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Path-based clustering algorithms typically generate clusters by optimizing a benchmark function. Most optimization methods in clustering algorithms often offer solutions close to the general optimal value. This study achieves the global optimum value for the criterion function in a shorter time using the minimax distance, Maximum Spanning Tree "MST", and meta-heuristic algorithms, including Genetic Algorithm "GA" and Particle Swarm Optimization "PSO". The Fast Path-based Clustering "FPC" algorithm proposed in this paper can find cluster centers correctly in most datasets and quickly perform clustering operations. The FPC does this operation using MST, the minimax distance, and a new hybrid meta-heuristic algorithm in a few rounds of algorithm iterations. This algorithm can achieve the global optimal value, and the main clustering process of the algorithm has a computational complexity of O (k² × n). However, due to the complexity of the minimum distance algorithm, the total computational complexity is O (n²). Experimental results of FPC on synthetic datasets with arbitrary shapes demonstrate that the algorithm is resistant to noise and outliers and can correctly identify clusters of varying sizes and numbers. In addition, the FPC requires the number of clusters as the only parameter to perform the clustering process. A comparative analysis of FPC and other clustering algorithms in this domain indicates that FPC exhibits superior speed, stability, and performance. [ABSTRACT FROM AUTHOR]

Ensuring confidentiality and privacy is critical when it comes to sharing images over unsecured networks such as the internet. Since widely used and secure encryption methods, such as AES, Twofish, and RSA, are not suitable for real-time image encryption due to their slow encryption speeds and high computational requirements, researchers have proposed specialized algorithms for image encryption. This paper provides an introduction and overview of the image encryption algorithms and metrics used, aiming to evaluate them and help researchers and practitioners starting in this field obtain adequate information to understand the current state of image encryption algorithms. This paper classifies image encryption into seven different approaches based on the techniques used and analyzes the strengths and weaknesses of each approach. Furthermore, this paper provides a detailed review of a comprehensive set of security, quality, and efficiency evaluation metrics for image encryption algorithms, and provides upper and lower bounds for these evaluation metrics. Finally, this paper discusses the pros and cons of different image encryption approaches as well as the suitability of different image encryption approaches for different applications. [ABSTRACT FROM AUTHOR]

This paper proposes a blockchain-based decentralized collaborative learning method for the Industrial Internet environment to solve the trust and security issues in Federated Learning. Deploy a decentralized network for collaborative learning based on the alliance chain, design a block data structure suitable for asynchronous learning, and model three stages of computing event triggering, computing task distribution, and computing result integration for cross-domain device collaborative learning. List the critical steps for network deployment, including inspection, tearing down old networks, creating organizational encryption material, creating channels, and deploying chaincode. It also introduces the development of crucial chaincode such as initialization, creation, query, and modification. Finally, the correlation between the number of data pieces of the network, the number of communications, and the time of communications are analyzed through experiments. This paper also proposes a decentralized asynchronous collaborative learning algorithm, develops chaincode middleware between the blockchain network and Artificial Intelligence training, and conducts experimental analysis on the industrial steam volume prediction data set in thermal power generation. The performance on the data set, and the experimental results prove that the asynchronous collaborative learning algorithm proposed in this paper can achieve a good convergence effect. It is also compared with the single-machine single-card regression prediction algorithm, proving that the proposed model has better generalization. [ABSTRACT FROM AUTHOR]

Homogeneous and isotropic thick spheres loaded with constant internal and/or external pressures can not be economically designed because the maximum equivalent stress is a local value. It was analytically demonstrated that, by neglecting the body loads, a functionally graded material (FGM) may be characterized in linear static analyses by two material constants: Young's modulus E(r) and Poisson's ratio ν(r). If these two functions are both known, the solution of the problem, displacement and stress distributions may be relatively easy obtained. For the inverse problem, in which a desired stress combination distribution is imposed, finding of E(r) and ν(r) is more difficult, if such a solution exists. More than that, if the solution exists, it is not unique, because two unknown functions are involved. For ν(r) = const., analytical solutions are available for E(r), but only for two particular stress conditions. In this paper, the inverse problem is solved iteratively using a finite element model and an algorithm of stress uniformization developed by the authors of this paper is proposed. In this original approach, the existing solutions were reproduced as a verification and afterwards new solutions were obtained for the remaining classical theories of resistance. The new obtained solutions were also verified by using the analytical solutions of the direct problem. [ABSTRACT FROM AUTHOR]

The existence of specular highlights hinders high-level computer algorithms. In this paper, we propose a novel approach to remove specular highlights from a single grayscale image by regarding the problem as an image-to-image translation task between the highlight domain and the diffuse domain. We solve this problem by using the generative adversarial network framework, where a generator removes highlights and discriminator judges whether outputs of the generator are clear and high- light-free. Specular highlight removal is intractable as we should remove specular highlights while keeping as many details as possible. Considering the similarity between the highlight image and diffuse image, we adopt an attention-based submodule that generates a mask image, which we call the highlight intensity mask, to locate pixels that contain specular highlights and help the skip-connected autoencoder to remove highlights. A pixel discriminator and Structural Similarity loss are utilized to ensure that more details can be retained in the output images. For training and testing models, we build a grayscale highlight images dataset. It consists of more than a thousand sets of grayscale highlight images with ground truth. Finally, quantitative and qualitative evaluations demonstrate the effectiveness of our method than other contrast generative adversarial network methods. [ABSTRACT FROM AUTHOR]

In this paper, we implemented two simple image processing algorithms in the MATLAB environment and then in the FPGA, and then we compared the results in terms of accuracy and execution time. The first algorithm deals with the pseudo-coloring of a monochrome image of 256 x 256 pixels, by assigning to each pixel in the original image three other values, corresponding to the RGB matrices in the colored image. The assignment of these values was based on a conversion table that generates 16 different colors in the HOT color scale. The second algorithm generates the negative image of the monochrome image by calculating the new values of the pixels making the difference between 255 and their initial value. For each algorithm discussed here, the images obtained in MATLAB R2014a are compared with those obtained in the Xilinx ISE 14.7 environment in terms of accuracy and execution speed. [ABSTRACT FROM AUTHOR]

Cloud Computing is known for majorly two features which makes it attractive for consumers and providers: 1. Automatic Resource Provisioning 2. Strictly following the decided QoS . Resource Provisioning needs to be dynamic and quick. Even the state of the art technology might take time in minutes to provision a VM and makes Cloud unattractive than an on-premise system. So we present a study of ASAP - A Self Adaptive Prediction System for instant demand provisioning and Cyclic Window Learning Algorithm to predict the requests. QoS is closely related to resource allocation along with but includes strictly following the SLOs. QoS prediction can help the users to select the best service as per the need for which we study ALPINE which is a system based on Bayesian Networks and another model has been discussed here. [ABSTRACT FROM AUTHOR]

The article presents a study on a new Neural Network (NN) algorithm for the retrieval of tropospheric ozone column from Ozone Monitoring Instrument (OMI) Level 1b data. The NN algorithm is extensively discussed, and validation results against independent ozone soundings and Chemistry/Transport Model (CTM) simulations are shown. The results show that the algorithm is able to retrieve the tropospheric ozone column with a Root Mean Square Error (RMSE) of around 5-6 DU in all the latitude bands.

The article presents a study which examines the application of tomographic algorithms in the analysis of mesospheric clouds based on maximum probability techniques. Short limb scans of the altitude range of Polar Mesospheric Clouds by the Swedish-led Odin satellite are highlighted. Results of simulating the Optical Spectrograph and InfraRed Imager System (OSIRIS) tomography are investigated.

This methods paper describes the methodological and statistical underpinnings of the highly adaptive testing design (HAT), which was developed for the Programme for International Student Assessment (PISA). The aim of HAT is to allow for a maximum of adaptivity in selecting items while taking the constraints of PISA into account with appropriate computer algorithms. HAT combines established methods from the area of computerized adaptive testing (a) to improve item selection when items are nested in units, (b) to make use of the correlation between the dimensions measured, (c) to efficiently accomplish constraint management, (d) to control for item position effects, and (e) to foster students' test-taking experience. The algorithm is implemented using the programming language R and readers are provided with the necessary code. This should facilitate future implementations of the HAT design and inspire other adaptive testing designs that aim to maximize adaptivity while meeting constraints. [ABSTRACT FROM AUTHOR]

The aim of this paper is to apply all the existing pyranometric methods to estimate the sunshine duration from global solar irradiation in order to find the most suitable method for a tropical site in its original form. Then, in a second step, one of these methods will be optimized to effectively fit tropical sites. Five methods in the literature (Step algorithm, Carpentras Algorithm, Slob and Monna Algorithm, Slob and Monna 2 Algorithm, and linear algorithm) were applied with eleven years of global and diffuse solar radiation data. As a result, with regard to its original form, the step algorithm is in the first rank. But in the second step, after improving its main coefficients, the Carpentras Algorithm was found to be the best algorithm for tropical sites in the southern hemisphere. [ABSTRACT FROM AUTHOR]

Batch scheduling is a crucial part of pipeline enterprise operation management, especially in the context of market-oriented operation. It involves 3 main tasks: quickly preparing batch plans, accurately tracking interface movement, and operation condition in real time. Normally, the completion of multi-product pipeline batch scheduling depends on simulation models or optimization models and corresponding conventional solving algorithm. However, this approach becomes inefficient when applied to large-scale systems. The rapid development of machine learning has brought new ideas to batch scheduling research. This paper first reviews the current state of batch scheduling technology, and suggests that applying machine learning to it is a promising development direction. Then, we summarize the progress of machine learning applications in batch planning, interface movement tracking, and operational condition monitoring, and point out their limitations. Finally, considering the separation of refined oil production, transportation, and sales processes, 5 recommendations are put forward: oil supply and demand prediction and pipeline capacity prediction, batch planning, batch interface movement tracking, mixed oil development monitoring, and pipeline operation condition identification. [ABSTRACT FROM AUTHOR]

In image processing, computer vision algorithms are applied to regions bounded by closed contours. These contours are often irregular, poorly defined, and contain holes or unavailable areas inside. A common problem in computational geometry includes finding the k-sided polygon (k-gon) of maximum area or maximum perimeter inscribed within a contour. This paper presents a generic method to obtain user-defined polygons within a region. Users can specify the number k of sides of the polygon to obtain. Additionally, users can also decide whether the calculated polygon should be the largest in area or perimeter. This algorithm produces a polygon or set of polygons that can be used to segment an image, allowing only relevant areas to be processed. In a real-world application, the validity and versatility of the proposed method are demonstrated. In addition, the source code developed in Java and Python is available in a GitHub repository so that researchers can use it freely. [ABSTRACT FROM AUTHOR]

Effective algorithm visualization in computer science education greatly enhances the level of understanding and interest in the subject. This paper presents a development and evaluation of a web-based visualizer of data structures intended for learning sorting algorithms. Our system allows interactive and dynamic visualization of various sorting techniques, including Bubble Sort, Merge Sort, Quick Sort, and Heap Sort. We provide a real-time graphical display for the user to view step-by-step executions of such algorithms. This provides insight into their operational mechanics and performance characteristics. We present the architecture of the visualizer, methodologies for algorithm visualization, and some feedback from users during testing phases. Both qualitative and quantitative metrics are used for assessing the improvement in algorithmic understanding. Our findings indicate that the visualizer significantly enhances students' understanding of the concepts in sorting algorithms and that it is indeed a very important tool for computer science educators and learners alike. [ABSTRACT FROM AUTHOR]

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The article presents a study which conducts ground-based measurements of stratospheric chlorine monoxide (ClO) in Atacama, Chile. The study uses radiometer which is developed by the Nagoya University and is capable of providing high sensitivity and stable performance needed in the calibration of stratospheric chlorine monoxide (ClO) lines. Using weighted-damped least squares algorithm, the study is able to retrieve vertical mixing ratio 10 profiles.

The article presents a study which describes an algorithm for retrieval of aerosol characteristics in dust storms and biomass burning plumes. The retrieval process applied in the study is divided into three parts including satellite data analysis, model simulations and radiative-transfer calculations. It also illustrates how the method of successive order of scattering (MSOS) is derived.

Nowadays, with the development of computer science and technology, computer intelligent algorithms are more and more widely used in various industries. Every calculation formula in the computer intelligent algorithm has systematic logic and singleness, in order to expound the dynamic algorithm of football training optimized by the computer intelligent algorithm in detail. In this paper, the monitoring system using the computer intelligent algorithm can dynamically observe people or objects and systematically analyze them. This paper mainly studies the research of a football training dynamic monitoring system based on the computer intelligent algorithm and the design and optimization of the computer intelligent dynamic monitoring system in football training. Finally, the overall composition of the computer intelligent dynamic monitoring system and the application of the optimized computer intelligent dynamic monitoring system to the analysis of sample data are studied. [ABSTRACT FROM AUTHOR]

The volume of digital text data is continuously increasing both online and offline storage, which makes it difficult to read across documents on a particular topic and find the desired information within a possible available time. This necessitates the use of technique such as automatic text summarization. Many approaches and algorithms have been proposed for automatic text summarization including; supervised machine learning, clustering, graph-based and lexical chain, among others. This paper presents a novel systematic review of various graph-based automatic text summarization models. [ABSTRACT FROM AUTHOR]

In many applications, top-k query is an important operation to return a set of interesting points in a potentially huge data space. It is analyzed in this paper that the existing algorithms cannot process top- k query on massive data efficiently. This paper proposes a novel table-scan-based T2S algorithm to efficiently compute top-k results on massive data. T2S first constructs the presorted table, whose tuples are arranged in the order of the round-robin retrieval on the sorted lists. T2S maintains only fixed number of tuples to compute results. The early termination checking for T2S is presented in this paper, along with the analysis of scan depth. The selective retrieval is devised to skip the tuples in the presorted table which are not top-k results. The theoretical analysis proves that selective retrieval can reduce the number of the retrieved tuples significantly. The construction and incremental-update/batch-processing methods for the used structures are proposed in this paper. The extensive experimental results, conducted on synthetic and real-life data sets, show that T2S has a significant advantage over the existing algorithms. [ABSTRACT FROM AUTHOR]

In industrial practice, is sometimes needed to manufacture specific parts without blueprints. This part of engineering process is known as reverse engineering. Usually, this assumes that an existing piece is measured or scanned and based on this result is made a new part. If the part can be machined with universal cutting tools the manufacturing process don't rise problems, but in case of parts which needs specific tools, these must be designed to be suitable for machining the piece. In this paper, a tool profiling algorithm is proposed, which is based on the capacity to measure and represent some surfaces in a methodical cloud of points form. The proposed profiling algorithm is based on the "virtual pole" method. This method is used together with the intermediate surface theorem for profiling tools characterized by a helical primary peripheral surface. This article will represent a continuation of a research which was been published in 2021, in Bulletin of Polytechnic Institute from Jassy. In that published article, the profiling of the hob mill tool was presented in a theoretical way and the "virtual pole" theorem was applied for an any type of profiles. The proposed algorithm allowed the determination of the tool's profile reciprocally enveloping with a piece's profile known in discrete form. Now, in this paper, the discrete form profile will represent exactly a cycloidal disk from a cycloidal reducer whose coordinates were determined using specific reverse engineering processes. Considering that the determination of the relative movements between the tool and the part, as well as the determination of the enveloping condition are relatively complicated and can be a source of errors, the purpose of this work is to highlight the advantages of the "virtual pole" method compared to the other specific methods of tool profiling that generate by enwrapping. At the same time, it is investigated if the "virtual pole" method can provide a support for the identification of the intermediate surface, with the help of graphic design environments, for profiling, in this case, the worm tools, since the "virtual pole" method cannot be applied as such in the case of disk-type tools, worm tools and cylindricalfrontal tools, due to the fact that the theorem is intended for the study of planar gearing. The advantages of using the "virtual pole" method are obvious compared to the classical tool profiling methods. The results demonstrated the accuracy of the method, by the fact that, using the "virtual pole" method, the calculation time for profiling the hob mill tool for generating a profile known in discrete form, namely the profile of the cycloidal disk, is significantly reduced. [ABSTRACT FROM AUTHOR]

Food and fluid intake monitoring are essential for reducing the risk of dehydration, malnutrition, and obesity. The existing research has been preponderantly focused on dietary monitoring, while fluid intake monitoring, on the other hand, is often neglected. Food and fluid intake monitoring can be based on wearable sensors, environmental sensors, smart containers, and the collaborative use of multiple sensors. Vision-based intake monitoring methods have been widely exploited with the development of visual devices and computer vision algorithms. Vision-based methods provide non-intrusive solutions for monitoring. They have shown promising performance in food/beverage recognition and segmentation, human intake action detection and classification, and food volume/fluid amount estimation. However, occlusion, privacy, computational efficiency, and practicality pose significant challenges. This paper reviews the existing work (253 articles) on vision-based intake (food and fluid) monitoring methods to assess the size and scope of the available literature and identify the current challenges and research gaps. This paper uses tables and graphs to depict the patterns of device selection, viewing angle, tasks, algorithms, experimental settings, and performance of the existing monitoring systems. [ABSTRACT FROM AUTHOR]

Optically thin ice clouds play an important role in polar regions due to their effect on cloud radiative impact and precipitation on the surface. Cloud bases can be detected by lidar-based ceilometers that run continuously and therefore have the potential to provide basic cloud statistics including cloud frequency, base height and vertical structure. Despite their importance, thin clouds are however not well detected by the standard cloud base detection algorithm of most ceilometers operational at Arctic and Antarctic stations. This paper presents the Polar Threshold (PT) algorithm that was developed to detect optically thin hydrometeor layers (optical depth τ ⩾ 0.01). The PT algorithm detects the first hydrometeor layer in a vertical attenuated backscatter profile exceeding a predefined threshold in combination with noise reduction and averaging procedures. The optimal backscatter threshold of 3×10-4 km-1 sr-1 for cloud base detection was objectively derived based on a sensitivity analysis using data from Princess Elisabeth, Antarctica and Summit, Greenland. The algorithm defines cloudy conditions as any atmospheric profile containing a hydrometeor layer at least 50m thick. A comparison with relative humidity measurements from radiosondes at Summit illustrates the algorithm's ability to significantly differentiate between clear sky and cloudy conditions. Analysis of the cloud statistics derived from the PT algorithm indicates a year-round monthly mean cloud cover fraction of 72% at Summit without a seasonal cycle. The occurrence of optically thick layers, indicating the presence of supercooled liquid, shows a seasonal cycle at Summit with a monthly mean summer peak of 40 %. The monthly mean cloud occurrence frequency in summer at Princess Elisabeth is 47 %, which reduces to 14% for supercooled liquid cloud layers. Our analyses furthermore illustrate the importance of optically thin hydrometeor layers 25 located near the surface for both sites, with 87% of all detections below 500m for Summit and 80% below 2 km for Princess Elisabeth. These results have implications for using satellite-based remotely sensed cloud observations, like CloudSat, that may be insensitive for hydrometeors near the surface. The results of this study highlight the potential of the PT algorithm to extract information in polar regions about a wide range of hydrometeor types from measurements by the robust and relatively low-cost ceilometer instrument. [ABSTRACT FROM AUTHOR]

The article presents a study which focuses on the development of an algorithm for the representation of large spectral variations of albedo over vegetation surfaces based on Moderate Resolution Imaging Spectrometer (MODIS) observations. The traditional approaches for estimating the MODIS enhanced vegetation albedo (MEVA) are outlined. It also emphasizes the significance of MEVA in understanding climate processes and climate change.

The article presents a study which looks into carbon monoxide (CO) total column measurements through the SCanning Imaging Absorption spectroMeter for Atmospheric CHartographY (SCIAMACHY). The study derives the CO total column measurements from the Iterative Maximum Likelihood Method (IMLM) algorithm. It also applies vertically integrated profiles aircraft measurements which are taken from the MOZAIC project.

The article presents a study which describes the Magee algorithm for the analysis of coefficient measurements of aerosol absorption. The study applies a 7-wavelengths aethalometer in an effort to determine the existence of brown carbon (BrC), black carbon (BC) and organic carbon (OC) in fine atmospheric. It suggests that the proposed algorithm can be used in field studies on processes involving both BC and BrC aerosol and organic compounds.

The article presents a study which looks into the main noise sources and the best residual adjustment area of Ozone Monitoring Instrument (OMI) sulfur dioxide (SO2). The study analyzes various residual correction effects of OMI SO2. It also describes the optimization of the Band Residual Difference (BRD) algorithm for data noise and cites existing retrieval error sources.

The article presents a study on the averaged carbon dioxide (ACOS) XCO2 retrieval algorithm. The study looks into estimates of the column averaged carbon dioxide (CO)2 dry air mole fraction, XCO2, using U.S. National Aeronautics and Space Administration's (NASA) retrieval methods. It also explores the retrieval algorithm's on seasonal time scales and retrieved surface pressure.

The article presents a study which looks into the Lambertian Equivalent Reflection (LER) data from operating instruments including Total Ozone Mapping Spectrometers (TOMS) and Ozone Monitoring Instrument (OMI). The study applies the LER data to find out the ground-based ultraviolet radiation (UV) radiance and ground-based global solar irradiance. It also compares the spaceborne cloud modification factors (CMFs) and ground-based CMFs.

Background: Rapidly growing healthcare demand associated with global population aging has spurred the development of new digital tools for the assessment of cognitive performance in older adults. Objective: To develop a fully automated Mini-Mental State Examination (MMSE) assessment model and validate the model's rating consistency. Methods: The Automated Assessment Model for MMSE (AAM-MMSE) was an about 10-min computerized cognitive screening tool containing the same questions as the traditional paper-based Chinese MMSE. The validity of the AAM-MMSE was assessed in term of the consistency between the AAM-MMSE rating and physician rating. Results: A total of 427 participants were recruited for this study. The average age of these participants was 60.6 years old (ranging from 19 to 104 years old). According to the intraclass correlation coefficient (ICC), the interrater reliability between physicians and the AAM-MMSE for the full MMSE scale AAM-MMSE was high [ICC (2,1)=0.952; with its 95% CI of (0.883,0.974)]. According to the weighted kappa coefficients results the interrater agreement level for audio-related items showed high, but for items "Reading and obey", "Three-stage command", and "Writing complete sentence" were slight to fair. The AAM-MMSE rating accuracy was 87%. A Bland-Altman plot showed that the bias between the two total scores was 1.48 points with the upper and lower limits of agreement equal to 6.23 points and −3.26 points. Conclusions: Our work offers a promising fully automated MMSE assessment system for cognitive screening with pretty good accuracy. [ABSTRACT FROM AUTHOR]

The increasing impact of algorithmically driven processes on human societies, which can exacerbate political, economic, and cultural asymmetries, raises questions about reducing human agency by constraining platform structures. We draw on the theoretical concept of algorithmic imaginary, which captures users' appropriations and ideas of these processes. In this paper, we focus on the dynamics between agency and structure in algorithmic imaginaries regarding the future of digital media platforms in Europe. The paper takes structuration theory as a theoretical starting point and employs methods of futures studies to analyze how the future is constructed in scenarios developed by a diversity of experts participating in a series of workshops. The future scenarios analysis is mapped around four actors, namely platform users, platform corporations, algorithms and institutions. By considering the role of various actors, particularly institutions, and their interdependencies this paper contributes to more balanced conceptualizations of algorithmic imaginaries, which tend to be centered around users' perspectives. [ABSTRACT FROM AUTHOR]

Multi-objective reinforcement learning (MORL) extends traditional RL by seeking policies making different compromises among conflicting objectives. The recent surge of interest in MORL has led to diverse studies and solving methods, often drawing from existing knowledge in multi-objective optimization based on decomposition (MOO/D). Yet, a clear categorization based on both RL and MOO/D is lacking in the existing literature. Consequently, MORL researchers face difficulties when trying to classify contributions within a broader context due to the absence of a standardized taxonomy. To tackle such an issue, this paper introduces multi-objective reinforcement learning based on decomposition (MORL/D), a novel methodology bridging the literature of RL and MOO. A comprehensive taxonomy for MORL/D is presented, providing a structured foundation for categorizing existing and potential MORL works. The introduced taxonomy is then used to scrutinize MORL research, enhancing clarity and conciseness through well-defined categorization. Moreover, a flexible framework derived from the taxonomy is introduced. This framework accommodates diverse instantiations using tools from both RL and MOO/D. Its versatility is demonstrated by implementing it in different configurations and assessing it on contrasting benchmark problems. Results indicate MORL/D instantiations achieve comparable performance to current state-of-the-art approaches on the studied problems. By presenting the taxonomy and framework, this paper offers a comprehensive perspective and a unified vocabulary for MORL. This not only facilitates the identification of algorithmic contributions but also lays the groundwork for novel research avenues in MORL. [ABSTRACT FROM AUTHOR]

Personalized PageRank, as a basic algorithm in large graph analysis, has a wide range of applications in search engines, social recommendation, community detection, and other fields and it has been a hot problem of interest to researchers. The existing distributed personalized PageRank algorithms assume that all data are located in the same geographic location and the network environment is the same among the computing nodes where the data are located. However, in the real world, these data may be distributed in multiple data centers across continents, and these geo-distributed data centers are connected to each other through WANs, which are characterized by heterogeneous network bandwidth, huge hardware differences, and high communication costs. Moreover, the distributed personalized PageRank algorithm requires multiple iterations and random walk on the global graph. Therefore, the existing distributed personalized PageRank algorithms are not applicable to the geo-distributed environment. To address this problem, the GPPR (Geo-distributed Personalized PageRank) algorithm is proposed in this paper. The algorithm first preprocesses the big graph data in the geo-distributed environment and maps the graph data by using a heuristic algorithm to reduce the impact of network bandwidth heterogeneity on the iteration speed of the algorithm. Secondly, GPPR improves the random walk approach and proposes a probability-based push algorithm to further lower the number of iterations required by the algorithm by reducing the bandwidth load of data transmission between working nodes. We implement the GPPR algorithm based on the Spark framework and build a real geo-distributed environment in AliCloud to conduct experiments comparing the GPPR algorithm with several existing representative distributed personalized PageRank algorithms on eight open-source big graph datasets. The results show that the communication data volume of GPPRis reduced by 30% on average in the geo-distributed environment compared with that of other algorithms. In terms of algorithm running efficiency, GPPR improves by an average 2.5 factor compared with other algorithms. [ABSTRACT FROM AUTHOR]

Bi-mobile planar mechanisms are commonly used in various fields of robotics. By adding additional degrees of mobility (rotation and/or translation) to the basis, multi-mobile systems for manipulators or pedipulators (legs) found in various fields are obtained. The paper aims to highlight these mechanisms using bi-mobile planar kinematic linkages described in the classical literature of mechanism theory [1-5,16,17,23,25] in which the basis and the effector are nominated by inverse modelling [8-12,13,15]. The effector extremity of such mechanism may describe any trajectory in its field. This is equivalent to introduce a pair with two constrains which determines the structure to have zero degree of mobility, its inverse structural model could have only passive modular groups. All basis-effector solutions for planar bimobile mechanisms obtained from linkages with five degrees of mobility and two and three independent contours are mentioned. The mechanism mobility is given by two active kinematic pairs suitably placed in the same chain [8-15]. The structural algorithm included in the paper offers the possibility to create various optimal solution for bi-mobile mechanisms. At the same time, the base-effector structural solutions mentioned in the paper are useful in designing these systems, and can be used to verify or adopt appropriate solutions. [ABSTRACT FROM AUTHOR]

Incomplete fault signal characteristics and ease of noise contamination are issues with the current rolling bearing early fault diagnostic methods, making it challenging to ensure the fault diagnosis accuracy and reliability. A novel approach integrating enhanced Symplectic geometry mode decomposition with cosine difference limitation and calculus operator (ESGMD-CC) and artificial fish swarm algorithm (AFSA) optimized extreme learning machine (ELM) is proposed in this paper to enhance the extraction capability of fault features and thus improve the accuracy of fault diagnosis. Firstly, SGMD decomposes the raw vibration signal into multiple Symplectic geometry components (SGCs). Secondly, the iterations are reset by the cosine difference limitation to effectively separate the redundant components from the representative components. Additionally, the calculus operator is performed to strengthen weak fault features and make them easier to extract, and the singular value decomposition (SVD) weighted by power spectrum entropy (PSE) can be utilized as the sample feature representation. Finally, AFSA iteratively optimized ELM is adopted as the optimized classifier for fault identification. The superior performance of the proposed method has been validated by various experiments. [ABSTRACT FROM AUTHOR]

This paper tackles the asynchronous signature-free Byzantine consensus. One way to circumvent the FLP impossibility result consists in adding a Time-free assumption. This assumption is based on the pattern of messages that are exchanged. In the context of authenticated asynchronous Byzantine systems where at most t processes may exhibit a Byzantine behavior, Moumen and Mostéfaoui provide the main result. They assume at least one correct process p i , called ⋄ ⟨ t + 1 ⟩ -winning , and a set Q of t correct processes such that, eventually, for each query issued by p i , any process p j of Q receives a response from p i among the (n - t) first responses to that query. The main contribution of this paper is to show that a deterministic solution for the Signature-free Byzantine consensus problem is possible if the system model satisfies an additional assumption that relies on the pattern of exchanged messages. To solve the Consensus problem, we assume a correct process p i , called ⋄ ⟨ 2 t + 1 ⟩ -winning , and a set Q of (2 t + 1) correct processes (including p i itself) such that, eventually, for each query issued by p i , any process p j of Q receives a response from p i among the (n - t) first responses to that query. The processes of the set Q may change over time. Based on this assumption, a Signature-free Multivalued Byzantine consensus protocol is proposed. Whereas many time-free protocols have been designed for the consensus problem in the crash model and in the Byzantine Authenticated model, this is, to our knowledge, the first time-free deterministic solution to the Signature-free Byzantine consensus Problem. [ABSTRACT FROM AUTHOR]

In recent years, the popularity of food recommendation systems has significantly increased due to the overwhelming amount of food-related information available online. These systems aid users in discovering new and personalized food choices, thereby enhancing their dining experiences. This paper presents a hybrid approach that combines content-based filtering (CBF) and collaborative filtering (CF) techniques to develop a robust and accurate food recommendation system. The proposed system leverages CBF to generate recommendations based on the intrinsic characteristics of food items, such as ingredients, cuisine, and nutritional content. By analyzing these attributes, the system identifies similarities between items and recommends new dishes that align with users' preferences. Additionally, the system incorporates CF to offer personalized recommendations based on user behavior and preferences. Collaborative filtering utilizes the collective knowledge of a user community to suggest food items that are popular among individuals with similar tastes. To implement the hybrid approach, a dataset containing food item details, user preferences, and ratings is collected and preprocessed. The content-based filtering component utilizes natural language processing techniques to extract relevant features from textual data, while the collaborative filtering component employs matrix factorization algorithms to identify user-item interactions and uncover latent factors. The system's performance is evaluated using standard evaluation metrics, including precision, recall, and mean average precision. The interface enables users to provide feedback and ratings on recommended food items, further refining the system's recommendations. The proposed food recommendation system holds great potential for improving user satisfaction and engagement in the culinary domain. By combining the strengths of content-based and collaborative filtering techniques, the system effectively captures both item characteristics and user preferences, resulting in more accurate and diverse food recommendations. [ABSTRACT FROM AUTHOR]

Analytic methods development, like many other disciplines, relies on experimentation and data analysis. Determining the contribution of a paper or report on a study incorporating data analysis is typically left to the reviewer's experience and good sense, without reliance on structured guidelines. This is amplified by the growing role of machine learning driven analysis, where results are based on computer intensive algorithm applications. The evaluation of a predictive model where cross validation was used to fit its parameters adds challenges to the evaluation of regression models, where the estimates can be easily reproduced. This lack of structure to support reviews increases uncertainty and variability in reviews. In this paper, aspects of statistical assessment are considered. We provide checklists for reviewers of applied statistics work with a focus on analytic method development. The checklist covers six aspects relevant to a review of statistical analysis, namely: (1) study design, (2) algorithmic and inferential methods in frequentism analysis, (3) Bayesian methods in Bayesian analysis (if relevant), (4) selective inference aspects, (5) severe testing properties and (6) presentation of findings. We provide a brief overview of these elements providing references for a more elaborate treatment. The robustness analysis of an analytical method is used to illustrate how an improvement can be achieved in response to questions in the checklist. The paper is aimed at both engineers and seasoned researchers. [ABSTRACT FROM AUTHOR]

Applications using terrestrial laser scanners (TLS) have been skyrocketing in the past two decades. In a scanning project, the configuration of scans is a critical issue as it has significant effects on the project cost and the quality of the product. In this paper, a practical strategy is proposed to resolve the problem of the optimal placement of the terrestrial laser scanner. The method attempts to reduce the number of viewpoints under the premise that the scenes are fully covered. In addition, the approach is designed in a way that the solutions can be efficiently explored. The method has been tested on 540 polygons simulated with different sizes and complexities. The results have also been compared with a benchmark strategy in terms of the optimality of the solutions and runtime. It is concluded that our proposed algorithm ties or reduces the number of viewpoints in the benchmark paper in 85.6% of the 540 tests. For complex environments, the method can potentially reduce the project cost by 10%. Although with relatively lower efficiency, our method can still reach the solution within a few minutes for a polygon with up to 500 vertices. [ABSTRACT FROM AUTHOR]

Recently, wireless sensor networks have employed the concept of mobile agent to reduce energy consumption and obtain effective data gathering. Typically, in data gathering based on mobile agent, it is an important and essential step to find out the optimal itinerary planning for the mobile agent. However, single-agent itinerary planning suffers from two primary disadvantages: task delay and large size of mobile agent as the scale of the network is expanded. Thus, using multi-agent itinerary planning overcomes the drawbacks of single-agent itinerary planning. Despite the advantages of multi-agent itinerary planning, finding the optimal number of distributed mobile agents, source nodes grouping, and optimal itinerary of each mobile agent for simultaneous data gathering are still regarded as critical issues in wireless sensor network. Therefore, in this article, the existing algorithms that have been identified in the literature to address the above issues are reviewed. The review shows that most of the algorithms used one parameter to find the optimal number of mobile agents in multi-agent itinerary planning without utilizing other parameters. More importantly, the review showed that theses algorithms did not take into account the security of the data gathered by the mobile agent. Accordingly, we indicated the limitations of each proposed algorithm and new directions are provided for future research. [ABSTRACT FROM AUTHOR]

This article discusses how a hybrid algorithm based on a genetic algorithm (GA) and linear programming helps minimize distribution costs for the Scandinavian pulp producer Sodra Cell. The GA considers a planner's perspective by defining an individual schedule as the sequences of routes for all ships in a fixed fleet. Because the GA operates on a pool of potential solutions, constantly adjusting them to better fit the requirements, it can suggest multiple solutions. Furthermore, it can serve as a core algorithm in a support system where one can apply small changes to the set of constraints without a cold start. Applying the GA to Sodra Cell's real-world case and comparing it to mixed-integer-programming approach revealed competitive results. INSET: Related Work in Genetic Algorithms..

Data races are a common problem in concurrent and multi-threaded programming. Experience shows that the classical notion of a data race is not powerful enough to capture certain types of inconsistencies occurring in practice. This paper investigates data races on a higher abstraction layer. This enables detection of inconsistent uses of shared variables, even if no classical race condition occurs. For example, a data structure representing a coordinate pair may have to be treated atomically. By lifting the meaning of a data race to a higher level, such problems can now be covered. The paper defines the concepts ‘view’ and ‘view consistency’ to give a notation for this novel kind of property. It describes what kinds of errors can be detected with this new definition, and where its limitations are. It also gives a formal guideline for using data structures in a multi-threaded environment. © US Government copyright [ABSTRACT FROM AUTHOR]

By increasing load demands and extending power networks to respond to customers' needs, the complexity and integration of power systems have been boosted, increasing the system's short circuit current level, which may threaten the network's reliability. Over the years, some approaches have been proposed to deal with this issue. Proper examples are reconfiguring networks, increasing circuit breakers (CBs) capacity, and implementing fault current limiters (FCLs). Reconfiguration and increasing CB rating have applied exorbitant costs to the system, and in some cases, it may be infeasible. Hence, FCLs can play a pivotal role in the mitigation of the fault current level, but the effectiveness of FCLs depends on the numbers and impedance of FCLs. In this paper, a novel and multi-objective approach is presented to optimize three objective functions simultaneously: decreasing the short circuit level, increasing the systems reliability level, and minimizing the costs of FCL installation. The adaptive penalty factor and Pareto-based multi-objective evolutionary algorithm based on decomposition are used to optimize the objectives mentioned above. Numerical and graphical results of optimization studies in MATLAB software on the IEEE RTS 24-Bus system confirm the proposed method's competence. [ABSTRACT FROM AUTHOR]