Miracle-dz/UniFAR-FTUs huggingface dataset:
Optimal Terminating Impadances for Maximizing the Gains of a Four-Coil WPT Link

In this paper, a Wireless Power Transfer (WPT) link consisting of four magnetically coupled resonators (a transmitting and a receiving resonator coupled through two relay elements) is analyzed. Standard gain definitions are used for describing the performance of the link and the analytical expressions of the terminating impedances providing the optimal operating conditions are derived.
A Novel soft switching bidirectional dc dc converter

A novel soft-switching bidirectional dc-dc converter is proposed in this paper. In the proposed converter, zero-voltage-switching (ZVS) and Zero Current Switching (ZCS) are achieved by utilizing an auxiliary circuit, which consists of auxiliary switches, auxiliary inductor, diodes, and capacitors. Conduction losses is increases with increase in components. Switching losses are significantly reduced due to the ZVS operation of main switches. Therefore, total efficiency is improved. In addition, there is no reverse-recovery problem of the intrinsic body diodes of the main switches.
Towards Applications of Stimulated Raman Scattering in Nanophotonics

Nanophotonics is a rapidly evolving field, exploring behaviour of light on the micro/nano scale and the interaction of micro/nano objects with light. The driving force for its development is the promise to go far beyond the conventional boundaries of photonics. Because of the diffraction limit, high-density regular optical components do not have the ability to confine light down to the microscale dimension. The footprint of devices becomes more critical for applications with limited physical space, such as on-chip silicon-based photonic devices.In this paper, a review of some of the most significant accomplishments in the field of stimulated Raman scattering in microstructures is reported. These findings indicate promising perspectives for integrated Raman lasers.
Using DBpedia Categories to Evaluate and Explain Similarity in Linked Open Data

Similarity is defined as the degree of resemblance between two objects. In this paper we present a new method to evaluate similarity between resources in Linked Open Data. The input of our method is a pair of resources belonging to the same type (e.g. Person or Painter), described by their Dbpedia categories. We first compute the ’distance’ between each pair of categories. For that we need to explore the graph whose vertices are the categories and whose edges connect categories and sub-categories. Then we deduce a measure of the similarity/dissimilarity between the two resources. The output of our method is not limited to this measure but includes other quantitative and qualitative informations explaining similarity/dissimilarity of the two resources. In order to validate our method, we implemented it and applied it to a set of DBpedia resources that refer to painters belonging to different countries, centuries and artistic movements.
Factors Associated with the Prevalence of Diabetes Mellitus Among Elderly Men and Women Living in Mediterranean Islands: The MEDIS Study.

BACKGROUND The aim of the present work was to evaluate the relationships between socio-demographic, clinical, lifestyle and psychological characteristics and the presence of diabetes mellitus, among elderly individuals without known cardiovascular disease. METHODS During 2005-2007, 1190 elderly (aged 65 to 100 years) men and women (from Cyprus, Mitilini, Samothraki, Cephalonia, Crete, Lemnos, Corfu and Zakynthos) were enrolled. Socio-demographic, clinical and lifestyle factors were assessed using standard procedures. Diabetes mellitus was defined as fasting blood glucose >125 mg/dl or use of special medication. RESULTS 21% of males and 23% of females had diabetes. Only 70% of diabetic participants were on a special diet and 76% were receiving pharmaceutical treatment. Diabetic individuals had higher prevalence of hypertension (80% vs. 64%, p < 0.001) and hypercholesterolemia (63% vs. 51%, p < 0.001) and reported lower physical activity status (p < 0.001), compared with non-diabetic participants. After adjusting for various confounders, hypertension and hypercholesterolemia were associated with a 144% (95% CI, 1.37-4.35) and 83% (95% CI, 1.13-2.94) higher likelihood of having diabetes, while moderate and vigorous exercise correlated with a 82% (95% CI, 0.09-0.81) and 67% (95% CI, 0.11-0.97) lower likelihood of diabetes. CONCLUSIONS A considerable proportion of our elderly sample had diabetes and other metabolic disorders, almost 25% of which were untreated. Promotion of physical activities, even in the elderly, may contribute to reducing their burden of diabetes and provide them with a better quality of living.
Neural Explainable Collective Non-negative Matrix Factorization for Recommender Systems

Explainable recommender systems aim to generate explanations for users according to their predicted scores, the user’s history and their similarity to other users. Recently, researchers have proposed explainable recommender models using topic models and sentiment analysis methods providing explanations based on user’s reviews. However, such methods have neglected improvements in natural language processing, even if these methods are known to improve user satisfaction. In this paper, we propose a neural explainable collective nonnegative matrix factorization (NECoNMF) to predict ratings based on users’ feedback, for example, ratings and reviews. To do so, we use collective non-negative matrix factorization to predict user preferences according to different features and a natural language model to explain the prediction. Empirical experiments were conducted in two datasets, showing the model’s efficiency for predicting ratings and generating explanations. The results present that NECoNMF improves the accuracy for explainable recommendations in comparison with the state-of-art method in 18.3% for NDCG@5, 12.2% for HitRatio@5, 17.1% for NDCG@10, and 12.2% for HitRatio@10 in the Yelp dataset. A similar performance has been observed in the Amazon dataset 7.6% for NDCG@5, 1.3% for HitRatio@5, 7.9% for NDCG@10, and 3.9% for HitRatio@10.
Planning of Resilient OFDM-PON in Support of 5G Backhaul

Orthogonal Frequency Division Multiplexing Passive Optical Network (OFDM-PON) has been regarded as one of the promising solutions for 5G backhaul due to its excellence in spectral efficiency and transmission stability. However, the vulnerability of OFDM-PON to link failure cumbers its applicability in 5G backhaul, which is expected to offer enhanced network resilience. Although link duplication has evolved as a common practice for the protection of PON against link failure, the compromise between economical cost for backup links and QoS guarantee remains an open issue. In this paper, we focus on the network planning of resilient OFDM-PON and propose a novel backup mechanism to effectively mediate between network redundancy cost and performance degradation at both PHY and MAC layers.
Stackelberg Game Based Social-Aware Power Allocation for Cooperative D2D Communications

As one of the indispensable techniques of the future cellular network, Device-to-Device communications can be potentially exploited to alleviate the traffic load by distributing data for base station. In order to stimulate users to cooperate, a price-based power allocation scheme is proposed in this paper, in which the requester pays the cost of power provided by the cooperative user for data forwarding combined with the social tie. Particularly, since the requester and cooperative user pursue the rate enhancement and reward maximization, respectively, the utility of these two sides is quantified according to the power cost and constraint. By introducing a two-stage Stackelberg game, the final price and power allocation strategy are determined via a dynamic bidding process while approaching the optimal utility of the requester and cooperative user. Simulation results show that the studied power allocation scheme can effectively stimulate the user to forward data and improve the system performance.
The Future of India Creeping up in Building a Smart City: Intelligent Traffic Analysis Platform

The integration of various embedded technological devices makes our environment to enable IoT. Internet of things plays a major role in bringing out the smartness into the culture. The smart transportation system is one such example that mainly focuses on to provide safety, efficient usage and reliable transportation this in turn is known in the form of smartness, and sustainable environment that is established for the public. To provide intelligent transportation System (ITS) there exist many problems in supply of the resources such as controlling of air pollution, reduce traffic congestion and the continuous growth of population. Therefore a deep analysis is required to control the trajectory data. These data have to analyzed and predicted using Big Data Analytics techniques. To solve the congestion in traffic a proper Data mining methodology is to be adapted that provides/paves way for the prioritized vehicles or it can make the congested payment to be free. These technologies will provide proper guidance to the public sector in enabling a healthier environment. The use of GPS sensor and analyzing the data is being done on the trajectory big data which will enable us to generate a Smarter Environment. This paper has discussed about the various technological implementations that have been carried out in prior to develop a smart intelligent transportation and it also has pointed out the different mechanism in executing this system effectively.
Generalized Multichannel Variational Autoencoder for Underdetermined Source Separation

This paper deals with a multichannel audio source separation problem under underdetermined conditions. Multi-channel Non-negative Matrix Factorization (MNMF) is one of the powerful approaches, which adopts the NMF concept for source power spectrogram modeling. It works reasonably well for particular types of sound sources, however, one limitation is that it can fail to work for sources with spectrograms that do not comply with the NMF model. To address this limitation, a novel technique called the Multichannel Variational Autoencoder (MVAE) method was recently proposed, where a Conditional VAE (CVAE) is used instead of the NMF model for source power spectrogram modeling. This approach has shown to perform impressively in determined source separation tasks thanks to the representation power of DNNs. This paper generalizes MVAE originally formulated under determined mixing conditions so that it can also deal with underdetermined cases. The proposed method was evaluated on an underdetermined source separation task of separating out three sources from two microphone inputs. Experimental results revealed that the generalized MVAE method achieved better performance than the conventional MNMF method.
Performance Analysis of Radio-over-Fiber Based on Phase-Modulation and Direct-Detection for the Future 5G Network

Access transport network plays a vital role in actual mobile networks to support the need of high data rates, real time services and spectrum efficiency. The future 5G requires an access network with high performances especially in the mm-wave bands. In this work, we propose a radio-over-fiber architecture based on intensity modulation for the downlink and phase modulation for the uplink. This solution permits to cancel optical sources and reduces electrical consumption in the multiple base stations. We focus our study on the phase modulation and direct detection link by evaluation of the quality of the wireless transmitted signal.
Behavioral Analysis of Docker Swarm Under DoS/ DDoS Attack

In recent time, a lot of talks is going about Docker an open source application containerization technology for building, shipping, and running applications. Docker when compared to other technologies has the ability to simplify the things that would previously require a lot of efforts. Due to this simplification, the time and cost of deploying and managing services have reduced tremendously. When we further talk about the Docker, one of its prime features which come to mind is the concept of Docker Swarm, a native clustering solution for Docker. This swarm clustering concept has over shattered the old and out-dated computer clustering techniques. Now, when we look into this paper, it totally talks about Docker Swarm from building a swarm cluster to managing the tasks. At last, this paper shows the behavioral analysis of Docker Swarm under DoS/ DDoS attack. This analysis has been done at two levels first at the node level and second at the cluster level and based on the analysis conclusion have been drawn.
Cocaine Paired Environment Increases SATB2 Levels in the Rat Paraventricular Thalamus

SATB2 is a DNA binding protein that specifically binds the nuclear matrix attachment region and functions as a regulator of the transcription of large chromatin domains. Unlike its well addressed role during brain development, the role of SATB2 in adult brain is under-investigated. It has been shown that deletion of SATB2 from the forebrain of adult mice significantly impaired long-term memory for contextual fear and object recognition memory. The aim of the present study was to investigate the effects of appetitive stimuli such as cocaine and social interaction (SI) on SATB2 expression in the adult rat brain. For that, we performed conditioned place preference (CPP) to cocaine (15 mg/kg) and to SI, then assessed SATB2 expression in the brain 1 h (24 h after the last conditioning) and 24 h (48 h after the last conditioning) after the CPP test. We found that SATB2 expression in the paraventricular thalamus of rats was increased 1 h after the cocaine CPP test. This increase was selective for the cocaine-paired environment since the SI-paired environment did not increase SATB2 expression in the paraventricular thalamus. Also, the cocaine paired environment-induced increase of SATB2 levels in the paraventricular thalamus was due to cocaine conditioning as the unpaired cocaine group did not show an increase of SATB2 in the paraventricular thalamus. These results suggest that SATB2 in the paraventricular thalamus appears to be involved in the association between cocaine effects and environmental context. Further studies are needed to address the functional role of SATB2 in cocaine conditioning.
A LSTM Neural Network applied to Mobile Robots Path Planning

Mobile robots path planning is a central problem in every situation where human intervention is not desired or not possible to accept: full automated industrial warehouses or general stocking areas and every domestic application that involves a mobile robot and special cases where environment is prohibited for human accessing like toxic wastes and bombs defusing [1]. Currently, neural networks are applied to problems related to mobile robot navigation. However, they are not as popular as in applications like image processing, speech recognition or machine translation, where they are commercially relevant. In this paper we propose a Long Short-Term Memory (LSTM) neural network as an online search agent to tackle the problem of mobile robots path planning in unknown environments, meaning that the agent relies only on local map awareness realized with a LRF sensor and relative information between robot and goal position. Specifically, a final structure of LSTM network is analyzed and its performance is compared with the A* algorithm, a widely known method that follows the best-first search approach. Subsequently, an analysis of the method developed on a real robot is described.
Dynamic Camera Network Reconfiguration for Crowd Surveillance

Crowd surveillance will play a fundamental role in the coming generation of video surveillance systems, in particular for improving public safety and security. However, traditional camera networks are mostly not able to closely survey the entire monitoring area due to limitations in coverage, resolution and analytics performance. A smart camera network, on the other hand, offers the ability to reconfigure the sensing infrastructure by incorporating active devices such as pan-tilt-zoom (PTZ) cameras and UAV-based cameras, which enable the adaptation of coverage and target resolution over time. This paper proposes a novel decentralized approach for dynamic network reconfiguration, where cameras locally control their PTZ parameters and position, to optimally cover the entire scene. For crowded scenes, cameras must deal with a trade-off among global coverage and target resolution to effectively perform crowd analysis. We evaluate our approach in a simulated environment surveyed with fixed, PTZ, and UAV-based cameras.
Neural System Identification With Spike-Triggered Non-Negative Matrix Factorization

Neuronal circuits formed in the brain are complex with intricate connection patterns. Such complexity is also observed in the retina with a relatively simple neuronal circuit. A retinal ganglion cell (GC) receives excitatory inputs from neurons in previous layers as driving forces to fire spikes. Analytical methods are required to decipher these components in a systematic manner. Recently a method called spike-triggered non-negative matrix factorization (STNMF) has been proposed for this purpose. In this study, we extend the scope of the STNMF method. By using retinal GCs as a model system, we show that STNMF can detect various computational properties of upstream bipolar cells (BCs), including spatial receptive field, temporal filter, and transfer nonlinearity. In addition, we recover synaptic connection strengths from the weight matrix of STNMF. Furthermore, we show that STNMF can separate spikes of a GC into a few subsets of spikes, where each subset is contributed by one presynaptic BC. Taken together, these results corroborate that STNMF is a useful method for deciphering the structure of neuronal circuits.
Analysis and Classification of Stenosis Severity from Coronary Angiogram Images

Currently angiogram is the golden standard methodology used for evaluating coronary artery disease (CAD). CAD helps the physician to assess percentage of stenosis or block in blood vessel. The density of the contrast agent that passes through blood is different along the blood vessel. So to calculate the density variation, a quantitative method is used. This helps to determine the percentage of stenosis. Frames of images are extracted from the angiogram and Region of Interest (ROI) are segmented from each frame. Length and diameter of segmented blood vessel is calculated. Neural network classifier is used for determining stenosis percentage based on statistical parameters. Also the dye density along the blood vessel is calculated using volume information. The severity of stenosis is determined based on the density of dye at different blood vessel segments.
as Conformational Tools and Reporters for Peptide and Protein Chemistry

: Amide bonds at the proline nitrogen are particularly susceptible to rotation, affording cis and trans isomers. Installation of a stereochemically-defined electron withdrawing fluorine atom or fluorinated groups has the power to influence the cis - trans conformational preferences of the amide bond in X-(F)Pro (where X = another amino acid). Advanta-geously, this also provides a sensitive reporter for 19 F NMR studies of protein conformation, interactions and dynamics. We deliberately use the term ‘fluorinated prolines’ as an all-encompassing term to describe proline analogues containing one or more fluorine atoms and to avoid confusion with the more well-known 4-fluoroprolines. This review presents a critical discussion of the increasing repertoire of fluor-inated prolines that have been described; and importantly, provides a comparison of their uses and relative influence on amide bond conformation as well as discussing the significant potential of using 19 F NMR as a tool to probe for conformational change in polypeptides. Of the twenty
Configurable N-fold Hardware Architecture for Convolutional Neural Networks

A Convolutional Neural Network (CNN) is a class of deep feed-forward artificial neural network usually employed to analyze visual images. Recently, rapid progress in applications based on CNNs has urged research on efficient architectures and implementations that exploit the latest technological advancements. The growing complexity of CNN architectures and the usage of reconfigurable devices enlarge the design space to a range hard to fully explore. This paper proposes a new method to design and implement efficient and flexible CNN architectures on hardware. The method adopts an N-fold approach, particularly suitable for devices with strict restrictions on power consumption and featuring reconfigurability. An 8-layer CNN to classify handwritten was trained on a software and prototyped on a FPGA available in a Zynq 7000 Programmable System on a Chip (SoC) board using the proposed CNN architecture. The hardware architecture was described and implemented using High Level Synthesis, enabling a fast development and easy configuration. Experimental results show that the best performance is achieved by using a pipelined design with a partial unfolding sublayer. A processing time of 3.4ms to classify was achieved with 41.16% of resources and 2.1W of power consumption. In contrast, a low-power design «2W), consuming 27.99% of the resources of the board, required 16.7ms to process the same CNN.
Mortality in ischemic stroke score: A predictive score of mortality for acute ischemic stroke

OBJECTIVE: This prospective study was planned to formulate and evaluate a predictive score for in-hospital mortality in cases of acute ischemic stroke. MATERIALS AND METHODS: In this study, 188 consecutive patients of ischemic stroke were included over 19 months. Only patients with renal failure and malignancy were excluded from the study. All patients were subjected to clinical evaluation along with Glasgow Coma Scale (GCS), National Institute of Health Science scale (NIHSS) score, and modified Rankin score (mRS). Investigations total leukocyte count (TLC), capillary blood sugar at admission, high-sensitivity C-reactive protein (HS-CRP), and troponin I, electrocardiogram, and neuroimaging were performed. The patients were followed up till their outcome in the hospital, and patients who expired were grouped as “mortality group” and the rest as “discharged group. ” One-way anova analysis was carried out among the significant parameters to identify independent predictors of mortality in cases of ischemic stroke. RESULTS: After statistical analysis, it was found that late presentation to the hospital, pyrexia (temperature >99F), low diastolic blood pressure at the time of admission, hypoxia (saturation of oxygen <94%), NIHSS score >15, mRS >3, GCS <8, hyperglycemia (random blood sugar >200 mg/dL), raised TLC, and HS-CRP (>10 mg/L) are positive predictive factors of mortality in cases of ischemic stroke. Based on the above findings, a simple and easily applicable mortality in ischemic stroke (MIS) score is developed. CONCLUSION: This MIS score system will help the clinicians in better management of the patient and improved counseling the relatives of patients with ischemic stroke.