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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]

The sun serves as the primary energy source, providing our planet with the essential energy for sustaining life. To efficiently harness this energy, photovoltaic cells, commonly known as PV cells, are employed. These cells convert the solar energy they receive into electrical energy. The operational point of the solar cell, delivering maximum output power, is referred to as the maximum power point (MPP). However, as light availability and temperature fluctuate throughout the day, the MPP also varies accordingly. To maintain constant operation at the MPP, Maximum Power Point Tracking (MPPT) algorithms are employed to trace the MPP during module operation. These algorithms can be categorized into four groups: classical, intelligent, optimization, and hybrid, based on the tracking algorithm utilized. Each MPPT algorithm, existing in these categories, comes with its own set of advantages and limitations. This paper extensively reviews fifteen algorithms categorized under different groups. The review concludes with a comparative analysis of these algorithms, considering various parameters such as cost, complexity, tracking accuracy, and sensed parameters in a succinct manner. The paper focuses on elucidating the necessity of MPPT algorithms, their classification as per existing literature, and a comparative assessment of the studied MPPT algorithms. This comprehensive review aims to address advancements in this field, paving the way for further research. [ABSTRACT FROM AUTHOR]

This article emphasises the importance of computer models in forensics and their potential for predicting criminal activity and preventing offences in the context of military confrontation. The purpose of this paper is to determine the possibility of using computer models to analyse and predict criminal activity during military confrontation. The research employed the following methods: historical method of studying law, logical approach, qualitative method, method of analysis, and method of legal modelling. The study found that forensic modelling is mainly used at the pre-trial investigation or trial stage. The analysis of modern armed conflicts gives grounds to conclude about the high efficiency of computer modelling in preventing wartime crimes. The academic novelty is the obtained results of using computer modelling to prevent potential offences as opposed to its widespread use at the stage of pre-trial investigation. The research prospects include the possibility of the potential study of forensic computer modelling algorithms for accurate prediction of types of offences during the war. [ABSTRACT FROM AUTHOR]

In the design of experiments, an optimal design should minimize the confounding between factorial effects, especially main effects and two-factor interaction effects. The general minimum lower-order confounding (GMC) criterion can be used to choose optimal regular designs based on the aliased component-number pattern. This paper aims to study the confounding properties of lower-order effects and provide several computer algorithms to calculate the lower-order confounding in regular designs. We provide a search algorithm to obtain GMC designs. Through python software, we conduct these algorithms. Some examples are analyzed to illustrate the effectiveness of the proposed algorithms. [ABSTRACT FROM AUTHOR]

Labeling is required by the interpretive system. When a head merges with a phrase, the head provides the label. However, lexical heads and T with poor inflectional features are too weak to be labels. Although insightful, this theory leaves at least one problem that needs prompt solutions: are there other kinds of weak heads? In this paper, we address this issue by proposing that phonological features play a crucial role in the labeling algorithm and by putting forward an additional version of weak heads. That is, a head that loses phonological features in the syntax is also weak. This approach to weak heads, together with the constraint that a structure must be labeled for interpretation, can capture the distribution of empty categories in topicalization, relativization, ellipsis, and other phenomena, some of which have not received enough scholarly attention. Therefore, our syntactic-phonological approach to labeling can open up new possibilities to account for the distribution of empty categories in a principled manner. [ABSTRACT FROM AUTHOR]

The development of a 3-DOF robotic arm powered by pneumatic artificial muscles is represented as an innovative approach in the field of robotics, with the advantages of lightweight and flexible design being combined with the power and control benefits of pneumatic actuation. The design process, challenges, and solutions that were encountered in the development of the robotic arm are outlined in this paper, with an emphasis on its potential applications in industrial and research settings being highlighted. Experimental results demonstrated that the arm can handle loads up to 250 grams, maintain precise horizontal positioning, achieve a 90° rotation at each joint, and operate within a minimum workspace radius of 250 mm. Advanced control algorithms, including sliding mode control, were implemented to optimize the arm's performance. The control system successfully maintained the set angles within a tolerance range, achieving a minimum angular error of approximately 0.7°. Additionally, the robotic arm's modular design is enabled to allow easy customization and scalability, making it possible for the arm to be tailored to a wide range of tasks, from precise laboratory work to more robust industrial applications. [ABSTRACT FROM AUTHOR]

The article presents a study which describes a Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm-based cloud/snow screening technique. The study applies spectral and spatial analysis in an effort to implement the aforementioned technique as part of MAIAC aerosol retrievals. Through the Moderate Resolution Imaging Spectroradiometer (MODIS) data analysis, the study discovers that unresolved clouds and snow suppress aerosol optical depth outliers.

The article presents a study which uses a polarimetric X-band radar in an effort to derive observations of tropical rain. The study describes the polarimetric X-band radar's specifications and discusses the calculation of polarimetric moments from disdrometer measurements. It also presents results from the data processing with the methods of Kalman filter (EKF) and ZPHI.

The article presents a study which describes two retrieval approaches to the assessment of long-term time series of methane (CH4) through the spectral fitting algorithm PROFFIT. The study applies the Fourier Transform InfraRed (FTIR) Hydrogen Fluoride (HF) and identifies the tropospheric column-averaged dry-air mole fractions of methane (troXCH4). It claims that the approaches are robust enough in retrieving middle infrared ground-based FTIR CH4 profile.

The article presents a study which investigates the proposed CloudIce Mission for the European Space Agency's (ESA) Earth Explorer 8 (EE8). The study cites scientific objectives and requirements and observation technique and technical requirements and describes retrieval algorithms for CloudIce data products. It also discusses the passive submillimeter-wave sensor.

The article presents a study on a comparison of desert dust satellite retrieval algorithms. The study describes several instruments including Advanced Along-Track Scanning Radiometer (AATSR), Medium Resolution Imaging Spectrometer (MERIS) and Moderate-resolution Imaging Spectroradiometer (MODIS). It discovers that one source of discrepancies of the instruments is removal of data for quality. It suggests that identification of differences is helpful in improving future retrieval algorithms.

The article presents a study which describes a new Markov switching model-based classification algorithm for inferring dry and rainy periods. The study suggests that the proposed Markov switching model is capable of improving telecommunication microwave link (MWL) data's signal processing. It illustrates the existing classification methods and the experimental set-up which are needed in the quantification of performances of the aforementioned classification algorithms.

The article presents a study which examines two methods for the calibration of optical particle counters (OPCs) which are both based on two atmospheric instruments such as Passive Cavity Aerosol Spectrometer Probe (PCASP) and Cloud Droplet Probe (CDP). The study describes two methods for calibrating OPCs such as discrete and scanning methods. It also applies three software tools including MieConScat, PCASP Calibrator and CStoDConverter.

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]

An introduction to the journal is presented in which the editor discusses various reports within the issue on topics related to reconfigurable computing including calculation of logarithm function of data, graphical processing units (GPUs), and used of dynamic instruction merging (DIM) approach.

The article discusses various reports published within the issue including one by Kemal Akkaya and colleagues on sensor-actor network in which sensors and another by Keqin Li on multihop wireless networks.

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]

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]

The satisfaction of linear-time temporal properties within fairness introduces an implicit fairness constraint to the verification process. To be applied to practical verification tasks, weakly continuation-closed abstractions preserve properties satisfied within fairness. Being defined on the complete behaviour of a distributed system, weakly continuation-closed abstractions require, in principle, an exhaustive state space construction prior to abstraction. Constructing the state space of a practically relevant specification exhaustively, however, is usually not feasible. Based on the notion of traces, i.e. certain equivalence classes of behaviours, trace reductions are defined in this paper. Trace reduction is a particular partial-order reduction based on the persistent-set selective search technique. It is shown that a trace reduction can be used on behalf of the complete behaviour of a distributed system in order to compute abstractions as well as to check whether the abstractions are weakly continuation closed. Thus, trace reductions allow one, in the discussed context, to overcome the requirement of an exhaustive state-space construction prior to abstraction. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The authors insist that monotonicity is a necessary property of a good cohesion metric and the violation of the monotonicity property limits the application of CBMC. They also state that the augmented CBMC can also be used as a guideline for quality evaluation and restructuring of poorly designed classes. This paper raises the question about the necessity of monotonicity by analyzing the reason that causes CBMC to violate the monotonicity property. In addition, we give a detailed description of the restructuring procedure based on CBMC. Copyright © 2003 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Applications that utilize a broker-based architecture are often composed of components that need to be tested individually and in combination. Furthermore, adequacy assessment of tests of components is useful in that it assists testers in identifying weaknesses in the tests generated so far and in offering hints on what the new tests must be. Traditional test adequacy criteria have limitations for commercial use, especially when tests for large components are to be assessed for their adequacy. This paper describes a test adequacy criterion based on interface mutation and a method, based on the criterion, to test components. This method requires the mutation of elements only from within a component's interface and not from within the code that implements the interface. The adequacy criterion based on interface mutation was evaluated empirically and compared with coverage criteria based on control flow for its relative effectiveness in revealing errors and in the cost incurred in developing test sets that satisfy the criterion. Copyright © 2001 John Wiley & Sons, Ltd. [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]

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]

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]

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]

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]

In this paper, the Recurrent Singular Spectrum Decomposition (R-SSD) algorithm is proposed as an improvement over the Recurrent Singular Spectrum Analysis (R-SSA) algorithm for forecasting non-linear and non-stationary narrowband time series. R-SSD modifies the embedding step of the basic SSA method to reduce energy residuals. This paper conducts simulations and real-case studies to investigate the properties of the R-SSD method and compare its performance with R-SSA. The results show that R-SSD yields more accurate forecasts in terms of ratio root mean squared errors (RRMSEs) and ratio mean absolute errors (RMAEs) criteria. Additionally, the Kolmogorov–Smirnov Predictive Accuracy (KSPA) test indicates significant accuracy gains with R-SSD over R-SSA, as it measures the maximum distance between the empirical cumulative distribution functions of recurrent prediction errors and determines whether a lower error leads to stochastically less error. Finally, the non-parametric Wilcoxon test confirms that R-SSD outperforms R-SSA in filtering and forecasting new data points. [ABSTRACT FROM AUTHOR]

A new algorithm is presented to reduce computational costs in solving harmonic balance equations obtained by separating state variables. In the author's previous works, an approach was proposed where the vector (matrix) of unknowns is replaced by two matrices of small dimension, which leads to two systems of balance equations that are solved iteratively. The first equation reduces the number of harmonics in the balance equations, the second equation reduces the number of circuit nodes. In this paper, it is proposed to further reduce computational costs by approximating part of the elements of the balance equations using the decomposition procedure based on singular values. It is proposed to construct a matrix of sets of responses of nonlinear dependencies of circuit models before solving the problem by an iterative method. This matrix reflects all the main changes in nonlinear dependencies with changes in the amplitudes of the input effect and over time. The resulting matrix is then approximated by applying decomposition based on singular values. Comparison of the proposed algorithm with the standard harmonic balance method [ABSTRACT FROM AUTHOR]

The widespread use of E-commerce websites has drastically increased the need for automatic recommendation systems with machine learning. In recent years, many ML-based recommenders and analysers have been built; however, their scope is limited to using a single filtering technique and processing with clustering-based predictions. This paper aims to provide a systematic year-wise survey and evolution of these existing recommenders and analysers in specific deep learning-based hybrid filtering categories using movie datasets. They are compared to others based on their problem analysis, learning factors, data sets, performance, and limitations. Most contributions are found with collaborative filtering using user or item similarity and deep learning for the IMDB datasets. In this direction, this paper introduces a new and efficient Hybrid Filtering based Recommendation System using Deep Learning (HFRS-DL), which includes multiple layers and stages to provide a better solution for generating recommendations. [ABSTRACT FROM AUTHOR]

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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]

People's lives are undergoing tremendous changes with the development of the times. Compared with the past, people's pursuit of spiritual and cultural life also makes our education field usher in a huge development to adapt to the changes in the context of the times. But, at the same time, the development of labor education is gradually being downplayed by people, resulting in a series of problems such as people preferring comfort and not working. Aiming at this common problem, this paper will use the ant colony algorithm and particle swarm optimization algorithm in the computer intelligent algorithm to optimize the way of labor education. It includes the principle and basic process of the ant colony algorithm, the establishment of the mathematical model of the original ant colony algorithm, and the improved algorithm of the ant colony algorithm. The research results of the optimization method of labor education showed the following: when the number of ant colonies reaches 51, the number of iterations of the algorithm will be the least, and the corresponding shortest path is also the best solution; when the combination of pheromone intensity and volatility factor is 3, the optimal solution can be quickly found, and the algorithm inflection point of MMAS is 44.82. From the research results, it can be seen that the computer intelligent algorithm has a good choice for the optimization of labor education and can achieve a major breakthrough in the traditional model of labor education. [ABSTRACT FROM AUTHOR]

In the field of computer research, the increase of data in result of societal progress has been remarkable, and the management of this data and the analysis of linked businesses have grown in popularity. There are numerous practical uses for the capability to extract key characteristics from secondary property data and utilize these characteristics to forecast home prices. Using regression methods in machine learning to segment the data set, examine the major factors affecting it, and forecast home prices is the most popular method for examining pricing information. It is challenging to generate precise forecasts since many of the regression models currently being utilized in research are unable to efficiently collect data on the distinctive elements that correlate y with a high degree of house price movement. In today's forecasting studies, ensemble learning is a very prevalent and well-liked study methodology. The regression integration computation of large housing datasets can use a lot of computer resources as well as computation time, and ensemble learning uses more resources and calls for more machine support in integrating diverse models. The Average Model suggested in this paper uses the concept of fusion to produce integrated analysis findings from several models, combining the best benefits of separate models. The Average Model has a strong applicability in the field of regression prediction and significantly increases computational efficiency. The technique is also easier to replicate and very effective in regression investigations. Before using regression processing techniques, this work creates an average of different regression models using theAM(Average Model) algorithm in a novelway. By evaluating essential models with 90% accuracy, this technique significantly increases the accuracy of house price predictions. The experimental results show that the AM algorithm proposed in this paper has lower prediction error than other comparison algorithms, and the prediction accuracy is greatly improved compared with other algorithms, and has a good experimental effect in house price prediction. [ABSTRACT FROM AUTHOR]

In this paper, focus has been given to design and implement signed binary subtraction in quantum logic. Since the type of operand may be positive or negative, therefore a novel algorithm has been developed to detect the type of operand and as per the selection of the type of operands, separate design techniques have been developed to make the circuit compact and work very efficiently. Two separate methods have been shown in the paper to perform the signed subtraction. The results show promising for the second method in respect of ancillary input count and garbage output count but at the cost of quantum cost. [ABSTRACT FROM AUTHOR]

Based on projection pursuit and particle swarm algorithm, a new method of establishing a nonlinear system model and its algorithm implementation are proposed, and two simulation examples are given. The simulation results show that the method proposed in this paper is used to establish a nonlinear system model. It has the advantages of high prediction accuracy and good engineering practicability. [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.

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]