The average iteration time for the proposed method is 588.83 ms, a substantial 81.7% reduction compared to the 3210.5 ms required by the Levenberg–Marquardt fitting. These results decisively demonstrate the efficacy of the proposed method in reducing iteration time and enhancing measurement precision. [ABSTRACT FROM AUTHOR]

After the service provider temporarily selects the required edge nodes based on social and storage capabilities, application execution causes the edge nodes to cache part of the application data. Therefore, offloading part of the application computing tasks to the selected edge nodes can effectively improve application execution performance. However, in cases where the resources of user's IoT devices are insufficient, tasks can be further offloaded to traditional edge servers or even to the cloud to maximize application execution efficiency. In this paper, the entire uninstall utility is modeled as a weighted sum of task completion time and energy consumption. Under the premise of considering users' preferences for completion time and energy consumption, a game-based uninstallation algorithm is proposed. The algorithm performs uninstallation by optimizing the uninstallation decision. Based on user preferences, the total system overhead is relatively small. The subsequent simulation experiments show that the algorithm can reduce system overhead on the basis of satisfying user preferences and has relatively good adaptability. [ABSTRACT FROM AUTHOR]

Economic Dispatch Problems (EDP) refer to the process of determining the power output of generation units such that the electricity demand of the system is satisfied at a minimum cost while technical and operational constraints of the system are satisfied. This procedure is vital in the efficient energy management of electricity networks since it can ensure the reliable and efficient operation of power systems. As power systems transition from conventional to modern ones, new components and constraints are introduced to power systems, making the EDP increasingly complex. This highlights the importance of developing advanced optimization techniques that can efficiently handle these new complexities to ensure optimal operation and cost-effectiveness of power systems. This review paper provides a comprehensive exploration of the EDP, encompassing its mathematical formulation and the examination of commonly used problem formulation techniques, including single and multi-objective optimization methods. It also explores the progression of paradigms in economic dispatch, tracing the journey from traditional methods to contemporary strategies in power system management. The paper categorizes the commonly utilized techniques for solving EDP into four groups: conventional mathematical approaches, uncertainty modelling methods, artificial intelligence-driven techniques, and hybrid algorithms. It identifies critical research gaps, a predominant focus on single-case studies that limit the generalizability of findings, and the challenge of comparing research due to arbitrary system choices and formulation variations. The present paper calls for the implementation of standardized evaluation criteria and the inclusion of a diverse range of case studies to enhance the practicality of optimization techniques in the field. [ABSTRACT FROM AUTHOR]

The data collected during the fault monitoring of industrial processes are often uncertain because of measurement noise, sensor error, and complex working environments. Currently, most of this uncertainty is transformed into interval-value data for processing. However, the traditional interval fault monitoring method (TIFMM) only considers the global information of the interval-valued data and only rarely considers the local neighborhood information, which can characterize the topological relationship between the interval-valued data points, so that the sample points with some nonlinear relationship still maintain this nonlinear relationship after dimensionality reduction. Therefore, to take into account both local and global information, a new interval-valued data projection algorithm called local and global interval embedding algorithms (LGIEA) is proposed in this paper. First, the measurement error estimation method, based on the principle of reasonable granularity, converts the imprecise variable measurement values into interval-value data that can reflect uncertainty. Then, an interval-matrix feature extraction method based on local and global information fusion, proposed in this paper, extracts process features by minimizing local scattering and maximizing global scattering of interval-valued data. Thereby, it preserves the optimal local neighborhood and global information in low-dimensional space. In addition, four statistical indicators and corresponding variable-contribution graphs have been defined to use online fault monitoring and fault variable identification. Finally, the superiority of the LGIEA was verified using simulation data of the Tennessee Eastman process and actual operating data of a shearer in an inclined-ditch coal mine. The experimental results show that, Compared with TIFMM such as midpoint-radius principal component analysis (PCA), complete information PCA and spectral radius-based interval PCA, proposed method significantly reduces the error alarm rate and the false monitoring rate and improves the accuracy of data classification. It also has better fault identification accuracy and robustness. [ABSTRACT FROM AUTHOR]

In this paper, we propose a one-dimensional convolutional neural network model based on the adaptive batch normalization (AdaBN) algorithm to improve the CNN model, which is difficult to extract features from multi-rotor unmanned aerial vehicle (UAV) rotor structural faults under variable conditions and has poor fault diagnosis performance. The method accomplishes fault diagnosis and classification by feature extraction from lower dimensional multi-rotor UAV data. The AdaBN algorithm adjusts the parameters of the BN layer in the model during the testing phase to improve the domain adaptive capability of the model in scenarios with variable operating conditions. Also, to improve the robustness of the model under noisy conditions, the first layer of convolutional kernel dropout operation is introduced to improve the noise immunity of the model. To reduce the complexity of manual tuning and to find the optimal combination of hyperparameters for the network model more effectively, the grey wolf optimizer algorithm is used to optimize the hyperparameters and further improve the model performance. Finally, the effectiveness of the proposed method is verified through comparison tests, and it shows good diagnostic effects in noise and variable conditions compared with several commonly used fault diagnosis methods. [ABSTRACT FROM AUTHOR]

Guaranteed lower Dirichlet eigenvalue bounds (GLB) can be computed for the m-th Laplace operator with a recently introduced extra-stabilized nonconforming Crouzeix–Raviart ( m = 1 ) or Morley ( m = 2 ) finite element eigensolver. Striking numerical evidence for the superiority of a new adaptive eigensolver motivates the convergence analysis in this paper with a proof of optimal convergence rates of the GLB towards a simple eigenvalue. The proof is based on (a generalization of) known abstract arguments entitled as the axioms of adaptivity. Beyond the known a priori convergence rates, a medius analysis is enfolded in this paper for the proof of best-approximation results. This and subordinated L 2 error estimates for locally refined triangulations appear of independent interest. The analysis of optimal convergence rates of an adaptive mesh-refining algorithm is performed in 3D and highlights a new version of discrete reliability. [ABSTRACT FROM AUTHOR]

This review paper discusses the development trends of agricultural autonomous all-terrain vehicles (AATVs) from four cornerstones, such as (1) control strategy and algorithms, (2) sensors, (3) data communication tools and systems, and (4) controllers and actuators, based on 221 papers published in peer-reviewed journals for 1960–2023. The paper highlights a comparative analysis of commonly employed control methods and algorithms by highlighting their advantages and disadvantages. It gives comparative analyses of sensors, data communication tools, actuators, and hardware-embedded controllers. In recent years, many novel developments in AATVs have been made due to advancements in wireless and remote communication, high-speed data processors, sensors, computer vision, and broader applications of AI tools. Technical advancements in fully autonomous control of AATVs remain limited, requiring research into accurate estimation of terrain mechanics, identifying uncertainties, and making fast and accurate decisions, as well as utilizing wireless communication and edge cloud computing. Furthermore, most of the developments are at the research level and have many practical limitations due to terrain and weather conditions. [ABSTRACT FROM AUTHOR]

As an important part of the power system, it is necessary to ensure the safe and stable operation of transmission lines. Due to long-term exposure to the outdoors, the lines face many insecurity factors, and foreign object intrusion is one of them. Traditional foreign object (bird's nest, kite, balloon, trash bag) detection algorithms suffer from low efficiency, poor accuracy, and small coverage, etc. To address the above problems, this paper introduces the RCDAM-Net. In order to prevent feature loss or useful feature compression, the RevCol (Reversible Column Networks) is used as the backbone network to ensure that the total information remains unchanged during feature decoupling. DySnakeConv (Dynamic Snake Convolution) is adopted and embedded into the C2f structure, which is named C2D and integrates low-level features and high-level features. Compared to the original BottleNeck structure of C2f, the DySnakeConv enhances the feature extraction ability for elongated and weak targets. In addition, MPDIoU (Maximum Performance Diagonal Intersection over Union) is used to improve the regression performance of model bounding boxes, solving the problem of predicted bounding boxes having the same aspect ratio as true bounding boxes, but with different values. Further, we adopt Decoupled Head for detection and add additional auxiliary training heads to improve the detection accuracy of the model. The experimental results show that the model achieves mAP50, Precision, and Recall of 97.98%, 98.15%, and 95.16% on the transmission tower line foreign object dataset, which is better to existing multi-target detection algorithms. [ABSTRACT FROM AUTHOR]

This paper proposes a YOLO fire detection algorithm based on an attention-enhanced ghost mode, mixed convolutional pyramids, and flame-centre detection (AEGG-FD). Specifically, the enhanced ghost bottleneck is stacked to reduce redundant feature mapping operations in the process for achieving lightweight reconfiguration of the backbone, while attention is added to compensate for accuracy loss. Furthermore, a feature pyramid built using mixed convolution is introduced to accelerate network inference speed. Finally, the local information is extracted by the designed flame-centre detection (FD) module for furnishing auxiliary information in effective firefighting. Experimental results on both the benchmark fire dataset and the video dataset show that the AEGG-FD performs better than the classical YOLO-based models such as YOLOv5, YOLOv7 and YOLOv8. Specifically, both the mean accuracy (mAP0.5, reaching 84.7%) and the inferred speed (FPS) are improved by 6.5 and 8.4 respectively, and both the number of model parameters and model size are compressed to 72.4% and 44.6% those of YOLOv5, respectively. Therefore, AEGG-FD achieves an effective balance between model weight, detection speed, and accuracy in firefighting. [ABSTRACT FROM AUTHOR]

Background: The frequency of primary joint replacement surgery continues to increase worldwide. While largely considered biologically inert entities, an increasing body of evidence continues to validate a not insignificant incidence of allergic reactions to such implants. Little previous work has explored genuinely immune-mediated reactivity in this context. In the absence of a contemporary published summary on the topic, this paper explored the current state of understanding of cobalt/chromium allergy and proposes a patient management algorithm whereby such immune reactions are clinically suggested. Methods: A structured, systematic literature review was performed by following PRISMA search principles to provide an updated review of this area. Results: Thirty-six topic-related articles were identified, the majority reflecting lower tiers of scientific evidence with a lack of homogeneous quantitative data to facilitate valid cohort comparisons. Largely, the available literature represented small case series' or expert opinions. Conclusions: Despite increasing clinical awareness and acknowledgement of true allergy to joint replacement components, this review highlighted that the evidence base underpinning the diagnosis and management of such patients is limited. Both patient-reported metal allergy or skin patch testing are grossly unreliable methods and show almost no correlation with true immune reactivity. Recent studies suggested a potential role for patient-specific in vitro cellular activation testing and/or targeted genetic testing when cobalt/chromium allergy is clinically suspected. However, while likely representing the contemporary "best available" approaches both can be costly undertakings, are not yet universally available, and still require broader validation in non-research settings before wider uptake can be championed. [ABSTRACT FROM AUTHOR]

Ocean currents make it difficult for unmanned surface vehicles (USVs) to keep a safe distance from obstacles. Effective path planning should adequately consider the effect of ocean currents on USVs. This paper proposes an improved A* algorithm based on an artificial potential field (APF) for USV path planning in a current environment. There are three main improvements to the A* algorithm. Firstly, the proposed algorithm ignores unnecessary perilous nodes to decrease calculation. Secondly, an adaptive guidance angle is developed to guide the search in the most appropriate direction to reduce the computing time. Thirdly, the potential field force function is introduced into the cost function to ensure that the path designed for the USV always maintains a safe distance from obstacles under the influence of ocean currents. Furthermore, the Bezier curve is adapted to smooth the path. The experimental results show that the USV path-planning algorithm proposed in this paper, which synthesizes the APF and A* algorithms, runs 22.5% faster on average than the traditional A* algorithm. Additionally, the path developed by the proposed A* algorithm effectively keeps appropriate and different distances from obstacles by considering different ocean currents. [ABSTRACT FROM AUTHOR]

Amid the conflict between the current huge data volumes and the requirement for large-compression-ratio compression in space-borne multichannel SAR, this paper proposes a data compression method that uses the adaptive bit allocation BAQ method in the Doppler domain of space-borne multichannel SAR. Specifically, 3MBAQ, which denotes multichannel, multipulse, multiweight block-adaptive quantization, performs signal reconstruction through a Krieger filter to obtain the Doppler spectrum under azimuthal multichannel and multipulse conditions and then achieves nonuniform bit allocation by using the multiple weight contributions between subband spectra. Furthermore, large-compression-ratio compression can be realized in homogeneous scenarios by using the adaptive bit allocation BAQ method together with the improved bit error control (BEC) algorithm. In this paper, the effectiveness of the 3MBAQ method is verified on GF3 measured data. Moreover, compared to the SQNR and NMSE of the BAQ, A-BAQ, A-BAQ-BEC, MCBAQ-ASQ, and MCBAQ-BEC methods under the same conditions, it is shown that there is an optimal 1.3434 dB improvement in SQNR and a 0.0241 improvement in NMSE using the 3MBAQ algorithm, and the complexity of the algorithm for 3MBAQ is O (N 2 l o g (N)) , which is within acceptable limits, clearly proving the superiority of the 3MBAQ method. [ABSTRACT FROM AUTHOR]

As the performance of Radio Frequency Identification (RFID) devices is susceptible to the influence of the surrounding environment, making the original data collected by RFID devices with uncertain, of which missed data and redundant data are the main source of uncertainty data, these uncertainty data will seriously affect the quality of RFID upper layer applications. Therefore, to solve the uncertainty in RFID data, the original data must be cleaned. For the shortcomings of tag dynamics detection in the traditional data cleaning algorithm named Statistical Smoothing for Unreliable RFID data (SMURF), an adaptive sliding window-based data cleaning algorithm for RFID single-tag is proposed. The method takes into account the influence of tag speed on tag integrity judgment, and divides sliding sub-windows to accurately detect tag changes and then reasonably adjusts the sliding window size. In addition, considering that the average reading rate of tags is affected by the collision between multiple tags, an RFID multi-tag cleaning method based on twice-tag number estimation is proposed. The method accurately estimates the number of tags by the twice-tag estimation method, controls the read period by the Markov chain, and reduces the occurrence of multiple tag collisions by using an unequal time slot optimization method. Experimental results show that the proposed method in this paper can form a complete set of RFID data stream cleaning algorithms, which effectively reduces the uncertain data and improves the data accuracy. [ABSTRACT FROM AUTHOR]

In Intelligent Transport Systems (ITSs), Internet of Vehicles (IoV) communications and computation offloading technology have been introduced to assist with the burdensome sensing task processing, thus prompting a new design paradigm called mobile sensing–communication–computation (MSCC) synergy. Most researchers have focused on offloading strategy design to reduce energy consumption or execution costs, but ignore the intrinsic characteristics of tasks, which may lead to poor performance. This paper studies the offloading strategy of vehicle MSCC tasks represented by a Directed Acyclic Graph (DAG) structure. According to the DAG dependency of the subtasks, this paper proposes a computation offloading strategy to optimize energy consumption under time constraints. An energy consumption model for task execution is established. Then, the Simulated Annealing and Tabu Search hybrid optimization algorithm (ST-HO) is designed to solve the problem of minimizing the energy consumption. Crucially, this research integrates the concept of symmetry into the typical DAG structure of MSCC tasks, ensuring the integrity and efficiency of task execution in ITS. The simulation results show that ST-HO reduces energy consumption by at least 5.58% compared to the conventional algorithm. Particularly, the convergence speed of ST-HO is improved by 52.63% when the replication strategy of symmetric task is considered. [ABSTRACT FROM AUTHOR]

INTRODUCTION: In present days, Automation of everything has become essential. Internet of things (IoT) play an important role among all medical advances of IT. In this paper, feasible solutions are discussed to compare and design better healthcare systems. A thorough investigation and survey of suitable approaches were done to select IoT based systems in hospitals consisting of various high precision sensors. OBJECTIVES: The challenge healthcare system face is to manage the real time patient’s data with high accuracy. Second challenge is at fog devices level to manage the load distribution to all sensors with limited availability of bandwidth. METHODS: This paper summarizes the selection criterions of suitable load balancing algorithms to reduce energy consumption and computational cost of fog devices and increase the network usage that are supposed to be used in IoT based healthcare systems. According to the survey BNBKnapack algorithm has been selected as best suitable approach to analyze the overall performance of fog devices and results are also verify the same. RESULTS: Comparative analysis of Overall performance of fog devices has been proposed with using SJF algorithm and Adaptive BNBKnapsack algorithm. It has been observed by analysing system performance, which is found as best among other load balancing algorithm Adaptive BNBKnapsack is successfully reduce the energy consumption by (99.29%), computational cost by (98.34%) and increase the network usage by (99.95%) of system CONCLUSION: It has been observed by analysing system performance, Adaptive BNBKnapsack Load balancing is successfully able to reduce the computational cost and energy consumption also increase the network usage of the fog network. The performance of the system is found best among other load balancing algorithm. [ABSTRACT FROM AUTHOR]

The article focuses on the importance of clinical informatics in neonatal care, highlighting its potential to provide critical resources for clinicians. Topics include the specialized data needed for neonatal care, the challenges in transitioning from paper to electronic health records, and the impact of informatics on real-time patient management and research.

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They give fixed parameter tractable algorithms for the problem parameterized by various structural parameters. The authors give a greedy loop-free algorithm for the exhaustive generation, a successor algorithm that runs in constant amortized time, among other algorithms, as well as results for the fixed spin generalization of this problem. IWOCA (International Workshop on Combinatorial Algorithms) is an annual conference series covering all aspects of combinatorial algorithms. [Extracted from the article]

In this paper we present the Markovian Multiagent Monte-Carlo Second Order Self-Consistent Field Algorithm (M3-SOSCF). This algorithm provides a highly reliable methodology for converging SCF calculations in single-reference methods using a modified differential evolution approach. Additionally, M3 is embarrassingly parallel and modular in regards to Newton–Raphson subroutines. We show that M3 is able to surpass contemporary SOSCFs in reliability, which is illustrated by a benchmark employing poor initial guesses and a second benchmark with SCF calculations which face difficulties using standard SCF algorithms. Furthermore, we analyse inherent properties of M3 and show that in addition to its robustness and efficiency, it is more user-friendly than current SOSCFs. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

Copyright of Journal of Computer Engineering & Applications is the property of Beijing Journal of Computer Engineering & Applications Journal Co Ltd. 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.)

To overcome the deficiencies in segmenting hot spots from thermal infrared images, such as difficulty extracting the edge features, low accuracy, and a high missed detection rate, an improved Mask R-CNN photovoltaic hot spot thermal image segmentation algorithm has been proposed in this paper. Firstly, the edge image features of hot spots were extracted based on residual neural networks. Secondly, by combining the feature pyramid structure, an edge-guided feature pyramid structure was designed, and the hot spot edge features were injected into a Mask R-CNN network. Thirdly, an infrared spatial attention module was introduced into the Mask R-CNN network when feature extraction and the infrared features of the detected hot spots were enhanced. Fourthly, the size ratio of the candidate frames was adjusted self-adaptively according to the structural characteristics of the aspect ratio of the hot spots. Finally, the validation experiments were conducted, and the results demonstrated that the hot spot contours of thermal infrared images were enhanced through the algorithm proposed in this paper, and the segmentation accuracy was significantly improved. [ABSTRACT FROM AUTHOR]

Recursive queries have been traditionally studied in the framework of datalog, a language that restricts recursion to monotone queries over sets, which is guaranteed to converge in polynomial time in the size of the input. But modern big data systems require recursive computations beyond the Boolean space. In this article, we study the convergence of datalog when it is interpreted over an arbitrary semiring. We consider an ordered semiring, define the semantics of a datalog program as a least fixpoint in this semiring, and study the number of steps required to reach that fixpoint, if ever. We identify algebraic properties of the semiring that correspond to certain convergence properties of datalog programs. Finally, we describe a class of ordered semirings on which one can use the semi-naïve evaluation algorithm on any datalog program. [ABSTRACT FROM AUTHOR]

In today's digital entertainment environment, movie recommendation algorithms are essential for intensifying user ecstasy and content engagement. Online streaming services like Netflix can potentially increase revenue by implementing a recommendation system that offers tailored movie suggestions to users based on their past interactions with the platform. The strategies we'll discuss here are not simply limited to films; they may be used for any product for which you want to develop a Recommendation System (RS). This paper gives a preamble of several types of recommendation strategies based on user preferences, ratings, domain knowledge, users’ demographic data, user’s context. This work also envisions the singular value decomposition plus-plus (SVD++) and collaborative filtering-based movie recommendation method. The suggested method is compared to well-known machine learning methods such as k closest neighbour (K-NN), singular value decomposition (SVD), and co-clustering. The suggested method is experimentally tested using MovieLens 100 K datasets, and the error of the RS is assessed using Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). In order to address issues like scalability, data sparsity, and the cold start problem, our study is driven by the requirement to determine the most efficient method for offering individualized movie suggestions. This technique is used by numerous e-commerce companies, including Amazon, Flipkart, and Mantra, to understand their customers' purchasing patterns and provide recommendations to them about the goods they are most likely to purchase. [ABSTRACT FROM AUTHOR]

This paper proposes a novel Very Large-Scale Integration (VLSI) design for the Turbo decoder, a popular communication tool to achieve optimal data reception with minimal errors. The Turbo decoder includes SISO decoders, interleavers, and deinterleavers, which use the Maximum-a-Posteriori (MAP) algorithm to reduce the number of iterations required for decoding. Additionally, the encoder part uses two Recursive convolutional encoders and a pseudorandom interleaver to enhance the error correction capability of the Turbo code. The paper also suggests using clock gating technology to improve the decoder's performance by selectively activating specific blocks based on the decoding performance. The proposed Turbo decoder is designed in Xilinx Vivado 2018.3 achieves high performance and provides a scalable and efficient solution for implementing Turbo decoders in communication systems. [ABSTRACT FROM AUTHOR]

This paper aims to build a machine learning and a neural network model to detect the probability of money laundering in banks. The paper's data came from a simulation of actual transactions flagged for money laundering in Middle Eastern banks. The main findings highlight that criminal networks mainly use the integration stage to integrate money into the financial system. Fraudsters prefer to launder funds in the early hours, morning followed by the business day's afternoon time intervals. Additionally, the Naïve Bayes and Random Forest classifiers were identified as the two best-performing models to predict bank money laundering transactions. [ABSTRACT FROM AUTHOR]

Purpose: Hexahedral meshing is one of the most important steps in performing an accurate simulation using the finite element analysis (FEA). However, the current hexahedral meshing method in the industry is a nonautomatic and inefficient method, i.e. manually decomposing the model into suitable blocks and obtaining the hexahedral mesh from these blocks by mapping or sweeping algorithms. The purpose of this paper is to propose an almost automatic decomposition algorithm based on the 3D frame field and model features to replace the traditional time-consuming and laborious manual decomposition method. Design/methodology/approach: The proposed algorithm is based on the 3D frame field and features, where features are used to construct feature-cutting surfaces and the 3D frame field is used to construct singular-cutting surfaces. The feature-cutting surfaces constructed from concave features first reduce the complexity of the model and decompose it into some coarse blocks. Then, an improved 3D frame field algorithm is performed on these coarse blocks to extract the singular structure and construct singular-cutting surfaces to further decompose the coarse blocks. In most modeling examples, the proposed algorithm uses both types of cutting surfaces to decompose models fully automatically. In a few examples with special requirements for hexahedral meshes, the algorithm requires manual input of some user-defined cutting surfaces and constructs different singular-cutting surfaces to ensure the effectiveness of the decomposition. Findings: Benefiting from the feature decomposition and the 3D frame field algorithm, the output blocks of the proposed algorithm have no inner singular structure and are suitable for the mapping or sweeping algorithm. The introduction of internal constraints makes 3D frame field generation more robust in this paper, and it can automatically correct some invalid 3–5 singular structures. In a few examples with special requirements, the proposed algorithm successfully generates valid blocks even though the singular structure of the model is modified by user-defined cutting surfaces. Originality/value: The proposed algorithm takes the advantage of feature decomposition and the 3D frame field to generate suitable blocks for a mapping or sweeping algorithm, which saves a lot of simulation time and requires less experience. The user-defined cutting surfaces enable the creation of special hexahedral meshes, which was difficult with previous algorithms. An improved 3D frame field generation method is proposed to correct some invalid singular structures and improve the robustness of the previous methods. [ABSTRACT FROM AUTHOR]

A reasonable method for estimating alighting stops can provide a data guarantee for schedule design and vehicle scheduling. However, few studies, to our best knowledge, have addressed this problem using the variable distance threshold for different types of passengers. This paper is based on available smart card data, and a two-stage algorithm is designed to obtain missing alighting stations in the entry-only urban public transportation system. This algorithm divides passengers into seven types, determines varying distance thresholds for different passengers, and includes five assumptions. Based on the first three assumptions, the corresponding subalgorithms are designed, respectively, constituting the first-stage algorithm, and the first match for all passengers' alighting stations is carried out. The second-stage algorithm is designed on the basis of the latter two assumptions. A secondary match is conducted on data that were not successfully matched in the first stage to ensure that alighting stops for all passengers are identified. The proposed methods are verified using urban public transport data from the city of Zhuhai as an example. Research results show that the drop-off stops where approximately 74% of passengers can be determined after matching the first-stage algorithm. The distance thresholds for various passengers are different, and the distance thresholds for the same type of passengers over different days are also different. Compared with the traditional fixed distance threshold, the variable distance threshold for various passengers proposed in this paper can improve the match rate of alighting station estimate, about 9%. By appropriately extending the distance thresholds for different passengers, the match rate for passengers who work or live in the suburbs can be further improved, which is in line with the actual travel status of such passengers. The second-stage algorithm can identify alighting sites for a single or unlinked trip. The efficiency of the methodology proposed in this study can meet the needs for practical applications, which may be applied to urban public transport systems. [ABSTRACT FROM AUTHOR]

Intelligent gangue sorting with high precision is of vital importance for improving coal quality. To tackle the challenges associated with coal gangue target detection, including algorithm performance imbalance and hardware deployment difficulties, in this paper, an intelligent gangue separation system that adopts the elevated YOLO-v5 algorithm and dual-energy X-rays is proposed. Firstly, images of dual-energy X-ray transmission coal gangue mixture under the actual operation of a coal mine were collected, and datasets for training and validation were self-constructed. Then, in the YOLOv5 backbone network, the EfficientNetv2 was used to replace the original cross stage partial darknet (CSPDarknet) to achieve the lightweight of the backbone network; in the neck, a light path aggregation network (LPAN) was designed based on PAN, and a convolutional block attention module (CBAM) was integrated into the BottleneckCSP of the feature fusion block to raise the feature acquisition capability of the network and maximize the learning effect. Subsequently, to accelerate the rate of convergence, an efficient intersection over union (EIOU) was used instead of the complete intersection over union (CIOU) loss function. Finally, to address the problem of low resolution of small targets leading to missed detection, an L2 detection head was introduced to the head section to improve the multi-scale target detection performance of the algorithm. The experimental results indicate that in comparison with YOLOv5-S, the same version of the algorithm proposed in this paper increases by 19.2% and 32.4% on mAP @.5 and mAP @.5:.95, respectively. The number of parameters decline by 51.5%, and the calculation complexity declines by 14.7%. The algorithm suggested in this article offers new ideas for the design of identification algorithms for coal gangue sorting systems, which is expected to save energy and reduce consumption, reduce labor, improve efficiency, and be more friendly to the embedded platform. [ABSTRACT FROM AUTHOR]

The exponential growth of Internet of Things (IoT) devices has resulted in an unparalleled surge in the need for energy-efficient strategies to guarantee the sustainability and durability of IoT networks. This study article provides a thorough examination of energyefficiency optimization in Internet of Things (IoT) networks, with a specific emphasis on the creation of algorithms, methodologies, and empirical investigations. The article commences by providing an overview of the significant significance of energy efficiency in Internet of Things (IoT) networks and its direct influence on the lifetime of devices, scalability of networks, and environmental sustainability. This emphasizes the urgent need for inventive approaches to tackle the difficulties presented by resource-limited Internet of Things (IoT) devices. Subsequently, the present study undertakes an in-depth examination of existing methodologies and algorithms for optimizing energy efficiency in Internet of Things (IoT) networks. This paper presents a comprehensive examination of each category, including insights into their respective strengths, limits, and suitability for various Internet of Things (IoT) applications. This study presents innovative algorithms and strategies that are especially developed to improve energy efficiency in Internet of Things (IoT) networks. These advancements use cutting-edge technology such as machine learning, edge computing, and low-power device design. The paper provides comprehensive explanations of these methodologies, accompanied by simulations and performance assessments, to showcase their efficacy in attaining energy conservation while maintaining network dependability and service excellence. The case studies presented provide valuable perspectives on the practical use of energy-efficient technologies, demonstrating their effectiveness in reducing operating expenses, carbon emissions, and system downtime. Moreover, this study aims to discuss the many problems and unresolved research inquiries pertaining to energy-efficient Internet of Things (IoT) networks. The aforementioned points underscore the need for further investigation in several aspects, including scalability challenges, security implications, and standardization efforts. These areas of focus are crucial in order to foster the ongoing expansion and long-term viability of Internet of Things (IoT) ecosystems. [ABSTRACT FROM AUTHOR]

Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles. [ABSTRACT FROM AUTHOR]

With the continuous breakthrough of natural language processing, the application of intelligent question-answering technology in electric power systems has attracted wide attention. However, at present, the traditional question-answering system has poor performance and is difficult to apply in engineering practice. This paper proposes an improved BERTserini algorithm for the intelligent answering of electric power regulations based on a BERT model. The proposed algorithm is implemented in two stages. The first stage is the text-segmentation stage, where a multi-document long text preprocessing technique is utilized that accommodates the rules and regulations text, and then Anserini is used to extract paragraphs with high relevance to the given question. The second stage is the answer-generation and source-retrieval stage, where a two-step fine-tuning based on the Chinese BERT model is applied to generate precise answers based on given questions, while the information regarding documents, chapters, and page numbers of these answers are also output simultaneously. The algorithm proposed in this paper eliminates the necessity for the manual organization of professional question–answer pairs, thereby effectively reducing the manual labor cost compared to traditional question-answering systems. Additionally, this algorithm exhibits a higher degree of exact match rate and a faster response time for providing answers. [ABSTRACT FROM AUTHOR]

In this paper, we propose a Grid-based Non-uniform Probability Density Sampling Probabilistic Roadmap algorithm (GN-PRM) in response to the challenges of difficult sampling in narrow passages and low-probability map generation in traditional Probabilistic Roadmap algorithms (PRM). The improved algorithm incorporates grid-based processing for map segmentation, employing non-uniform probability density sampling based on the different attributes of each block to enhance sampling probability in narrow passages. Additionally, considering the computational cost and frequent ineffective searches in traditional PRM algorithms during pathfinding, this paper optimizes the time required for query route planning by altering connection strategies to improve the algorithm's runtime. Finally, the simulation results indicate that, with a reduction of over 50% in undirected line segments and a reduction of over 85% in runtime, the GN-PRM algorithm achieves a 100% success rate in complex planning scenarios with a sampling point value of K = 500. In comparison, the traditional PRM algorithm has a success rate of no more than 10%, with a sampling point value of K = 500. [ABSTRACT FROM AUTHOR]

Autonomous driving technology has made a lot of outstanding achievements with deep learning, and the vehicle detection and classification algorithm has become one of the critical technologies of autonomous driving systems. The vehicle instance segmentation can perform instance-level semantic parsing of vehicle information, which is more accurate and reliable than object detection. However, the existing instance segmentation algorithms still have the problems of poor mask prediction accuracy and low detection speed. Therefore, this paper proposes an advanced real-time instance segmentation model named FIR-YOLACT, which fuses the ICIoU (Improved Complete Intersection over Union) and Res2Net for the YOLACT algorithm. Specifically, the ICIoU function can effectively solve the degradation problem of the original CIoU loss function, and improve the training convergence speed and detection accuracy. The Res2Net module fused with the ECA (Efficient Channel Attention) Net is added to the model’s backbone network, which improves the multi-scale detection capability and mask prediction accuracy. Furthermore, the Cluster NMS (Non-Maximum Suppression) algorithm is introduced in the model’s bounding box regression to enhance the performance of detecting similarly occluded objects. The experimental results demonstrate the superiority of FIR-YOLACT to the based methods and the effectiveness of all components. The processing speed reaches 28 FPS, which meets the demands of real-time vehicle instance segmentation. [ABSTRACT FROM AUTHOR]

With the rapid advancement of wearable devices, Human Activities Recognition (HAR) based on these devices has emerged as a prominent research field. The objective of this study is to enhance the recognition performance of HAR by proposing an LSTM-1DCNN recognition algorithm that utilizes a single triaxial accelerometer. This algorithm comprises two branches: one branch consists of a Long and Short-Term Memory Network (LSTM), while the other parallel branch incorporates a one-dimensional Convolutional Neural Network (1DCNN). The parallel architecture of LSTM-1DCNN initially extracts spatial and temporal features from the accelerometer data separately, which are then concatenated and fed into a fully connected neural network for information fusion. In the LSTM-1DCNN architecture, the 1DCNN branch primarily focuses on extracting spatial features during convolution operations, whereas the LSTM branch mainly captures temporal features. Nine sets of accelerometer data from five publicly availableHAR datasets are employed for training and evaluation purposes. The performance of the proposed LSTM-1DCNN model is compared with five other HAR algorithms including Decision Tree, Random Forest, Support Vector Machine, 1DCNN, and LSTM on these five public datasets. Experimental results demonstrate that the F1-score achieved by the proposed LSTM-1DCNN ranges from 90.36% to 99.68%, with a mean value of 96.22% and standard deviation of 0.03 across all evaluated metrics on these five public datasetsoutperforming other existingHAR algorithms significantly in terms of evaluationmetrics used in this study. Finally the proposed LSTM-1DCNN is validated in real-world applications by collecting acceleration data of seven human activities for training and testing purposes. Subsequently, the trained HAR algorithm is deployed on Android phones to evaluate its performance. Experimental results demonstrate that the proposed LSTM-1DCNNalgorithm achieves an impressive F1-score of 97.67% on our self-built dataset. In conclusion, the fusion of temporal and spatial information in the measured data contributes to the excellent HAR performance and robustness exhibited by the proposed 1DCNN-LSTM architecture. [ABSTRACT FROM AUTHOR]

Existing Decision-Making Trial and Evaluation Laboratory (DEMATEL) methods are mostly suitable for simple systems with fewer factors, and lack effective integration of expert knowledge and experience from large-scale group populations, resulting in a potential compromise of the quality of the initial direct relation (IDR) matrix. To make DEMATEL better suited for the identification of critical factors in complex systems, this paper proposes a hierarchical DEMATEL method for large-scale group decision-making. Considering the limitations of expert knowledge and experience, a method based on expert consistency network for constructing the expert weight matrix is designed. The expert consistency network is constructed for different elements, and the weights of experts in different elements are determined using the clustering coefficient. Following the principles of the classic DEMATEL method, the steps for identifying key elements in complex systems using the large-scale group-hierarchical DEMATEL method are summarized. To objectively test the effectiveness and superiority of the decision algorithm, the robustness of the algorithm is analyzed in an interference environment. Finally, the superiority of the proposed method and algorithm is verified through a case study, which demonstrating that the proposed decision-making method is suitable for group decision-making in complex systems, with high algorithm stability and low algorithm deviation. [ABSTRACT FROM AUTHOR]

Ten Gigabit-capable ethernet passive optical network (GEPON) can quickly improve network bandwidth, meet multilevel business needs, and quickly improve the comprehensive competitiveness of the network. It is well known that the 10G EPON access network technology is used to carry IPTV services, VoIP services and high-speed Internet access, known as "triple play", and IPTV services have been particularly popular in recent years. As the demand for IPTV service increases exponentially, a large amount of data will be generated, occupying a large access bandwidth and causing an impact on other services. Therefore, reasonable scheduling of IPTV service queue is an urgent problem to be solved under multiservice environment at present. Therefore, the design of PON bearing IPTV robustness algorithm to meet user experience is the focus of this paper. This paper proposes a Forward Error Correction with Weight Round Robin algorithm (FWRR), which adopts RS error correction code and Weighted Round Robin algorithm based on IP layer. When IPTV packet is lost at optical line terminal (OLT) end, downlink optical network unit (ONU) can recover the lost data through received packets. This mechanism can effectively solve the problem of packet loss caused by the blocking of OLT end, and realize the scheme of PON carrying IPTV multicast service. Simulations results in OPNET show that compared with weighted round robin (WRR) algorithm and round robin (RR) algorithm, FWRR mechanism can reduce packet loss, delay and jitter, in addition, improve the robustness of IPTV service in GEPON. [ABSTRACT FROM AUTHOR]

The vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults. [ABSTRACT FROM AUTHOR]