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Copyright of Die Anaesthesiologie is the property of Springer Nature 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.)

Nowadays, the application of artificial intelligence (AI) algorithms and techniques is ubiquitous and transversal. Fields that take advantage of AI advances include sound and music processing. The advances in interdisciplinary research potentially yield new insights that may further advance the AI methods in this field. This special issue aims to report recent progress and spur new research lines in AI-driven sound and music processing, especially within interdisciplinary research scenarios. [ABSTRACT FROM AUTHOR]

The Distributed power flow controller can be considered as advance power flow controller which is combination of series and shunt compensator without DC link. In this paper Space vector Pulse width Modulation employs in Distributed power flow Controller for Harmonics Reduction and power quality improvement. A new technique SVPWM adopted which reduces the harmonics and the same time improves power quality and increase transient stability. In this paper a new technique of Multi objective Grey wolf algorithm used to optimize the controller parameter of DPFC. For the design of PI controller GWO algorithm can be used for better optimistic performance of DPFC. As a result of that power quality and voltage profile improves drastically and the same time harmonics also reduced remarkable level of 4.62% of Voltage THD and 1.8% of current THD. The simulation result and hardware model Prove the feasibility of proposed Configuration. [ABSTRACT FROM AUTHOR]

Human pose estimation is an important task in computer vision, which can provide key point detection of human body and obtain bone information. At present, human pose estimation is mainly utilized for detection of large targets, and there is no solution for detection of small targets. This paper proposes a multi-channel spatial information feature based human pose (MCSF-Pose) estimation algorithm to address the issue of medium and small targets inaccurate detection of human key points in scenarios involving occlusion and multiple poses. The MCSF-Pose network is a bottom-up regression network. Firstly, an UP-Focus module is designed to expand the feature information while reducing parameter computation during the up-sampling process. Then, the channel segmentation strategy is adopted to cut the features, and the feature information of multiple dimensions is retained through different convolutional groups, which reduces the parameter lightweight network model and makes up for the loss of the feature information associated with the depth of the network. Finally, the three-layer PANet structure is designed to reduce the complexity of the model. With the aid of the structure, it also to improve the detection accuracy and anti-interference ability of human key points. The experimental results indicate that the proposed algorithm outperforms YOLO-Pose and other human pose estimation algorithms on COCO2017 and MPII human pose datasets. [ABSTRACT FROM AUTHOR]

In response to the challenges of accurately identifying event triggers and elements in long texts related to aquaculture, existing models struggle to differentiate between elements and triggers, as well as effectively recognize complete entity texts. To tackle this issue, this study proposes an algorithm for extracting fish disease events based on pseudo triggers and augmented event element data. The method starts by constructing pseudo samples using the original dataset. Two types of noise datasets are then generated: a trigger noise dataset constructed based on fish disease triggers and an entity noise dataset with varying levels of entity noise constructed based on fish disease entities. Next, three parallel neural networks are deployed to extract sample features from these datasets. The fish disease event extraction for the source dataset employs multi-label classification. For the trigger noise dataset, the sample features are activated using the sigmoid function, and the MRSE loss is utilized for optimization of this branch. For the entity noise dataset, the sample features are activated using the Relu function, and the XOR loss is used for optimization. Finally, the losses from the three branches are combined with weighted summation to obtain the fusion loss. The experimental results on the fish disease dataset used in this paper show that the proposed algorithm achieves an average accuracy of 78.71%, 78.95%, and 79.43% on F1, recall, and precision, respectively, which is a maximum improvement of 11.201%, 11.849%, and 12.421% in accuracy with respect to the baseline model on F1, recall, and precision, respectively. [ABSTRACT FROM AUTHOR]

Cloud model similarity algorithm is an important part of cloud modelling theory. Most of the existing cloud model similarity algorithms suffer from poor discriminability, poor classification, unstable results, and low time efficiency. In this paper, a new similarity algorithm is proposed that considers the triangular cloud model distance and shape. First, according to the D T distance formula, a new exponential closeness measure is defined, with which the distance similarity of cloud models is characterized. Then, the shape similarity is calculated according to the variance of the cloud model cloud drops. Finally, the two similarities are synthesized to define a similarity algorithm for determining the distance from the D T distance formula and shape based on the triangular cloud model (DDTSTCM). In this paper, discriminability, stability, efficiency and theoretical interpretability are taken as the evaluation indices. Equipment security system capability evaluation experiment, cloud model differentiation simulation experiment and time series classification accuracy experiment are set up to verify the effectiveness of the algorithm in terms of the four above aspects. The experimental results show that DDTSTCM has good differentiation and excellent classification effects. In the classification experiment for the time series, the average classification accuracy of DDTSTCM reaches 91.78%, which is at least 2.78% higher than those of the other seven commonly used algorithms. The CPU running efficiency of DDTSTCM is also extremely high, and the average CPU running time of group training is always on the order of milliseconds, which effectively reduces the time cost. Finally, a case study is conducted to analyse a risk assessment problem for China's island microgrid industry, and the evaluation results based on DDTSTCM are in line with human cognition and have good value for engineering applications. [ABSTRACT FROM AUTHOR]

Aiming at the received signal strength index (RSSI) in wireless positioning system, an improved weighted KNN fingerprint positioning algorithm is proposed in this paper. The algorithm pre-processes fingerprint data in offline stage that including eliminating outliers and Kalman filtering first, in order to improve the accuracy of data acquisition. Secondly, the fingerprint data is partitioned by using RSSI to attenuate obstacles such as walls. Then, points with significant RSSI differences in each region are selected as regional feature points, and the distance between RSSI of test points and feature points in each region is calculated respectively to determine the region in which the test points are located. Geometric method is used to analyse and define the correlation degree, and KNN is re-weighted to achieve accurate positioning in the region. Finally, experiments were carried out in the indoor environment to complete the establishment of the fingerprint database. Compared with the existing NN, KNN and WKNN, the experimental analysis results show that the accumulated error and average error are better than the traditional algorithm with the increase of measurement points, which has reference value for the complex environment positioning technology. [ABSTRACT FROM AUTHOR]

This research paper presents a comprehensive study of the simultaneous localization and mapping (SLAM) algorithm for robot localization and navigation in unknown environments. The SLAM algorithm is a widely used approach for building a map of an environment and estimating the robot's position within it, which is especially useful in dynamic and unstructured environments. The paper discusses various SLAM techniques, including the Kalman filter (KF) and GraphSLAM algorithms, and their use in probabilistic estimation of the robot's position and orientation. The paper also explores different path-planning techniques that can be used with the map created by the SLAM algorithm to generate collision-free paths for the robot to navigate toward its goal. The paper also discusses recent advances in deep learning-based SLAM algorithms and their applications in indoor navigation with ORB and RGB-D cameras. The research concludes that SLAM-based robot localization and navigation algorithms are a promising approach for robots navigating in unstructured environments and present various opportunities for future research. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative method to improve loop closure detection within the domain of Simultaneous Localization And Mapping (SLAM) by integrating a Multi-Scale Attention and Semantic Guidance (MSA-SG) framework. In SLAM systems, accurate loop closure detection is essential for minimizing localization errors over time and ensuring the reliability of the constructed maps in robotics navigation through uncharted environments. Our proposed method utilizes EfficientNet-EA for robust feature extraction and introduces MSA-SG, a novel mechanism that synergizes multiscale attention with semantic guidance to focus on critical semantic features essential for loop closure detection. This approach ensures the prioritization of static environmental landmarks over transient and irrelevant objects, significantly enhancing the accuracy and efficiency of loop closure detection in complex and dynamic settings. Experimental validations on recognized datasets underscore the superiority of our approach, demonstrating marked improvements in precision, recall, and overall SLAM performance. This research highlights the significant benefits of leveraging semantic insights and attentional focus in advancing the capabilities of loop closure detection for SLAM applications. [ABSTRACT FROM AUTHOR]

There is increasing focus on applying deep learning methods to electrocardiograms (ECGs), with recent studies showing that neural networks (NNs) can predict future heart failure or atrial fibrillation from the ECG alone. However, large numbers of ECGs are needed to train NNs, and many ECGs are currently only in paper format, which are not suitable for NN training. We developed a fully-automated online ECG digitisation tool to convert scanned paper ECGs into digital signals. Using automated horizontal and vertical anchor point detection, the algorithm automatically segments the ECG image into separate images for the 12 leads and a dynamical morphological algorithm is then applied to extract the signal of interest. We then validated the performance of the algorithm on 515 digital ECGs, of which 45 were printed, scanned and redigitised. The automated digitisation tool achieved 99.0% correlation between the digitised signals and the ground truth ECG (n = 515 standard 3-by-4 ECGs) after excluding ECGs with overlap of lead signals. Without exclusion, the performance of average correlation was from 90 to 97% across the leads on all 3-by-4 ECGs. There was a 97% correlation for 12-by-1 and 3-by-1 ECG formats after excluding ECGs with overlap of lead signals. Without exclusion, the average correlation of some leads in 12-by-1 ECGs was 60–70% and the average correlation of 3-by-1 ECGs achieved 80–90%. ECGs that were printed, scanned, and redigitised, our tool achieved 96% correlation with the original signals. We have developed and validated a fully-automated, user-friendly, online ECG digitisation tool. Unlike other available tools, this does not require any manual segmentation of ECG signals. Our tool can facilitate the rapid and automated digitisation of large repositories of paper ECGs to allow them to be used for deep learning projects. [ABSTRACT FROM AUTHOR]

Some special manufacturing fields such as aerospace may encounter mixed production of multiple research and development projects and multiple batch production projects. Under these special production conditions resource conflicts are more severe, resulting in uncertain operating times that are difficult to predict. In addition, a single project may have tens of thousands of supporting products, making it difficult to effectively control the total construction process. To address these challenges this paper proposes new methods. A model, EMA-DCPM (dynamic critical path method) incorporating attention mechanisms in Enterprise Resource Planning and Mechanical Engineering Society) has been proposed. This model predicts product job time through machine learning methods and discovers the predictive advantage of the attention mechanism through data comparison. The CPM control algorithm was improved to enhance its robustness and an efficient modeling method, "5+X" was proposed. This new method is suitable for mixed line planning management in sophisticated manufacturing projects and has value for practical applications. [ABSTRACT FROM AUTHOR]

The existing joint extraction methods for entity relationships in knowledge data only target specific fields or datasets, which may have insufficient coverage for large-scale and diverse manufacturing knowledge data. In order to achieve more accurate and efficient joint extraction of manufacturing knowledge data entity relationship, an improved method based on BERT algorithm is proposed in this paper. This method constructs a manufacturing specific knowledge graph, treating the extraction of quantitative knowledge as the extraction of manufacturing entity attributes, thereby achieving joint extraction of knowledge reasoning and data entity relationships. The improved BERT algorithm was used to establish a distribution feature set of entity relationships in manufacturing knowledge data, and a manufacturing specific knowledge graph was constructed, providing an effective way to analyze and extract entity relationships in manufacturing data, thereby helping decision-makers better understand knowledge in the manufacturing field and improving production efficiency and quality. The test results show that the knowledge graph generated by the method has good expression ability and strong logical reasoning ability, and the running time required by the application of the method is only 12 s, indicating that it can realize the joint clustering extraction of entity relationships in manufacturing knowledge data more efficiently and accurately. The innovation of this paper lies in applying BERT algorithm to joint extraction of entity relationship of manufacturing knowledge data, and improving the algorithm to improve the accuracy and efficiency of extraction, providing a new solution for the construction and application of manufacturing knowledge graph. [ABSTRACT FROM AUTHOR]

We consider standard T-interval dynamic networks, under the synchronous timing model and the broadcast CONGEST model. In a T-interval dynamic network, the set of nodes is always fixed and there are no node failures. The edges in the network are always undirected, but the set of edges in the topology may change arbitrarily from round to round, as determined by some adversary and subject to the following constraint: For every T consecutive rounds, the topologies in those rounds must contain a common connected spanning subgraph. Let H r to be the maximum (in terms of number of edges) such subgraph for round r through r + T - 1 . We define the backbone diameterd of a T-interval dynamic network to be the maximum diameter of all such H r 's, for r ≥ 1 . We use n to denote the number of nodes in the network. Within such a context, we consider a range of fundamental distributed computing problems including Count/Max/Median/Sum/LeaderElect/Consensus/ConfirmedFlood. Existing algorithms for these problems all have time complexity of Ω (n) rounds, even for T = ∞ and even when d is as small as O(1). This paper presents a novel approach/framework, based on the idea of massively parallel aggregation. Following this approach, we develop a novel deterministic Count algorithm with O (d 3 log 2 n) complexity, for T-interval dynamic networks with T ≥ c · d 2 log 2 n . Here c is a (sufficiently large) constant independent of d, n, and T. To our knowledge, our algorithm is the very first such algorithm whose complexity does not contain a Θ (n) term. This paper further develops novel algorithms for solving Max/Median/Sum/LeaderElect/Consensus/ConfirmedFlood, while incurring O (d 3 polylog (n)) complexity. Again, for all these problems, our algorithms are the first ones whose time complexity does not contain a Θ (n) term. [ABSTRACT FROM AUTHOR]

In this paper we consider the following problem: Given a Hamiltonian graph G, and a Hamiltonian cycle C of G, can we compute a second Hamiltonian cycle C ′ ≠ C of G, and if yes, how quickly? If the input graph G satisfies certain conditions (e.g. if every vertex of G is odd, or if the minimum degree is large enough), it is known that such a second Hamiltonian cycle always exists. Despite substantial efforts, no subexponential-time algorithm is known for this problem. In this paper we relax the problem of computing a second Hamiltonian cycle in two ways. First, we consider approximating the length of a second longest cycle on n-vertex graphs with minimum degree δ and maximum degree Δ . We provide a linear-time algorithm for computing a cycle C ′ ≠ C of length at least n - 4 α (n + 2 α) + 8 , where α = Δ - 2 δ - 2 . This results provides a constructive proof of a recent result by Girão, Kittipassorn, and Narayanan in the regime of Δ δ = o (n) . Our second relaxation of the problem is probabilistic. We propose a randomized algorithm which computes a second Hamiltonian cycle with high probability, given that the input graph G has a large enough minimum degree. More specifically, we prove that for every 0 < p ≤ 0.02 , if the minimum degree of G is at least 8 p log 8 n + 4 , then a second Hamiltonian cycle can be computed with probability at least 1 - 1 n 50 p 4 + 1 in p o l y (n) · 2 4 p n time. This result implies that, when the minimum degree δ is sufficiently large, we can compute with high probability a second Hamiltonian cycle faster than any known deterministic algorithm. In particular, when δ = ω (log n) , our probabilistic algorithm works in 2 o (n) time. [ABSTRACT FROM AUTHOR]

Deep Cross-Modal Hashing (DCMH) has garnered significant attention in the field of cross-modal retrieval due to its advantages such as high computational efficiency and small storage space. However, existing DCMH methods still face certain limitations: (1) they neglect the correlation between labels, while label features exhibit high sparsity; (2) they lack fine-grained semantic alignment; (3) they fail to effectively address data imbalance. In order to tackle these issues, this paper introduces a framework named Semantic-Alignment Transformer and Adversary Hashing for Cross-modal Retrieval (SATAH). To the best of our knowledge, this is the first attempt at the Semantic-Alignment Transformer algorithm. Specifically, this paper first designs a label learning network that utilizes a crafted transformer module to extract label information, guiding adversarial learning and hash function learning accordingly. Subsequently, a Balanced Conditional Generative Adversarial Network (BCGAN) is constructed, marking the first instance of adversarial training guided by label information. Furthermore, a Weighted Semi-Hard Cosine Triplet Constraint is proposed to better ensure high-ranking similarity relationships among all items. Lastly, considering the correlation between labels, a semantic-alignment constraint is introduced to handle label correlation from a fine-grained perspective, capturing similarity on a global scale more effectively. Extensive experiments are conducted on multiple representative cross-modal datasets. In experiments with 64-bit hash code length, SATAH achieves average mAP values of 84.75%, 68.87%, and 68.73% on MIR Flickr, NUS-WIDE, and MS COCO datasets, respectively, outperforming state-of-the-art methods. The code is available at https://github.com/Daydaylight/SATAH. [ABSTRACT FROM AUTHOR]

Gautschi proposed a method for computing incomplete beta functions I x (a , b) using Miller's algorithm with a three-term recurrence relation and showed a computation program in ALGOL. In this paper, first, Miller's algorithm using the recurrence relation satisfied by f k (x) = I x (a + k , b) is described. Next, another solution that is first-order independent of f k (x) of the recurrence relation is given, and its general solution can be expressed as a linear sum of these. Using this general solution, an error analysis for the function I x (a , b) is performed for the first time. The relative error of the function values is then expressed in a new formula to a trend of the error behavior. Also, Miller's algorithm with a normalizing sum is explained and its error analysis is performed. Since Miller's algorithm requires a predefined number of iterations of the recurrence relation, it is necessary to repeat the computation of the recurrence relation with increasing number of iterations until the required accuracy will be met. Therefore, in this paper, we apply Deuflhard's algorithm, which can automatically obtain the function value with the required accuracy. This algorithm requires far fewer iterations than Gautschi's algorithm to obtain the same accuracy. [ABSTRACT FROM AUTHOR]

In this paper we propose an algorithm for testing whether the independent observations come from finite-variance distribution. The preliminary knowledge about the data properties may be crucial for its further analysis and selection of the appropriate model. The idea of the testing procedure is based on the simple observation that the empirical cumulative even moment (ECEM) for data from finite-moments distribution tends to some constant whereas for data coming from heavy-tailed distribution, the ECEM exhibits irregular chaotic behavior. Based on this fact, in this paper we parameterize the regular/irregular behavior of the ECEM and construct a new test statistic. The efficiency of the testing procedure is verified for simulated data from three heavy-tailed distributions with possible finite and infinite variances. The effectiveness is analyzed for data represented in time domain. The simulation study is supported by analysis of real vibration signals from rotating machines. Here, the analyses are provided for data in both the time and time-frequency domains. [ABSTRACT FROM AUTHOR]

Remote Sensing Image Object Detection (RSIOD) faces the challenges of multi-scale objects, dense overlap of objects and uneven data distribution in practical applications. In order to solve these problems, this paper proposes a YOLO-ACPHD RSIOD algorithm. The algorithm adopts Adaptive Condition Awareness Technology (ACAT), which can dynamically adjust the parameters of the convolution kernel, so as to adapt to the objects of different scales and positions. Compared with the traditional fixed convolution kernel, this dynamic adjustment can better adapt to the diversity of scale, direction and shape of the object, thus improving the accuracy and robustness of Object Detection (OD). In addition, a High-Dimensional Decoupling Technology (HDDT) is used to reduce the amount of calculation to 1/N by performing deep convolution on the input data and then performing spatial convolution on each channel. When dealing with large-scale Remote Sensing Image (RSI) data, this reduction in computation can significantly improve the efficiency of the algorithm and accelerate the speed of OD, so as to better adapt to the needs of practical application scenarios. Through the experimental verification of the RSOD RSI data set, the YOLO-ACPHD model in this paper shows very satisfactory performance. The F1 value reaches 0.99, the Precision value reaches 1, the Precision-Recall value reaches 0.994, the Recall value reaches 1, and the mAP value reaches 99.36 % , which indicates that the model shows the highest level in the accuracy and comprehensiveness of OD. [ABSTRACT FROM AUTHOR]

Reoptimization is a setting in which we are given a good approximate solution of an optimization problem instance and a local modification that slightly changes the instance. The main goal is that of finding a good approximate solution of the modified instance. We investigate one of the most studied scenarios in reoptimization known as Steiner tree reoptimization. Steiner tree reoptimization is a collection of strongly NP -hard optimization problems that are defined on top of the classical Steiner tree problem and for which several constant-factor approximation algorithms have been designed in the last decades. In this paper we improve upon all these results by developing a novel technique that allows us to design polynomial-time approximation schemes. Remarkably, prior to this paper, no approximation algorithm better than recomputing a solution from scratch was known for the elusive scenario in which the cost of a single edge decreases. Our results are best possible since none of the problems addressed in this paper admits a fully polynomial-time approximation scheme, unless P = NP [ABSTRACT FROM AUTHOR]

Due to the complex underwater environment and the selective absorption and scattering effect of water on light waves, underwater images often suffer from issues such as low contrast, color distortion, and blurred details. This paper presents a stable and effective algorithm for enhancing underwater images to address these challenges. Firstly, an improved color correction algorithm based on the gray world and minimum information loss is employed to remove the blue-green bias present in the images. Secondly, a contrast enhancement algorithm is based on the guided filter and wavelet decomposition to make the texture details of the image clearer. Then, the normalized weight map of the image is obtained to carry out multi-scale fusion. Finally, the fused image is applied to perform the multi-scale decomposition. The experimental results show that the algorithm proposed in this paper can correct the image color deviation, improve the image contrast and enhance the image details. [ABSTRACT FROM AUTHOR]

For the limitations of global feature-based deep learning point cloud registration algorithms in partial point cloud registration, this paper proposes a partial point cloud registration algorithm NcPE-PNLK combining global features and correspondence. The NcPE-PNLK algorithm introduces a feature interaction module to complete the information interaction between two point clouds in the feature extraction stage, which can improve the credibility of the feature. Moreover, the algorithm predicts the correspondence through the non-corresponding point estimation module, which reduces the influence of non-overlapping regions on the global feature and effectively solves the problem of performance degradation of the global feature registration algorithms in partial point cloud registration. We test the registration performance of NcPE-PNLK on synthetic scene dataset and real dataset in this paper. The experimental results show that NcPE-PNLK can effectively reduce the impact of non-overlapping regions in the registration process and achieve better performance compared with the global feature-based registration algorithms. In addition, compared with the correspondence-based registration algorithms, the NcPE-PNLK algorithm does not need to calculate correspondence precisely, which can achieve high-precision partial point cloud registration with guaranteed efficiency. [ABSTRACT FROM AUTHOR]

Deploying static wireless sensor nodes is prone to network coverage gaps, resulting in poor network coverage. In this paper, an attempt is made to improve the network coverage by moving the locations of the nodes. A surrogate-assisted sine Phasmatodea population evolution algorithm (SASPPE) is used to evaluate the network coverage. A 50 × 50 hill simulation environment was tested for the number of nodes of 30 and 40 and radii of 3, 5 and 7, respectively. The results show that the SASPPE algorithm has the highest coverage, which can be up to 23.624% higher than the PPE algorithm, and up to 5.196% higher than the PPE algorithm, ceteris paribus. The SASPPE algorithm mixes the GSAM with LSAMs, which balances the computational cost of the algorithm and the algorithm's ability to find optimal results. The use of hierarchical clustering enhances the stable type of the LSAMs. In addition, LSAMs are easy to fall into local optimality when they are modeled with local data, and the use of sine Phasmatodea population evolution algorithm (Sine-PPE) for searching in LSAMs alleviates the time for the algorithm to fall into local optimality. On 30D, 50D, and 100D, the proposed algorithm was tested by 7 test functions. The results show that the algorithm has significant advantages on most functions. [ABSTRACT FROM AUTHOR]

With the increasing number of terminal devices in the sensor network and the Internet of things, non-orthogonal multiple access (NOMA) will be widely used in the future to improve spectrum resource utilization. In this paper, we considered how to control massive devices in the downlink in of scenario of space-based Internet of Things (S-IoT). In the case of multiple base stations (BSs), user fairness is a difficult problem, which usually requires a traversal algorithm to achieve the optimal solution. However, the time complexity caused by this method is the product of the complexity of user grouping and power allocation, which is unbearable. We split the problem into two sub-problems changing complexity from product form to additive form, and solve them by using low time complexity algorithms respectively. Firstly, we use a threshold-based algorithm to implement user grouping, and then use a bisection iterative algorithm for power allocation. In the simulation, we compared design with several benchmarks and results show that the minimum user rate is significantly improved and closed to optimal value by using proposed algorithm in this paper. [ABSTRACT FROM AUTHOR]

Currently, high hospital readmission rates have become a problem for mental health services, because it is directly associated with the quality of patient care. The development of predictive models with machine learning algorithms allows the assessment of readmission risk in hospitals. The main objective of this paper is to predict the readmission risk of patients with schizophrenia in a region of Spain, using machine learning algorithms. In this study, we used a dataset with 6089 electronic admission records corresponding to 3065 patients with schizophrenia disorders. Data were collected in the period 2005–2015 from acute units of 11 public hospitals in a Spain region. The Random Forest classifier obtained the best results in predicting the readmission risk, in the metrics accuracy = 0.817, recall = 0.887, F1-score = 0.877, and AUC = 0.879. This paper shows the algorithm with highest accuracy value and determines the factors associated with readmission risk of patients with schizophrenia in this population. It also shows that the development of predictive models with a machine learning approach can help improve patient care quality and develop preventive treatments. [ABSTRACT FROM AUTHOR]

Recent rank-based attacks have reduced the security of Rainbow, which is one of the multi-layer UOV signatures, below the NIST security requirements by speeding up iterative kernel-finding operations using classical mathematics techniques. If quantum algorithms are applied to perform these iterative operations, the rank-based attacks may be more threatening to multi-layer UOV, including Rainbow. In this paper, we propose a quantum rectangular MinRank attack called the Q-rMinRank attack, the first quantum approach to key recovery attacks on multi-layer UOV signatures. Our attack is a general model applicable to multi-layer UOV signature schemes, and in this paper, we provide examples of its application to Rainbow and the Korean TTA standard, HiMQ. We design two quantum oracle circuits to find the kernel in consideration of the depth-width trade-off of quantum circuits. One is to reduce the width of the quantum circuits using qubits as a minimum, and the other is to reduce the depth using parallelization instead of using a lot of qubits. By designing quantum circuits to find kernels with fewer quantum resources and complexity by adding mathematical techniques, we achieve quadratic speedup for the MinRank attack to recover the private keys of multi-layer UOV signatures. We also estimate quantum resources for the designed quantum circuits and analyze quantum complexity based on them. The width-optimized circuit recovers the private keys of Rainbow parameter set V with only 1089 logical qubits. The depth-optimized circuit recovers the private keys of Rainbow parameter set V with a quantum complexity of 2 174 , which is lower than the complexity of 2 221 recovering the secret key of AES-192, which provides the same security level as parameter set III. [ABSTRACT FROM AUTHOR]

In recent times, video inpainting techniques have intended to fill the missing areas or gaps in a video by utilizing known pixels. The variety in brightness or difference of the patches causes the state-of-the-art video inpainting techniques to exhibit high computation complexity and create seams in the target areas. To resolve these issues, this paper introduces a novel video inpainting technique that employs the Morphological Haar Wavelet Transform combined with the Krill Herd based Criminisi algorithm (MHWT-KHCA) to address the challenges of high computational demand and visible seam artifacts in current inpainting practices. The proposed MHWT-KHCA algorithm strategically reduces computation times and enhances the seamlessness of the inpainting process in videos. Through a series of experiments, the technique is validated against standard metrics such as peak signal-to-noise ratio (PSNR) and structural similarity index (SSIM), where it demonstrates superior performance compared to existing methods. Additionally, the paper outlines potential real-world applications ranging from video restoration to real-time surveillance enhancement, highlighting the technique's versatility and effectiveness. Future research directions include optimizing the algorithm for diverse video formats and integrating machine learning models to advance its capabilities further. [ABSTRACT FROM AUTHOR]

Disjoint paths are defined as paths between the source and destination nodes where the intermediate nodes in any two paths are disjoint. They are helpful in fault-tolerance routing and securing message distribution in the network. Several research papers were proposed to solve the problem of finding disjoint paths for a variety of interconnection networks such as Hypercube, Generalized Hypercube, Mesh, Torus, Gaussian, Eisenstein–Jacobi, and many other topologies. In this research, we have developed a general algorithm that constructs maximal node-to-node disjoint paths for symmetric networks where all paths are shortest. The algorithm presented in this paper outperforms other algorithms in finding not only the disjoint paths but shortest and maximal disjoint paths with a complexity of O (n 2) . In addition, we have simulated the proposed algorithm on different networks. The solution of unsolved problem in Cube-Connected-Cycles is given in the simulation results. [ABSTRACT FROM AUTHOR]

The problem of finding optimal block designs can be formulated as a combinatorial optimization, but its resolution is still a formidable challenge. This paper presents a general and user-friendly algorithm, namely Modified Particle Swarm Optimization (MPSO), to construct optimal or near-optimal block designs. It can be used for several classes of block designs such as binary, non-binary and test-control block designs with correlated or uncorrelated observations. In order to evaluate the algorithm, we compare our results with the optimal designs presented in some published papers. An advantage of our algorithm is its independency to the sizes of blocks and the structure of correlations. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the AAPF* algorithm, an innovative approach that synergistically integrates the A-star search algorithm (A*) with the artificial potential field (APF) method. This algorithm is designed to enhance safety and ensure smoother global path planning for autonomous vehicles, particularly addressing vehicle cornering constraints. Initially, for augmenting the safety of autonomous vehicle, we implement an obstacle expansion strategy with a factor of 2 units, enhancing environmental adaptability. The study then delves into the classical A* algorithm, examining its core principles and characteristics, leading to the development of novel heuristic functions and search strategies that address the limitations inherent in the classic A* algorithm. Subsequently, we explore the APF algorithm, recognized for its excellence in obstacle avoidance in path planning. The paper culminates in the amalgamation of the APF's repulsive field concept with the improved A* algorithm, crafting a comprehensive global planning algorithm tailored for autonomous vehicle path planning schemes. Experiments conducted in a simulated environment model validate the AAPF* algorithm's efficacy in improving both the safety and smoothness, demonstrating its potential for real-world applications. [ABSTRACT FROM AUTHOR]

Many sensitive applications require that data remain confidential and undisclosed, even for intrusion detection objectives. For this purpose, the detection of anomalies in encrypted data has become increasingly vital. Deep learning models are becoming good tools to detect anomalies in encrypted data without the need to pass through data decryption. This paper presents a systematic review focusing on the advancements made in deep learning models for intrusion detection over encrypted data with privacy preservation. This study aims to guide researchers on how to select the right tools to set up an intrusion detection system over encrypted data with privacy preservation. The study presented the context and challenges of intrusion detection on encrypted data and how machine learning-based solutions can circumvent these challenges. The paper looks at recently proposed solutions, examines metrics for assessing model performance, and evaluates frequently used reference datasets. Deep learning models are also evaluated with statistics on the most frequent models, datasets, and encryption tools. The performance metrics of the studied solutions are investigated as a function of the encryption tools, the deployed deep learning models, the privacy preservation tools, the deployed datasets, and the eventual additional tools and algorithms. Our recommendations help researchers evaluate their proposals for preserving privacy and detecting intrusions on encrypted data using deep learning techniques. [ABSTRACT FROM AUTHOR]

Manual term extraction is similar to literal meaning: A translator browses text, classifies words, and prepares for translation. Terminology, as a centralized carrier of expertise, creation, popularization, and disappearance, dynamically reflects the development and evolution of an industry. The automatic extraction of terminology is a key technology for creating a professional terminology database, and it is also a key topic in the field of natural language processing. The purpose of this paper is to study how to analyse a term extraction algorithm based on machine learning and a comprehensive feature strategy. Focusing on the problems of poor generality and single statistical features of current term extraction algorithms, this paper proposes an improved domain ontology term extraction algorithm based on a comprehensive feature strategy. Moreover, automatic term extraction experiments based on a word-based maximum entropy model and a conditional random field model based on machine learning are conducted in this paper. Its word-based conditional random field model outperforms the maximum entropy model. The experimental results show that the algorithm based on the comprehensive feature strategy improves the accuracy by 8.6% compared with the TF-IDF algorithm and the C-value term extraction algorithm. This algorithm can be used to effectively extract the terms in a text and has good generality. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles are useful tools for many applications. However, autonomous path planning for Unmanned Aerial Vehicles in unfamiliar environments is a challenging problem when facing a series of problems such as poor consistency, high influence by the native controller of the Unmanned Aerial Vehicles. In this paper, we investigate reinforcement learning-based autonomous local path planning methods for Unmanned Aerial Vehicles with high autonomous decision-making capability and locally high portability. We propose an autonomous local path planning algorithm based on the TD3 strategy to solve the problem of local obstacle avoidance and path planning in unfamiliar environments using autonomous decision-making of Unmanned Aerial Vehicles. The simulation results on Gazebo show that our method can effectively realize the autonomous local path planning task for Unmanned Aerial Vehicles, the success rate of path planning with our method can reach 93% under the interference of no obstacles, and 92% in the environment with obstacles. Finally, our method can be used for autonomous path planning of Unmanned Aerial Vehicles in unfamiliar environments. [ABSTRACT FROM AUTHOR]

In the petroleum and coal industries, digital image technology and acoustic emission technology are employed to study rock properties, but both exhibit flaws during data processing. Digital image technology is vulnerable to interference from fractures and scaling, leading to potential loss of image data; while acoustic emission technology is not hindered by these issues, noise from rock destruction can interfere with the electrical signals, causing errors. The monitoring errors of these techniques can undermine the effectiveness of rock damage analysis. To address this issue, this paper focuses on the restoration of image data acquired through digital image technology, leveraging deep learning techniques, and using soft and hard rocks made of similar materials as research subjects, an improved Incremental Transformer image algorithm is employed to repair distorted or missing strain nephograms during uniaxial compression experiments. The concrete implementation entails using a comprehensive training set of strain nephograms derived from digital image technology, fabricating masks for absent image segments, and predicting strain nephograms with full strain detail. Additionally, we adopt deep separable convolutional networks to optimize the algorithm's operational efficiency. Based on this, the analysis of rock damage is conducted using the repaired strain nephograms, achieving a closer correlation with the actual physical processes of rock damage compared to conventional digital image technology and acoustic emission techniques. The improved incremental Transformer algorithm presented in this paper will contribute to enhancing the efficiency of digital image technology in the realm of rock damage, saving time and money, and offering an innovative approach to traditional rock damage analysis. [ABSTRACT FROM AUTHOR]

The increasing ubiquity of machine learning (ML) motivates research on algorithms to "explain" models and their predictions—so-called Explainable Artificial Intelligence (XAI). Despite many publications and discussions, the goals and capabilities of such algorithms are far from being well understood. We argue that this is because of a problematic reasoning scheme in the literature: Such algorithms are said to complement machine learning models with desired capabilities, such as interpretability or explainability. These capabilities are in turn assumed to contribute to a goal, such as trust in a system. But most capabilities lack precise definitions and their relationship to such goals is far from obvious. The result is a reasoning scheme that obfuscates research results and leaves an important question unanswered: What can one expect from XAI algorithms? In this paper, we clarify the modest capabilities of these algorithms from a concrete perspective: that of their users. We show that current algorithms can only answer user questions that can be traced back to the question: "How can one represent an ML model as a simple function that uses interpreted attributes?". Answering this core question can be trivial, difficult or even impossible, depending on the application. The result of the paper is the identification of two key challenges for XAI research: the approximation and the translation of ML models. [ABSTRACT FROM AUTHOR]

Recent research has focused on applying blockchain technology to solve security-related problems in Internet of Things (IoT) networks. However, the inherent scalability issues of blockchain technology become apparent in the presence of a vast number of IoT devices and the substantial data generated by these networks. Therefore, in this paper, we use a lightweight consensus algorithm to cater to these problems. We propose a scalable blockchain-based framework for managing IoT data, catering to a large number of devices. This framework utilizes the Delegated Proof of Stake (DPoS) consensus algorithm to ensure enhanced performance and efficiency in resource-constrained IoT networks. DPoS being a lightweight consensus algorithm leverages a selected number of elected delegates to validate and confirm transactions, thus mitigating the performance and efficiency degradation in the blockchain-based IoT networks. In this paper, we implemented an Interplanetary File System (IPFS) for distributed storage, and Docker to evaluate the network performance in terms of throughput, latency, and resource utilization. We divided our analysis into four parts: Latency, throughput, resource utilization, and file upload time and speed in distributed storage evaluation. Our empirical findings demonstrate that our framework exhibits low latency, measuring less than 0.976 ms. The proposed technique outperforms Proof of Stake (PoS), representing a state-of-the-art consensus technique. We also demonstrate that the proposed approach is useful in IoT applications where low latency or resource efficiency is required. [ABSTRACT FROM AUTHOR]

Flocking cohesion is critical for maintaining a group's aggregation and integrity. Designing a potential function to maintain flocking cohesion unaffected by social distance is challenging due to the uncertainty of real-world conditions and environments that cause changes in agents' social distance. Previous flocking research based on potential functions has primarily focused on agents' same social distance and the attraction–repulsion of the potential function, ignoring another property affecting flocking cohesion: well depth, as well as the effect of changes in agents' social distance on well depth. This paper investigates the effect of potential function well depths and agent's social distances on the multi-agent flocking cohesion. Through the analysis, proofs, and classification of these potential functions, we have found that the potential function well depth is proportional to the flocking cohesion. Moreover, we observe that the potential function well depth varies with the agents' social distance changes. Therefore, we design a segmentation potential function and combine it with the flocking control algorithm in this paper. It enhances flocking cohesion significantly and has good robustness to ensure the flocking cohesion is unaffected by variations in the agents' social distance. Meanwhile, it reduces the time required for flocking formation. Subsequently, the Lyapunov theorem and the LaSalle invariance principle prove the stability and convergence of the proposed control algorithm. Finally, this paper adopts two subgroups with different potential function well depths and social distances to encounter for simulation verification. The corresponding simulation results demonstrate and verify the effectiveness of the flocking control algorithm. [ABSTRACT FROM AUTHOR]

In order to address the challenges of identifying, detecting, and tracking moving objects in video surveillance, this paper emphasizes image-based dynamic entity detection. It delves into the complexities of numerous moving objects, dense targets, and intricate backgrounds. Leveraging the You Only Look Once (YOLOv3) algorithm framework, this paper proposes improvements in image segmentation and data filtering to address these challenges. These enhancements form a novel multi-object detection algorithm based on an improved YOLOv3 framework, specifically designed for video applications. Experimental validation demonstrates the feasibility of this algorithm, with success rates exceeding 60% for videos such as "jogging", "subway", "video 1", and "video 2". Notably, the detection success rates for "jogging" and "video 1" consistently surpass 80%, indicating outstanding detection performance. Although the accuracy slightly decreases for "Bolt" and "Walking2", success rates still hover around 70%. Comparative analysis with other algorithms reveals that this method's tracking accuracy surpasses that of particle filters, Discriminative Scale Space Tracker (DSST), and Scale Adaptive Multiple Features (SAMF) algorithms, with an accuracy of 0.822. This indicates superior overall performance in target tracking. Therefore, the improved YOLOv3-based multi-object detection and tracking algorithm demonstrates robust filtering and detection capabilities in noise-resistant experiments, making it highly suitable for various detection tasks in practical applications. It can address inherent limitations such as missed detections, false positives, and imprecise localization. These improvements significantly enhance the efficiency and accuracy of target detection, providing valuable insights for researchers in the field of object detection, tracking, and recognition in video surveillance. [ABSTRACT FROM AUTHOR]

Since 2000, there have been more than 8000 publications on radiology artificial intelligence (AI). AI breakthroughs allow complex tasks to be automated and even performed beyond human capabilities. However, the lack of details on the methods and algorithm code undercuts its scientific value. Many science subfields have recently faced a reproducibility crisis, eroding trust in processes and results, and influencing the rise in retractions of scientific papers. For the same reasons, conducting research in deep learning (DL) also requires reproducibility. Although several valuable manuscript checklists for AI in medical imaging exist, they are not focused specifically on reproducibility. In this study, we conducted a systematic review of recently published papers in the field of DL to evaluate if the description of their methodology could allow the reproducibility of their findings. We focused on the Journal of Digital Imaging (JDI), a specialized journal that publishes papers on AI and medical imaging. We used the keyword "Deep Learning" and collected the articles published between January 2020 and January 2022. We screened all the articles and included the ones which reported the development of a DL tool in medical imaging. We extracted the reported details about the dataset, data handling steps, data splitting, model details, and performance metrics of each included article. We found 148 articles. Eighty were included after screening for articles that reported developing a DL model for medical image analysis. Five studies have made their code publicly available, and 35 studies have utilized publicly available datasets. We provided figures to show the ratio and absolute count of reported items from included studies. According to our cross-sectional study, in JDI publications on DL in medical imaging, authors infrequently report the key elements of their study to make it reproducible. [ABSTRACT FROM AUTHOR]

This special issue contains six revised and extended versions of tool papers that appeared in the proceedings of TACAS 2021, the 27th International Conference on Tools and Algorithms for the Construction and Analysis of Systems. The issue is dedicated to the realization of algorithms in tools and the studies of the application of these tools for analysing hard- and software systems. [ABSTRACT FROM AUTHOR]

The Grey Wolf Optimizer (GWO) is one of the more successful swarm-based intelligent algorithms in recent years, but the shortcomings of the Grey Wolf Optimizer are revealed as the problems handled become progressively more complex. For this purpose, this paper presents a new variant of GWO and names its Hybrid Contact List Subpopulation Mixed Evolution Grey Wolf Optimizer (CSELGWO). In the paper first introduces the Contact List Mechanism (CLM) to obtain high quality local optimal information in the search space. This is followed by the Hybrid Contact List Subpopulation Generation (HCSG) mechanism, which utilizes the information in the Contact List to assist in the updating of the Subpopulation and interacts with the main population through Subpopulation Mixed Evolution (SME) to interact with the main population, thus significantly improving population diversity and convergence accuracy. In addition, the proposed Levy Flight with archives and Activation Mechanism (LFAA) can moving away from local optimality by reasonable judgment. We evaluated it using 66 test functions and showed excellent convergence speed, stability and accuracy. Additionally, when compared with the top-performing algorithm from the CEC2020 Real World Competition, CSELGWO demonstrates effective solutions to real-world problems. Finally, we compared LSHADE_cnEpSin with LSHADE_SPACMA. Although CSELGWO does not outperform these LSHADE variants in terms of convergence accuracy and standard deviation obtained, it shows excellent performance on certain types of functions, indicating excellent potential. [ABSTRACT FROM AUTHOR]

Identifying discriminative patterns, known as shapelets, within time series is a critical step in many time series classification tasks. A major limitation of shapelet learning is that often hindered by their unsupervised methods, treating shapelet learning as an unsupervised subsequence clustering process and discovery based on pre-defined metric, which performed sequentially. This sequential procedure presents challenges, as it fails to establish a direct connection between shapelets and samples, and lacks the capacity to explicitly incorporate label information. In this paper, we proposed a novel shapelet learning algorithm called Discriminative Shapelet Learning via Temporal Clustering and Matrix Factorization (DSLMF). DSLMF introduced a joint framework that combines matrix factorization and coherent temporal clustering to discovery salient and coherent feature subsets. To further enhance discriminability and prevent arbitrary shapelet shapes, DSLMF integrates a label-specific shapelet regularization as a guiding mechanism enabling the learning of shapelets optimized for higher classification performance. The proposed algorithm has shown to be effective for capturing the temporal cluster structure and interpretability of shapelet-based method. The results of experiments showcased in this paper highlight DSLMF's effectiveness in capturing temporal cluster structures and learning meaningful shapelets, ultimately leading to promising performance on benchmark datasets. [ABSTRACT FROM AUTHOR]

In industry, the task of defect classification and defect localization is an important part of defect detection system. However, existing studies only focus on one task and it is difficult to ensure the accuracy of both tasks. This paper proposes a defect detection system based on improved Yolo_v4, which greatly improves the detection ability of minor defects. For K_Means algorithm clustering prianchors question with strong subjectivity, the paper proposes the Density Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm to determine the number of Anchors. To solve the problem of low detection rate of small targets caused by insufficient reuse rate of low-level features in CSPDarknet53 feature extraction network, this paper proposes an ECA-DenseNet-BC-121 feature extraction network to improve it. And the Dual Channel Feature Enhancement (DCFE) module is proposed to improve the local information loss and gradient propagation obstruction caused by quad chain convolution in PANet networks to improve the robustness of the model. The experimental results on the fabric surface defect detection datasets show that the mAP of the improved Yolo_v4 is 98.97%, which is 7.67% higher than SSD, 3.75% higher than Faster_RCNN, 10.82% higher than Yolo_v4 tiny, and 5.35% higher than Yolo_v4, and the detection speed reaches 39.4 fps. It can meet the real-time monitoring needs of industrial sites. [ABSTRACT FROM AUTHOR]

Fruit recognition and location are the premises of robot automatic picking. YOLOv3 has been used to detect different fruits in complex environment. However, for the object with definite features, the complex network structure will increase the computing time and may cause overfitting. Therefore, this paper has carried out a lightweight design for the YOLOv3. This paper proposed an improved T-Net to detect tomato images. Firstly, the T-Net reduces the residual network layers. This paper changed the number of cycles in each group of the residual unit to 1, 2, 2, 1, and 1. Second, two feature layers with different scales are selected according to the features of tomatoes. Meanwhile, the convolutional layer at the neck has been reduced by two layers. Finally, the location and approximate diameter of the ripe tomato are obtained by combining the node information of the Intel D435i camera and T-Net in the Robot Operation System. T-Net obtains mean average precision (mAP) of 99.2%, F1-score of 98.9%, precision of 99.0%, and recall of 98.8% at a detection rate of 104.2 FPS. The proposed T-Net has outperformed the YOLOv3 with 0.4%, 0.1%, and 0.2% increase in precision, mAP, and F1-score. The detection speed of T-Net is 1.8 times faster than YOLOv3. The mean errors of the center coordinates and diameter of the tomato are 8.5 mm and 2.5 mm, respectively. This model provides a method for efficient real-time detection and location of tomatoes. [ABSTRACT FROM AUTHOR]

This paper aims to propose a table tennis target detection (TD) method based on deep learning (DL) and multi-scale feature fusion (MFF) to improve the detection accuracy of the ball in table tennis competition, optimize the training process of athletes, and improve the technical level. In this paper, DL technology is used to improve the accuracy of table tennis TD through MFF guidance. Initially, based on the FAST Region-based Convolutional Neural Network (FAST R-CNN), the TD is carried out in the table tennis match. Then, through the method of MFF guidance, different levels of feature information are fused, which improves the accuracy of TD. Through the experimental verification on the test set, it is found that the mean Average Precision (mAP) value of the target detection algorithm (TDA) proposed here reaches 87.3%, which is obviously superior to other TDAs and has higher robustness. The DL TDA combined with the proposed MFF can be applied to various detection fields and can help the application of TD in real life. [ABSTRACT FROM AUTHOR]

Elliptical curves are dedicated for several security applications including Radio Frequency Identification (RFID) devices, smart cards, bankcards, etc. To guarantee effective security of such applications, these cryptographic systems require effective resistance to various types of physical attack. Differential Power-Analysis (DPA) attacks were considered the most efficient attacks against scalar multiplication calculation algorithms. In this paper, we propose a countermeasure method against the DPA attacks, for a scalar multiplication algorithm that is basically secure against Simple Power Analysis (SPA) and safe-error attacks. Our proposal is intended for Elliptic Curves Cryptosystems (ECC) algorithms dedicated to low cost applications. We first introduce the different types of side-channel attacks that ECC-based cryptographic algorithms can suffer, as well as their countermeasure methods existing in the literature. We then present an optimized hardware implementation of the most effective scalar multiplication algorithm against SPA and safe-error attacks. Finally, we present our proposed DPA countermeasure method and its effectiveness against other extensions of DPA attacks. Our proposed method is similar to the Basic Random Initial Point (BRIP) method except that the latter is only applicable for the left-to-right algorithm. The proposed method is based on the randomization of processed data during the computation of the scalar multiplication algorithm and prevents vulnerability to Zero-value Point Attack (ZPA), Refined Power analysis (RPA) attack and double attack. In the last part of our paper, we present comparative analysis in terms of computational cost between our proposed method and other countermeasure algorithms presented in the literature, such as Montgomery-ladder, the BRIP algorithm, the left-to-right algorithm and the Co-Z Mont-Ladder algorithm. [ABSTRACT FROM AUTHOR]

This paper addresses the problem of generating an evenly distributed set of Pareto solutions. It appears in real-life applications related to multi-objective optimization when it is important to represent the entire Pareto front with a minimal cost. There exist only a few algorithms which are able to tackle this problem in a general formulation. The Directed Search Domain (DSD) algorithm has proved to be efficient and quite universal. It has successfully been applied to different challengeable test cases. In this paper for the first time the DSD approach is systematically extended and applied to problems with higher dimensions. The modified algorithm does not have any formal limitation on the number of objective functions that is important for practical applications. The efficacy of the algorithm is demonstrated on a number of test cases. [ABSTRACT FROM AUTHOR]

The growing depletion of fossil fuels has led to the use of distributed renewable energy sources. This shift has altered the grid structure from centralized to distributed, where energy flows from multiple sources through multiple paths, besides producing a more competitive and dynamic energy market, posing new problems to power system management. To tackle the issue of effectively utilizing renewable energy sources, the energy internet (EI) was developed, in which devices are connected by energy routers. Therefore, efficiently transmitting energy within the EI has become a prominent topic in research. Despite the existence of algorithms and reviews in the literature on energy routing, there is still a lack of a thorough and comprehensive comparison of these existing algorithms. This paper classifies current energy routers and discusses and categorizes energy routing algorithms based on their methods. Additionally, the paper conducts extensive simulations to compare several energy routing algorithms in detail. [ABSTRACT FROM AUTHOR]

In this work, we propose a multiple feedback-based successive interference cancellation (SIC) scheme for an ultra-dense Internet of Things (IoT) device network. Non-orthogonal multiple access (NOMA) enables massive connectivity with improved user fairness and spectral efficiency and is envisaged as a multiple access technique for IoT devices. NOMA simultaneously serves multiple users within a single resource block, leading to unbounded yet regulated multi-user interference. SIC is widely adopted in the NOMA system to detect users' symbols. Nevertheless, multi-user interference and error propagation in the SIC layer are inherent challenges in NOMA. Recent studies have aimed to minimize interference and error propagation, imposing stringent conditions on the number of users and power allocation. Thus, this paper proposes novel multiple feedback-based SIC algorithms for the uplink multi-user NOMA scenarios that outperform the conventional SIC. Further, the proposed algorithm's performance is analyzed under the practical case of imperfect channel state information at the receiver node to validate the robustness. The computational complexity of multiple feedback SIC is compared with the conventional SIC. [ABSTRACT FROM AUTHOR]

In this paper, a multifunction three-phase balance relay based on normalized correlation coefficients is proposed to detect and estimate imbalances and perturbations in synchronous machine output signals. Furthermore, fresh definitions of imbalance and disturbance indicators derived using the correlation estimators are introduced, taking into account the changes in the waveform phase displacement, frequency, amplitude, and shape of the machine three-phase waves. Experimental tests are performed on a motor–generator set connected to a three-phase load, which is used to identify and evaluate the imbalance and disturbance conditions of the voltage and current measurements. Extensive tests for different fault types have been presented. The practical results show that the proposed protection can respond quickly to faults, and assess online the level of the imbalance/disturbance with high accuracy. Its running time is within one cycle. In addition, the proposed algorithm's reliability and accuracy are its most significant attributes, whose percentages exceed 96.6%. The present algorithm considers the impact of both negative and zero sequence components when measuring di-symmetry factors, while some conventional approaches merely rely on the negative sequence component computed for the machine voltages and currents. [ABSTRACT FROM AUTHOR]