Miracle-dz/UniFAR-FTUs huggingface dataset:
The role of certain Brauer and Rado results in the nonnegative inverse spectral problems

We say that a list $\Lambda =\{ \lambda _{1},\ldots ,\lambda _{n}\}$ of complex numbers is realizable, if it is the spectrum of a nonnegative matrix $A$ (the realizing matrix). We say that $\Lambda $ is universally realizable if it is realizable for each possible Jordan canonical form allowed by $ \Lambda . $ This work does not contain new results. As its title says, our goal is to show and emphasize the relevance of certain results of Brauer and Rado in the study of nonnegative inverse spectral problems. We show that virtually all known results, which give sufficient conditions for the list $ \Lambda $ to be realizable or universally realizable, can be obtained from the results of Brauer or Rado. Moreover, in this case, we may always compute a realizing matrix.
Enabling High Performance Fog Computing through Fog-2-Fog Coordination Model

Fog computing is a promising network paradigm in the IoT area as it has a great potential to reduce processing time for time-sensitive IoT applications. However, fog can get congested very easily due to fog resources limitations in term of capacity and computational power. In this paper, we tackle the issue of fog congestion through a request offloading algorithm. The result shows that the performance of fogs nodes can be increased be sharing fog's overload over several fog nodes. The proposed offloading algorithm could have the potential to achieve a sustainable network paradigm and highlights the significant benefits of fog offloading for the future networking paradigm.
Method of loss calculation based on mechanical quality factor measurement

Calculation of the mechanical quality factor from the impedance spectrum for the resonance and antiresonance in standard set of ceramic samples published previously was extended for square plate resonator. Such results allow for the evaluation of all losses, but from different samples with problematic uniformity of properties. This contribution presents evaluation of the full set of elastic, dielectric and piezoelectric losses from the mechanical quality factor measurements on disc and square plate resonators. Measured impedance spectra are compared with calculations and used for the characterization of losses on lead-free BCZT and lead-based PZT piezoelectric ceramics as an example.
PREVALENCE OF NONTOXIC NODULAR GOITER AFTER A NEARLY TWO-DECADE UNIVERSAL SALT IODIZATION IN A LITTORAL REGION OF SHANDONG PROVINCE, CHINA.

Introduction With the introduction of iodized salt, more and more people are exposed to iodine sufficiency in some regions. The purpose of this study was to investigate the prevalence of nontoxic nodular goiter (NTNG) in the littoral region with high iodine supply after a nearly two- decade universal salt iodization. Subjects and Methods Eight hundred and thirty- five participants (from 25~65 years; males 421 and females 414) were invited for the study from Huan-cui District of Weihai City, Shandong Province from January 2013 to September 2014. All participants were inspected and diagnosed by endocrinologists according to the thyroid function tests and the thyroid gland imaging. After the normal diet of three days, the urine samples of the participants were collected between 8:00AM and 9:00AM and the urinary iodine (UI) concentrations were analyzed using Urinary Iodide Test Kit. Results The overall prevalence of NTNG in the region was 40.1%, and different prevalence occurred in the different age ranges (p<0.01). The prevalence of NTNG was 32.51%, 37.44%, 49.70%, 58.57 and 74.77% in the age group of ≤ 30, 31-40, 41-50, 51-60 and >60 years, respectively. Meanwhile, the prevalence of NTNG in women (42.08%) was higher than that in men (34.29%, p<0.05). The median of UI concentrations were 139. 4μg/L and 101. 5μg/L for the group with NTNG and without NTNG, respectively (p<0.01). However, there was no significant difference in UI concentrations among the groups with different age ranges (p>0.05), and statistical difference was not observed for UI concentrations between women and men (p>0.05). Intriguingly, higher UI concentrations were found in the group with larger thyroid size (p<0.01). Conclusion The iodine excess can lead to the high occurrence of nodular goiter in the littoral region, and individual UI concentration detection is recommended for the iodine nutritional status analysis among normal people when Universal Salt Iodization (USI) continues to be implemented in the region.
QP-based task-space hybrid / parallel control for multi-contact motion in a torque-controlled humanoid robot

Humanoid robots rely on precise interaction force to locomote and perform various tasks. Controlling torque usually allows humanoid robots to produce these desired forces on known environments. However, the tracking may be imperfect in the absence of torque feedback or with an imprecise environment model. Furthermore, the presence of geometric errors, regarding the model of the environment, can also lead to discrepancies between desired and actual forces. In this paper, we extend our previous QP-based robust torque control framework to allow force control without requiring joint torque feedback. The control relies only on force/torque sensors at the end effectors, joint encoders and IMUs for kinematic feedback. Additionally, it is formulated to keep consistency with the internal state of the QP solver. We show that hybrid or parallel control, where position and force can be controlled independently, is possible with this approach. The framework is validated with stabilizer-free locomotion on uneven terrain and a multi-contact scenario with reference forces.
A Machine Learning Based Approach to Crack Detection in Asphalt Pavements

Asphalt roads are always prone to surface cracks because of the stress produced by automobiles, drastic climatic changes and improper laying of the roads. These cracks reduce the load-bearing capacity of the roads, penetrate deeper into the structures and lead to severe degradation of the roads. There are several existing methods to detect the cracks, including destructive methods and Non-Destructive Testing (NDT) methods. Non-destructive methods such as Ground Penetrating Radar based techniques are preferred now-a-days but suffer from the necessity of high-cost equipment. In this study, twelve models: six deep learning based models and six models designed by combining image processing techniques and machine learning, are proposed and evaluated for their effectiveness in detecting cracks on the road surface. Five different measures are employed for performance evaluation. It is observed from the results that deep learning based models are well suited to detection of cracks on the road surface.
Monitoring System in Lora Network Architecture using Smart Gateway in Simple LoRa Protocol

LoRa is a low power wide area network communication that has ability to transmit data in a long range with a low power. Lora Alliance has designed a network architecture that usually used, called LoRaWan. The implementation of LoRaWan itself has not meet the requirement of LoRa as one of the LPWAN protocol because it still depend on the internet in processing and displaying data. To meet the purpose of Low Power Wide Area Network (LPWAN) such as simple implementation, low cost, and simple architecture, this research using Simple LoRa Protocol (SLP) with Raspberry Pi 3b+ as the smart gateway. The module used is Dragino LoRa that work in 868 MHz with Spreading Factor 7 and Arduino UNO for the client. The architecture build used in this research implemented to a monitoring system. The system build only to access a Local Area Network. Result showed that the smart gateway could handle the LoRa communication and the monitoring information system together. It also showed that the system average throughput is 489 bit/s and has packet loss 26% at 1-meter distance.
Do domiciliary visits in elderly people with psychiatric problems fulfil the expectations of general practitioners

The domiciliary visit (DV) is a well-established component of psychiatric care in the UK. Nevertheless, little is known about general practitioners' expectations of DVs or about what types of psychiatric services are likely to result from the DV. This prospective study set out to investigate the relationship between GPs' expectations and actual outcome after psychiatric DVs. It involved three old-age psychiatry services covering urban and rural areas in the UK and 197 older people who were seen at home by consultants in old-age psychiatry. The majority of GPs (80.6%) were very satisfied by the result of the DVs. However, GPs' expectations were not well matched by the services offered for three types of outcome: inpatient admission (p < 0.001), community psychiatric nurse input (P < 0.01) and outpatient follow-up (P < 0.0001). Lower levels of GP satisfaction were associated with their expectations about follow-up not being met. Psychiatrists need to bear in mind GPs' expectations and if possible speak to GPs after the visit to discuss possible management and follow-up.
A Sequence-to-Sequence Model Approach for ImageCLEF 2018 Medical Domain Visual Question Answering

Numerous attempts have been made in the recent past for the task of free-form and open-ended Visual Question Answering (VQA). Solving VQA problem typically requires techniques from both computer vision for a deeper understanding of the images and Natural language processing for understanding the semantics of the question and generating appropriate answers. It has caught the attention of a lot of researchers because of its enormous applications in the real-world scenarios. But none of the existing approaches are designed for the medical image-question pairs which require a sequence of words as an answer. We propose a novel approach by combining the tasks of Image captioning and Machine translation and provided a comprehensive model that takes a medical image-question pair as an input and generates a sequence of words as an answer. We evaluate our model on the dataset provided by ImageCLEF as a part of the ImageCLEF 2018 VQA-med challenge. We outperformed all the contestants of the challenge by achieving the best BLEU and WBSS scores. Furthermore, we provide additional insights that can be adopted to develop our baseline model and the challenges that lie ahead of us while building Machine learning models for medical datasets.
Revealing dynamical symmetries by bicircular high-harmonic spectroscopy beyond the Born-Oppenheimer approximation

We explore the possibility of bicircular high-harmonic spectroscopy to probe the laser-induced dynamics of molecules in a non-Born-Oppenheimer treatment. The numerical solutions of the timedependent Schrödinger equation for aligned H2 and its isotopologues in ω-2ω bicircular fields show that the intensity ratio between D2 and H2 for harmonic orders 3q is lower than that for orders 3q ± 1 (q ∈ N). Based on the symmetry analysis in the strong-field approximation, we demonstrate that the vibrational wave-packet motion and the dynamical symmetry lead to the different ratio. In general, the vibrational motion causes the ratio for both 3q and 3q ± 1 to increase with q, but a higher degree of symmetry breaking owing to the faster nuclear vibration in H2 leads to stronger harmonics of otherwise symmetry-forbidden orders 3q and only slight change in orders 3q±1, which causes the lower ratio of 3q (D2/H2). Therefore, the nuclear dynamics is imprinted in the ratio for the orders 3q ± 1, while the sensitivity of harmonic orders 3q to symmetry breaking gives access to the attosecond probing of dynamical symmetries.
Early Detection of Mild Cognitive Impairment Using Quantitative Analysis of EEG Signals

Detection of Alzheimer’s Disease (AD) at initial onset is urgent because it greatly influences the success of therapy. Mild cognitive impairment (MCI) is a condition of transition to (AD) although not all subjects with MCI will develop AD. However, the detection of MCI is still important to avoid the risk of getting worse. There are several biomarkers that can be used, one of which is an electroencephalogram (EEG), which is of much concern because it is non-invasive, inexpensive and safe to use continuously. In this study, quantitative EEG (QEEG) analysis was carried out for MCI detection compared to the normal group. Linear QEEG based on power spectral features was applied. EEG signals in the temporal, parietal and occipital electrode areas of 27 subjects (16 normal and 11 MCI) were observed. The simulation results using K-NN, shows an accuracy of 81.5%. Thus EEG can be used as a potential reference as a tool for diagnosing MCI in large populations by considering costs, convenience, and non-invasive.
Arrhythmia Classification Based on Combination of Heart Rate, Auto Regressive Coefficient and Spectral Entropy Using Probabilistic Neural Network

This paper presents a classification method for arrhythmia using probabilistic neural network, based on unique combination of three Electrocardiogram (ECG) features; heart rate, Auto Regressive (AR) coefficients & spectral entropy (SE). Heart rate has been very critical parameter for the detection of life-threatening arrhythmia. The purpose of this paper is to develop a Probabilistic Neural Network (PNN) based algorithm for improved detection and broader classification of cardiac arrhythmia. The results show that the unique combination of ECG features considered in this work provides more accurate and robust classification of arrhythmias.
Learn to Coloring: Fast Response to Perturbation in UAV-Assisted Disaster Relief Networks

In this paper, we address the unmanned aerial vehicle (UAV)-assisted disaster relief networks, where UAVs can disseminate the emergency information to those terrestrial users in a multicast manner. Due to the limitation of spectrum resources, multiple UAVs have to reuse the same channel while the co-channel interference management is needed. The dynamic topology structure induced by mobility can be modeled as a dynamic graph in which the existence of an edge (interference relationship) between two vertices (multicast clusters) is dynamically changing. As such, the channel selection problem can be transformed into a dynamic graph coloring problem in which the graph structure evolves in continuous time slots and the previous coloring strategy becomes valid. In this regard, we propose a stochatic learning based algorithm which can converge rapidly. Simulation results demonstrate that our proposed method is fast in response to the perturbations in dynamic environments.
Disposition kinetic and bioavailability of florfenicol in buffalo calves

The Pharmacokinetics of florfenicol was studied in buffalo calves following single intravenous and intramuscular administration of 20 mg kg -1 b.wt. Florfenicol concentration in both serum and urine were determined by microbiological assay using Bacillus subtilis (ATCC 6633) as test organism. After intravenous injection the serum florfenicol concentration time course obeys two-compartment open model with distribution (t0.5 (α)) and elimination (t0.5 (β)) half lives of 0.381 ± 0.004 and 2.89 + 0.263 h. , respectively. Total body clearance (CLB) and steady state volume of distribution (Vdss) were 3.6 ± 0.30 ml kg -1 min -1 and 1.70 ± 0.010 l kg -1 . , respectively. After intramuscular administration the observed mean peak serum concentration (Cmax) was 2.32 ± 0.052 μg ml -1 achieved after maximum time (Tmax) of one hour postinjection. The systemic bioavailability after intramuscular administration was 27.43 % and the plasma protein binding was 13.5 %.
Myopic traction maculopathies and their treatments

ABSTRACT Introduction: Pathologic myopia is associated with structural and physiological posterior segment changes, including myopic tractional maculopathies, which can be associated with sight threatening complications. With higher resolution ophthalmic imaging techniques and access to more advanced surgical treatment options, myopic tractional maculopathies are better understood and effective management strategies are being developed. Areas covered: In this review we will examine the clinical characteristics and the current management, of myopic traction maculopathies. An extensive literature search was made using the following key words: pathologic myopia, high myopia, dome shaped maculopathy, posterior staphyloma, myopic foveoschisis, myopic maculoschisis, myopic tractional maculopathy, myopic macular hole, myopia macular hole retinal detachment, scleral buckling, macular buckling, and imbrication. This review used Medline, PubMed, and The Cochrane Library databases. Expert commentary: Rapid advances in retinal and ocular imaging have been accompanied by rapid technical improvements in vitreoretinal surgery. In cases with an axial length greater than 30mm, a marked staphyloma, foveoschisis and macula hole (with or without retinal detachment) vitrectomy surgery alone becomes less effective. It is becoming clear that some form of intervention, such as macular buckling, in terms of limiting the staphyloma or axial length may be required.
CNN-Based Learnable Gammatone Filterbank and Equal-Loudness Normalization for Environmental Sound Classification

For environmental sound classification (ESC), this letter presents a learnable auditory filterbank based on a one-dimensional (1D) convolutional neural network with strong psychophysiological inductive bias in the form of a gammatone filterbank and an equal-loudness prompting normalization. In the past, a number of ESC methods based on learnable auditory features obtained by performing plain 1D convolutions on raw input waveforms for outperforming traditional handcrafted features such as a mel-frequency filterbank have been proposed. However, the large number of parameters involved in the convolutions suggests that these methods will not generalize better than a model defined by a smaller number of parameters, which is considered in this letter. Here, a learnable gammatone filterbank layer consisting of 1D kernels represented by a parametric form of the bandpass gammatone filters is proposed for acquiring a time-frequency representation of the raw waveform. A normalization with learnable parameters that control the trade-off between energy equalization and structure preservation in the spectro-temporal domain is proposed. To verify the effectiveness of the considered network and the normalization, ESC experiments on the ESC-50 and UrbanSound8K datasets were conducted. Compared to other state-of-the-art networks, the considered network performed better on the two datasets. In addition, an ensemble architecture achieved further performance improvement.
Gamification-Based Apps for PTSD: An Analysis of Functionality and Characteristics

Post-traumatic stress disorder (PTSD) is a mental health disorder that is triggered by exposure to traumatic events. PTSD is associated with severe emotional disturbances that impair the person's functional abilities like social interactions and the ability to work. If left untreated, PTSD can even lead to suicide. Even though PTSD is a manageable and treatable disorder, many people are not able to get the appropriate treatment, either because of cost problems, stigma, mobility problems, or other causes. Since mobile phones are devices that are owned by a large number of people, m-health is an aspect that can facilitate access to the needed treatment and care for PTSD in many ways, one being via mobile applications (apps). Using gamification features in apps can be helpful in the management and treatment of PTSD. This paper identifies a list of 24 gamification-based apps for PTSD, 14 Android apps and 10 iOS apps, and assesses their functionalities and characteristics. The results highlighted mainly the need for more consideration of security aspects, incorporation of medical devices and physical indicators, and the apps' availability in multiple languages.
Pre-service Teachers' Usage of Internet Technology at Faculty of Education-Amran University, Yemen

This study aimed to find out the usage of internet technology among the pre-service teachers at Faculty of education in Amran University, Yemen. To bring about this aim, a descriptive research design (i.e. survey, and open interview) was used. A constructed questionnaire includes 28 items that were distributed on three domains of the internet usage: academic, professional and general internet usage. Interview, as an aid tool, was used to explore the reasons beyond why some of the study subjects did not answer the questionnaire. Results of the questionnaire revealed that most of the subjects utilize the internet but not in favor of development their academic, or profession specialization as teachers in the future. On the other hand, the results of the interview showed that reasons beyond why some subjects did not answer the questionnaire were: the absence of internet usage facilities for them as well as the negative attitudes towards its usage. Based on the results of the study, some recommendations were recommended.
Prediction of Blast Furnace Slag Concrete Compressive Strength Using Artificial Neural Networks and Multiple Regression Analysis

High performance concrete compressive strength modeling is a complex process. This study investigates the relationship between compressive strength of blast furnace slag concrete with its constituents and to predict blast furnace slag concrete compressive strength using two methods: (1) multiple regression analysis and (2) artificial neural networks. Results from study showed that the use of artificial neural networks in compressive strength modeling provides higher accuracy in predicting compressive strength of a given mix proportion. However, the multiple regression model is able to give an equation representing the relationship between the compressive strength of concrete with its inputs. Both compressive strength prediction models can be used as additional tools in the decision making of a blast furnace slag concrete mix design.
A Rare Variant Formation Of The Median Nerve: A Case Report

Variations in the formation of median nerve are common but this case report is the rare variation of formation of the median nerve posterior to the third part of axillary artery. During educational dissection of undergraduate students of the academic year 2007-2008 in our department of anatomy, the two roots of the median nerve joined behind the axillary artery to form the median nerve, which coursed behind the brachial artery and then crossed to the medial part of cubital fossa. The lateral root passed between the axillary artery and anterior circumflex humeral artery to course to the back to join the medial root of median nerve. This variation may be clinically important because symptoms of median nerve compression arising from similar variations are often confused with more common causes such as radiculopathy and carpal tunnel syndrome.
A Survey of IoT Platform Comparison for Building Cyber-Physical System Architecture

Increased Internet of Things (IoT) technology has increased the number of IoT platform technology developments that are used to facilitate the development of IoT. But in the industrial era 4.0, the application of IoT to Smart Factory has evolved into a new paradigm, namely the Cyber-Physical System (CPS). Therefore, this paper aims to do a survey to determine the parameters and criteria to build a system and architecture of the CPS platform. Additionally, it surveys and analyzes using several parameters or criteria to compare several IoT platforms such as thing management, connectivity, data storage, data abstraction, interface, analytical, feedback & collaboration, security, scalability, microservices, plug, and play. The result shows that these parameters and criteria can be used as references to develop the CPS platform system. In the next step, we expected that the creation of a CPS platform can be based on references from the results of this research survey and analysis.