Miracle-dz/UniFAR-FTUs huggingface dataset:
Open-source multi-protocol gateway for Internet of Things

Internet of Things (IoT) is becoming increasingly ubiquitous with global applicability in a very wide spectrum of commercial sectors, making use of diverse technologies for custom usage in certain environments. The diversity of applications and protocols and the custom needs for each environment introduce a big challenge for IoT applications, demanding the support of communication and message exchanges in implementations that aggregate diverse protocols. The paper addresses the design of an open-source multiprotocol gateway which supports two communication protocols, namely, Bluetooth Low Energy (BLE) and LoRa. The proposed gateway represents the bridge between the devices and the network server and transports the received messages via a standard format readable by the network server. The messages between the gateway and the network server are transported over the Message Queuing Telemetry Transport (MQTT) protocol. The paper details the implementation of the proposed multi-protocol gateway and highlights its benefits. In particular, the proposed gateway demonstrates support for multiple protocols on the same hardware base and eliminates the need of using multiple hardware components for each type of protocol.
Aalborg Universitet Nutritional status , self-identification as a traditional food consumer and motives for food choice in six European countries

Purpose – The aim of the study is to analyse whether self-identifying as a traditional food consumer is associated with obesity or overweight, and to investigate the motives for consuming traditional food among people with normal weight, overweight and obesity. Design/methodology/approach – Cross-sectional data were collected through a pan-European consumer survey (n 1⁄4 4,828) with samples representative for age, gender and region in Belgium, France, Italy, Norway, Poland and Spain. Data analyses included multivariate logistic regressions and a multi-group structural equation modelling analysis. Findings – Individuals with overweight or obesity attach more importance to weight control when purchasing food, and tend to self-identify themselves more as consumers of traditional foods. Among individuals with obesity, importance attached to the natural content of food is directly associated with traditional food consumption, and importance attached to sensory appeal in food choice is indirectly associated with self-identification as a traditional food consumer. Among individuals with normal weight, importance attached to healthiness in food choice associates negatively with self-identification as a traditional food consumer. Originality/value – This study provides a unique approach for testing the motives for consuming traditional food among people with different nutritional status, particularly with normal weight, overweight and obesity. The strength of this paper pertains further to its international scope and large representative data set.
Self inductance of a wire loop as a curve integral

It is shown that the self inductance of a wire loop may be written as a curve integral akin to the Neumann formula for the mutual inductance of two wire loops. The only difference is that contributions where the two integration variables get too close to each other must be excluded from the curve integral and evaluated in detail. The contributions of these excluded segments depend on the distribution of the current in the cross section of the wire. They add to a simple constant proportional to the wire length. The error of the new expression is of first order in the wire radius if there are sharp corners and of second order in the wire radius for smooth wire loops.
A proposal to support ubiquitous libraries

Ubiquitous computing aims to make tasks that depend on computing transparent to the users, providing resources and services anytime and anywhere. This paper proposes a computational model to support ubiquitous libraries. The model provides appropriate information and tools to the librarian to maintain resources and services in a library, as well tools that allow offering better services to the library users. We developed a prototype that has been evaluated by thirteen volunteers in the Library of Centro Universitario Univates (Univates). The results showed a good acceptance of the model. Keywords: Ubiquitous Computing, Mobile Computing, Ubiquitous Libraries, Intelligent Systems.
Latex allergy as risk factor for the sensitization to aero-and tropho-allergens among Albanian dental students

Latex allergy is a common occupational disease among healthcare workers who use latex gloves. The aim of the present study was to determine the prevalence of allergy to latex gloves among dental students and its role on the sensitization to different aeroand tropho-allergens. In this prospective study, a total of 240 students separated as nonexposed, shortly-exposed and longer-exposed during school practice completed a self-administrated questionnaire that comprised of a total of different items and gave information about the participants and their glove use, working habits and glove use, signs and symptoms related to glove use, additional allergic diseases, etc. Challenge and patch tests were performed through latex gloves, skin prick test for latex, aeroallergens and trophoallergens with commercial extracts. Questionnaire items and diagnostic tests revealed that one-fourth of subjects were suspicious for latex gloves hypersensitivity. Contact urticaria, irritant or allergic dermatitis was observed on 10% to 14% of students, while the noncutaneous symptoms were observed on less than 5% of them. In general, students who experienced latex exposure over 2 years during their school practice have shown about a two-fold positive response for statements or diagnostic tests about latex gloves hypersensitivity. Sensitization to aeroallergens and trophoallergens is shown in 38% and 12% of subjects respectively. Because of relationship between allergic reactions to latex gloves and some medical histories, it seems to be necessary for pre-matriculation evaluation and periodic health surveillance of dental students. Latex allergy also seems to be a risk factor for the sensitization to diverse atopenes.
Clinical Response to Oral Ibandronate in Paget ’ s Disease of Bone

Introduction: The main goals of treatment of Paget’s disease of the bone (PDB) are to restore normal bone metabolism, relieve bone pain and prevent future complications. Bisphosphonates have been used for several decades, as the class of choice for the treatment of PDB. Oral alendronate and risedronate are used in higher doses than those used for osteoporosis that is 30mg/day and 20 to 40mg/day for 2 and 6 months respectively. Pamidronate (60-180mg), ibandronate (2mg) and zolendronate (5mg) can also be used intravenously. There is no data on the treatment of PDB with oral ibandronate. Objective: To evaluate the clinical and laboratory response of the treatment with oral ibandronate (150mg/month) for 6 months in 12 patients with PDB. Methods: We evaluated the intensity of bone pain, the alkaline phosphatase (AP) and serum ß-cross-links-C telopeptide (sCTX) in 12 patients with PDB, mean age 68.6 ± 11,2 years-old, serum AP 1.49 ± 0,98 times the upper limit of normal (ULN). sCTX was elevated in 10 patients (mean 2,13 ± 1,2 times ULN), before treatment. Results: After 6 months of treatment, the mean reduction in sCTX was 65,24 ± 28,9%, reaching 80% in 7 patients. The mean reduction of sAP was 49,21 ± 37,9%, with all patients having normal values in sAP after treatment. There was a significant clinical response in all patients, with marked improvement in bone pain. Conclusion: Our data demonstrate high efficacy of oral ibandronate in the treatment of PDB at equivalent doses used in the treatment of osteoporosis.
14_Artificial Insemination and Its Role in Transmission of Swine Viruses

Artificial insemination (AI) in swine is not a new technique and reports as early as the 1930s (Lush, 1925) describe collecting semen for AI. However, because of farm structure changes, increasing farm sizes and separation of production stages, interest in intensive pig production is growing and AI has become a critical component in modern pig production. In 2001, nearly 60% of North American swine herds utilized AI (Singleton, 2001), a drastic increase from the estimated 5% in the 1990’s (Flowers & Esbenshade, 1993). This is still relatively low compared to the 90% or greater use of AI in Western Europe (Madsen, 2005; Maes et al., 2008). The extensive use of AI in pig reproduction in the last decade has facilitated the exchange of desirable genetic characteristics at an international level, allowing producers to make greater use of superior genetics at a lower cost than some natural-service systems (Gerrits et al., 2005). However, the growth in use of AI has increased the risk of quick and widespread transmission of venereally transmissible pathogens (Thacker et al., 1984). It has been reported that the porcine male reproductive tract is highly susceptible to viral infections (Phillips et al., 1972; Spradbrow, 1968). This, coupled with the ability of boars to produce tens to thousands of insemination doses per week and the widespread distribution of the processed semen (both nationally and internationally), further increases the risk of wide transmission of viral pathogens by semen.
Intelligent nanophotonics: merging photonics and artificial intelligence at the nanoscale

Abstract Nanophotonics has been an active research field over the past two decades, triggered by the rising interests in exploring new physics and technologies with light at the nanoscale. As the demands of performance and integration level keep increasing, the design and optimization of nanophotonic devices become computationally expensive and time-inefficient. Advanced computational methods and artificial intelligence, especially its subfield of machine learning, have led to revolutionary development in many applications, such as web searches, computer vision, and speech/image recognition. The complex models and algorithms help to exploit the enormous parameter space in a highly efficient way. In this review, we summarize the recent advances on the emerging field where nanophotonics and machine learning blend. We provide an overview of different computational methods, with the focus on deep learning, for the nanophotonic inverse design. The implementation of deep neural networks with photonic platforms is also discussed. This review aims at sketching an illustration of the nanophotonic design with machine learning and giving a perspective on the future tasks.
Insect Pathogenic Properties Of Serratia Marcescens. Passive And Active Resistance To Insect Immunity Studied With Protease-Deficient And Phage-Resistant Mutants

SUMMARY: Many insects have a cell-free immune system in which small basic proteins called cecropins are the main defence against Gram-negative bacteria. We have earlier shown that an insect pathogenic strain of Serratia marcescens was resistant to insect immunity and that certain mutants resistant to phage φJ become sensitive to cecropins. We have found that protease-deficient mutants with and without resistance to φJ appear to be deficient in the mechanism of protease induction. Three different protease fractions exist and for two of the enzymes we describe a partial purification and characterization. The proteases show pronounced autodegradation which increases with the purity. Both enzymes are only partly affected by EDTA and they are highly toxic to Drosophila melanogaster. All three enzymes destroy cecropins in immune haemolymph from Cecropia pupae. However, in vivo experiments with different mutants indicate that in Serratia, passive resistance to insect immunity is more important for virulence in Drosophila than the production of proteases which can destroy cecropins.
The Determinants of New Venture Performance: An Extended Model

This article argues that new venture formation is a special case of strategic management theory. Thus, Sandberg & Hofer's (1987) model of new venture performance, which states that new venture performance is a function of industry structure, venture strategy, and the founding entrepreneur, must be extended to include the resources and the organizational structure, processes, and systems developed by the venture to implement its strategy and achieve its objectives. The key assumptions underlying this model are presented, and specific propositions concerning how resources and organizational structure, processes, and systems affect new venture performance are developed.
A Stabilizing Controller for Hopping Motions

Feedback linearization is employed to explicitly control the ground reaction force exerted onto a kangaroo locomotor to describe its periodic motion and robustness to floor perturbations. Such description is enabled through kinematic constraints that allow the determination of an inverse for the leg's redundant motion. To determine the locomotor's stabilizing force, a rest Length Modulated Viscoelastic force subject to the Floating structure's posture control (LMVF) is here derived, yielding an automatic energy regulation of the gait, which is a product of a force feedback modulation paired with dissipation.
Identification of Differences between Dutch Language Varieties with the VarDial2018 Dutch-Flemish Subtitle Data

With the goal of discovering differences between Belgian and Netherlandic Dutch, we participated as Team Taurus in the Dutch-Flemish Subtitles task of VarDial2018. We used a rather simple marker-based method, but a wide range of features, including lexical, lexico-syntactic and syntactic ones, and achieved a second position in the ranking. Inspection of highly distin-guishing features did point towards differences between the two language varieties, but because of the nature of the experimental data, we have to treat our observations as very tentative and in need of further investigation.
Discretionary Policy Interactions and the Fiscal Theory of the Price Level: A SVAR Analysis on French Data

We estimate a structural vector autoregressive (SVAR) model of the French economy. The econometric method originates in Blanchard and Perotti [Quarterly Journal of Economics, 2002] but owes also extensively to the fiscal theory of the price level (FTPL) that investigates the interactions between government surplus, debt accumulation and price dynamics. We have the objective, on the one hand, of assessing the effects of fiscal and monetary policy shocks on the economy; and, on the other, of studying the strategic interactions between fiscal and monetary authorities. As a consequence, the theoretical restrictions to identify our model are derived from a FTPL framework. Our estimations reveal so-called Keynesian features of fiscal and monetary shocks; meanwhile, they are consistent with the prediction of the FTPL as regards price dynamics. Although the first part of our findings agree with most of the recent literature on the subject, the non-rejection of the FTPL is an originality.
Neccessasity of Bio-imaging Hybrid Approaches Accelerating Drug Discovery Process (Mini-Review)

Imaging technologies have made a significant improvement in the past few decades and their application made a great impact on accelerating the process of drug discovery and development. The ability to non-invasively image an animal model or co-cultured live cells and validate potential drug target, biomarkers of drug efficacy and assess a pharmacological drug interaction significantly contributes to the process of translating molecules into medicines. This paper summarizes current trends in bio-imaging technologies and their application on the process of drug discovery. In particular, High Content Screening (HCS) and Virtual Screening (VS) are reviewed, and their respective examples are discussed to gain insight into state-of-the-art bio-imaging methodologies used for extracting knowledge and its application to drug discovery. This paper argues the need to reduce the gap between experimental (e.g. HCS based assays) and theoretical (e.g. VS based assays) assays. Although HCS and VS are leading drug discovery choices for the pharmaceutical industry and such investigations have been carried out in their respective campaign, the potential effects of these approaches together to facilitate the process of drug discovery has rarely been reported. Further, the prevalent research trends on developing hybrid approaches such as VS complementing HCS implies substantial enhancement to the goal of reliable drug candidate identification.
STUDENTS ' PERCEPTIONS ACCORDING TO ACTIVE METHODOLOGIES IN THE CONTEX OF THE EUROPEAN HIGHER EDUCATION AREA

The learning methodologies (i.e., participative), the perceived institutional support and the learning performance linked to the use of information and communication technologies (ICT) are considered as the background to student satisfaction and the development of generic and specific skills for professional results after graduating. In particular, the main objective of this paper is to research about students` perceptions according to participative learning methodologies, the use of ICT and the institutional support in the University context. This study, firstly, provides an analysis of the expectations of current undergraduate students in the business and management area. Secondly, it analyzes a causal model related to the use of participative teaching methodologies, perceived institutional support, and the use of ICT in the teachinglearning process, as determinants of student satisfaction and skills development in the context of the European Higher Education Area (EHEA). Previous analysis about expectations and the theoretical model and the hypotheses discussed above are validated through a non-probabilistic sampling and self-selection. The data are particularly collected from a sample of questionnaires filled out by second course undergraduate students (University of Seville, Spain).
Aggression Detection in Social Media using Deep Neural Networks

With the rise of user-generated content in social media coupled with almost non-existent moderation in many such systems, aggressive contents have been observed to rise in such forums. In this paper, we work on the problem of aggression detection in social media. Aggression can sometimes be expressed directly or overtly or it can be hidden or covert in the text. On the other hand, most of the content in social media is non-aggressive in nature. We propose an ensemble based system to classify an input post to into one of three classes, namely, Overtly Aggressive, Covertly Aggressive, and Non-aggressive. Our approach uses three deep learning methods, namely, Convolutional Neural Networks (CNN) with five layers (input, convolution, pooling, hidden, and output), Long Short Term Memory networks (LSTM), and Bi-directional Long Short Term Memory networks (Bi-LSTM). A majority voting based ensemble method is used to combine these classifiers (CNN, LSTM, and Bi-LSTM). We trained our method on Facebook comments dataset and tested on Facebook comments (in-domain) and other social media posts (cross-domain). Our system achieves the F1-score (weighted) of 0.604 for Facebook posts and 0.508 for social media posts.
Predictions of protein–RNA interactions

Ribonucleoprotein interactions play important roles in a wide variety of cellular processes, ranging from transcriptional and posttranscriptional regulation of gene expression to host defense against pathogens. High throughput experiments to identify RNA–protein interactions provide information about the complexity of interaction networks, but require time and considerable efforts. Thus, there is need for reliable computational methods for predicting ribonucleoprotein interactions. In this review, we discuss a number of approaches that have been developed to predict the ability of proteins and RNA molecules to associate. © 2012 John Wiley & Sons, Ltd.
Work in Progress: Performance Modeling for Data Distribution in Heterogeneous Computing Systems

Balanced data distribution among devices in Heterogeneous Computing System (HCS) is key to improved application performance. This work presents a model that estimates data distribution ratio for CPU-GPU co-execution of a data-parallel application in an HCS node. This estimation is based on relative application performance on the devices. To find the relative performance, we build individual performance models of each device (CPU-GPU) using off-line profile information. Our CPU and GPU models have an average performance estimation error of 8.64% and 9.03% respectively w.r. t. the measured performance, across a set of data-parallel benchmarks. With CPU-GPU co-execution, an average performance improvement of 37.3% is seen across benchmarks.
Data-mining Approaches for the Study of Emotional Responses in Healthy Controls and Traumatic Brain Injured Patients: Comparative Analysis and Validation

Relationship between Heart Rate Variability (HRV) and emotions subjectively reported by 26 healthy subjects during symphonic music listening have been investigated through Data Mining approaches. Most reliable decision models have been successively adopted to forecast an emotional assessment on a group of 16 Traumatic Brain Injured patients during the same type of stimulation, without algorithms retraining. The most performing decisional models have been a Rule Learner (ONE-R) and a Multi Layer Perceptron (MLP) but, comparing them, the first one was the best in terms of reliability both on validation and independent test phases. Furthermore, ONE-R provides a simple “human-understandable” rule useful to evaluate emotional status of a subjects depending only on one HRV parameter: the normalized unit of Low Frequancy BandPower (nu_LF). Specifically, the classification by HRV nu_LF matched that on reported emotions, with 76.0% of correct classification; tenfold cross-validation: 70.2%; leave-one-out validation: 71.1%. On the other hand, MLP approache has provided an accuracy of 82.69% on healthy controls, but it has decreased to 47.11% and 46.15% on 10folds-cross and leave-one-out validation respectively. Finally, the accuracy has resulted in 51.56% when the MLP model has been applied to the posttraumatic subjects, while the ONE-R accuracy has resulted in 70.31%. Data mining proved applicable in psychophysiological human research.
Why Tourists Don’t Visit Again?

The population of tourists has grown rapidly with the development of social media. The rise of social media has changed the behaviors that passengers visit sightseeing spots. Online consumer reviews could be considered as the main channel for providing valuable information to consumers. Revisit intention could directly influence the future behavior of customers. It’s also one of the crucial factors that enhance the income growth of tourism. However, relatively few researchers focused on why passengers don’t revisit directly. Therefore, this study will focus on the topic that why passengers didn’t revisit again. We’ll use textual reviews of social media instead of questionnaire survey. And text mining and feature selection (Least Absolute Shrinkage and Selection Operator, LASSO) methods have been employed to identify the factors that affect passenger non-revisit intention. From the results, this study will provide some suggestions for the tourism industry to improve their service quality and increase their revisit intentions.