Miracle-dz/UniFAR-FTUs huggingface dataset:
A quantitative study of the value of ergonomic training at a Middle Eastern post secondary institution

The use of portable, compact technology is prevalent in today’s society, particularly among the student population. It would be assumed that the more a user of this technology is aware of ergonomic principles and safe usage then the lower the risk of mobile technology related musculoskeletal injuries (MSIs). Unfortunately, many users do not have the knowledge to successfully employ these technologies safely and comfortably. A study was carried out at the College of the North Atlantic, Qatar campus (CNA-Q) in three stages: 1) to determine mobility usage prevalence and associated musculoskeletal risk factors, 2) identification of a suitable introductory ergonomics training program, and 3) identification of a suitable delivery method of the ergonomics training program (instructor-led versus web-based learning). Results of the study found that of students who use mobile technology improper postures were adopted approximately 100% of the time. To assist in decreasing the probability of future soft tissue injuries, an Introduction to Ergonomics program was selected and delivered to students. Upon comparison of the presentation formats, the students who received the information by a teacher retained the greatest amount of information (as compared to the group that received the information via the web and those that received no training) in the short-term (immediately following the training session). However, there was no statistically significant difference in retention among the three groups after one month following the training.
Low-complexity 0.55-V 2.5-μW bandgap reference and power-on reset hybrid circuit

A near-threshold low-complexity bandgap reference (BGR) and power-on reset (POR) hybrid circuit is implemented in 65 nm CMOS, with an ultra-low supply voltage of 0.55 V and power consumption of 2.5 μW. The BGR employing amplifier offset voltage and current matching, and the POR based on offset voltage matching and loop settling feature, are proposed. The circuit-level simulation results verify that the presented implementation, not only generates a stable reference voltage with the typical temperature coefficient of 120 ppm/°C and line-regulation feature of 3.3%/V, but provides a robust reset signal with power-on and brown-out detection function. The proposed near-threshold circuit has ultra-low power supply and design complexity, both for BGR and POR modules, when compared to the reported.
INFLUENCE OF BETAMETHASONE DIPROPIONATE AND PREDNISONE ON ENZYME KINETICS

Most pharmaceutical and nutritional compounds are marketed as enzyme inhibitors, and such inhibitors show their specific action in inhibiting enzymes within cells. Since their discovery, corticosteroid treatment has been one of the most used and effective treatments for various inflammatory and autoimmune disorders. Corticosteroids have anti-inflammatory and immunosuppressive effects, so it is hypothesized that these corticosteroids will have an inhibitory effect on the enzyme catalase. This study aimed to examine the effect of the corticosteroid’s betamethasone dipropionate and prednisone on catalase activity in vitro. A spectrophotometric method was used based on the reaction between hydrogen peroxide, enzymes, and corticosteroids. After incubation at 37°C with the addition of ammonium heptamolybdate, a stable complex was formed, and the absorbances were analysed. The obtained results indicate an uncompetitive type of inhibition in betamethasone dipropionate, while prednisone acts as a partial competitive agent.
MEASUREMENT OF MECHANICAL VIBRATIONS AND MUSCULAR ACTIVITY IN CYCLING ON COBBLESTONES AT RACE PACE – A SINGLE CASE STUDY

The aim of this research is to reveal the best bike configuration, wheels, and tyre pressure for competitive cycling on cobblestones. One former professional cyclist performed 16.75 laps on a cobbled road track (1.55 km). Three accelerometers were mounted on the stem, the seat post and the down tube. sEMG of the rectus femoris, vastus lateralis, tibialis anterior, extensor digitorum, and thoracolumbar fascia were measured by wireless sensors that measured also soft tissue longitudinal accelerations. The pressure inflated on tubular tyres led to significant differences on effective values of vibrations, each time higher at 5.5 rather than 5 bar. The methods implemented in this study appear to be consistent in revealing the best bike configuration and settings when vibrations are the main elements in races.
Integrating Collaborative Awareness in Synchronous Distributed Groupware

This paper presents a model for integrating collaborative awareness in development of a synchronous distributed groupware, which was expected to enhance team collaboration in home modification tasks. The model, which is a synthesis of Situation Awareness and Activity theory, has three levels of collaborative awareness, comprising subject-collaborator member, object-outcome, spatial-temporal relationship, and mediation-projector components. These components were hypothesized to support remote collaborators in performing collaborative tasks effectively. We report the findings of the first-level perception that tested the importance of subject-collaborator member and object-outcome components in enhancing awareness. Our experiment revealed that both components were frequently acquired during collaboration. If only the subject-collaborator member component was available, subjects made several errors, and if only the object-outcome component was present, subjects could not achieve common ground, nor distribute the task easily, or share their customization errors. Therefore, a groupware that supported both subject-collaborator member and object-outcome component is preferred in collaborative task performance.
A velocity program using the Kanade–Lucas–Tomasi feature‐tracking algorithm with demonstration for pressure and electroosmosis conditions

Investigating microfluidic flow profiles is of interest in the microfluidics field for the determination of various characteristics of a lab‐on‐a‐chip system. Microparticle tracking velocimetry uses computational methods upon recording video footage of microfluidic flow to ultimately visualize motion within a microfluidic system across all frames of a video. Current methods are computationally expensive or require extensive instrumentation. A computational method suited to microparticle tracking applications is the robust Kanade–Lucas–Tomasi (KLT) feature‐tracking algorithm. This work explores a microparticle tracking velocimetry program using the KLT feature‐tracking algorithm. The developed program is demonstrated using pressure‐driven and EOF and compared with the respective mathematical fluid flow models. An electrostatics analysis of EOF conditions is performed in the development of the mathematical using a Poisson's Equation solver. This analysis is used to quantify the zeta potential of the electroosmotic system. Overall, the KLT feature‐tracking algorithm presented in this work proved to be highly reliable and computationally efficient for investigations of pressure‐driven and EOF in a microfluidic system.
NFVinc: Disponibilizando Serviços Inovadores na Rede com Tecnologia NFV

A Virtualização de Funções de Rede (Network Function Virtualization - NFV) permite a substituição de funções de rede tradicionalmente implementadas em hardware dedicado por software. Apesar de que a maioria das VNFs implementa funcionalidades de middleboxes clássicas, como firewall ou detector de intrusão, a tecnologia NFV pode ser utilizada para disponibilizar serviços oferecidos pela própria rede que são utilizados por aplicações de usuários finais. Desta forma, aplicações e sistemas normalmente executados pelos usuários em hosts da borda, passam a ser inteiramente executados dentro da rede, no paradigma que tem sido chamado de In-Network Computing (INC). Implementar INC em NFV tem diversas vantagens, incluindo aumento da ﬂexibilidade e diminuição de custos. Neste trabalho descrevemos a arquitetura NFVinc que permite, de forma compatível com a arquitetura de referência NFV-MANO, a disponibilização tanto de serviços INC pelos desenvolvedores como a exportação de APIs que são usadas por aplicações de usuários finais para o acesso a estes serviços INC. São apresentados três estudos de caso demonstrando a ﬂexibilidade e eficácia da arquitetura NFVinc.
Minimally invasive spine surgery as treatment for persistent infectious lumbar spondylodiscitis: a systematic review and meta-analysis.

Background Antibiotic resistant infectious spondylodiscitis (IS) can cause significant morbidity for patients. Open surgical techniques were previously the only option for patients who failed antibiotic therapy. However, advances in minimally invasive surgical techniques may provide a new alternative for some patients. Methods A systematic review was conducted using Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology to identify studies that reported inflammatory [erythrocyte sedimentation rate (ESR) and C-reactive protein (CRP)] and functional outcomes [visual analog scale (VAS)] for patients with antibiotic resistant IS treated with either minimally invasive or open surgery. Searches were preformed using PubMed, Embase, and Scopus from January 2015 to June 2021. Fourteen articles met inclusion criteria. One study was a Level III evidence study and the other 13 included studies were Level IV. Results The minimally invasive surgery group showed significantly lower post-operative CRP and VAS pain scores and significantly higher post-operative ESR levels than the open group. All studies included were measured by the Downs and Black tool for potential bias. Discussion This study showed that minimally invasive surgery is efficacious in the treatment of antibiotic resistant IS. These outcomes support minimally invasive surgery (MIS) as an effective alternative to previous open surgery techniques in certain patients after failed trails of antibiotic therapy.
Quantifying Location Privacy for Navigation Services in Sustainable Vehicular Networks

Current connected and autonomous vehicles will contribute to various and green vehicular services. However, sharing personal data with untrustworthy Navigation Service Providers (NSPs) raises serious location concerns. To address this issue, many Location Privacy-Preserving Mechanisms (LPPMs) have been proposed. In addition, several quantification methods have been designed to help understand location privacy and illustrate how location privacy is leaked. However, their assessment is insufficient due to the incomplete assumptions about the adversary’s model. In particular, users tend to request the same navigation routes from home to workplace and acquire traffic information along the route. An adversary can collect the coordinates of adjacent locations and infer the two true locations. In this paper, we provide a formal framework for the analysis of LPPMs in navigation services. Our framework captures extra information that is available to an adversary performing localization attacks. By formalizing the adversary’s performance, we also propose and justify two new metrics to quantify location privacy in navigation services, namely accuracy and visibility. We assess the efficacy of two popular LPPMs for location privacy, i.e., differential privacy and ${k}$ -anonymity. Experimental results demonstrate that the adversary can recover users’ locations with a high probability.
Is Data Mining for Gold "Statistical déjà vu"?

In the past, mining for gold consisted of choosing a site and then sifting through endless amounts of dirt. Sometimes the miner only found a few valuable nuggets, sometimes he hit upon an entire vein, but most of the time, he found nothing at all and decided to either move on to another promising spot or give up mining altogether and stop wasting his time. Today, with scientific methods and specialised tools, mineral mining is much more accurate and productive. Mining for data and learning from it has evolved in much the same way. Older methods executed by statisticians took a long time to yield constructive information. Now, current software and techniques help make data mining a lucrative and more accessible process in the information age that we live in. The field of data mining, like statistics, concerns itself with “learning from data” or “turning data into information”. So we may want to ask ourselves whether data mining for gold is “statistical deja vu”.
Multi Criteria Decision Making: a tool to take decisions in Multi Objective Optimization

Many real-world engineering design involve simultaneous optimization of multiple objectives, with remarkable differences with the single-objective optimization. The goal of the single-objective optimization is finding the best solution, which corresponds to the maximum value of one objective function. On the contrary, in the multi-objective case, the goal is to find the best compromise solutions between the different conflicting criterions. For these reasons, new optimization approaches are needed. The Pareto approach (inside the Game Theory methodology) has been demonstrated as an efficient way to solve problems with multicriterion choice; by following this approach one could achieve not only a solution, but a set with the best compromise designs among different objectives. Since only one design among all the alternatives could be put on production, an efficient way to select the more appropriate alternative is needed. In the final Pareto set all the solutions are non dominated but only one is the best compromise according to the project manager requirements. In order to solve the choosing problem, a useful numeric tool comes to the aid: Multi Criteria Decision Making (MCDM). MCDM is both an approach and a set of techniques, with the goal of providing an overall ordering of designs, starting from the most preferred to the least preferred one. In this paper different MCDM techniques are adopted, in order to understand the capability and the differences between the different algorithms. The database is the optimal Pareto set of an aeronautic example, the multi objective design of a transonic airfoil with uncertainties.
Graphical design method for unequal power dividers based on phase-balanced SIW tee-junctions

This paper presents a detailed procedure to design substrate integrated waveguide (SIW) Tee-junctions with wide-range split-power ratio (from 1:1 to 1:8) and phase-balanced outputs. A set of curves was generated using an electromagnetic (EM) simulator to graphically determine the required parameters for the desired junction. Since such curves are provided, no further electromagnetic simulation is required when applying the proposed method. Input return loss at central frequency is guaranteed to be better than 18 dB. If an optimization procedure is required, the parameters obtained from this methodology may be used as initial values for such a process. Moreover, design rules are given for implementing 1 → 2N unequal power dividers with phase-balanced outputs. To demonstrate the accuracy of the proposed graphical method, three implementation examples are provided: two Tee-junctions and a 1 → 4 power divider.
Calisto: Sistema para processamento rápido de pacotes com baixa latência em centro de dados

A demanda por suporte a baixa latência em ambientes de alta vazão tem aumentado nosúltimos anos. Sistemas como Shenango permitem que servidores processem pacotes rapidamente, mantendo a eficiência na utilização de CPU através de um algoritmo de detecção de congestionamento e o IOKernel, componente dedicado a orquestrar a alocação de novos núcleos. O sistema porém aloca núcleos utilizando a política de escalonamento round-robin. Neste trabalho, apresenta-se Calisto, um novo sistema que implementa uma política eficiente de distribuição de pacotes entre núcleos que mantém afinidade de ﬂuxos, reduzindo a latência em até 10%.
Prevalence, Antimicrobial Resistance, and Whole Genome Sequencing Analysis of Shiga Toxin-Producing Escherichia coli (STEC) and Enteropathogenic Escherichia coli (EPEC) from Imported Foods in China during 2015–2021

Shiga toxin-producing Escherichia coli (STEC) and enteropathogenic Escherichia coli (EPEC) are foodborne pathogens that cause hemolytic uremic syndrome and fatal infant diarrhea, respectively, but the characterization of these bacteria from imported food in China are unknown. A total of 1577 food samples from various countries during 2015–2021 were screened for STEC and EPEC, and the obtained isolates were tested for antimicrobial resistance and whole genome sequencing analysis was performed. The prevalence of STEC and EPEC was 1.01% (16/1577) and 0.51% (8/1577), respectively. Antimicrobial resistances to tetracycline (8%), chloramphenicol (8%), ampicillin (4%), ceftazidime (4%), cefotaxime (4%), and trimethoprim-sulfamethoxazole (4%) were observed. The antimicrobial resistance phenotypes corresponded with genotypes for most strains, and some resistance genes were related to mobile genetic elements. All 16 STEC isolates were eae negative, two solely contained stx1 (stx1a or stx1c), 12 merely carried stx2 (stx2a, stx2d, or stx2e), and two had both stx1 and stx2 (stx1c + stx2b, stx1a + stx2a + stx2c). Although they were eae negative, several STEC isolates carried other adherence factors, such as iha (5/16), sab (1/16), and lpfA (8/16), and belonged to serotypes (O130:H11, O8:H19, and O100:H30) or STs (ST297, ST360), which have caused human infections. All the eight EPEC isolates were atypical EPEC; six serotypes and seven STs were found, and clinically relevant EPEC serotypes O26:H11, O103:H2, and O145:H28 were identified. Two STEC/ETEC (enterotoxigenic E. coli) hybrids and one EPEC/ETEC hybrid were observed, since they harbored sta1 and/or stb. The results revealed that food can act as a reservoir of STEC/EPEC with pathogenic potential, and had the potential ability to transfer antibiotic resistance and virulence genes.
Assessment on Performance Using Two Kinds of Optimized Fault-Tolerance Neural Network in Fault Diagnosis Models of Transmission and Distribution Networks

In order to enhance the fault-tolerance performance(FTP)of the fault diagnosis system,the extend- ed fault samples considering the distortion of fault information because of stochastic factors are used in the fault- tolerance training of neural network(NN). Two kinds of optimized NN are constructed based on the ant colony optimization algorithm(ACOA)and genetic algorithm(GA)and are used in the fault diagnosis of transmission and distribution systems,and their FTP is assessed.The simulation results show that the FIT of ACOA-NN is superior to that of GA-NN though the structure of models and the distribution of sample space are different.It can enhance 5.91% and 4.95% respectively.ACOA-NN possesses not only excellent generalization ability but quick convergence rate.
Environmental Impact Characteristics of Common Rail Diesel Engine Fueled with Jatropha Biodiesel Blends

The experimental study on global warming potential(GWP),photochemical ozone creation potential(POCP),acid potential(AP),health toxic potential(HTP),and air quality potential(AQP) of a common rail diesel engine fueled with jatropha biodiesel blends was carried out on the full load speed characteristics and the load characteristics at rated speed and max.torque speed.The results show that when fueled with jatropha biodiesel blends,the POCP and AQP at all speed conditions on the full load speed characteristics decrease with the biodiesel fraction increasing,and in range of speed over 1 600 r/min,GWP,AP and HTP lower,moreover,the POCP and AQP lower on the load characteristics of rated speed and max.torque speed,its GWP,AP and HTP lower at rated speed and max.torque speed under the load over 75 %.
Aerodynamic Optimization Design for Airfoil Based on Kriging Model

The approximation technology based on Kriging model is applied to the aerodynamic optimization design. The characteristics of the optimization method based on the approximation technology, the traditional gradient-based search algorithm and the standard genetic algorithm are studied, through calculating global extreme of two test functions and re-constructing the geometry of airfoil. The result shows that the optimization method based on approximation technology has good global performance and also can greatly save the calculating resources. This method is applied to the aerodynamic optimization design of airfoil, defining the lift-drag ratio as the goal function, when Mach number is 0. 7 and the angle of attack is 3°. The application indicates that the lift-drag ratio of optimized airfoil is increased by 81 % comparing with the original airfoil, and the cross section area of the optimized airfoil is decreased less than 1%.
Prediction of the Transition-Temperature Shift Using Machine Learning Algorithms and the Plotter Database

The long-term operating strategy of nuclear plants must ensure the integrity of the vessel, which is subjected to neutron irradiation, causing its embrittlement over time. Embrittlement trend curves used to predict the dependence of the Charpy transition-temperature shift, ΔT41J, with neutron fluence, such as the one adopted in ASTM E900-15, are empirical or semi-empirical formulas based on parameters that characterize irradiation conditions (neutron fluence, flux and temperature), the chemical composition of the steel (copper, nickel, phosphorus and manganese), and the product type (plates, forgings, welds, or so-called standard reference materials (SRMs)). The ASTM (American Society for Testing and Materials) E900-15 trend curve was obtained as a combination of physical and phenomenological models with free parameters fitted using the available surveillance data from nuclear power plants. These data, collected to support ASTM’s E900 effort, open the way to an alternative, purely data-driven approach using machine learning algorithms. In this study, the ASTM PLOTTER database that was used to inform the ASTM E900-15 fit has been employed to train and validate a number of machine learning regression models (multilinear, k-nearest neighbors, decision trees, support vector machines, random forest, AdaBoost, gradient boosting, XGB, and multi-layer perceptron). Optimal results were obtained with gradient boosting, which provided a value of R2 = 0.91 and a root mean squared error ≈10.5 °C for the test dataset. These results outperform the prediction ability of existing trend curves, including ASTM E900-15, reducing the prediction uncertainty by ≈20%. In addition, impurity-based and permutation-based feature importance algorithms were used to identify the variables that most influence ΔT41J (copper, fluence, nickel and temperature, in this order), and individual conditional expectation and interaction plots were used to estimate the specific influence of each of the features.