Miracle-dz/UniFAR-FTUs huggingface dataset:
Identifying the End-to-End Backbone of Information Diffusion

In recent years, online social networks (OSNs) have undoubtedly become the most popular information diffusion channel. However, existing work has rarely focused on the end-to-end diffusion path, which means information diffusion between user pairs is still a problem unresolved. In this paper, an end-to-end independent cascade model (EICM) is proposed. Then, we present an efficient approach called end-to-end diffusion flow pruning (EDFP) that simplify large-scale social networks until main diffusion paths of end-to-end networks are identified.
Wearable Devices and Measurement Data: An Empirical Study on eHealth and Data Sharing

The development of medical applications and services is growing but is hampered by security and privacy concerns and a lack of trust by users. This interview study with 29 users of wearable devices reports perspectives on privacy concerns towards sharing of measured data in general, and measured stress data in the workplace. Participants consider stress data to be sensitive (73%), and report that privacy protection is a requirement for both the technology and the workplace. Sharing behavior is shown to be strongly coupled with clear purposes and incentives. Sharing measured fitness data is accepted (72%), and sharing stress measured data for the common good (48%), despite privacy concerns. Over half mistrust the technology not to leak data. Technology solutions should provide clear and sound purposes for storing, sharing, and processing measured data, as well as provide assurances from workplace and cloud.
Hardware Security Reconnaissance Application using FCC ID Lookup and Computer Vision

The reconnaissance process is a time consuming and risky task for hardware researchers and pen-testers. First, the exterior of the device is assessed and then opened to inspect the interior. The device can be seriously damaged due to this and cannot be reused. With the help of Computer Vision – Optical Character Recognition with Machine Learning, implemented in the app, one has to click on the text containing FCC ID real-time and is directed to the site containing all the required information. This saves time and the information of the device is readily available without the need to open up the device. FCC ID is considered as one of the protocols by the pen-testers.
Evolution of Machine Learning in Smart Grids

The objective of this work is to investigate the evolution of the application of ma-chine learning (ML) in the area of smart grids. It presents an overview of research that used ML in the area of smart grids, through the quantitative descriptive analysis of periodical articles and newspapers, registered in the IEEE Xplore Library database, in the period between the years 2010 and 2019. A total of 108 research publications were identified that address the application of machine learning in the area of smart grids. The study also present the incidence of each topic related to smart grids as well as the types of machine learning used in each document. As a result, it was concluded that the number of surveys has increased in the previous 3 years, meaning this is between 2017 and 2019, with the main research topics related to smart grids being safety and reliability of the electrical network and energy management / forecasting. Also, was indicated that the main-ly two techineques of machine learning that have been used in smart grid area was Neural networks and Support Vector Machine (SVM).
Parallel-Type Asymmetric Memristive Diode-Bridge Emulator and Its Induced Asymmetric Attractor

Symmetry brings beauty, while asymmetry is the general law of nature. This paper reports a novel parallel-type asymmetric memristive diode-bridge (AMDB) emulator, which is implemented by an unbalance diode-bridge linked with a RC filter. Following the voltage constraints of the unbalance diode-bridge, the mathematical model of the parallel-type AMDB emulator is established. Thereafter, the asymmetric property of the hysteresis loops is demonstrated by the numerical simulations and confirmed by the hardware experiments. Furthermore, by importing the parallel-type AMDB emulator into the classical Chua’s circuit, a novel memristive Chua’s circuit is proposed, so that the asymmetric double-scroll chaotic attractor, asymmetric coexisting single-scroll chaotic attractors, and asymmetric coexisting limit cycles can be revealed herein. The parallel-type AMDB emulator enriches the types of memristor emulators and it can mimic the asymmetric property of the physical memristor device.
Stand-alone Photovoltaic System with integrated Energy Storage using Cascaded Predictive Direct Power Control

This paper presents a stand-alone photovoltaic (PV) system, which contains a predictive PV emulator (P-PVE), a boosting stage used for Maximum Power Point Tracking (MPPT) and integrated energy storage system, using the cascaded predictive direct power control (CPPC). Generally, in such PV applications with batteries, the used DC/DC converters are separately controlled using several controllers, for that, the proposed CPPC assembles the control of the used converters in the same controller and minimizes only one cost function for all the system by giving more flexibility for the predictive system modelling. The MPPT of the PV emulator and the power management between PV power, batteries power and demanded power are directly included in the proposed control. Simulation and experimental results are carried out to well illustrate and confirm the correctness and the effectiveness of the proposed control.
SysEvoRecomd: Network Reconstruction by Graph Evolution and Change Learning

We introduce a <italic>System Evolution Recommender</italic> (SysEvoRecomd) algorithm that uses a novel algorithm <italic>Graph Evolution and Change Learning</italic> (GECL) to do <italic>system network reconstruction</italic>. Internally, GECL uses <italic>Deep Evolution Learner</italic> (DEL) to learn about evolution and changes happened over a system state series. The DEL is an extension of the deep learning algorithm, which uses an <italic>Evolving Connection Matrix</italic> (ECM) representing temporal patterns of the <italic>evolving entity-connections</italic> for training incremental states. The DEL generates a <italic>Deep System Neural Network</italic> (Deep SysNN) to do network (graph) reconstruction. The SysEvoRecomd extracts the evolving characteristic of graph with deep neural network techniques. It aims to learn the evolution and changes of the system state series to reconstruct the system network. Our key idea is to design three variants of GECL based on three remodeled deep learning techniques: Restricted Boltzmann Machine (RBM), Deep Belief Network (DBN), and denoising Autoencoder (dA). Based on proposed SysEvoRecomd algorithm, we developed a SysEvoRecomd-Tool, which is applied on different evolving systems: software, natural language, multisport event, retail market, and IMDb movie genre. We demonstrated the usefulness of intelligent recommendations using three variants of GECL based on <italic>RBM</italic>, <italic>DBN</italic>, and <italic>dA. </italic>
A multidimensional human-centric framework for environmental intelligence: Air pollution and noise in smart cities

For the important problem of increasing levels of air pollution and noise in urban and rural areas, we propose a holistic, multi-dimensional approach to gather, monitor and analyze heterogeneous data sources of air pollutants and noise indicators, into an integrated, intelligent computational system. Although several interesting approaches have been developed for monitoring pollution and noise, however the challenge remains for even more detailed, precise, large scale monitoring.To overcome the limitations of current systems, we envision an integrated approach to human-centric environmental intelligence, bringing together modern IoT technology and the human factor. In particular, our approach emphasizes selected behavioural and health aspects, and the complementary use of sensing technology with citizen engagement and crowdsourcing methods. The proposed system will collect diverse data from heterogeneous sources, such as mobile and static wireless sensor networks, crowdsourcing, citizen questionnaires and social media analytics, to continuously combine objective estimations with subjective perception of air quality and noise. With the use of advanced AI and Deep Learning algorithms, our system will be able to estimate air pollutants concentration and noise levels in micro-scale with adequate precision over large urban-scale environments. Furthermore, tracking of behavioral and psychological users’ input, as well as personal exposure to pollution, will allow studying the impact of air quality and noise on the users’ daily habits and the interplay of ambient conditions with behavioural factors, towards an active engagement of citizens in a hybrid techno-social manner. A reference architecture for the realization of this human-centric environmental intelligence approach is presented. Also, a planned implementation at the city of Patras, Greece is discussed. To the best of our knowledge, this is one of the first holistic, multifaceted approaches to a surveillance system for air quality and noise in urban areas.
High-Frequency Memristor-Based BFSK and 8-QAM Demodulators

This paper presents developed memristor-based demodulators for 8 circular Quadrature Amplitude Modulation (8-QAM) and Binary Frequency Shift Keying (BFSK) operating at 25MHz. In our implementations, the experimental-based ‘nonlinear’ dopant drift model is adopted for all the proposed circuits which incorporates all known nonidealities of practically realized memristor devices and achieves high frequency of operation. The suggested designs leverage the distinctive characteristics of the memristor device, which is the variation of average memristance versus the frequency, phase, and amplitude of the periodic excitation input. The proposed demodulators feature small integration area, low power consumption, and easy implementation. Moreover, the proposed QAM demodulator precludes the requirement for the carrier recovery circuits. In doing so, the designs were validated by transient simulations using the nonlinear dopant drift memristor model. The simulations results show high agreement with the theory presented.
A model for the identification of students at risk of dropout at a university of technology

Development has been seen in the advanced education segment in South Africa. With this development, an expansion in the dropout rate is noticed. This study explores the adequacy of dimensional decrease and concentrates the significant data covered up in the student information for the identification of students in danger of dropout. This study depends on educational data mining techniques and makes forecasts of dropout goal of understudies from courses. In the test, the researchers show promising outcomes with information from the recognition courses of a University of Technology.
Lipid Fatty Acid Chain Length Influence over Liposome Physicochemical Characteristics Produced in a Periodic Disturbance Mixer

Liposomes nanoparticles (LNPs) are versatile delivery systems that transport from drugs to genes. Liposome physicochemical properties determine how lipid nanoparticles will interact with biological systems. Thus, tuning these characteristics is of paramount importance for developing innovative formulations. Micromixers have shown to control average liposome size in a range relevant for drug delivery applications (50–200 nm) by changing the flow conditions. However, other factors might dramatically change liposome properties, such as the lipid fatty acid chain length. In this work, liposomes were produced using a periodic disturbance mixer (PDM) utilizing two different types of lipid mixtures, which differ solely in the number of carbons in the lipid fatty acid chain of the primary lipid as well as in the lipid mixture concentration. A clear relationship between these properties and liposome size and size distribution was found. The studied factors did not influence zeta potential results. The bending elasticity modulus of the studied lipids and their terminal head groups might play a role in the observed liposome properties.
A new low-rank sparse image denoising algorithm based on non-local self-similarity

Aiming at the existing problems of denoising methods based low-rank and sparse representation that easily lose image details, resulting in low image denoising quality, we propose an image denoising method based on low-rank and sparse representations in a non-local framework. The proposed algorithm consists of two steps: firstly, similar image blocks are matched and grouped, a low-rank matrix recovery model is established, and then a random matrix theory is used to implement preliminary denoising; secondly, the artifacts in the image are removed by non-local sparse representation, and the noise atoms are clipped. Theoretical analysis and experimental results show that the proposed method can filter out noise better, retain image detail information, and obtain better image visual effects compared with the current popular denoising methods of the same kind.
Compliance with hygiene practices among healthcare workers in the intensive care unit.

BACKGROUND Intensive care unit (ICU)-related healthcare-associated infections (HCAIs) are two to three times higher in lower-income countries than in higher-income ones. Hand cleansing and other hygiene measures have been documented as one of the most effective measures in combating the transmission of HCAIs. There is a paucity of data pertaining to hygiene practices in the ICU in developing countries. OBJECTIVES To determine compliance with hygiene practices among healthcare workers in a tertiary hospital ICU. METHODS Hygiene practices of healthcare workers in a tertiary academic hospital ICU in Johannesburg, South Africa, were discreetly observed over an 8-week period. Compliance with hand cleansing and other hygiene practices was documented and analysed. Retrospective consent was obtained, and subject confidentiality was maintained. RESULTS A total of 745 hygiene opportunities were observed. Of the 156 opportunities where handwashing with soap and water was indicated (20.9%), compliance was noted in 89 cases (57.1%), while an alcohol-based hand rub was inappropriately used in 34 cases (21.8%) and no hand hygiene was performed in the remaining 33 cases (21.1%). Of the 589 opportunities where an alcohol-based hand-rub was indicated, it was used in 312 cases (53.0%). Compliance with the donning of disposable surgical gloves, disposable plastic aprons and being 'bare below the elbows' was noted in 114 (90.6%), 108 (71.1%) and 355 (47.7%) opportunities, respectively, where these were indicated. CONCLUSIONS Overall compliance with hygiene measures among healthcare workers in the ICU was suboptimal in this study, but in keeping with general international trends. Regular retraining of staff, frequent reminders, peer oversight and regular audits may improve compliance.
Semantic Segmentation of Brain Tumor from MRI Images and SVM Classification using GLCM Features

The brain tumor is a disease that affects or harms the brain with unwanted tissues. This is very difficult to detect brain tumor tissue from whole brain. Early detection of tumor is very important to save patient's life. Detection or segmentation techniques are used to detect and segment the brain-tumor region from the MRI images of brain and it is very useful method in recent days. In medical, magnetic-resonance-imaging is a tough field in image processing because accuracy percentage must be very high so doctors could get proper idea about diseases to save patient's life. Some MRI images have been taken as inputs data. The brain-tumor segmentation process is performed for separating brain-tumor tissues from brain MRI images, The MRI images should be filtering such as with the median filtering technique and skull stripping should be done in pre-processing, the thresholding process is being done on the given MRI images with using the watershed segmentation method. Then at last the segmented tumor region is obtained. And then in other phase features extracted by GLCM methods using MATLAB software. Then, the some images have been classified using support vector machine (SVM), this system obtained with the average accuracy of 93.05%. Which is quite better than other conventional models.
Hypothermia impedes calcium induced mitochondrial permeability transition pore opening in mitochondria harvested after cold ischemia of isolated hearts

Hypothermia decreases ischemia‐reperfusion (IR) injury. IR injury has been associated with mitochondrial permeability transition pore (PTP) opening, and decreased activity of the electron transport chain. We studied mitochondria from isolated hearts subjected to normothermic and hypothermic ischemia. Guinea pig isolated hearts were perfused at constant pressure with Krebs‐Ringer's solution at 37 ºC and subjected to 30 min of ischemia at 37ºC or 17ºC. After 5 min of reperfusion mitochondria were isolated. Mitochondrial [Ca2+]m, pH, membrane potential (ΔΨm) and NADH were measured spectrophotometrically with indo 1, BCECF, rhodamine 123 and autofluorescence. After energizing with pyruvic acid, [CaCl2] (0‐100 μM) and ADP were added. Adding CaCl2 caused a dose dependent [Ca2+]m increase. Ca2+ induced PTP opening, assessed by collapse of ΔΨm, and prevented by cyclosporine A, occurred after adding 50 μM CaCl2 after 37ºC IR, and adding 100 μM CaCl2 after 17ºC IR. ADP decreased ΔΨm, pH and NADH and increased [Ca2+]m in all mitochondria. These bioenergetic markers recovered after 17ºC IR, but not after 37ºC IR. Our study shows that hypothermia protects mitochondria against IR injury as shown by resistance to Ca2+ induced PTP opening and appropriate responses to ADP. This study emphasizes the importance of mitochondrial function in protecting against IR injury, and gives insight into how hypothermia reduces IR injury.
Distributed Salinity Sensor With a Polyimide-Coated Photonic Crystal Fiber Based on Brillouin Dynamic Grating

The distributed measurement of salinity is experimentally demonstrated for the first time, to the best of our knowledge, by Brillouin dynamic grating (BDG) with a polyimide-coated (PI-coated) polarization-maintaining photonic crystal fiber as the active sensing component. The position-related salinity change can be measured by mapping the birefringence change through the BDG, where the salinity altering can induce swelling or shrinking actions of the PI coating through absorbing or releasing water. To reveal the systematic characterizations of the proposed sensor, four PI-coated sensors with different coating thicknesses of 5 μm, 8 μm, 15 μm and 20 μm were fabricated as demos, achieving salinity sensitivities of 30.1 MHz/(mol/L) for 5 μm, 75.4 MHz/(mol/L) for 8 μm, 119.2 MHz/(mol/L) for 15 μm, and 139.6 MHz/(mol/L) for 20 μm, respectively. The proposed sensor shows a linear and reproducible response to the salinity change. And its measurement uncertainty is less than 10 MHz, yielding a salinity accuracy of 0.072 mol/L. The temperature crosstalk can be compensated through measuring the temperature-induced Brillouin frequency shift changes using Brillouin optical time-domain analysis.
Passive Third-, Fourth-, and Fifth-Order Reconfigurable D-Band Frequency Multipliers Based on Switched-Capacitor Varactors

Shunt switched-capacitor varactors (SCVs) are proposed to replace MOS varactors in millimeter-wave (mm-wave) parametric circuits, and a reconfigurable frequency multiplier (RFM) is presented to demonstrate the advantages of the SCVs. The SCVs are shown to achieve ratios of maximum to minimum capacitance exceeding 20, which corresponds to ~10× improvement relative to mm-wave or terahertz (THz) MOS varactors. A single circuit topology based on both SCV pairs and novel C-V shapes is presented and acts as a ×3, ×4, and ×5 RFM. A similar RFM is also designed using MOS varactors for comparison with the SCV-based RFM. Improvements of 11.4, 6.6, and 7.9 dB in minimum conversion loss, and 3.3%, 90%, and 400% in relative bandwidth are achieved respectively with the SCV-based RFM, demonstrating the potential of SCVs to significantly improve the performance of mm-wave and THz parametric circuits.
Thioredoxin-Related Transmembrane Proteins: TMX1 and Little Brothers TMX2, TMX3, TMX4 and TMX5

The endoplasmic reticulum (ER) is site of synthesis and maturation of membrane and secretory proteins in eukaryotic cells. The ER contains more than 20 members of the Protein Disulfide Isomerase (PDI) family. These enzymes regulate formation, isomerization and disassembly of covalent bonds between cysteine residues. As such, PDIs ensure protein folding, which is required to attain functional and transport-competent structure, and protein unfolding, which facilitates dislocation of defective gene products across the ER membrane for ER-associated degradation (ERAD). The PDI family includes over a dozen of soluble members and few membrane-bound ones. Among these latter, there are five PDIs grouped in the thioredoxin-related transmembrane (TMX) protein family. In this review, we summarize the current knowledge on TMX1, TMX2, TMX3, TMX4 and TMX5, their structural features, regulation and roles in biogenesis and control of the mammalian cell’s proteome.
Expression of ADR-α1, 2 and ADR-β2 in cumulus cell culture of infertile women with polycystic ovary syndrome and poor responder who are a candidate for IVF: the novel strategic role of clonidine in this expression

Abstract Objective Alpha and beta-adrenoceptors (ADR-α1, 2, and β2) play a regulatory role in the folliculogenesis and steroidogenesis in the ovarian follicles. This study aimed to measure these adrenoceptors mRNA and its protein levels in cumulus cells (CCs) culture of poor ovarian reserve (POR) and polycystic ovarian syndrome (PCOS) infertile women (IVF candidate) and the effect of clonidine treatment at CCs culture. Methods This case/control study was conducted in 2017 includes a control (donation oocytes) and two studies (PCO and POR) groups. The ovulation induction drugs were prescribed in all groups. After the oocyte puncture, the follicular fluid was collected and CCs were isolated were cultured. RNA was extracted and cDNA was synthesized and designed the primer for the ADR-α1, 2 and ADR-β2 gene expression. The protein levels were investigated by Western Blot. Results The results showed a high level of three adrenergic expressions in PCO women compared to the control group (p-value <.001), which can be reduced by clonidine. POR group showed a significant decrease in the gene expression of ADR-α1 (p-value = .004) and ADR-α2 (p-value = .003) compared to the control group and clonidine treatment had no effect. Conclusion The significant increase of three adrenoceptors gene expression and protein levels in CCs culture indicate to the hyperactivity of the ovarian sympathetic nervous system at the receptor levels in women with PCOS, and clonidine confirmed it by reducing this expression. In POR women, the reduction of ADR-α1, 2 expressions maybe lead to the aging process in the ovary.