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The occurrence of hotspots in photovoltaic panels is one of the most common problems of solar power plants, which reduces the output power of photovoltaic arrays and can also cause irreparable damage to the solar cells. There are several ways to identify hotspots, including using custom datasets using thermographic camera images, which will be later used to teach YOLO and Faster R-CNN computer vision algorithms. In practice, it is observed that the YOLO algorithm is many times faster than the Faster R-CNN in high-density solar panels. Therefore, the applied method is the safest choice for automatic hotspot detection in large-scale photovoltaic power plants to improve overall efficiency. In this paper, by comparing the performance of methods such as Faster R-CNN with YOLO, we concluded that the YOLO algorithm has far better advantages in terms of quality of detection, and speed. Therefore, this factor makes the use of YOLO significantly helps to speed up the troubleshooting of solar modules caused by hotspots, and this factor improves the efficiency of solar power plants in the long run. Meanwhile, in the studies for this paper, the results extracted by Python have been optimized as an algorithm to be used for hotspot detection. [ABSTRACT FROM AUTHOR]

Nowadays, Web browsers have become an important carrier of 3D model visualization because of their convenience and portability. During the process of large-scale 3D model visualization based on Web scenes with the problems of slow rendering speed and low FPS (Frames Per Second), occlusion culling, as an important method for rendering optimization, can remove most of the occluded objects and improve rendering efficiency. The traditional occlusion culling algorithm (TOCA) is calculated by traversing all objects in the scene, which involves a large amount of repeated calculation and time consumption. To advance the rendering process and enhance rendering efficiency, this paper proposes an occlusion culling with three different optimization methods based on theWebGPU Computing Pipeline. Firstly, for the problem of large amounts of repeated calculation processes in TOCA, these units are moved from the CPU to the GPU for parallel computing, thereby accelerating the calculation of the Potential Visible Sets (PVS); Then, for the huge overhead of creating pipeline caused by too many 3D models in a certain scene, the Breaking Occlusion Culling Algorithm (BOCA) is introduced, which removes some nodes according to building a Hierarchical Bounding Volume (BVH) scene tree to reduce the overhead of creating pipelines; After that, the structure of the scene tree is transmitted to the GPU in the order of depth-first traversal and finally, the PVS is obtained by parallel computing. In the experiments, 3D geological models with five different scales from 1:5,000 to 1:500,000 are used for testing. The results show that the proposed methods can reduce the time overhead of repeated calculation caused by the computing pipeline creation and scene tree recursive traversal in the occlusion culling algorithm effectively, with 97% rendering efficiency improvement compared with BOCA, thereby accelerating the rendering process on Web browsers. [ABSTRACT FROM AUTHOR]

Intelligent transportation systems (ITSs) are becoming increasingly popular as they support efficient coordinated transport. ITSs aim to improve the safety, efficiency and reliability of road transportation through integrated approaches to the exchange of relevant information. Mobile adhoc networks (MANETs) and vehicle ad-hoc networks (VANETs) are integral components of ITS. The VANET is composed of interconnected vehicles with sensitivity capabilities to exchange traffic, positioning, weather and emergency information. One of the main challenges in VANET is the reliable and timely dissemination of information between vehicular nodes to improve decision-making processes. This paper illustrates challenges in VANET and reviews possible solutions to improve road safety control and management using V2V and V2I communications. This paper also summarizes existing rules-based and optimized-based solutions, including reducing the effect of mixed environments, obstacles, malfunctions and short wireless ranges on transportation efficiency and reducing false messages that cause unintended vehicle actions and unreliable transportation systems. Additionally, an event simulation algorithm was designed to maximize the benefits of exchangeable messages among vehicles. Furthermore, simulatedVANETenvironmentswere developed to demonstrate how the algorithm can be used for transformable messages. Experimental results show that coupling of both V2V and V2I messages yielded better results in terms of end-to-end delay and average time. Future research directions were highlighted to be taken into account in the development of ITS and intelligent routing mechanisms. [ABSTRACT FROM AUTHOR]

Black fungus is a rare and dangerous mycology that usually affects the brain and lungs and could be life-threatening in diabetic cases. Recently, some COVID-19 survivors, especially those with co-morbid diseases, have been susceptible to black fungus. Therefore, recovered COVID-19 patients should seek medical support when they notice mucormycosis symptoms. This paper proposes a novel ensemble deep-learning model that includes three pre-trained models: reset (50), VGG (19), and Inception. Our approach is medically intuitive and efficient compared to the traditional deep learning models. An image dataset was aggregated from various resources and divided into two classes: a black fungus class and a skin infection class. To the best of our knowledge, our study is the first that is concerned with building black fungus detection models based on deep learning algorithms. The proposed approach can significantly improve the performance of the classification task and increase the generalization ability of such a binary classification task. According to the reported results, it has empirically achieved a sensitivity value of 0.9907, a specificity value of 0.9938, a precision value of 0.9938, and a negative predictive value of 0.9907. [ABSTRACT FROM AUTHOR]

Community detection is a highly active research area that aims to identify groups of vertices with similar properties or interests within complex real-world networks. Over the years, a large number of papers have been published, resulting in the development of various community detection algorithms that consider factors such as the type of networks, the nature of communities, and applications. Despite numerous relevant review and survey papers, the literature lacks a comprehensive analysis of the computational complexity of existing community detection algorithms. This review aims to address this gap by providing a more detailed analysis and evaluation of the computational complexity of hierarchical clustering algorithms for community detection, including two main categories: agglomerative and divisive algorithms. We also highlight the main theoretical concepts, emphasizing the benefits and drawbacks of each approach, both in theory and in practical applications. This review helps researchers and practitioners in this field better understand valuable information on the differences and unique features of community detection algorithms with hierarchical community structure. [ABSTRACT FROM AUTHOR]

This paper proposes a novel theory, coined as Topological Tail Dependence Theory, that links the mathematical theory behind Persistent Homology (PH) and the financial stock market theory. This study also proposes a novel algorithm to measure topological stock market changes as well as the incorporation of these topological changes into forecasting realized volatility (RV) models to improve their forecast performance during turbulent periods. The results of the empirical experimentation of this study provide evidence that the predictions drawn from the Topological Tail Dependence Theory are correct and indicate that the employment of PH information allows nonlinear and neural network models to better forecast RV during a turbulent period. [ABSTRACT FROM AUTHOR]

Today, the use of digital medicine is becoming more and more common in medicine. With the use of digital medicine, health data can be shared, processed, and visualized using computer algorithms. One of the problems currently facing digital medicine is the rapid transmission of large amounts of data and their appropriate visualization, even in 3D. Advances in technology offer the possibility to use new image processing, networking, and visualization solutions for the evaluation of medical samples. Because of the resolution of the samples, it is not uncommon that it takes a long time for them to be analyzed, processed, and shared. This is no different for 3D visualization. In order to be able to display digitalized medical samples in 3D at high resolution, a computer with computing power that is not necessarily available to doctors and researchers is needed. COVID-19 has shown that everyday work must continue even when there is a physical distance between the participants. Real-time network streaming can provide a solution to this, by creating a 3D environment that can be shared between doctors/researchers in which the sample being examined can be visualized. In order for this 3D environment to be available to everyone, it must also be usable on devices that do not have high computing capacity. Our goal was to design a general-purpose solution that would allow users to visualize large amounts of medical imaging data in 3D, regardless of the computational capacity of the device they are using. With the solution presented in this paper, our goal was to create a 3D environment for physicians and researchers to collaboratively evaluate 3D medical samples in an interdisciplinary way. [ABSTRACT FROM AUTHOR]

In this paper, we continue the study of efficient algorithms for the computation of the Riemann zeta function on the complex plane. We introduce two precalculated arrays-based modifications of MB-method. We perform numerical experiments with these algorithms using Zetafast as a benchmark and apply the algorithms for the visualizations of fractal structures associated with the Riemann zeta function. [ABSTRACT FROM AUTHOR]

Radial Basis Function Neural Network (RBFNN) ensembles have long suffered from non-efficient training, where incorrect parameter settings can be computationally disastrous. This paper examines different evolutionary algorithms for training the Symbolic Radial Basis Function Neural Network (SRBFNN) through the behavior's integration of satisfiability programming. Inspired by evolutionary algorithms, which can iteratively find the nearoptimal solution, different Evolutionary Algorithms (EAs) were designed to optimize the producer output weight of the SRBFNN that corresponds to the embedded logic programming 2Satisfiability representation (SRBFNN-2SAT). The SRBFNN's objective function that corresponds to Satisfiability logic programming can be minimized by different algorithms, including Genetic Algorithm (GA), Evolution Strategy Algorithm (ES), Differential Evolution Algorithm (DE), and Evolutionary Programming Algorithm (EP). Each of these methods is presented in the steps in the flowchart form which can be used for its straightforward implementation in any programming language. With the use of SRBFNN-2SAT, a training method based on these algorithms has been presented, then training has been compared among algorithms, which were applied in Microsoft Visual C++ software using multiple metrics of performance, including Mean Absolute Relative Error (MARE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), Mean Bias Error (MBE), Systematic Error (SD), Schwarz Bayesian Criterion (SBC), and Central Process Unit time (CPU time). Based on the results, the EP algorithm achieved a higher training rate and simple structure compared with the rest of the algorithms. It has been confirmed that the EP algorithm is quite effective in training and obtaining the best output weight, accompanied by the slightest iteration error, which minimizes the objective function of SRBFNN-2SAT. [ABSTRACT FROM AUTHOR]

Due to the anonymity of blockchain, frequent security incidents and attacks occur through it, among which the Ponzi scheme smart contract is a classic type of fraud resulting in huge economic losses. Machine learningbased methods are believed to be promising for detecting ethereum Ponzi schemes. However, there are still some flaws in current research, e.g., insufficient feature extraction of Ponzi scheme smart contracts, without considering class imbalance. In addition, there is room for improvement in detection precision. Aiming at the above problems, this paper proposes an ethereum Ponzi scheme detection scheme through opcode context analysis and adaptive boosting (AdaBoost) algorithm. Firstly, this paper uses the n-gram algorithm to extract more comprehensive contract opcode features and combine them with contract account features, which helps to improve the feature extraction effect. Meanwhile, adaptive synthetic sampling (ADASYN) is introduced to deal with class imbalanced data, and integrated with the Adaboost classifier. Finally, this paper uses the improved AdaBoost classifier for the identification of Ponzi scheme contracts. Experimentally, this paper tests our model in real-world smart contracts and compares it with representative methods in the aspect of F1-score and precision. Moreover, this article compares and discusses the state of art methods with our method in four aspects: data acquisition, data preprocessing, feature extraction, and classifier design. Both experiment and discussion validate the effectiveness of our model. [ABSTRACT FROM AUTHOR]

Rock condition perception based on tunnel boring machine (TBM) data is of great importance for not only ensuring tunnel boring safety but also improving construction efficiency. The prediction of TBM boring responses (i.e., torque and total thrust of the cutterhead) largely determines the reliability of rock condition perception. In this paper, a new architecture of a two-dimensional convolutional neural network (2D-CNN) with a dual-input strategy is proposed to predict the TBM responses. The TBM Lot 3 of the Yinsong project in Jilin province, China, is taken as the case study in this paper. Two types of models that follow different learning strategies are compared: one is defined as the point model, which only learns data of the stable phase, and the other is defined as the line model, which learns data from both the loading and stable boring phases. The line model is further improved by the weighted loss function method. The results indicate that the strategy of learning data from both the loading phase and stable boring phase and increasing the weight of samples from the stable phase is shown to be optimal in predicting TBM boring responses. In terms of learning strategies, the line model can learn the influence of active control parameters on passive response parameters, but the point model cannot. In terms of machine learning algorithms, 2D-CNN has the best performance, with R2 values of 0.865 and 0.923 for torque and total thrust, respectively. The proposed line model can overcome the problem that the traditional model failed to learn the influence of control parameters. Such a model can provide a solid base for the timely optimization of the control parameters in TBM boring process. [ABSTRACT FROM AUTHOR]

The increasing application of medical robots in the healthcare sector underscores the critical importance of intent recognition in enhancing the interaction and assistance capabilities of these robots. Traditional intent recognition methods utilize convolutional neural networks (CNNs) for text analysis but often fall short in capturing global features, resulting in incomplete information. To address this challenge, this paper introduces an innovative approach by combining an enhanced CNN with bidirectional gated recurrent units (BiGRU) to construct a dual-channel short-text intent recognition model. This model effectively leverages both local and global features to more accurately comprehend user needs and intentions. Experimental results demonstrate that this model excels, achieving an accuracy rate of 96.68% and an F1 score of 96.67% on the THUCNews_Title dataset. In comparison to conventional intent recognition models, it exhibits significantly improved performance, thereby providing substantial support for medical robots in patient care and assisting healthcare professionals. [ABSTRACT FROM AUTHOR]

Object detection is the use of computer to find out all the objects of interest in the image, determine their categories and locations, is one of the core problems in the field of computer vision. Traditional animal image target detection usually adopts the sliding window method, but due to the different sizes of the input images, this method has some problems such as insufficient training samples, low detection accuracy and slow speed. In order to solve such problems, based on the development of deep learning in recent years, this paper proposes an object detection algorithm based on convolutional neural network. YOLOv5 was used to effectively distinguish, identify and mark animal categories, which accelerated the training of the model and greatly improved the accuracy of target detection. Through the analysis of experimental data, it was concluded that the algorithm studied in this paper had good performance and good target detection results. Finally, the key problems of object detection research are summarized, and the future development trend of this field is prospected. When the number of training rounds is 30, the accuracy rate has reached about 70%, and after 50 rounds of training, some accuracy can reach 90%, which is excellent and better than other traditional algorithms. [ABSTRACT FROM AUTHOR]

Derivative-free iterative methods are useful to approximate the numerical solutions when the given function lacks explicit derivative information or when the derivatives are too expensive to compute. Exploring the convergence properties of such methods is crucial in their development. The convergence behavior of such approaches and determining their practical applicability require conducting local as well as semi-local convergence analysis. In this study, we explore the convergence properties of a sixth-order derivative-free method. Previous local convergence studies assumed the existence of derivatives of high order even when the method itself was not utilizing any derivatives. These assumptions imposed limitations on its applicability. In this paper, we extend the local analysis by providing estimates for the error bounds of the method. Consequently, its applicability expands across a broader range of problems. Moreover, the more important and challenging semi-local convergence not investigated in earlier studies is also developed. Additionally, we survey recent advancements in this field. The outcomes presented in this paper can be proved valuable to practitioners and researchers engaged in the development and analysis of derivative-free numerical algorithms. Numerical tests illuminate and validate further the theoretical results. [ABSTRACT FROM AUTHOR]

New versions of HTTP protocols have been developed to overcome many of the limitations of the original HTTP/1.1 protocol and its underlying transport mechanism over TCP. In this paper, we investigated the performance of modern Internet protocols such as HTTP/2 over TCP and HTTP/3 over QUIC in high-latency satellite links. The goal was to uncover the interaction of the new features of HTTP such as parallel streams and optimized security handshake with modern congestion control algorithms such as CUBIC and BBR over high-latency links. An experimental satellite network emulation testbed was developed for the evaluation. The study analyzed several user-level web performance metrics such as average page load time, First Contentful Paint and Largest Contentful Paint. The results indicate an overhead problem with HTTP/3 that becomes more significant when using a loss-based congestion control algorithm such as CUBIC which is widely used on the Internet. Also, the results highlight the significance of the web page structure and how objects are distributed in it. Among the various Internet protocols evaluated, the results show that HTTP/3 over QUIC will perform better by an average of 35% than HTTP/2 over TCP in satellites links specifically with a more aggressive congestion algorithm such as BBR. This can be attributed to the non-blocking stream multiplexing feature of QUIC and the reduced TLS handshake of HTTP/3. [ABSTRACT FROM AUTHOR]

In this paper, we establish some embedding relationships between Muckenhoupt and Gehring classes Ap and Gr by proving transition and inclusion relations. We also identify the exact range of τ > 1 for which wτ ∈ Ap. Additionally, we show that the weights that satisfy the Ap -condition also meet the A∞ -condition. Next, we prove the Jones factorization property of Ap weights in terms of two A1 weights by employing the discrete Rubio De Francia iterated algorithm. Finally, we determine the specific ranges of indices (sharp exponents) for which w belongs to Gr (Ap) if w belongs to Ap (Gr) and the precise range of q< p for which w belongs to Aq given that it belongs to Ap. [ABSTRACT FROM AUTHOR]

This research paper will start by presenting background information to risk management in the manufacturing sector followed by review of the literature for state of the art knowledge in this subject area. An algorithm for generic risk management system will be presented highlighting the various stages from establishing the risk context, assessment, evaluation and managing it. Finally, a risk management framework will be developed showing the sequence for assessing potential risk factors associated with the typical manufacturing process This research paper will start by presenting background information to risk management in the manufacturing sector followed by review of the literature for state of the art knowledge in this subject area. An algorithm for generic risk management system will be presented highlighting the various stages from establishing the risk context, assessment, evaluation and managing it. Finally, a risk management framework will be developed showing the sequence for assessing potential risk factors associated with the typical manufacturing process This research paper will start by presenting background information to risk management in the manufacturing sector followed by review of the literature for state of the art knowledge in this subject area. An algorithm for generic risk management system will be presented highlighting the various stages from establishing the risk context, assessment, evaluation and managing it. Finally, a risk management framework will be developed showing the sequence for assessing potential risk factors associated with the typical manufacturing process This research paper will start by presenting background information to risk management in the manufacturing sector followed by review of the literature for state-of-the-art knowledge in this subject area. An algorithm for generic risk management system will be presented highlighting the various stages from establishing the risk context, assessment, evaluation and managing it. Finally, a risk management framework will be developed showing the sequence for assessing potential risk factors associated with the typical manufacturing process This research paper will start by presenting background information to risk management in the manufacturing sector followed by review of the literature for state of the art knowledge in this subject area. An algorithm for generic risk management system will be presented highlighting the various stages from establishing the risk context, assessment, evaluation and managing it. Finally, a risk management framework will be developed showing the sequence for assessing potential risk factors associated with the typical manufacturing process Project activities are carried out in conditions of uncertainty. Therefore, knowledge of the basics of project risk management necessary in the professional activities of economists, managers and entrepreneurs. At the stage of creating a business in the process of developing a business plan, an entrepreneur needs to evaluate economic efficiency and risks of this business project, develop a risk management plan, plan a response to possible risks. When the established enterprise is located at the stage of its further development, then in the process of implementation various projects - to expand business, organizational projects - risk management is also required. In addition, at the stage of decline, when the organization is in crisis, it is necessary to develop projects for the improvement of this enterprise, in the process implementation of which risk management is required. All this emphasizes the relevance of the issues considered in this study guide. [ABSTRACT FROM AUTHOR]

Multi-area economic distribution of load (MAED) is a generalization of the basic problems of economic distribution of load (ED), which evaluate the optimal power distribution of multiple areas in terms of operating costs. In the following parts of this paper, the concept of MAED is extended to multi-regional economic/environmental distribution (MAEED) by considering environmental issues. The purpose of MAEED is to distribute power among different areas by minimizing operating costs and emissions at the same time. In this paper, first the MAEED problem was expressed as a formula; then, a multi-objective and advanced particle optimization (MOPSO) algorithm was expressed to deduce its Pareto optimal solutions, during the optimization process. The transmission limits of communication lines were expressed as a set of constraints to ensure the security of the system. In addition, the rotating storage area was to increase the reliability of the system. A reserve sharing pattern was applied to ensure each region's capability to meet reserve demand. The simulation was based on the four-zone experimental power system, and it showed the effect of the proposed optimization process and the effects caused by different issues. [ABSTRACT FROM AUTHOR]

Intra prediction algorithm used in High Efficiency Video Coding (HEVC) standard has very high computational complexity. Therefore, in this paper, a novel technique is proposed for reducing amount of computations performed by HEVC intra prediction algorithm and, therefore, reducing energy consumption of HEVC intra prediction hardware. The proposed technique significantly reduced the amount of computations performed by 4x4, 8x8, 16x16 and 32x32 luminance angular prediction modes. The proposed technique does not affect the PSNR and bit rate. In this paper, a low energy HEVC intra prediction hardware for 4x4, 8x8, 16x16 and 32x32 angular prediction modes is also designed and implemented using Verilog HDL. The proposed technique significantly reduced the energy consumption of the HEVC intra prediction hardware. Therefore, it can be used in portable consumer electronics products that require a real-time HEVC encoder. [ABSTRACT FROM PUBLISHER]

This paper comprehensively analyzes the characteristics of flexible job shop scheduling problem (FJSSP), takes the dynamic factors in the actual scheduling process such as the arrival and departure of jobs, the breakdown and recovery of machines into account at the same time, and establishes a new dynamic multi-objective mathematical model. Take the Squirrel Search Algorithm (SSA) as the core evolution strategy, and combine the multi-objective framework and the dynamic processing technology to solve the established mathematical model. Experimental results show that the mathematical model proposed in this paper can solve the flexible job shop scheduling problem effectively. Compared with other mathematical models, the mathematical model established in this paper can keep better balance between the efficiency and stability. [ABSTRACT FROM AUTHOR]

Android malware is one of the most dangerous threats on the Internet. It has been on the rise for several years. As a result, it has impacted many applications such as healthcare, banking, transportation, government, e-commerce, etc. One of the most growing attacks is on Android systems due to its use in many devices worldwide. De-spite significant efforts in detecting and classifying Android malware, there is still a long way to improve the detection process and the classification performance. There is a necessity to provide a basic understanding of the behavior displayed by the most common Android malware categories and families. Hence, understand the distinct ob-jective of malware after identifying their family and category. This paper proposes an effective systematic and functional parallel machinelearning model for the dynamic detection of Android malware categories and families. Standard machine learning classifiers are implemented to analyze a massive malware dataset with 14 major mal-ware categories and 180 prominent malware families of the CCCS-CICAndMal2020 on dynamic layers to detect Android malware categories and families. The paper ex-periments with many machine learning algorithms and compares the proposed model with the most recent related work. The results indicate more than 96 % accuracy for Android Malware Category detection and more than 99% for Android Malware family detection overperforming the current related methods. The proposed model offers a highly accurate method for dynamic analysis of Android malware that cuts down the time required to analyze smartphone malware. [ABSTRACT FROM AUTHOR]

Literature search is one of the most important steps of academic research. With more than 100,000 papers published each year just in computer science, performing a complete literature search becomes a Herculean task. Some of the existing approaches and tools for literature search cannot compete with the characteristics of today’s literature, and they suffer from ambiguity and homonymy. Techniques based on citation information are more robust to the mentioned issues. Thus, we recently built a Web service called the advisor, which provides personalized recommendations to researchers based on their papers of interest. Since most recommendation methods may return redundant results, diversifying the results of the search process is necessary to increase the amount of information that one can reach via an automated search. This article targets the problem of result diversification in citation-based bibliographic search, assuming that the citation graph itself is the only information available and no categories or intents are known. The contribution of this work is threefold. We survey various random walk--based diversification methods and enhance them with the direction awareness property to allow users to reach either old, foundational (possibly well-cited and well-known) research papers or recent (most likely less-known) ones. Next, we propose a set of novel algorithms based on vertex selection and query refinement. A set of experiments with various evaluation criteria shows that the proposed γ-RLM algorithm performs better than the existing approaches and is suitable for real-time bibliographic search in practice. [ABSTRACT FROM AUTHOR]

: In computational science literature including, e.g., bioinformatics, computational statistics or machine learning, most published articles are devoted to the development of “new methods”, while comparison studies are generally appreciated by readers but surprisingly given poor consideration by many journals. This paper stresses the importance of neutral comparison studies for the objective evaluation of existing methods and the establishment of standards by drawing parallels with clinical research. The goal of the paper is twofold. Firstly, we present a survey of recent computational papers on supervised classification published in seven high-ranking computational science journals. The aim is to provide an up-to-date picture of current scientific practice with respect to the comparison of methods in both articles presenting new methods and articles focusing on the comparison study itself. Secondly, based on the results of our survey we critically discuss the necessity, impact and limitations of neutral comparison studies in computational sciences. We define three reasonable criteria a comparison study has to fulfill in order to be considered as neutral, and explicate general considerations on the individual components of a “tidy neutral comparison study”. R codes for completely replicating our statistical analyses and figures are available from the companion website http://www.ibe.med.uni-muenchen.de/organisation/mitarbeiter/020_professuren/boulesteix/plea2013. [ABSTRACT FROM AUTHOR]

The article presents a study which assesses the retrieval of MetOp-A/IASI carbon monoxide (CO) profiles. Two retrieval algorithms are examined which include the Software for a Fast Retrieval of IASI Data (SOFRID) and Fast Optimal Retrievals on Layers for IASI (FORLI). It also outlines CO profiles from the measurements of ozone, water vapour, carbon monoxide and nitrogen oxides by airbus in-service aircraft (MOZAIC) program.

The article presents a study which describes a cloud screening and quality control algorithm for charge coupled device (CCD) camera-based star photometer as detector. The study evaluates the proposed algorithm through lidar system-registered correlative measurements. Employing various aerosol conditions, the cloud screening algorithm is also assessed in the study.

This paper, based on differential privacy protecting K-means clustering algorithm, realizes privacy protection by adding data-disturbing Laplace noise to cluster center point. In order to solve the problem of Laplace noise randomness which causes the center point to deviate, especially when poor availability of clustering results appears because of small privacy budget parameters, an improved differential privacy protecting K-means clustering algorithm was raised in this paper. The improved algorithm uses the contour coefficients to quantitatively evaluate the clustering effect of each iteration and add different noise to different clusters. In order to be adapted to the huge number of data, this paper provides an algorithm design in MapReduce Framework. Experimental finding shows that the new algorithm improves the availability of the algorithm clustering results under the condition of ensuring individual privacy without significantly increasing its operating time. [ABSTRACT FROM AUTHOR]

Overlapping community is a response to the real network structure in social networks and in real society in order to solve the problems such as the parameters of the existing overlapping community discovery algorithm being too large, excessive overlap and no guarantee of stability of multiple runs. In this paper, the method of calculating the node degree of membership was proposed, and an overlapping community discovery algorithm based on the local optimal expansion cohesion idea was designed. Firstly, the initial core community was constructed with the highest importance node and its neighbor nodes. Secondly, the core community was extended by node attribution degree until the termination condition of the algorithm was satisfied. Finally, the experimental results were compared with the existing algorithms. The experiments show that the result of the division by the improved algorithm has been significantly improved compared to the other algorithms, and the community structure after the division is more reasonable. [ABSTRACT FROM AUTHOR]

The article discusses the application of the information operator approach for retrieving the vertical profile information from ground-based Fourier Transform infrared (FTIR) measurements. It explores the adoption and testing of the algorithm within the retrieval code, SFIT2. It also examines the feasibility of the method when applied to FTIR spectra taken in the frame of the Network for the Detection of Atmospheric Composition Change (NDACC) at Ile de La R'eunion, France.

Rather than seeking to recuperate the ideal of a digital public sphere or lament its demise with the rise of social media platforms, in this paper I seek to identify the dangers of precisely this insistence to imagine the Internet as a public sphere. It is this curious insistence and persistence that, I claim, may feed into precisely those post-truth media dynamics such critical accounts worry about and rally against. The success of viral conspiracy narratives like Pizzagate and QAnon, as well as other forms of mis- and disinformation, hinges not (only) on the absence or distortion of a healthy democratic public sphere, as is typically assumed, but (also) on its persistence as an imaginary in an environment that obeys an altogether different set of logics, namely that of ‘communicative capitalism’ and ‘information warfare.’ Whereas the former has drawn most critical attention in connection to current post-truth dynamics (e.g., the effects of targeted advertising and the role of algorithms in creating polarizing echo chambers and filter bubbles), I will instead focus on the latter. The unique problem and ‘cunning’ of what I refer to as ‘post-truth conspiracism’ is that it draws on idea(l)s of digital publicness to establish its own epistemic legitimacy, as well as derive its unique powers of persuasion, while also mobilizing the full tactical arsenal of information warfare in a global attention economy. The resulting weaponization of digital public sphere imaginaries complicates attempts to recuperate the idea(l) of a digital public sphere as a solution to a ‘polluted’ information environment. [ABSTRACT FROM AUTHOR]

Dementia, not a particular disease but rather a gathering of conditions which ascribes the debilitation of atleast two cerebrum capacities, cognitive decline and memory judgment has became ubiquous all over the world. Achieving accuracy and timely diagnosis is a major challenge in dementia detection. The recent advancements in neuroimaging techniques has setup some benchmarks. The amalgamation of computer aided algorithms with various imaging modalities proved their significance in dementia detection and classification. In this paper, a rigrous review of existing work in dementia detection and classification is presented based on the existing literature. Additionally this paper surveys the most important AI (Artificial Intellligence) and profound learning approaches for early identification of dementia. [ABSTRACT FROM AUTHOR]

Linear-time temporal logic on finite traces (LTLf) is rapidly becoming a de-facto standard to produce specifications in many application domains (including planning, business process management, run-time monitoring, and reactive synthesis). Several studies have challenged the satisfiability problem thus far. In this paper, we focus instead on unsatisfiable LTLf specifications, with the objective of extracting the subset of formulae that cause inconsistencies within them, i.e., the unsatisfiable cores. We provide four algorithms to this end, which leverage the adaptation of a range of state-of-the-art algorithms to LTLf satisfiability checking. We implement those algorithms extending the respective implementations and carry out an experimental evaluation on a set of reference benchmarks, restricting to the unsatisfiable specifications. The results put in evidence that the different algorithms and tools exhibit complementary features determining their efficiency and efficacy. Indeed, our findings suggest exploring different strategies and algorithmic solutions for the extraction of unsatisfiable cores from LTLf specifications, thus confirming the challenging and multi-faceted nature of this problem. [ABSTRACT FROM AUTHOR]

In recent years, there has been a proliferation of competing conceptions of what it means for a predictive algorithm to treat its subjects fairly. Most approaches focus on explicating a notion of group fairness, i.e. of what it means for an algorithm to treat one group unfairly in comparison to another. In contrast, Dwork et al. (2012) attempt to carve out a formalised conception of individual fairness, i.e. of what it means for an algorithm to treat an individual fairly or unfairly. In this paper, I demonstrate that the conception of individual fairness advocated by Dwork et al. is closely related to a criterion of group fairness, called ‘base rate tracking’, introduced in Eva (2022). I subsequently show that base rate tracking solves some fundamental conceptual problems associated with the Lipschitz criterion, before arguing that group level fairness criteria are at least as powerful as their individual level counterparts when it comes to diagnosing algorithmic bias. [ABSTRACT FROM AUTHOR]

The task of modeling and analyzing intercepted multifunction radars (MFRs) pulse trains is vital for cognitive electronic reconnaissance. Existing methodologies predominantly rely on prior information or heavily constrained models, posing challenges for non-cooperative applications. This paper introduces a novel approach to model MFRs using a Bayesian network, where the conditional probability density function is approximated by an autoregressive kernel mixture network (ARKMN). Utilizing the estimated probability density function, a dynamic programming algorithm is proposed for denoising and detecting change points in the intercepted MFRs pulse trains. Simulation results affirm the proposed method's efficacy in modeling MFRs, outperforming the state-of-the-art in pulse train denoising and change point detection. [ABSTRACT FROM AUTHOR]

A road multi‐object detection algorithm is one of the core algorithms for intelligent road cleaning robots relying on machine vision. Most existing object detection algorithms analyse all image regions and finally calculate the category and location of each object. However, it is not necessary to analyse all areas of the image when detecting objects on the road surface where the background changes little, and the number of objects is small because there will be a lot of invalid calculations. If we can perform targeted local analysis on images instead of analysing all image regions, it will improve the detection efficiency. Therefore, this paper proposes a multi‐object detection method using a binocular camera and a convolutional neural network (CNN) that effectively reduces invalid calculations during the detection and improves detection efficiency. In the developed method, the binocular vision image acquired by the binocular camera is stereo matched and equalized, while linear regression and coordinate transformation eliminate the angle of the camera pair concerning the road surface. Then, the coordinates of the regions of interest (ROI) is calculated in the left vision image and the features within the ROI is extracted from the corresponding CNN's feature map. Next, ROI pooling resizes the extracted feature maps of different sizes to the same size, which are then input to the fully connected layers to output the results. The proposed binocular network and faster R‐CNN (VGG16) are trained and tested on a dataset involving 1000 road waste images. The experimental results demonstrate that the developed binocular network improves the detection accuracy and speed by 28.56% and 78.39%, respectively, compared with faster R‐CNN (VGG16), providing a reliable basis for a machine vision‐based intelligent road cleaning robot. [ABSTRACT FROM AUTHOR]

This paper presents a comprehensive approach to optimizing furniture design processes and facilitating wood identification through the development of a mobile application. The application is designed to provide users with intuitive tools and resources for informed decisionmaking in furniture design and wood selection. The first component of the mobile application focuses on optimizing furniture design through furniture size and quality consideration. This encompasses the creation of an intuitive user interface that enables users to specify their furniture design preferences, including material selection criteria. A comprehensive wood database is meticulously curated, furnishing users with comprehensive details on wood sizes, qualities, and thicknesses, thereby aiding in material selection and waste reduction initiatives. Algorithms are employed to recommend optimal wood sizes, considering factors such as material selection, waste reduction, and cost analysis. Visual representation tools are included, allowing users to preview furniture designs with different wood options, facilitating informed decisionmaking. Furthermore, feedback mechanisms have been integrated to enable users to provide feedback on recommended wood sizes and qualities, thereby facilitating continuous enhancement and refinement of recommendations. The second component aims to identify the type of wood by its appearance. Image processing features have been developed to precisely extract visual characteristics from wood sample images, thereby aiding in the identification of wood species. Machine learning models are trained to classify wood species based on visual characteristics obtained from images, thereby enhancing the precision of identification. User interaction tools have been designed to ensure ease of use and accessibility for users in capturing and submitting wood sample images. Within the application, educational resources and materials are provided to help users learn about different wood species, aiding their understanding and selection process. [ABSTRACT FROM AUTHOR]

To elucidate the combinatorial architecture of neural codes, the neural ideal , an algebraic object, was introduced. Represented in its canonical form, provides a succinct characterization of the inherent receptive field architecture within the code. The polynomials in are also instrumental in determining the relationships among the neurons' receptive fields. Consequently, the computation of the collection of canonical forms is pivotal. In this paper, based on the study of relations between pseudo-monomials, the authors present a computationally efficient iterative algorithm for the canonical forms of the neural ideal. Additionally, we introduce a new relationship among the neurons' receptive fields, which can be characterized by if-and-only-if statements, relating both to and to a larger ideal of a code . [ABSTRACT FROM AUTHOR]

An important prerequisite for improving the reliability of lung cancer surveillance and clinical interventions is accurate lung nodule segmentation. Although deep learning is effective at performing medical image segmentation, lung CT image heterogeneity, nodule size, shape, and location variations, convolutional localized feature extraction characteristics, the receptive field limitations of continuous downsampling, lesion edge information losses, fuzzy boundary segmentation challenges, and the low segmentation accuracy achieved when segmenting lung CT images using deep learning remain. An edge-enhanced multiscale Sobel coordinate attention-atrous spatial convolutional pooling pyramid V-Net (SCA-VNet) algorithm for lung nodule segmentation was proposed to solve these problems. First, a residual edge enhancement module was designed, which was used to enhance the edges of the original data. Using an edge detection operator in combination with a residual module, this module could reduce data redundancy and alleviate the gray level similarity between the foreground and background. Then, a 3D atrous spatial convolutional pooling pyramid module set different expansion rates, which could obtain feature maps under different receptive fields and capture the multiscale information of the segmentation target. Finally, a three-dimensional coordinate attention network (3D CA-Net) module was added to the encoding and decoding paths to extract channel weights from multiple dimensions. This step propagated the spatial information in the coding layer to the subsequent layers, and it could reduce the loss of information during the forward propagation process. The proposed method achieved a Dice coefficient of 87.50% on the lung image database consortium and image database resource initiative (LIDC-IDRI). It significantly outperformed the existing lung nodule segmentation models (UGS-Net, REMU-Net, and multitask models) and compared favorably with the Med3D, CENet, and PCAM_Net segmentation models in terms of their Dice coefficients, which were 3.37%, 2.2%, and 1.43%, respectively. The experimental results showed that the proposed SCA-VNet model attained improved lung nodule segmentation accuracy and laid a good foundation for improving the early detection rate of lung cancer. [ABSTRACT FROM AUTHOR]

To assure operational safety in the airport apron area and track the process of ground service, it is necessary to analyze key targets and their activities in the airport apron surveillance videos. This research shows an activity identification algorithm for ground service objects in an airport apron area and proposes an improved YOLOv5 algorithm to increase the precision of small object detection by introducing an SPD-Conv (spath-to-depth-Conv) block in YOLOv5's backbone layer. The improved algorithm can efficiently extract the information features of small-sized objects, medium-sized objects, and moving objects in large scenes, and it achieves effective detection of activities of ground service in the apron area. The experimental results show that the detection average precision of all objects is more than 90%, and the whole class mean average precision (mAP) is 98.7%. At the same time, the original model was converted to TensorRT and OpenVINO format models, which increased the inference efficiency of the GPU and CPU by 55.3 and 137.1%, respectively. [ABSTRACT FROM AUTHOR]

High-pressure pipelines are critical for transporting hazardous materials over long distances, but they face threats from third-party interference activities. Preventive measures are implemented, but interference accidents can still occur, making the need for high-quality detection strategies vital. This paper proposes an end-to-end Artificial Intelligence of Things (AIoT) solution to detect potential interference threats in real time. The solution involves developing a smart visual sensor capable of processing images using state-of-the-art computer vision algorithms and transmitting alerts to pipeline operators in real time. The system's core is based on the object-detection model (e.g., You Only Look Once version 4 (YOLOv4) and DETR with Improved deNoising anchOr boxes (DINO)), trained on a custom Pipeline Visual Threat Assessment (Pipe-VisTA) dataset. Among the trained models, DINO was able to achieve the best Mean Average Precision (mAP) of 71.2% for the unseen test dataset. However, for the deployment on a limited computational-ability edge computer (i.e., the NVIDIA Jetson Nano), the simpler and TensorRT-optimized YOLOv4 model was used, which achieved a mAP of 61.8% for the test dataset. The developed AIoT device captures the image using a camera, processes on the edge using the trained YOLOv4 model to detect the potential threat, transmits the threat alert to a Fleet Portal via LoRaWAN, and hosts the alert on a dashboard via a satellite network. The device has been fully tested in the field to ensure its functionality prior to deployment for the SEA Gas use-case. The AIoT smart solution has been deployed across the 10km stretch of the SEA Gas pipeline across the Murray Bridge section. In total, 48 AIoT devices and three Fleet Portals are installed to ensure the line-of-sight communication between the devices and portals. [ABSTRACT FROM AUTHOR]

This paper aims to develop a MAGDM model using single-valued neutrosophic credibility matrix (SVNCM) energy in a SVNCM scenario. To do it, first, SVNCM energy and its score function are presented as a conceptual extension of existing single-valued neutrosophic matrix (SVNM) energy. Then, a MAGDM model is developed in terms of SVNCM energy and its score function in a SVNCM scenario and also its decision algorithm is provided to solve MAGDM problems with SVNCMs. Finally, the developed MAGDM model is applied in the school site selection problem as an actual example, then the comparative investigation of the decision results in the SVNM and SVNCM scenarios indicates the superiority of the developed model over existing MAGDM model. [ABSTRACT FROM AUTHOR]

Variable parameters (VP) schemes are the most effective adaptive schemes in increasing control charts' sensitivity to detect small to moderate shift sizes. In this paper, we develop four VP adaptive memory-type control charts to monitor multivariate multiple linear regression profiles. All the proposed control charts are single-chart (single-statistic) control charts, two use a Max operator and two use an SS (squared sum) operator to create the final statistic. Moreover, two of the charts monitor the regression parameters, and the other two monitor the residuals. After developing the VP control charts, we developed a computer algorithm with which the charts' time-to-signal and run-length-based performances can be measured. Then, we perform extensive numerical analysis and simulation studies to evaluate the charts' performance and the result shows significant improvements by using the VP schemes. Finally, we use real data from the national quality register for stroke care in Sweden, Riksstroke, to illustrate how the proposed control charts can be implemented in practice. [ABSTRACT FROM AUTHOR]

Purpose: This study explores the potential of computational design processes in creating contextually responsive envelopes for high-rise residential buildings in the Middle East. This includes considering both physical constraints and social preferences, with a focus on balancing sunlight exposure, privacy and views. Design/methodology/approach: A two-phase simulation study analyzed various exterior envelope systems in Baghdad high-rise buildings. The first phase examined two commonly used exterior envelopes – fully glazed and window-based – to assess sunlight exposure, privacy and views. In the second phase, a multi-objective optimization process was applied to derive contextually optimized design solutions addressing the challenges identified in the first phase. Findings: The study reveals that contextually optimized design solutions significantly improved direct sunlight exposure and privacy while maintaining satisfactory views. Although fully glazed exterior envelopes provided better-uninterrupted views, the optimized solutions offered more balanced performance across all factors, demonstrating the potential of computational design processes in creating contextually responsive building envelopes. Originality/value: This paper emphasizes the importance of considering both physical and social contexts in the development of algorithms for architecture in the Middle East. This paper supports a progressive interpretation of traditional building references and demonstrates how computational design processes can create contextually responsive building envelopes that satisfy social needs and provide better-performing buildings for inhabitants. [ABSTRACT FROM AUTHOR]

Wireless Body Area Networks (WBANs) are based on connected and dedicated sensor nodes for patient monitoring in the healthcare sector. The sensor nodes are implanted inside or outside the patient's body for sensing the vital signs and transmitting the sensed data to the end devices for decision-making. These sensor nodes use advanced communication technologies for data communication. However, they have limited capabilities in terms of computation power, battery life, storage, and memory, and these constraints make networks more vulnerable to security breaches and routing challenges. Important and sensitive information is exchanged over an unsecured channel in the network. Several devices are involved in handling the data in WBANs, including sink nodes, coordinator, or gateway nodes. Many cryptographic schemes have been introduced to ensure security in WBANs by using traditional confidentiality and key-sharing strategies. However, these techniques are not suitable for limited resource-based sensor nodes. In this paper, we propose a Lightweight Hybrid Cryptography Algorithm (LWHCA) that uses cryptographicbased techniques for WBAN networks to improve network security, minimize network overhead and delay issues, and improve the healthcare monitoring processes. The proposed solution is evaluated in a simulation scenario and compared with state-of-the-art schemes in terms of energy consumption, and ciphertext size. [ABSTRACT FROM AUTHOR]

Reinforcement learning has been applied to air combat problems in recent years, and the idea of curriculum learning is often used for reinforcement learning, but traditional curriculum learning suffers from the problem of plasticity loss in neural networks. Plasticity loss is the difficulty of learning new knowledge after the network has converged. To this end, we propose a motivational curriculum learning distributed proximal policy optimization (MCLDPPO) algorithm, through which trained agents can significantly outperform the predictive game tree and mainstream reinforcement learning methods. The motivational curriculum learning is designed to help the agent gradually improve its combat ability by observing the agent's unsatisfactory performance and providing appropriate rewards as a guide. Furthermore, a complete tactical maneuver is encapsulated based on the existing air combat knowledge, and through the flexible use of these maneuvers, some tactics beyond human knowledge can be realized. In addition, we designed an interruption mechanism for the agent to increase the frequency of decisionmaking when the agent faces an emergency. When the number of threats received by the agent changes, the current action is interrupted in order to reacquire observations and make decisions again. Using the interruption mechanism can significantly improve the performance of the agent. To simulate actual air combat better, we use digital twin technology to simulate real air battles and propose a parallel battlefield mechanism that can run multiple simulation environments simultaneously, effectively improving data throughput. The experimental results demonstrate that the agent can fully utilize the situational information to make reasonable decisions and provide tactical adaptation in the air combat, verifying the effectiveness of the algorithmic framework proposed in this paper. [ABSTRACT FROM AUTHOR]