Miracle-dz/UniFAR-FTUs huggingface dataset:
Investigation of Linear and Nonlinear Properties of a Heartbeat Time Series Using Multiscale Rényi Entropy

The time series of interbeat intervals of the heart reveals much information about disease and disease progression. An area of intense research has been associated with cardiac autonomic neuropathy (CAN). In this work we have investigated the value of additional information derived from the magnitude, sign and acceleration of the RR intervals. When quantified using an entropy measure, these time series show statistically significant differences between disease classes of Normal, Early CAN and Definite CAN. In addition, pathophysiological characteristics of heartbeat dynamics provide information not only on the change in the system using the first difference but also the magnitude and direction of the change measured by the second difference (acceleration) with respect to sequence length. These additional measures provide disease categories to be discriminated and could prove useful for non-invasive diagnosis and understanding changes in heart rhythm associated with CAN.
Variational proof of the existence of brake orbits in the planar 2-center problem

The restricted three-body problem is an important subject that deals with significant issues referring to scientific fields of celestial mechanics, such as analyzing asteroid movement behavior and orbit designing for space probes. The 2-center problem is its simplified model. The goal of this paper is to show the existence of brake orbits, which means orbits whose velocities are zero at some times, under some particular conditions in the 2-center problem by using variational methods.
Effect of vortex-induced vibration of finned cylinders on heat transfer enhancement

Two-degree-of-freedom vortex-induced vibration (VIV) of a finned cylinder with heat transfer is studied numerically at the Reynolds number Re = 150. The governing equations in the Arbitrary Lagrangian-Eulerian frame are solved by the finite volume method. The dynamics of the oscillating cylinder (with or without fins) in the fluid flow was approximated as a mass-spring system. The effects of the number and arrangement of the fins (14 different cases) on the vortex shedding pattern, vibration amplitude, and frequency and heat transfer of the cylinder are investigated and discussed. The results indicate that in comparison with the stationary state, the effects of the number and arrangement of the fins on the wake pattern and the heat transfer enhancement in the VIV state are significant. Different vortex shedding pattern like 2S, P, 2P, S + P and combination of them with stable or unstable interactions between vortices and cylinders are observed in an oscillating cylinder. In the vibration state of finned cylinders, the heat transfer enhances up to 50.4% with respect to the stationary state and increases up to 64% with respect to the stationary smooth cylinder.Two-degree-of-freedom vortex-induced vibration (VIV) of a finned cylinder with heat transfer is studied numerically at the Reynolds number Re = 150. The governing equations in the Arbitrary Lagrangian-Eulerian frame are solved by the finite volume method. The dynamics of the oscillating cylinder (with or without fins) in the fluid flow was approximated as a mass-spring system. The effects of the number and arrangement of the fins (14 different cases) on the vortex shedding pattern, vibration amplitude, and frequency and heat transfer of the cylinder are investigated and discussed. The results indicate that in comparison with the stationary state, the effects of the number and arrangement of the fins on the wake pattern and the heat transfer enhancement in the VIV state are significant. Different vortex shedding pattern like 2S, P, 2P, S + P and combination of them with stable or unstable interactions between vortices and cylinders are observed in an oscillating cylinder. In the vibration state of finned c.
Study of Majorana Fermion Dark Matter

We construct a generic model of Majorana fermion dark matter (DM). Starting with two Z2-odd Weyl spinor multiplets η1,2 ∼ (I,∓Y ) coupled to the standard model (SM) Higgs, eight additional Z2-odd Weyl spinor multiplets with (I± 1/2,±(Y ± 1/2)) are needed in general. It contains 13 parameters in total, five mass parameters and eight Yukawa couplings. The DM sector of the minimal extension of supersymmetric stand model (MSSM) is a special case of the model with (I,Y ) = (1/2,1/2). Therefore, this model can be viewed as an natural extension of the MSSM case. We consider the cases of MSSM-like (A), MSSM-like (B), and an extended case. We study the constraints from the observation of dark matter in relic density, the direct detection of LUX and XENON100 experiments, and the indirect detection of Fermi-LAT data. From these constraints we can find the allowed range for each coupling strength. Without considering the outlier samples, we find that the DM mass for finding H, B-, W and X-like DM particles should be greater than 445 GeV, 270 GeV, 1025 GeV, and 700 GeV respectively.
A Comparative Study of [F-18] Florbetaben (FBB) PET Imaging, Pathology, and Cognition between Normal and Alzheimer Transgenic Mice

Alzheimer's disease (AD) is highly prevalent in dementia, with no specifically effective treatment having yet been discovered. Amyloid plaques are one of the key hallmarks of AD. Transgenic mouse models exhibiting Alzheimer's disease-like pathology have been widely used to study the pathophysiology of Alzheimer's disease. In this study, we showed an age-dependent correlation between cognitive function, pathological findings, and [F-18] Florbetaben (FBB) PET images. Nineteen transgenic mice (12 with AD, 7 with controls) were used for this study. We observed an increase in β -Amyloid deposition (A β ) in brain tissue and [F-18] FBB amyloid PET imaging in the AD group. The [F-18] FBB data showed a mildly negative trend with cognitive function. Pathological findings were negatively correlated with cognitive functions. These finding suggests that amyloid beta deposition can be well-monitored with [F-18] FBB PET and a decline in cognitive function is related to the increase in amyloid plaque burden.
Existence, blow-up and exponential decay estimates for a system of nonlinear viscoelastic wave equations with nonlinear boundary conditions

This paper is devoted to the study of a system of nonlinear viscoelastic wave equations with nonlinear boundary conditions. Based on the Faedo-Galerkin method and standard arguments of density corresponding to the regularity of initial conditions, we first establish two local existence theorems of weak solutions. By the construction of a suitable Lyapunov functional, we next prove a blow up result and a decay result of global solutions.
The causal interpretation of estimated associations in regression models

Abstract A common causal identification strategy in political science is selection on observables. This strategy assumes one observes a set of covariates that is, after statistical adjustment, sufficient to make treatment status as-if random. Under adjustment methods such as matching or inverse probability weighting, coefficients for control variables are treated as nuisance parameters and are not directly estimated. This is in direct contrast to regression approaches where estimated parameters are obtained for all covariates. Analysts often find it tempting to give a causal interpretation to all the parameters in such regression models—indeed, such interpretations are often central to the proposed research design. In this paper, we ask when we can justify interpreting two or more coefficients in a regression model as causal parameters. We demonstrate that analysts must appeal to causal identification assumptions to give estimates causal interpretations. Under selection on observables, this task is complicated by the fact that more than one causal effect might be identified. We show how causal graphs provide a framework for clearly delineating which effects are presumed to be identified and thus merit a causal interpretation, and which are not. We conclude with a set of recommendations for how researchers should interpret estimates from regression models when causal inference is the goal.
Folded aperture coupled patch antenna fabricated on FPC with vertically polarised end‐fire radiation for fifth‐generation millimetre‐wave massive MIMO systems

The authors proposed a folded aperture-coupled patch antenna (ACPA) on a flexible printed circuit (FPC) with vertically polarised end-fire radiation, suitable for fifth-generation millimetre-wave massive multi-input-multi-output (MIMO) systems. For low-cost implementation, antenna elements were fabricated on a single polyimide substrate. The fabricated FPC was first folded to implement a three-layer ACPA, and then the feed line was bent 90° to achieve end-fire radiation. Measurement of five antenna samples demonstrates that the resonance frequency lies between 27.7 and 28.3 GHz. Each antenna sample has the bandwidth (| S 11 | 1.2 GHz. The measured antenna gain is 2.74 dBi at 28.1 GHz, and the difference between co- and cross-polarised radiation intensities is >15 dB in all directions of interest. To improve the impedance bandwidth and the gain further, they increased the distance between the patch and the aperture of the proposed antenna by folding the fabricated FPC multiple times without an additional layer. Experimental results confirm the bandwidth of 2.64 GHz (26.27-28.91 GHz) and the gain of 3.80 dBi at 28 GHz. Additionally, the 1 × 8-folded APCA array is simulated to verify the feasibility for a practical application of multi-beam massive MIMO technology.
A novel automatic segmentation workflow of axial breast DCE-MRI

In this paper we propose a novel process of a fully automatic breast tissue segmentation which is independent from expert calibration and contrast. The proposed algorithm is composed by two major steps. The first step consists in the detection of breast boundaries. It is based on image content analysis and Moore-Neighbour tracing algorithm. As a processing step, Otsu thresholding and neighbors algorithm are applied. Then, the external area of breast is removed to get an approximated breast region. The second preprocessing step is the delineation of the chest wall which is considered as the lowest cost path linking three key points; These points are located automatically at the breast. They are respectively, the left and right boundary points and the middle upper point placed at the sternum region using statistical method. For the minimum cost path search problem, we resolve it through Dijkstra algorithm. Evaluation results reveal the robustness of our process face to different breast densities, complex forms and challenging cases. In fact, the mean overlap between manual segmentation and automatic segmentation through our method is 96.5%. A comparative study shows that our proposed process is competitive and faster than existing methods. The segmentation of 120 slices with our method is achieved at least in 20.57±5.2s.
An algorithm for mining frequent itemsets on uncertain dataset

Mining frequent itemsets from uncertain transaction dataset is a research topic in data mining. Some algorithms are based on FPGrowth, but they construct the tree structure in a manner that cannot be as compact as the original FP-Tree, so the tree is easily developed to huge size and this hinders their performance. In this paper, we propose a new tree structure called IT-Tree (Itemset Tailnode Tree) to efficiently maintain probability information of itemsets in tail-nodes; we also propose a corresponding algorithm ITMine to mine frequent itemsets from IT-Tree without additional dataset scans. We evaluate our approach on real sparse and dense datasets with different minimum support numbers that can produce non-null frequent k-itemsets (k≥2); the results show that IT-Mine outperforms other algorithms in terms of execution time, especially for large dataset or small minimum expected support number.
Anti-Spoofing for Text Independent Speaker Verification

This paper describes the scientific study of vulnerability of automatic speaker verification to various vary of spoofing attacks. We start with radical analysis of the spoofing effects of five speech syntheses and eight voice conversion system and also the vulnerability of three speaker verification system under those attacks. Then we introduce variety of countermeasures to prevent the spoofing attacks from the each noted and unknown attackers. Known attacker’s square measure spoofing system those output was used to train the countermeasures, whereas associate unknown assailant is a spoofing system whose output offers to the countermeasures during coaching. Finally we describe benchmark automatic system against human performance on each speaker verification and spoofing deduction task. The main objective of the proposed method is to protect from the spoofing attack for text independent speaker verification.
Assessment of performance of a distributed information system based on time profile

The problem of determining performance parameters for distributed information systems, which are formed by heterogeneous hardware, is examined. The information system, designed to perform distributed computing, based on the interaction between elements, is presented in the form of a combination of operation processes of software objects (agents) in interaction with operators. To provide a possibility of algorithmic and quantitative analysis of the system’s operation process, the authors used time diagrams, which can be obtained based of time profiles of serial and distributed actors. Construction of the time profile of an actor is provided by knowledge of the sequence of performed actions and the average time to perform each action. Based on the knowledge of a typical time profile, estimations of different kinds of performance were obtained and the ratio, linking the main forms of performance to a variety of quantitative characteristics of the system, was derived. As the key indicator of performance assessment, it is proposed to apply throughput, which for a serial actor is the ratio of loading of an actor and total time of actions per task. It was shown that within a single distributed actor, total throughput remains constant regardless of redistribution of the number of tasks between serial actors. As another performance indicator, it is proposed to apply response time of the system as the average time to fulfil a task by an actor. Relationship between response time and throughput of the system was established analytically. It was determined by modeling that at an increase in throughput, response time of the system decreases, reaching 0 at a certain ratio of throughput and the number of fulfilled tasks. The introduction of these ratios, in addition to the key indicators, also makes it possible to determine derivative parameters of the system, such as minimal computation time, average time of waiting for requests and amount of memory, required to fulfil tasks. In this case, it was determined that the minimum computation time is a magnitude, dependent on the capacity of an actor and the number of actions performed, as well as the ratio of computation time and exchange time. The average time of waiting for a request is the difference between total operation time and direct time for fulfilling the task by actors. The amount of required memory is determined based on knowledge of amount of memory, involved in performance of certain processes and atomic operations. Presented ratios make it possible to evaluate quantitatively parameters of distributed information systems and to synthesize systems with assigned parameters of throughput and response time
Emerging Trends on Big Data & Cloud Computing

In this paper we focused on the emerging trends and various approaches for carrying out analytics on clouds for Big Data application. It revolves around four important analytics and Big Data. We also discussed about the some of the real world challenges in this cloud and Big Data computing era. This paper also focused on the implementation strategy of Big Data like Management, Data Varity etc. It helps to identify the technology gaps which may help to research communities so that they will have a directions for future scope of Big Data based on cloud computing.
Forensic research on detecting seam carving in digital images

FORENSIC RESEARCH ON DETECTING SEAM CARVING IN DIGITAL IMAGES by Jingyu Ye Digital images have been playing an important role in our daily life for the last several decades. Naturally, image editing technologies have been tremendously developed due to the increasing demands. As a result, digital images can be easily manipulated on a personal computer or even a cellphone for many purposes nowadays, so that the authenticity of digital images becomes an important issue. In this dissertation research, four machine learning based forensic methods are presented to detect one of the popular image editing techniques, called ‘seam carving’. To reveal seam carving applied to uncompressed images from the perspective of energy distribution change, an energy based statistical model is proposed as the first work in this dissertation. Features measured global energy of images, remaining optimal seams, and noise level are extracted from four local derivative pattern (LDP) domains instead of from the original pixel domain to heighten the energy change caused by seam carving. A support vector machine (SVM) based classifier is employed to determine whether an image has been seam carved or not. In the second work, an advanced feature model is presented for seam carving detection by investigating the statistical variation among neighboring pixels. Comprised with three types of statistical features, i.e., LDP features, Markov features, and SPAM features, the powerful feature model significantly improved the state-of-the-art accuracy in detecting low carving rate seam carving. After the feature selection by utilizing SVM based recursive feature elimination (SVM-RFE), with a small amount of features selected from the proposed model the overall performance is further improved. Combining above mentioned two works, a hybrid feature model is then proposed as the third work to further boost the accuracy in detecting seam carving at low carving rate. The proposed model consists of two sets of features, which capture energy change and neighboring relationship variation respectively, achieves remarkable performance on revealing seam carving, especially low carving rate seam carving, in digital images. Besides these three hand crafted feature models, a deep convolutional neural network is designed for seam carving detection. It is the first work that successfully utilizes deep learning technology to solve this forensic problem. The experimental works demonstrate their much more improved performance in the cases where the amount of seam carving is not serious. Although these four pieces of work move the seam carving detection ahead substantially, future research works with more advanced statistical model or deep neural network along this line are expected. FORENSIC RESEARCH ON DETECTING SEAM CARVING IN DIGITAL IMAGES
Evaluation of the Medical Image Compression using Wavelet Packet Transform and SPIHT Coding

Wavelet transforms and wavelet packets are widely imposed in the analysis and resolution of problems related to science and technical engineering. Decomposition wavelet packet allows several frequency bands according to various levels of resolutions. We apply this transform (PWT) coupled with the SPIHT coder to reduce the limitations of conventional wavelet filter bank. The results obtained using the applied algorithm, are very satisfactory and encouraging compared to many of the best coders cited in the literature and show a visual and numerical superiority over conventional methods. These the promising results are confirmed by visual evaluation parameters (PSNR, MSSIM and VIF).
Perbandingan Konverter CUK dan SEPIC Untuk Pelacakan Titik Daya Maksimum Berbasis Panel Surya

To meet the increasing demand for electricity, is required the power plants that utilize renewable energy.The rapid development of technology currently, it has been able to take advantage of solar energy into electrical energy by using solar panels.To get the maximum power from the solar panel dc-dc converter is needed, which is controlled by using the maximum power point tracking algorithm(MPPT). This paper presents a comparison between CUK and SEPIC converter in maximum power point tracking of solar panel system. Advantages and disadvantages of both converters are described in this paper. Furthermore, Perturb and Observe (P&O) are used as maximum power point tracking algorithm. The evaluation of the output has been carried out and compared by software simulation.
Development of Intelligent Distribution Network Simulation System Based on Distributed Generation

With the rapid development of distribution network and distributed power generation, and the improvement of network intelligence and digitization, intelligent power consumption and the access of distributed power generation access, as the main function of intelligent distribution network, has impact on the operation of intelligent distribution network. A simulation system based on the intelligent power consumption system, photovoltaic generation, wind power generation and other distributed generations is researched and developed. By researching the operating characteristics of each subsystem, the protection and control strategy of intelligent distribution network, it can be used to research the influence of distributed power generation and intelligent power consumption on the operation of intelligent distribution network. At the same time, it can also be used as a training system.
The Role of Technology in Supporting Communication for Real Estate Agents – Evaluation of the Phenomenon

Globalization and the development of modern information technology have contributed to the higher importance of communication technological support and its use between the client and the entrepreneur in the service industry. Such tendencies also apply to real estate companies, in particular to real estate agents and brokers. The purpose of this article is to identify the level of service adaptation of the real estate brokerage industry to the general tendencies characterizing the service sector. Two research questions were proposed in the article. The first one is about adjustment of the communication tools used by the real estate brokerage industry to the trends in the services market. The second one includes the assessment of the tools’ efficiency in the opinion of the estate brokers and agents. The literature and critical analysis as well as an introductory survey of real estate agents and brokers were all used in this paper. The introductory survey was conducted with the use of webform among real estate agents affiliated to Powszechne Towarzystwo Ekspertów i Doradców Rynku Nieruchomości, between the 2nd of October and 15th of November 2017. After a basic analysis, we can conclude that the real estate sector is quite similar to other services’ sectors as far as the new means of communication usage in contacts with clients are concerned. The empirical studies results show that tools such as smartphone and laptop in combination with social media play an important role in today’s communication trends. The role of traditional means of communication, on the other hand, such as leaflets and newspapers has significantly decreased.