Miracle-dz/UniFAR-FTUs huggingface dataset:
Interpretation of Electrocardiogram (ECG) Rhythm by Combined CNN and BiLSTM

Computer-aided detection and diagnosis in ECG signals for heart diseases are gaining increasing attention. However, developing and selecting the highly performing diagnostic model suitable for clinical implications is still challenging. In this paper, we proposed a combined network of convolutional neural network (CNN) and Recurrent Neural Network (RNN), designed for the classification of ECG heart signals for diagnostic purpose. The proposed network consists of 2 convolutional layers with $5\times 5$ kernels and ReLU activations, followed by 4 residual blocks, 2 bidirectional long short-term memory (biLSTM) layers, as well as 2 fully connected layers. Each residual block involved the structure of a Squeeze-and-Excitation Network (SENet) with lightweight property to recalibrate the feature map of the network. The last dense layer has 5 outputs, equivalent to the classes considered: non-ectopic beats, supraventricular ectopic beats, ventricular ectopic beats, fusion beats, and unknown beats. To train and evaluate the combined CNN and RNN, we transferred the knowledge acquired on beat classification tasks in 2017 PhysioNet/CinC Challenge to that in PhysionNet’s MIT-BIH dataset. The developed network achieved a recognition sensitivity of 95.90%, accuracy = 95.90% and specificity = 96.34% with classification time of single sample = 6.23 s in detecting 5 ECG classes. A comparative analysis proved the high performance of the proposed combined CNN and RNN against previous methods, demonstrating the potential of our proposed network in the analysis of beat patterns. The proposed model can be applied in cloud computing or implemented in mobile devices to evaluate cardiac health with the highest precision.
Internet of Things and social platforms: an empirical analysis from Indian consumer behavioural perspective

ABSTRACT Internet technology has flourished in India keeping pace with other developing countries. In keeping pace with this advancement, the Internet of Things (IoT) technology is also spreading in India. Government of India (GOI) has realised IoT’s business prospect and published draft policy on IoT in 2015. To achieve a better result, the prospective users are required to be kept aware regarding the prospect of IoT and for this, social media is expected to play a pivotal role. Social media can highlight advantages of IoT and in turn, these advantages would be made known to other potential users through Word of Month (WOM). This would bring in projection of a new business paradigm as contemplated by GOI. The purpose of this paper is to identify the factors affecting actual use of IoT by the potential users of India and to develop a conceptual model. The model has been verified through survey with inputs from 208 participants identified from three metropolitan cities of India. The responses have been quantified through 5-Point Likert scale. The result shows the implication that social media and WOM almost equally influence Indians to use IoT.
The Effect of Inhibition on the Processing and Comprehension of L2 Garden Path Sentences

The present research explored the relationship between syntactic disambiguation and inhibition in L2 learners by conducting eye movement tracking experiment and a nonverbal Stroop task. As L2 learners, 53 Chinese college students participated in the present experiment, in which the eye movement tracking technique was used to monitor the eye movement patterns of the participants while they were reading L2 (English) garden path sentences, and the nonverbal Stroop task was completed to investigate the inhibitory control ability of the participants. The results showed that the individual differences in inhibition ability do not affect the initial analysis process, but they do affect the disambiguation process. Also, the results suggested that there seems no relation between the individual inhibition ability and the garden path recovery. In the processing of L2 garden path sentences, the role of inhibitory control may be general domain.
A Novelty Approach to Retina Diagnosing Using Biometric Techniques With SVM and Clustering Algorithms

A method to extract the retina characteristic points for the purpose of medical diagnosis of the human eye is presented in this research. The proposed method helps to make the primary decision about the illness faster and can be used on mobile devices. The algorithm is mostly based on the characteristic points (the so-called minutiae). These structures are commonly used in the biometric applications for fingerprint-based people recognition. In the case of the conducted research, this trait was used to differentiate healthy eyes from unhealthy ones. The methods were evaluated by appropriately implemented algorithms, showing promising results. Each solution was created with object-oriented programming language. The accuracy of the classification (healthy versus samples with pathological changes) was evaluated using four algorithms: $k$ -Nearest Neighbors, $k$ -Means and Support Vector Machines (SVM) with linear and third-degree polynomial as well as our own approach based on counting the minutiae number. Performance requirements were also checked, and it was verified that the computing power of modern mobile devices is sufficient to implement the proposed solution. The highest accuracy result was equal to 96,45% and was obtained with the third-degree polynomial SVM. This was a novel approach. For comparative purposes, we also implemented currently used solutions for image analysis – deep learning (DL) and Convolution Neural Networks (CNNs). Both medical and computer science backgrounds are presented in the work with the main methodology components to include image segmentation using the Gaussian Matched Filter, binarization by Local Entropy Thresholding and classification with the previously mentioned approaches.
Parasitological and Comparative Pathological Studies on Monogenean Infestation of Cultured Sea Bream (Sparus aurata, Spariidae) in Egypt

This investigation was realized between December 2012 to November 2013. A total of 400 Sea bream (Sparus aurata) were sampled from a private fish farm in Ezbet Elborg area, Domietta province, Egypt and were investigated in terms of monogenean parasites . The prevalence, seasonal dynamics, mean intensity as well as the histopathological influence of the detected monogeneans were estimated. The standard parasitological methods were used in the survey and yielded four monogenean species on the Sea bream gills: Furnestinia Echeneis (58.59? of invaded fish), Encotyllabe spari 32.81? , Sparicotyle chrysophrii 25.78% and Choriocotyle chrysophrii 17.19? The total prevalence was 32? and the highest rate was recorded during summer. With the exception of Encotyllabe spari, the detected parasites are new records for the monogenean fauna of Egypt ( new geographical record) and also Sea bream (Sparus aurata) is a new host record for the monogenean Choriocotyle chrysophrii. Histopathological examination revealed different pathological changes in the affected bronchial tissue. The lesions were depended on the type of the detected monogenea. Some parts of the brachial tissue showed hyperplasia of mucous secreting cells, hemorrhages and edema. Necrosis of the gill lamellar epithelium was a common finding at the sites of parasites attachment. In these sites, the necrotic tissue debris was noticed between gill lamellae. In the necrotic part, the secondary lamellae were fused, atrophied with mononuclear cells infiltration. The monogenean parasites were prominently noticed and attached to the gill filaments. The anterior part of the parasites in some cases appeared attached to the epithelial lining of the secondary lamellae with desquamated cells. In other cases, cross sections of the parasites were embedded in the bronchial tissue either gill filaments or gill rakers. Focal areas of denuded epithelium were noticed, in such cases, prominent mononuclear cells infiltration was common.
Amblyseius californicus (McGregor, 1954) (Acari: Phytoseiidae), a new record for the Turkish fauna.

During 2001-2003, Amblyseius californicus (McGregor) was collected from the strawberry, peach, bean and pepper, where it was associated with Tetranychus urticae and Panonychus ulmi in Kusadasi, Aydin, Turkey. The descriptions and drawings of male and female A. californicus, which is a new record for the Turkish fauna, are given.
Studies On Entomopathogenic Fungi: Evaluations Of Germination Protocols For Assesssing Conidial Quality And Modified Atmosphere Packaging For Enhancing High-Temperature Shelf Life

A substantial number of mycoinsecticides and mycoacaricides have been developed worldwide since the 1960s. Here I present information generated from an updated, comprehensive list of these products. At least 12 species or subspecies (varieties) of fungi have been employed as active ingredients of mycoinsecticides and mycoacaricides for inundative and inoculative applications, although some are no longer in use. Products based on Beauveria bassiana (33.9%), Metarhizium anisopliae (33.9%), Isaria fumosorosea (5.8%), and B. brongniartii (4.1%) are the most common among the 171 products presented in this Chapter. Approximately 75% of all listed products are currently registered, undergoing registration or commercially available (in some cases without registration), whereas 15% are no longer available. I was unable to determine the status of the remaining 10%. Insects in the orders Hemiptera, Coleoptera, Lepidoptera, Thysanoptera, and Orthoptera comprise most of the targets, distributed among at least 48 families. A total of 28 products are claimed to control acarines (mites and ticks) in at least 4 families, although only three products (all based * Faria, M.R. de, Wraight, S.P., 2007. Mycoinsecticides and mycoacaricides: a comprehensive list with worldwide coverage and international classification of formulation types. Biological Control 43, 237-
Self-Heating Investigation in SOI MOSFET Structures with High Thermal Conductivity Buried Insulator Layers

The self-heating of thin and short silicon on insulator (SOI) devices is a serious problem limiting operation and reducing device lifetime. The main source of this phenomenon is low thermal conductivity of SiO<inf>2</inf> buried oxide <tex>$(\lambda_{\mathrm{Si}\mathrm{O}2}=1.4\mathrm{W}/\mathrm{m}\cdot \mathrm{K}$</tex> 100 time lower than that of silicon <tex>$\lambda_{\mathrm{Si}}=140\mathrm{W}/\mathrm{m}\cdot \mathrm{K})$</tex>. In this work three novel buried oxide (BOX) configurations in SOI MOSFET structure which suppress the self-heating effect are analyzed: 1) ultra-thin SiO<inf>2</inf> BOX; 2) buried layers with high thermal conductivity material AlN, Al<inf>2</inf>O<inf>3</inf>, Si<inf>3</inf>N<inf>4</inf>; 3) stack of traditional SiO<inf>2</inf> BOX with AlN plug. TCAD was used to study the 2D temperature distribution in device structures. The simulation results show that the thermal dissipation and self-heating were dramatically improved in all the devices. The UTB and UTBB ultra-thin SiO<inf>2</inf> BOXes reduce the maximum internal device temperature 25 K and 39 K lower than that of normal SiO<inf>2</inf> BOX; AlN, Al<inf>2</inf>O<inf>3</inf>, Si<inf>3</inf>N<inf>4</inf> BOXes reduce <tex>$\mathrm{T}_{\max}$</tex> by 31–24 K lower than that of SiO<inf>2</inf> BOX. The advantages and difficulties in practical use of the SOI MOSFETs with novel buried insulator layers are discussed.
Antimicrobial effects of yarrow (Achillea millefolium ) essential oils against Staphylococcus species

Staphylococcus aureus , a gram positive, non-motile, catalase and coagulase positive, facultative anaerobe coccus is a common type of bacteria that normally lives on the skin and nasal passages of healthy people. The genus Achillea (Asteraceae) was named after the mythological Greek warrior Achilles who used Achillea species for healing wounded-soldiers during the Trojan war. The genus Achillea comprises of ~85 species, most of which are endemic to Europe and the Middle East. The aim of this study was to evaluate antimicrobial effects of yarrow ( Achillea millefolium ) essential oils against Staphylococcus spp. , fourteen clinical isolates of Staphylococcus cultured from patients. The disc diffusion method was used for determination of antimicrobial activity of essential oil. Results showed that this inhibitory effect is dose-dependent to wit, by increasing the concentration of the extract in the culture media; reduction in growth was obviously revealed. In conclusion, it can be stated that yarrow essential oils have inhibitory effect against Staphylococcus spp.
Improving Availability and Confidentiality via Hyperchaotic Baseband Frequency Hopping Based on Optical OFDM in VLC Networks

The security in visible-light communication (VLC) networks has recently gained attention. This work presents a hyperchaotic baseband frequency hopping method based on optical orthogonal frequency-division multiplexing (Hyperchaos-BB-FH-OOFDM) that can enhance both confidentiality and availability in VLC networks. A four-dimensional hyperchaotic scheme is employed for chaotic mapping. The proposed method exploits the random feature of the input data and chaotic codes to improve the confidentiality against correlation and statistical attacks and to improve the availability against illumination, disguised, and noise jamming. Using the chaotic scheme, the bipolar real OFDM samples are exploited to create dynamic cypher keys and spreading codes that are updated at each frame throughout the entire session, leading to high security against malicious attacks. The proposed method employs a four-fold encryption approach that involves chaotic frequency/time-domain scrambling of OFDM subcarriers, chaotic phase scrambling of frequency-domain subcarriers, and chaotic spreading of the OFDM signals upon transmission. According to the results, in addition to enhancing the confidentiality and availability, the method supports multiplexing, reduces the peak-to-average-power ratio of the OFDM signal, and improves the bit error rate performance for OFDM-based VLC networks.
Nonlinearity Tolerant LUT-Based Probabilistic Shaping for Extended-Reach Single-Span Links

We propose Huffman-coded sphere shaping (HCSS) as a method for probabilistic constellation shaping which provides improved tolerance to fiber nonlinearities in single-span links. An implementation of this algorithm based on look-up-tables (LUTs) allows for low-complexity, multiplier-free shaping. The advantage of short-length shaping for mitigating fiber nonlinear impairments is experimentally demonstrated for a system employing dual–polarization 64–ary quadrature amplitude modulation (DP-64QAM) at 56 GBd and operating over 210 km of standard single-mode fiber (SSMF). A gain in achievable information rate (AIR) of 0.4 bits/4D-symbol compared with uniform signaling is measured, corresponding to a 100% improvement in shaping gain compared with ideal Maxwell–Boltzmann (MB) shaping. The combinatorial mapping and demapping algorithms can be implemented with integer addition and comparison operations only, utilizing an LUT with 100 kbit size.
Growth, Carcass and Meat Quality of Casertana, Italian Large White and Duroc x (Landrace x Italian Large White) Pigs Reared Outdoors

To compare growth, skeletal development, carcass traits and meat quality of different genotypes, 10 Casertana (CT), 10 Italian Large White (LW) and 10 Duroc x (Landrace x Italian Large White) (DU) crosses, barrows of 90 days of age, were allotted to the same outdoor rearing and feeding conditions. Live weight was recorded and average daily gain (ADG) was calculated. At slaughter (330-day-old) dressing and lean percentages were determined; backfat thickness and loin eye depth were measured. Carcasses were dissected into commercial cuts. Water holding capacity, pH and colour (45 min and 24 h post-mortem) were measured. Longissimus lumborum muscle samples were collected for cholesterol, α-tochopherol and intramuscular collagen (IMC) analyses. CT compared to DU and LW had the lowest growth rate and skeletal development. Casertana showed higher backfat thickness, lower lean cut/fatty cut ratio and less lean meat (P<0.05). Loin eye depth differed among genotypes with LW>DU>CT (P<0.05). CT showed higher red colour of the meat than DU and LW (P<0.05). CT compared to LW had the highest hydroxylysylpiridinoline (HLP) crosslink concentration and HLP/IMC ratio, and a lower IMC amount (P<0.05). Casertana pigs produced meat that could be tougher than that from the improved breed, but more acceptable from the technological point of view. At eleven months of age bone weight, length and diameter were clearly genetic type-related; differently, the bone maturity was similar among the genotypes studied.
Milk consumption and growth efficiency in Antarctic fur seal (Arctocephalus gazella) pups

The body composition, milk consumption, and growth efficiency of Antarctic fur seal (Arctocephalus gazella) pups were measured over three consecutive lactation periods (1990- 1992) at Bird Island, South Georgia. The body composition of pups differed between the sexes; whereas for any given age females were lighter than males, they had proportionately higher body lipid reserves than males. Milk consumption was determined on 388 occasions in 177 pups. Mean estimates of milk consumption ranged from 2.5 to 3.2 kg (42-53 MJ) during the 6-day perinatal period and from 2.9 to 3.6 kg (49-68 MJ) during the 1- to 2-day maternal attendance periods (feeding bouts). There were no differences in milk consumption between the sexes in any year. Both per-bout and per-day milk consumption increased steadily with age before decreasing significantly in the last 30-40 days of lactation. Per-bout milk consumption was positively related to the duration of the maternal foraging trip and attendance cycle, and both per-bout and per-day milk consumption were related to pup mass. Pups of both sexes consume the same amount of milk, but males direct more of their milk consumption into lean tissue growth than females, which accumulate greater adipose stores. Therefore, mass and mass changes may not be appropriate parameters for investigating differential maternal investment between the sexes in otariid pups.
Deletion of tumor necrosis factor death receptor inhibits amyloid β generation and prevents learning and memory deficits in Alzheimer's mice

The tumor necrosis factor type 1 death receptor (TNFR1) contributes to apoptosis. TNFR1, a subgroup of the TNFR superfamily, contains a cytoplasmic death domain. We recently demonstrated that the TNFR1 cascade is required for amyloid β protein (Aβ)–induced neuronal death. However, the function of TNFR1 in Aβ plaque pathology and amyloid precursor protein (APP) processing in Alzheimer's disease (AD) remains unclear. We report that the deletion of the TNFR1 gene in APP23 transgenic mice (APP23/TNFR1−/−) inhibits Aβ generation and diminishes Aβ plaque formation in the brain. Genetic deletion of TNFR1 leads to reduced β-secretase 1 (BACE1) levels and activity. TNFR1 regulates BACE1 promoter activity via the nuclear factor-κB pathway, and the deletion of TNFR1 in APP23 transgenic mice prevents learning and memory deficits. These findings suggest that TNFR1 not only contributes to neurodegeneration but also that it is involved in APP processing and Aβ plaque formation. Thus, TNFR1 is a novel therapeutic target for AD.
GAME-ON: A Multimodal Dataset for Cohesion and Group Analysis

This paper presents GAME-ON (Group Analysis of Multimodal Expression of cohesiON), a multimodal dataset specifically designed for studying group cohesion and for explicitly controlling its variation over time. Cohesion is here addressed according to the Severt and Estrada’s theoretical multidimensional integrative framework. More specifically, GAME-ON focuses on the social and task dimensions of the instrumental function of cohesion. The dataset consists of over 11 hours of synchronized multimodal recordings (audio, video, and motion capture data) of 17 small groups (3 persons) playing a social game, i.e., an escape game. The game consists of several tasks designed to manipulate the variation of cohesion over time. GAME-ON includes annotations consisting of self-assessment of cohesion and other constructs such as emotions, leadership, and warmth and competence. A first statistical analysis of these annotations shows that we successfully manipulated all the relative variations of cohesion (between tasks) over time. This holds for all tasks except for one where we observed a significant variation of cohesion in the opposite direction than expected. The dataset will be publicly available for research purposes. The motivation of our work is to provide the scientific community with an asset for studying cohesion and other group phenomena.
New Syndrome of Mixed Bacterial and Viral Etiology in Cultured Turbot Scophthalmus maximus

Abstract A new disease occurred from August to October 1987 in cultured turbot in northwestern Spain. During the outbreak, there was continuous but low mortality, with a total cumulative loss of 4% of the affected population. Diseased turbot did not display unusual swimming behavior; the external signs of the disease were abdominal distension and hemorrhagic foci in the anus and base of the fins. Internal examination of affected fish revealed a swollen stomach and intestine, which were filled with a mucous liquid. The internal wall of the peritoneal cavity was hemorrhagic, and there was an accumulation of reddish fluid in the cavity. Bacteriological analysis revealed a sucrose-negative Vibrio in the kidney, liver, and spleen. This bacterium's biochemical and serological characteristics resembled those of V. splendidus. Samples of internal organs were processed for virological analysis and inoculated on chinook salmon embryo (CHSE-214) cells, which showed a cytopathic effect after 14 d at 15°C. The viral a.
Performance Prediction of Big Data Transfer Through Experimental Analysis and Machine Learning

Big data transfer in next-generation scientific applications is now commonly carried out over connections with guaranteed bandwidth provisioned in High-performance Networks (HPNs) through advance bandwidth reservation. To use HPN resources efficiently, provisioning agents need to carefully schedule data transfer requests and allocate appropriate bandwidths. Such reserved bandwidths, if not fully utilized by the requesting user, could be simply wasted or cause extra overhead and complexity in management due to exclusive access. This calls for the capability of performance prediction to reserve bandwidth resources that match actual needs. Towards this goal, we employ machine learning algorithms to predict big data transfer performance based on extensive performance measurements, which are collected over a span of several years from a large number of data transfer tests using different protocols and toolkits between various end sites on several real-life physical or emulated HPN testbeds. We first identify a comprehensive list of attributes involved in a typical big data transfer process, including end host system configurations, network connection properties, and control parameters of data transfer methods. We then conduct an in-depth exploratory analysis of their impacts on application-level throughput, which provides insights into big data transfer performance and motivates the use of machine learning. We also investigate the applicability of machine learning algorithms and derive their general performance bounds for performance prediction of big data transfer in HPNs. Experimental results show that, with appropriate data preprocessing, the proposed machine learning-based approach achieves 95% or higher prediction accuracy in up to 90% of the cases with very noisy real-life performance measurements.