Miracle-dz/UniFAR-FTUs huggingface dataset:
Seroprevalence of Hepatitis E Virus Infection in Middle Eastern Countries: A Systematic Review and Meta-Analysis

Hepatitis E virus (HEV) is a hepatotropic virus that is a major public health concern worldwide. Autochthonous HEV is spread through oral feces in unsanitary environments, as well as vertical and, occasionally, blood transfusion. HEV is more common in developing countries, but it has recently become more widespread in developed countries as well. The Middle East (ME) has long been an endemic location for HEV infection. Therefore, the aim of this systematic review and meta-analysis was to assess the seroprevalence of anti-HEV antibodies in ME countries. The author systematically searched five databases, namely ScienceDirect, EMBASE, Scopus, PubMed, and Google Scholar, to identify English-language articles published on or before 25 April 2022. Comprehensive meta-analysis software was used for all statistical analyses (CMA, version 3, BioStat, Englewood, CO, USA). After quality control and exclusion of irrelevant studies, 80 studies were included in the qualitative synthesis and meta-analysis. A forest plot showed that the overall pooled seroprevalence of HEV infection in ME countries in the fixed-effect and random-effect models were 21.3% (95% CI: 0.209–0.216) and 11.8% (95% CI: 0.099–0.144), respectively. Furthermore, the findings showed a high level of heterogeneity (I2 = 98.733%) among the included studies. In both fixed-effect and random-effect models, the seroprevalence of HEV infection by country was high in Egypt as compared to other regions, at 35.0% (95% CI: 0.342–0.359), and 34.7% (95% CI: 0.153–0.611), respectively. The seroprevalence of HEV infection by country was high among pregnant women, at 47.9% (95% CI: 0.459–0.499) in the fixed-effect model, and in renal transplant recipients, at 30.8% (95% CI: 0.222–0.410) in the random-effect model. The seroprevalence of HEV infection varies by country and study population in the Middle East. More research is needed to determine the disease’s incidence, morbidity, and mortality in the region, where it is prevalent.
Pharmacist involvement in sepsis response and time to antibiotics: A systematic review

Sepsis is a life‐threatening medical emergency and a leading cause of morbidity and mortality worldwide. Reductions in time to antibiotics in patients presenting with sepsis or septic shock are associated with reduced mortality, and Surviving Sepsis Campaign guidelines recommend antibiotics within 1 h of recognition. Pharmacists are well‐equipped to help navigate the therapeutic and operational challenges associated with achieving this goal.
Insights into Genetic Characteristics and Virological Features of Endemic Avian Influenza A (H9N2) Viruses in Egypt from 2017–2021

From 2010 to 2013, genotype I avian influenza A(H9N2) viruses of the G1-lineage were isolated from several poultry species in Egypt. In 2014, novel reassortant H9N2 viruses were detected in pigeons designated as genotype II. To monitor the subsequent genetic evolution of Egyptian A(H9N2) viruses, we characterized the full genomes of 173 viruses isolated through active surveillance from 2017 to 2022. In addition, we compared the virological characteristics and pathogenicity of representative viruses. Phylogenetic analysis of the HA indicated that all studied sequences from 2017–2021 were grouped into G1-like H9N2 viruses previously detected in Egypt. Phylogenetic analysis indicated that the Egyptian A(H9N2) viruses had undergone further reassortment, inheriting four genes (PB2, PB1, PA, NS) from genotype II, with their remaining segments deriving from genotype I viruses (these viruses designated as genotype III). Studying the virological features of the two most dominant genotypes (I and III) of Egyptian H9N2 viruses in vitro and in vivo indicated that both replicated well in mammalian cells, but did not show any clinical signs in chickens, ducks, and mice. Monitoring avian influenza viruses through surveillance programs and understanding the genetic and antigenic characteristics of circulating H9N2 viruses are essential for risk assessment and influenza pandemic preparedness.
Performance Improvement of Red InGaN Micro-LEDs by Transfer Printing From Si Substrate Onto Glass Substrate

Red micro light-emitting diodes (micro-LEDs) on silicon substrates are important components for realizing large-scale and low-cost micro-LED displays. Recently, InGaN red micro-LEDs have received enormous attention because their external quantum efficiency (EQE) is not significantly affected by size effect and they are compatible with existing green and blue LED material systems. Here, taking advantage of the easy removal of silicon substrates, we successfully achieved the transfer printing of InGaN red micro-LEDs. By removing the light-absorbing Si substrate and introducing a metal mirror, the transfer printed micro-LEDs exhibited better color purity, higher optical power, and higher EQE. At 0.5 A/cm2, the optical power of micro-LEDs on glass was 300% higher than that of micro-LEDs on silicon. And the peak EQE of transfer printed micro-LEDs was 2.3 times that of micro-LEDs on native substrate. This work is applicable to most transfer printing techniques requiring PDMS stamps, and shows the potential to realize InGaN-based full-color micro-LED displays by mass transfer in the future.
Sur la décomposition modale empirique des signaux échantillonnés

This paper is intended to give new insights into a recent technique of signal decomposition, referred to as “Empirical Mode Decomposition” (EMD) and more precisely into the effect of sampling on its behavior. Therefore we analyze the EMD of sampled sinusoidal signals while varying the sampling parameters. The observed effects related to sampling are first characterized and quantified and then justified theoretically.
On the k-restricted structure ratio in planar and outerplanar graphs

A planar k-restricted structure is a simple graph whose blocks are planar and each has at most k vertices. Planar k-restricted structures are used by approximation algorithms for Maximum Weight Planar Subgraph, which motivates this work. The planar k-restricted ratio is the infimum, over simple planar graphs H, of the ratio of the number of edges in a maximum k-restricted structure subgraph of H to the number edges of H. We prove that, as k tends to infinity, the planar k-restricted ratio tends to 1 = 2. The same result holds for the weighted version. Our results are based on analyzing the analogous ratios for outerplanar and weighted outerplanar graphs. Here both ratios tend to 1 as k goes to infinity, and we provide good estimates of the rates of convergence, showing that they differ in the weighted from the unweighted case.
Mass Spectrometry-Based Quantification of the Antigens in Aluminum Hydroxide-Adjuvanted Diphtheria-Tetanus-Acellular-Pertussis Combination Vaccines

Vaccines undergo stringent batch-release testing, most often including in-vivo assays for potency. For combination vaccines, such as diphtheria-tetanus-pertussis (DTaP), chemical modification induced by formaldehyde inactivation, as well as adsorption to aluminum-based adjuvants, complicates antigen-specific in-vitro analysis. Here, a mass spectrometric method was developed that allows the identification and quantitation of DTaP antigens in a combination vaccine. Isotopically labeled, antigen-specific internal standard peptides were employed that permitted absolute quantitation of their antigen-derived peptide counterparts and, consequently, the individual antigens. We evaluated the applicability of the method on monovalent non-adjuvanted antigens, on final vaccine lots and on experimental vaccine batches, where certain antigens were omitted from the drug product. Apart from the applicability for final batch release, we demonstrated the suitability of the approach for in-process control monitoring. The peptide quantification method facilitates antigen-specific identification and quantification of combination vaccines in a single assay. This may contribute, as part of the consistency approach, to a reduction in the number of animal tests required for vaccine-batch release.
An Overview of Shade Selection in Clinical Dentistry

The selection of an accurate tooth shade has always been a challenging task for dental practitioners in restoring the natural appearance of teeth. Various factors can influence shade selection, such as different lighting conditions, clearness and opaqueness of teeth, eye fatigue, aging and color vision problems. It is imperative to have a sound knowledge about the concept of shades and its selection protocol for obtaining good esthetics outcomes. To attain the best esthetics, four elementary contributing factors are essential: exact position, shape, surface texture and shade. The current analysis focuses on several features of shade, sensitivity of shades, optical properties of the teeth, visual and innovative instrumental techniques for shade selection, protocols for shade selection and factors affecting dental shade selection.
Automatic pixel-level detection method for concrete crack with channel-spatial attention convolution neural network

Concrete crack detection is a significant research problem in structural safety. However, the traditional manual inspection is a laborious and time-consuming method, and the detection accuracy is greatly limited by the work experience of engineers. Hence, automatic image-based crack detection has attracted wide attention from both academia and industry. In this study, a novel crack detection method using attention convolution neural networks, ATCrack, is proposed for automatic crack identification. ATCrack uses a symmetric structure consisting of an encoder and a decoder by imposing channel-spatial attention to achieve end-to-end crack prediction. Channel attention module is introduced in the encoder to improve the effective utilization of crack features, and spatial attention is added in the decoder to suppress the background features. Combining with channel and spatial attention modules, the codec network will be more sensitive to the characteristics of cracks and increase detection accuracy and robustness. Moreover, a complex crack dataset of buildings and pavements is collected to verify the effectiveness and feasibility of ATCrack. Finally, experiment results are tested on several public datasets and self-collected (CBCrack) database, and it shows that the proposed method during the five-fold cross-validation can achieve state-of-the-art performance compared with other existing methods in terms of precision, recall, F1-score, and mIoU.
A new switching technique for direct torque Control of Induction Motor using Four-Switch Three-Phase Inverter

Faculty of Electrical and Electronic Engineering, HCMC University of Technology, Ho Chi Minh City, VietNam; Siemens AG Presentation, Ho Chi Minh City, Viet Nam; NARC, Ulsan University, South Korea Abstract: This paper presents a new switching technique for Direct Torque Control of Induction Motor usingFour-Switch Three Phase Inverter (DTC-FSTPI-IM) for low power applications. The modified switchingtable in this method is based on the principle of similarity between FSTPI and SSTPI (Six-Switch ThreePhase Inverter), where the αβ plan is divided into 6 sectors and the formation of the voltage space vector isdone in the same way as for SSTPI by using effective (mean) vectors. This approach allows using the well-knowing established switching table of SSTPI for FSTPI, in order to reduce torque ripples in comparisonwith the conventional DTC method for FSTPI. The validity of new proposed technique is verified bysimulation results using Matlab/Simulink and also experimentally by using DSpace 1104 Card. © 2007IEEE. Author Keywords: Direct Torque Control; Four-Switch Three-Phase Inverter (FSTPI); Optimal SwitchingTable; Pulse-width-modulation; Six-Switch Three-Phase Inverter (SSTPI); Space vector Index Keywords: AC motors; Electric inverters; Motors; Power electronics; Switching; Torque; Torquecontrol; Vectors; Direct Torque Control; Drive systems; Four-Switch Three-Phase Inverter (FSTPI);International conferences; Low power applications; MATLAB /simulink; Optimal Switching Table; Pulse-width-modulation; Simulation results; Six-Switch Three-Phase Inverter (SSTPI); Space vector; Switchingtables; Switching techniques; Three-phase inverters; Torque ripples; Voltage space vectors; Inductionmotors Year: 2007 Source title: Proceedings of the International Conference on Power Electronics and Drive Systems Art. No.: 4487877 Page : 1331-1336 Cited by: 1 Link: Scorpus Link Correspondence Address: Dzung, P. Q. ; Faculty of Electrical and Electronic Engineering, HCMC Universityof Technology, Ho Chi Minh City, Viet Nam Conference name: 7th International Conference on Power Electronics and Drive Systems, PEDS 2007 Conference date: 27 November 2007 through 30 November 2007 Conference location: Bangkok Conference code: 73040 ISBN: 1424406455; 9781424406456 CODEN: 85RTA
Comparison of Online Learning during the COVID-19 Pandemic against the Traditional Face-to-Face Learning Experience for a STEM Related Subject, Analytical Mathematics

The Coronavirus (Covid-19) pandemic put a halt provisionally to face-to-face teaching throughout the educational sector impeding in addition, university students from receiving their traditional on campus teaching learning experience. Educators, academics, and the higher institutional authorities had to act fast and change their mode of teaching delivery to online platforms, with many students transferring their learning habits and customs to various online mediums and tools. In view of the struggles and adjustments many students underwent with this new method of teaching, a research study was conducted to identify whether online learning had an effect on student performance for a Science, Technology, Engineering and Mathematics (STEM) related module. On this purpose, this paper evaluates different statistical test measures to compare the two different teaching techniques adopted by a university foundation level module, Analytical Mathematics. Results from the pursued quantitative analysis show a decrease in the success of students’ performance whilst receiving online teaching, suggesting a lesser impact on improved learning compared to the customary face-to-face delivery for this specific module. With regards to engagement, observations from the qualitative analysis of the study show a lack of involvement and participation from students with their lecturer and peers whilst in an online environment, echoing its influence on student performance. The deductions of the analyses of this study, emphasize that the necessary transition to online delivery and learning during the pandemic did lead to a challenging and negative experience on students and their performance for this STEM related subject.
Misfire Detection in Automotive Engines Using a Smartphone through Wavelet and Chaos Analysis

Besides the failures that cause accidents, there are the ones responsible for preventing the car’s motion capacity. These failures cause inconveniences to the passengers and expose them to the dangers of the road. Although modern vehicles are equipped with a failure detection system, it does not provide an online approach to the drivers. Third-party devices and skilled labor are necessary to manage the data for failure characterization. One of the most common failures in engines is called misfire, and it happens when the spark is weak or inexistent, compromising the whole set. In this work, two algorithms are compared, based on Wavelet Multiresolution Analysis (WMA) and another using an approach performing signal analysis based on Chaos using the density of maxima (SAC-DM) to identify misfare in a combustion engine of a working automotive vehicle. Experimental tests were carried out in a car to validate the techniques for the engine without failure, with failure in one piston, and with two failed pistons. The results made it possible to obtain the failure diagnosis for 100% of the cases for both WMA and SAC-DM methods, but a shorter time window when using the last one.
Virtual Assistants: A Code of Ethics

An intelligent virtual assistant (IVA) like Amazon’s Alexa supports clients remotely by performing administrative, technological, or creative tasks. IVAs have become a part of our daily lives and collect large sums of data about their users, meaning we need a proper code of ethics to protect people’s rights to privacy as there are data leakage concerns. In this paper, I outline a code of ethics for IVAs based on research and a case study on Amazon Alexa to recognize the key principles that a code of ethics for IVAs requires to protect its users. This code of ethics can be used for development of new IVAs or updating of existing ones in order for them to follow improved ethics principles.
An Accurate and Efficient Scheduler for Hadoop MapReduce Framework

MapReduce is the preferred computing framework used in large data analysis and processing applications. Hadoop is a widely used MapReduce framework across different community due to its open source nature. Cloud service provider such as Microsoft azure HDInsight offers resources to its customer and only pays for their use. However, the critical challenges of cloud service provider is to meet user task Service level agreement (SLA) requirement (task deadline). Currently, the onus is on client to compute the amount of resource required to run a job on cloud. This work present a novel makespan model for Hadoop MapReduce framework namely OHMR (Optimized Hadoop MapReduce) to process data in real-time and utilize system resource efficiently. The OHMR present accurate model to compute job makespan time and also present a model to provision the amount of cloud resource required to meet task deadline. The OHMR first build a profile for each job and computes makespan time of job using greedy approach. Furthermore, to provision amount of resource required to meet task deadline Lagrange Multipliers technique is applied. Experiment are conducted on Microsoft Azure HDInsight cloud platform considering different application such as text computing and bioinformatics application to evaluate performance of OHMR of over existing model shows significant performance improvement in terms of computation time. Experiment are conducted on Microsoft Azure HDInsight cloud. Overall good correlation is reported between practical makespan values and theoretical makespan values.
Solid-State DC Circuit Breaker Based on HTS Fault Current Limiter and SiC MOSFET Modules

An improvement of the conventional solid state dc breaker (SSDCB) is developed for shipboard medium-voltage dc (MVDC) system protection. By combining a high temperature superconducting fault current limiter (HTS-FCL) with a SSDCB based on Slicon Carbide (SiC) MOSFETs, the efficiency of the SSDCB portion is improved while the number of switching devices necessary is significantly reduced with the assistance of the HTS-FCL. The natural current limiting properties of HTS cables are leveraged such that the HTS-FCL has negligible insertion losses and passive fault limitation. In normal operation, the nominal load current flows through the superconductive HTS-FCL and SiC MOSFETs with low conduction losses. During a fault, the HTS-FCL becomes a significant resistance which limits the peak magnitude of fault current. This reduces the number of SiC devices in the SSDCB which then interrupts the fault. After interruption, a metal-oxide varistor device clamps the transient overvoltage which limits voltage stress and excessive switching losses. Simulation results are provided to verify the main functionality of the breaker in a 20 kV shipboard MVDC power system.
Evaluation of Upper Limb Muscle Activation Using Musculoskeletal Model with Wearable Assistive Device

In recent years, wearable assistive device has been used to support upper arm movement training for rehabilitation purposes. A wearable assistive device could affect the muscle output during motor tasks to support upper limb disorder rehabilitation training. However, the investigation of muscle activity with the given assistive force is not widely investigated. In this study, the evaluation of upper limb muscle activities using musculoskeletal simulation systems with the developed wearable cable-driven assistive device has been carried out. An experimental protocol consisting of a series of motions was executed with five healthy subjects. Muscle activation on the brachioradialis, biceps, and triceps muscles was measured by using surface electromyography (EMG) and analyzed. The simulations with a musculoskeletal model to estimate muscle output with and without a wearable assistive device were performed for three tasks. An assistive upper arm device was integrated into the musculoskeletal model, and the desired assistive force is translated to the arm joint along with a tendon routing structure. Assisting movement by the wearable device was evaluated by measuring muscle activation with-assist and without-assist conditions. The results show that the use of the wearable assistive device can effectively assist in arm movement. Comparisons of measured EMG muscle data and the musculoskeletal model revealed that muscle force was generated throughout the arm. The integrated musculoskeletal model results show that muscle force values for two primary muscles (biceps and brachioradialis) were reduced during the simulated task when wearing the assistive device. These results are congruent with expectations, with the assistive device that supports the upper limb movement, providing practical assistance. The results highlight the importance of evaluating muscle output for the developed wearable assistive device to support the assistive movement. Lastly, the musculoskeletal simulation system could reduce the resource-intensive, and time consumed with the experimental testing could be achieved.
Learning From FM Communications: Toward Accurate, Efficient, All-Terrain Vehicle Localization

Vehicle localization service is a fundamental component of intelligent transportation systems. The widely used satellite navigation systems perform poorly in urban areas because the lines of sight to satellites are blocked by complex terrain characteristics, e.g., buildings, elevated streets and interchanges. In this paper, we design RadioLoc, a novel system achieving accurate, efficient, all-terrain vehicle localization with two key design points. First, RadioLoc harvests the frequency modulation (FM) signal, which has higher availability than satellite signal in complex terrains, as the signal source for localization. Second, RadioLoc integrates modern machine learning techniques into the processing of FM signals to efficiently learn the accurate vehicle localization in all-terrain environments. We validate the feasibility of FM-based vehicle localization and corresponding challenges and practical issues via field tests (e.g., signal distortion, signal inconsistency and limited in- vehicle radio bandwidth), and develop a series of advanced techniques in RadioLoc to address them, including adaptive batching, frequency sweeping, a novel multipath delay spread filter, a reconstructive PCA denoiser and a tailored FM feature extractor. We then develop a generic, modular localization module in RadioLoc, and design different learning-based 3D position identification algorithms for this module. We implement a prototype of RadioLoc and perform extensive field experiments to evaluate its efficiency and efficacy. Results show that (1) RadioLoc achieves a real-time localization latency of less than 100 milliseconds; (2) RadioLoc achieves a worst-case localization accuracy of 99.6% even in an underground parking lot, and (3) the horizontal error of RadioLoc is only one sixth of a dedicated GPS device even when the vehicle is moving at a high-speed (i.e., 80 km/h) in a complex highway scenario.