A new method based on a time diffusion technique is developed to mitigate the numerical instability induced by the high frequency electrostatic shear Alfvén wave (ωH mode) in the gyrokinetic simulation of electrostatic turbulence. It can preserve fully drift-kinetic electron effects and can be incorporated efficiently with explicit time integration scheme. This method has been implemented in the semi-Lagrangian

Numerical simulations of nonhydrostatic atmospheric flow, based on linearly decoupled semi-implicit or fully-implicit techniques, usually solve linear systems by a pre-conditioned Krylov method without preserving the skew-symmetry of convective operators. We propose to perform atmospheric simulations in such a fully-implicit manner that the difference operators preserve both the skew-symmetry and the

This paper discusses the algorithmic generation of Feynman graphs for QFT models that have some form of explicit propagator mixing, including some related theoretical background. Primarily, it is shown that an ordinary Feynman graph generator — ie one that is able to deal with models without mixed propagators — can be employed to simulate the former, more general type of graph generation. Most of the

We give a detailed presentation of the theory and numerical implementation of an expression for the adiabatic energy flux in extended systems, derived from density-functional theory. This expression can be used to estimate the heat conductivity from equilibrium ab initio molecular dynamics, using the Green-Kubo linear response theory of transport coefficients. Our expression is implemented in an open-source

Software testing depends on effective oracles. Implicit oracles, such as checks for program crashes, are widely applicable but narrow in scope. Oracles based on formal specifications can reveal application-specific failures, but specifications are expensive to obtain and maintain. Metamorphic oracles are somewhere in-between. They test equivalence among different procedures to detect semantic failures

No abstract is available for this article.

Crowdsourcing is simply the outsourcing of different tasks or work to a diverse group of individuals in an open call for the purpose of utilizing human intelligence. Crowdsourcing nowadays used to support and enhance software engineering in different aspects. In this proposed study, a systematic literature review was conducted for the last 10 years from 2010 to 2019. During the filtering process

Usage of agile methods for software development has increased in recent times. Rapid delivery of software products is ensured by these methods with less expense and high user gratification. Application of these methods on a large scale causes many De-Motivators or challenges as initially these methods were aimed at those developmental teams that worked on small scale. However, no effort has been made

In social networks, the strength of relationships between users can significantly affect the stability of the network. Two users are more likely to build the friendship if they share some common friends. Meanwhile, the breakdown or enhancement of critical connections may lead to a cascaded phenomenon and cause the social network collapsed or reinforced. In this paper, we leverage the k-truss model

Human-AI collaborative decision-making tools are being increasingly applied in critical domains such as healthcare. However, these tools are often seen as closed and intransparent for human decision-makers. An essential requirement for their success is the ability to provide explanations about themselves that are understandable and meaningful to the users. While explanations generally have positive

Medical information management has progressed in the last few years because of the advances in information technologies. Nowadays, it is possible to share medical images among specialists geographically distant to interpret, discuss, and get improved diagnostics. However, any alteration of transmitted image metadata may lead to issues related to information security, such as detachment and authentication

Accurate eye blink artifact detection is essential for electroencephalogram (EEG) analysis and auxiliary analysis of nervous system diseases, especially in the presence of the frontal epileptiform discharges. In this paper, we develop a novel eye blink artifact detection algorithm based on optimally selected multi-dimensional EEG features. Specific efforts have been paid to filtering the frontal epileptiform

This study investigates how external vertical forces on the pelvis change the stability of stairmill climbing and other gait parameters such as kinematics and muscle activity. We use a Tethered Pelvic Assist Device (TPAD) to apply forces on the pelvis during continuous ascent on a stairmill. Ten young healthy subjects participated in three one-minute stair ascent with no force, a 10% body weight (BW)

Enterprise architecture has become an important driver to facilitate digital transformation in companies, since it allows to manage IT and business in a holistic and integrated manner by establishing connections among technology concerns and strategical/motivational ones. Enterprise architecture modelling is critical to accurately represent business and their IT assets in combination. This modelling

In a rapidly digitizing country, Digital Financial Services (DFS) hold the promise of bringing financial inclusion to the last mile. Through the use of DFS, vulnerable and marginalized groups can reduce the risk and cost of using cash, and increase their access to a suite of financial services that would have been otherwise unavailable, or difficult to obtain, such as credit, savings, insurance, and

Software systems start to include other types of interfaces beyond the “traditional” Graphical-User Interfaces (GUIs). In particular, Conversational User Interfaces (CUIs) such as chat and voice are becoming more and more popular. These new types of interfaces embed smart natural language processing components to understand user requests and respond to them. To provide an integrated user experience

Financial wellness is regarded as a significant constituent of the overall well-being of individuals. Financial wellbeing is defined as having financial security and freedom of choice, in the present and in the future. This has a direct correlation to overall wellbeing: including productivity experienced at work, quality of relationship, quality of health and hence quality of life. Financial wellness

Household electricity consumption has been rising gradually with the improvement of living standards. Making short-term load forecasting at the small-scale users plays an increasingly important role in the future power network planning and operation. To meet the efficiency of the dispatching system and the demand of human daily power consumption, an optimal forecasting model Attention-CNN-GRU of small-scale

Pooled testing (also known as group testing), where diagnostic tests are performed on pooled samples, has broad applications in the surveillance of diseases in animals and humans. An increasingly common use case is molecular xenomonitoring (MX), where surveillance of vector-borne diseases is conducted by capturing and testing large numbers of vectors (e.g. mosquitoes). The R package PoolTestR was developed

(2021). Digital twin-enabled smart industrial systems: recent developments and future perspectives. International Journal of Computer Integrated Manufacturing. Ahead of Print.

The A64FX CPU is arguably the most powerful Arm-based processor design to date. Although it is a traditional cache-based multicore processor, its peak performance and memory bandwidth rival accelerator devices. A good understanding of its performance features is of paramount importance for developers who wish to leverage its full potential. We present an architectural analysis of the A64FX used in

In wireless sensor networks (WSNs), the clustering architecture balances the energy consumption of the sensor nodes as compared with the flat network architecture. The cluster heads which are nearer to the sink node spend more energy than other cluster heads since it relays the packets in addition to its essential functions. The mobile sinks avoid such an energy hole scenario in sensor networks. Several

Cyber-Physical Systems (CPSs) employed for Industrial Automation often require the adoption of a hybrid data processing approach mediating between cloud, edge, and fog computing paradigms. Nowadays, it is possible to shift data pre-processing capabilities closer to data sensing to collect environmental measurements locally on the edge or deep edge. In line with the emerging computing paradigms, this

We contemplate this article to help the teachers of programming in his aspiration for giving some appropriate and interesting examples. The work will be especially useful for students-future programmers, and for their lecturers. Some of the strong sides of these programming languages C/C++ and Java are the possibilities of low-level programming. Some of the means for this possibility are the introduced

Single flux quantum (SFQ) logic is a promising technology to replace complementary metal-oxide-semiconductor logic for future exa-scale supercomputing but requires the development of reliable EDA tools that are tailored to the unique characteristics of SFQ circuits, including the need for active splitters to support fanout and clocked logic gates. This article is the first work to present a physical

In this article, we present a low-energy inference method for convolutional neural networks in image classification applications. The lower energy consumption is achieved by using a highly pruned (lower-energy) network if the resulting network can provide a correct output. More specifically, the proposed inference method makes use of two pruned neural networks (NNs), namely mildly and aggressively

Numerical models of complex real-world phenomena often necessitate High Performance Computing (HPC). Uncertainties increase problem dimensionality further and pose even greater challenges. We present a parallelization strategy for multilevel Markov chain Monte Carlo, a state-of-the-art, algorithmically scalable Uncertainty Quantification (UQ) algorithm for Bayesian inverse problems, and a new software

This paper is designed to analyze the entropy production in hybrid nanofluid (MnZnFe2O4−NiZnFe2O4−H2O). Marangoni convection is the phenomena of mass transfer between two phases of fluids when gradient of surface tension take place. Darcy-Forchheimer flow is incorporated in momentum equation. In thermal equation we are studying additional effects of nonlinear heat source sink with thermal radiation

In this paper, three challenges often encountered when upper limb rehabilitation robots are integrated with impaired people are addressed. Firstly, estimation of Desired Intended Motion (DIM) of the robot’s wearer is achieved. Secondly, robust adaptive impedance control based on the Modified Function Approximation Technique (MFAT) is designed. Lastly, a new Integral Nonsingular Terminal Sliding Mode

The study aimed to analyze the application of diffusion tensor imaging (DTI) in the surgery of benign and malignant intracranial tumors through improved fuzzy C-means (FCM). First, a method of combining the maximum and minimum distances was proposed to improve the FCM algorithm. Then, the optimized FCM was applied to the diffusion tensor imaging (DTI) diagnosis. The patients were rolled into the benign

In order to realize the automatic recognition of intracranial hematoma and accurate measurement of hematoma volume in patients with ICH (intracerebral hemorrhage), the FCM algorithm (fuzzy c-means algorithm) was improved in this study, and a new level set segmentation algorithm based on FCM was obtained, FCRLS (fuzzy c-means regularized level set). Then, 120 ICH patients were used as the research objects

Green development is the hot spot of cold chain logistics today. Therefore, this paper converts carbon emission into carbon emission cost, comprehensively considers cargo damage, refrigeration, carbon emission, time window, and other factors, and establishes the optimization model of location of low-carbon cold chain logistics in the Beijing-Tianjin-Hebei metropolitan area. Aiming at the problems of

Recent work in recommender systems has emphasized the importance of fairness, with a particular interest in bias and transparency, in addition to predictive accuracy. In this paper, we focus on the state of the art pairwise ranking model, Bayesian Personalized Ranking (BPR), which has previously been found to outperform pointwise models in predictive accuracy, while also being able to handle implicit

Since culture influences expectations, perceptions, and satisfaction, a cross-culture study is necessary to understand the differences between Japan's biggest tourist populations, Chinese and Western tourists. However, with ever-increasing customer populations, this is hard to accomplish without extensive customer base studies. There is a need for an automated method for identifying these expectations

Artist similarity plays an important role in organizing, understanding, and subsequently, facilitating discovery in large collections of music. In this paper, we present a hybrid approach to computing similarity between artists using graph neural networks trained with triplet loss. The novelty of using a graph neural network architecture is to combine the topology of a graph of artist connections with

Approximate Nearest neighbor search (ANNS) plays a crucial role in information retrieval, which has a wide range of application scenarios. Therefore, during past several years, a lot of fast ANNS approaches have been proposed. Among these approaches, graph-based methods are one of the most popular type, as they have shown attractive theoretical guarantees and low query latency. In this paper, we propose

Deep Learning and factorization-based collaborative filtering recommendation models have undoubtedly dominated the scene of recommender systems in recent years. However, despite their outstanding performance, these methods require a training time proportional to the size of the embeddings and it further increases when also side information is considered for the computation of the recommendation list

Heart disease patients are continuously increasing. The patients face the problem of a delayed diagnosis as the subjects do not undergo routine tests and consult a doctor only after severe symptoms. Most medical expert systems are designed to aid the doctors in making wise decisions and only such data sets exist in the literature. We attack the problem of an early-stage diagnosis that can be done at

This article introduces a robust image watermarking primarily founded on DWT (discrete wavelet transform), BEMD (bi-dimensional empirical mode decomposition), DCT (discrete cosine transform), PSO (particle swarm optimization), and SVD (singular value decomposition). During the process of embedding, 2nd level DWT is used to decompose the cover image into sub-bands. DWT is also used for the decomposition

Every Virtual Reality (VR) experience has to end at some point. While there already exist concepts to design transitions for users to enter a virtual world, their return from the physical world should be considered, as well, as it is a part of the overall VR experience. We call the latter outro-transitions. In contrast to offboarding of VR experiences, that takes place after taking off VR hardware

Haze removal is an interesting topic in multimedia and image processing for many applications. Specially for the automatic piloting of ships, the haze removal technology for aqueous vapour regions plays a key role in safe piloting. However, the existing haze removal methods did not dehaze well for these areas. Based on this motive, this paper presents a new haze removal approach to improve the dehazing

Cardiac arrest is a common issue in Intensive Care Units (ICU) with low survival rate. Deep learning algorithms have been used to predict cardiac arrest unsuccessfully due to low sensitivity and high false alarm rate. The aims of this research are to decrease the false alarm rate and increase sensitivity and specificity. The proposed system is based on Medical Information Mart for Intensive Care (MIMIC-III)

1 INTRODUCTION Cyber-physical systems (CPS), characterized by the deep integration of computation, communication, and control of physical processes, are becoming increasingly important. To enable the sustainable development of such systems, innovative technologies of software and hardware designs for green and energy-efficient CPS, such as reducing energy consumption of computing infrastructures, improving

Disagreement is essential to scientific progress. However, the extent of disagreement in science, its evolution over time, and the fields in which it happens, remains largely unknown. Leveraging a massive collection of scientific texts, we develop a cue-phrase based approach to identify instances of disagreement citations across more than four million scientific articles. Using this method, we construct

In this paper, we propose a geospatial data management framework called IRIDEF which captures and analyzes user's exploratory feedback for an enriched guidance mechanism in the context of interactive analysis. We discuss that exploratory feedback can be a proxy for decision-making feedback when the latter is scarce or unavailable. IRIDEF identifies regions of interest (ROIs) via exploratory feedback

In this paper, a new cell-centered positivity-preserving finite volume scheme is proposed for the 3D anisotropic diffusion problems on distorted meshes. The primary unknowns and auxiliary unknowns are used in this scheme. First, the one-sided flux on each cell facet is discretized by a fixed stencil. Then, the nonlinear two-point flux approximation is applied to get the cell-centered discrete scheme

No area of computing is hungrier for performance than High Performance Computing (HPC), the demands of which continue to be a major driver for processor performance and adoption of accelerators, and also advances in memory, storage, and networking technologies. A key feature of the Intel processor domination of the past decade has been the extensive adoption of GPUs as coprocessors, whilst more recent

Augmented Reality (AR) technologies are used as human-machine interface within various types of safety-critical systems. Several studies have shown that AR improves human performance. However, the introduction of AR might introduce risks due to new types of dependability threats. In order to avoid unreasonable risk, it is required to detect new types of dependability threats (faults, errors, failures)

Data center is a fundamental infrastructure of computers and networking equipment to collect, store, process, and distribute huge amounts of data for a variety of applications such as Cyber-Physical-Social Systems, business enterprises and social networking. As the demands of remote data services keep increasing, both the workload of the data center and its power consumption are rapidly rising. An

The data in the off-chip main memory can be potentially extracted or tampered by an adversary having physical access to the device and thus it becomes inevitable to secure the data present in the off-chip memory. The modern designs consider storing the counters on-chip to prevent replay attacks. However, these designs have significant overheads in terms of the on-chip storage used to store counters

Argument selection defects, in which the programmer chooses the wrong argument to pass to a parameter from a potential set of arguments in a function call, is a widely investigated problem. The compiler can detect such misuse of arguments only through the argument and parameter type for statically typed programming languages. When adjacent parameters have the same type or can be converted between one

Context Validating discrete-event computer simulations for a particular problem domain often involves the help of a domain expert. This means that a certain structural closeness between the simulator’s inner workings and the modeled system is needed in order to allow the expert to follow the implementation in analogy. Process-based simulation imposes an object-oriented view onto a modeled system which

Context: Mobile applications (known as “apps”) usage continues to rapidly increase, with many new apps being developed and deployed. However, developing a mobile app is challenging due to its dependencies on devices, technologies, platforms, and deadlines to reach the market. One potential approach is to use Model Driven Development (MDD) techniques that simplify the app development process, reduce

In wet flue gas desulfurization (WFGD) process, the pH value of the absorption tower slurry is a crucial factor to the efficiency of desulfurization system. Aiming at the nonlinearity and large lag of the pH change in WFGD process, a predictive control strategy based on Hammerstein–Wiener inverse model compensation is proposed. During the calculation of optimal control, an anti-model of Wiener nonlinearity