Miracle-dz/UniFAR-FTUs huggingface dataset:
Lineage 1 Porcine Reproductive and Respiratory Syndrome Virus Attenuated Live Vaccine Provides Broad Cross-Protection against Homologous and Heterologous NADC30-Like Virus Challenge in Piglets

Porcine reproductive and respiratory syndrome virus (PRRSV) is an important pathogen that endangers the swine industry worldwide. Recently, lineage 1 PRRSVs, especially NADC30-like PRRSVs, have become the major endemic strains in many pig-breeding countries. Since 2016, NADC30-like PRRSV has become the predominant strain in China. Unfortunately, current commercial vaccines cannot provide sufficient protection against this strain. Here, an attenuated lineage 1 PRRSV strain, named SD-R, was obtained by passaging an NADC30-like PRRSV strain SD in Marc-145 cells for 125 passages. Four-week-old PRRSV-free piglets were vaccinated intramuscularly with 105.0TCID50 SD-R and then challenged intramuscularly (2 mL) and intranasally (2 mL) with homologous NADC30-like PRRSV SD (1 × 105.0TCID50/mL) and heterologous NADC30-like PRRSV HLJWK108-1711 (1 × 105.0TCID50/mL). The results showed that antibodies against specific PRRSVs in 5 of 5 immunized piglets were positive after a 14-day post-vaccination and did not develop fever or clinical diseases after NADC30-like PRRSV challenges. Additionally, compared with challenge control piglets, vaccinated piglets gained significantly more weight and showed much milder pathological lesions. Furthermore, the viral replication levels of the immunized group were significantly lower than those of the challenge control group. These results demonstrate that lineage 1 PRRSV SD-R is a good candidate for an efficacious vaccine, providing complete clinical protection for piglets against NADC30-like PRRSVs.
Diagnostic Performance of SARS-CoV-2 Rapid Antigen Test in relation to RT-PCR Cq Value

Background Early detection of the SARS-CoV-2 is crucial for both the improvement of turnaround time and limiting the spread of the virus in the community. Thus, this study aims to establish rapid antigen tests as an effective diagnostic tool to improve the testing strategies of COVID-19 diagnosis. Methods A laboratory based cross-sectional study was performed on the patients that visited Sukraraj Tropical and Infectious Disease Hospital (STIDH) in Kathmandu, Nepal, from November 2020 to January 2021. A total of 213 nasopharyngeal swabs were collected from both symptomatic and asymptomatic patients for rapid antigen test, followed by RT-PCR assay as reference test for confirmation of COVID-19. A standard questionnaire was administered to collect other information from patients. Data were collected and analyzed using SPSS version 20. Results Out of 213 individuals, 75 tested positive in Ag-RDT test, while 118 tested positive for SARS-CoV-2 RNA genome via Real time PCR assay. The overall diagnostic performance of Ag-RDT showed 63.6% sensitivity and 97.9% specificity. The diagnostic accuracy of Ag- RDT was 78.9% with κ value 0.590, showing moderate agreement with RT-PCR. Significant difference (p value <0.001) was observed between Ag- RDT+ and Ag- RDT− results when compared to Cq values obtained from RT- PCR. Conclusion The promising performance of Ag-RDT renders it useful as screening tool alongside RT-PCR to reduce transmission via improving contact tracing, implementation of local mitigation strategies, and refining existing testing protocol for diagnosis of COVID-19.
Catering for the Audience Needs in English-Arabic Interpreting: The Case Study of Arabicized Words in Conferences

This research sheds light on whether Arab interpreters take into consideration the audience preferences when interpreting Arabicized words in conferences. The researchers selected four groups; three of them are students at Birzeit University majoring in the English language with a minor in translation or English language only, as well as students coming from different majors but not English language majors. Forty contextualized Arabicized words were used in a simulated conference situation, and students were asked to select either the loan words or the loan translations they preferred to hear for the Arabicized words. Five certified interpreters also participated in this study. The results showed that there were clear trends; the English language majors with a minor in translation, as well as the English language majors, opted for the loan translations, while the other students not majoring in English selected the loan words. Finally, the five interpreters supported the loan translation options. Thus, it became clear that those who major in language and or translation tend to prefer loan translation rather than loan words since they think these translations are more formal, more standard, and purer.
NUMERICAL ANALYSIS OF THE IN-CYLINDER FLOW FIELD AND COMPARISON WITH EXPERIMENTAL DATA IN A SINGLE CYLINDER RESEARCH ENGINE

In this paper, the 3D numerical analysis of the flow field in a single cylinder research engine with optical access, running cold flow, is presented. The simulations were carried out in the commercial software Star-CD with es-ICE module and the two different turbulence models: RNG-k-e and k-w SST were compared with experimental data of the engine running at 1000 rpm in motored conditions. A grid independency study is presented using five grids varying from approximately 500,000 to 1,500,000 cells on piston bottom dead center. An optical window inside the engine cylinder was used to measure the velocity components along the crank angle with the PIV technique and a comparison was made with calculated velocity curves. Moreover, a qualitative analysis of the scalar flow field in the region of interest is presented. The results showed a better agreement with the RNG-k-e and experimental mean velocity magnitude curve than k-ω-SST. However, the qualitative scalar velocity field of the k-ω model captured more details of the flow than the k-e model. Keywords: Turbulence, Internal Combustion Engine, Star-CD, 3D Simulation, RANS
Can unexpected support promote environmental policy acceptability? An experimental investigation of norm source and strength

Two experiments tested how environmental policy acceptability of US conservatives and liberals was influenced by manipulating the level (minority vs. majority) and source (in-group vs. outgroup) of normative support for policy. Results from 928 MechanicalTurk users (Study 1: N = 268, Study 2: N = 660) indicated that when evaluating an in-group policy (that participants expect their own political group to support), communicating outgroup support increases acceptability compared with communicating in-group support. The outgroup norm has a positive indirect effect via the inference that the in-group is even more supportive of the policy than the outgroup is. In contrast, when evaluating an outgroup policy, communicating in-group support indirectly yields higher acceptability than communicating outgroup support, via the inference that the outgroup is more supportive than the in-group is. This effect mainly occurred for individuals with strong ideological identification and was independent of level of support (minority vs. majority). Results indicate that bipartisan support for environmental policies can be achieved by strategic communication of normative information about political groups.
A Shunt DC Electric Spring-Based Control Strategy for Real-Time Critical and Noncritical Load Management in DC Microgrid

Deployment of DC Electric Spring (DCES) technology is an innovative demand side management (DSM) program that helps to mitigate voltage interruptions. Conventional control strategy uses proportional-integral controllers to regulate DC spring parameters. As PI controllers have a trade-off between settling time and peak overshoot, they may not be able to achieve the desired dynamic performance under varying DC microgrid (DCMG) conditions. In this regard, this paper proposes an inverted zero compensator for the control of shunt DCES (ShDCES) in DCMG. This controller regulates the ShDCES current and terminal voltage under all operating conditions with reduced settling time and peak overshoot. A power management algorithm is also proposed to optimize the operation of ShDCES under the presence of varying PV generation and critical and noncritical loads. A detailed mathematical analysis of ShDCES and inverted zero compensator design are furthermore incorporated to validate the strategy. The simulation of the proposed method is performed in MATLAB/Simulink environment. The comparison analysis with the conventional controller proves the effectiveness of the new controller. The results are also validated using real-time simulator OP4510RTS.
AI bot to detect fake COVID‐19 vaccine certificate

Abstract As the world is now fighting against rampant virus COVID‐19, the development of vaccines on a large scale and making it reach millions of people to be immunised has become quintessential. So far 40.9% of the world got vaccinated. Still, there are more to get vaccinated. Those who got vaccinated have the chance of getting the vaccine certificate as proof to move, work, etc., based on their daily requirements. But others create their own forged vaccine certificate using advanced software and digital tools which will create complex problems where we cannot distinguish between real and fake vaccine certificates. Also, it will create immense pressure on the government and as well as healthcare workers as they have been trying to save people from day 1, but parallelly people who have fake vaccine certificates roam around even if they are COVID/Non‐COVID patients. So, to avoid this huge problem, this paper focuses on detecting fake vaccine certificates using a bot powered by Artificial Intelligence and neurologically powered by Deep Learning in which the following are the stages: a) Data Collection, b) Preprocessing to remove noise from the data, and convert to grayscale and normalised, c) Error level analysis, d) Texture‐based feature extraction for extracting logo, symbol and for the signature we extract Crest‐Trough parameter, and e) Classification using DenseNet201 and thereby giving the results as fake/real certificate. The evaluation of the model is taken over performance measures like accuracy, specificity, sensitivity, detection rate, recall, f1‐score, and computation time over state‐of‐art models such as SVM, RNN, VGG16, Alexnet, and CNN in which the proposed model (D201‐LBP) outperforms with an accuracy of 0.94.
DNA methylation prediction using reduced features obtained via Gappy Pair Kernel and Partial Least Square

It is critical to correctly identify DNA methylation because it has been linked to a variety of human disorders, particularly cancer. DNA methylation is an epigenetic process that allows cells to alter gene expression. This work deals with a type of DNA methylation called 5-methyl cytosine (m5c), in which the methyl group (CH3) is attached to the 5th carbon of cytosine. The performances of different machine learning algorithms used for methylation identification are greatly degraded due to poor representation of input sequential data. In the current work, we have proposed a classification model that is based on the extraction of high differentiating features from the sample sequences using gappy pair kernel. Increasing the number of features to better represent a sequence leads to the curse of dimensionality, which is handled by a dimensionality reduction technique called PLS (Partial Least Square). The obtained features are then subjected to multiple classifiers to test the discriminating power of these features. Results are computed for cross species i.e human and mouse, to check the robustness of our proposed model. Finally, the obtained results are compared in terms of sensitivity, specificity, and accuracy with the state-of-the-art approaches. Our proposed approach has outperformed state-of-the-art techniques in all three metrics for both datasets. For research community to test our technique, we have uploaded our code on github (https://github.com/sajidshahbs/gappypairKernel_Rcode).
Cyclic tests of steel plate shear walls using box‐shape vertical boundary elements with or without infill concrete

The steel plate shear wall (SPSW) is an effective lateral force resisting system in which unstiffened steel infill plates are connected to the horizontal and vertical boundary elements (VBEs) on all sides of the plates. The boundary elements must be designed to resist the tension field force of the infill panels. When the VBEs are made from a steel box section, the flange of each box VBE connected with the infill panels can be pulled out‐of‐plane by significant panel forces, called pull‐out action. This study investigates capacity design methods for box VBEs in SPSWs. Simplified fixed beam and portal frame models aim to estimate the pull‐out responses of the flange of the box sections with and without infill concrete, respectively. In this study, cyclic tests of three full‐scale two‐story SPSWs using box VBEs with or without the infill concrete are conducted. Inelastic pushover analyses of the finite element models are conducted. The tests and analytical results confirm that the proposed design methods, which aimed to prevent the full yield of the flange under the pull‐out action, are applicable. Furthermore, the test and analytical results suggest that the initial yielding of the flange of box VBEs under the collective effects of the pull‐out action on the flange, the gravity load, and the sway action on the SPSW represents a local yielding. A strict prohibition of the initial yielding on the flange under the aforementioned collective effects is not recommended for pursuing a cost‐effective design. Copyright © 2017 John Wiley & Sons, Ltd.
A Cancelable Biometric Security Framework Based on RNA Encryption and Genetic Algorithms

Cancelable biometric recognition techniques play a vital role in the privacy and security of remote surveillance systems to keep the genuine users’ confidential data safe and away from intruders. This research work presents an efficient cancelable biometric recognition framework that exploits an irreversible hybrid encryption algorithm. It incorporates Deoxyribonucleic, Ribonucleic Acid sequence (DNA and RNA) encryption technique, and an evolutionary optimization technique, namely Genetic Algorithms (GAs). These techniques are employed to create completely deformed templates from their original ones. Hence, the main contribution is introducing a novel biometric security framework that achieves unique randomness characteristics using RNA and DNA sequences and the evolutionary GA technique. The proposed framework produces entirely deformed biometric templates by ciphering the main discriminative features of the biometric traits of the authorized clients. It is firstly initialized by creating several encrypted biometric images for the original users with the logistic map. After that, the initially encrypted images are transformed into vectors of a binary array. Then, they are converted to their corresponding introns, and exons, and consequently, their relevant codons are stored in the cloud database. These relevant codons are replaced by new ones after generating encrypted RNA lists. The utilized encryption key for each template is extracted from the original biometric image through excessive permutations between pixels. The GA optimization technique is applied to select the most convenient biometric features. Finally, after employing the GA-based cross-over and mutation operations, the chosen features are used to generate the cancelable biometric traits. To assess the proposed framework, six different biometric databases are considered. These databases are Olivetti Research Laboratory (ORL) Faces (gray), CASIA v.5 Faces (color), UPOL Iris (gray), Indian Institute of Technology Delhi (IIT Delhi) Ear (color and gray), Fingerprint, and CASIA Palmprint (color and gray). The security performance of the proposed encryption algorithm is compared to those of recent studies in this field, such as Optical Scanning Holography (OSH) and Double Random Phase Encoding (DRPE). The simulation results prove the superior performance of the proposed framework in terms of all adopted evaluation metrics. The proposed framework provides high Area under the Receiver Operating Characteristic (AROC) curve that reaches 0.9990, low False Acceptance Rate (FAR) of 0.0015, more uniform histograms, high correlation values for genuine users, and completely hidden biometric features. In addition, from the security perspective, the proposed framework achieves good entropy, Unified Average Changing Intensity (UACI), and Number of Pixels Change Rate (NPCR) values that reach 7.9960, 33.55%, and 99.65%, respectively.
Design and Analysis of Base Valve of Twin Tube Dampers

In this article we seek to model a base valve of a twin tube shock absorber and study the behaviour of the valve under loaded conditions replacing a mono tube shock absorber. A top down approach was adopted for this work wherein a commercially available shock absorber was stripped down to obtain the individual valves inside. Product teardown was done for the various subassemblies and the base valve was isolated. Modelling was done in PTC Creo 2.0 and the FEA study for the stress and flow analysis was done using ANSYS 15.0. The materials as well as the hydraulic fluid selected for the design were validated and proved to give a better performance than a mono tube shock absorber currently being used in automobiles.
A Practical Approach to Assessing Physical Freshness: Utility of a Simple Perceived Physical Freshness Status Scale

Background: Monitoring physical freshness is essential in assessing athletes’ conditions during training periods, training sessions, or competitions. To date, no single physical freshness scale has been successfully validated against training load variables and widely used scales measuring different facets of physical freshness. Objective: In this study, we develop and test the practical utility of a perceived physical freshness (RPF) scale to monitor the condition of the athletes and to prevent excessive fatigue and insufficient recovery during training sessions or competitions. Methods: Sixteen professional male soccer players (mean ± SD age 26 ± 4 years) were enrolled. Training load (TL), monotony, strain, rate of perceived exertion (RPE), well-being indices (sleep, stress, fatigue, and muscle soreness), total quality recovery (TQR) and RPF were determined each day for two weeks of training, including a week intensified training (IW) and a week taper (TW). The validity of the RPF scale was assessed by measuring the level of agreement of a player’s perceived physical freshness relative to their TL variables, recovery state and well-being indices during each training phase (IW and TW) and during the overall training period (TP). Results: RPF increased during the TW compared to IW (ES = 2.31, p < 0.001, large). For the TP, IW and TW, weekly RPF was related to weekly TL (r = −0.81, r = −0.80, r = −0.69, respectively), well-being (r = −0.91, r = −0.82, r = −0.84, respectively) and TQR (r = 0.76, r = 0.91, r = 0.52, respectively), all p < 0.01. For the TP, IW and TW, daily RPF was related to TL (r = −0.75, r = −0.66, r = −0.70, respectively), well-being (r = −0.84, r = −0.81, r = −0.78, respectively) and TQR (r = 0.82, r = 0.81, r = 0.75, respectively), all p < 0.01. Conclusions: RPF was effective for evaluating the professional soccer players’ physical freshness and may be a strategy for coaches to monitor the physical, psycho-physiological, and psychometric state of the players before training session or matches.
Reconceptualizing Enterprise Systems

Enterprise Systems (ES) are traditionally seen as a specific category of Information Systems (IS). They offer a set of functional modules, generally based on industry best practices (Markus and Tanis 2000). In contrast to traditional IS, ES target large-scale integration of data and business processes across a company’s functional areas (Devadoss and Pan 2007). Consequently, the complexity of ES is higher than in traditional IS as they tend to have organization-wide impact rather than localized effect (Strong and Volkoff 2010). Due to this complexity, organization-wide resources are involved and significant change is generally required during the (post-) implementation phase (Devadoss and Pan 2007).
Parametric Analysis of Vertical Ultimate Bearing Capacity of Precast Concrete Pile Using Artificial Neural Network

An approach of predicting vertical ultimate bearing capacity of precast concrete pile is presented using backpropagation(BP) artificial neural network(ANN). Some parameters are analyzed to see how to influence vertical ultimate bearing capacity of precast concrete pile.Through analyzing it is found that five parameters may influence vertical ultimate bearing capacity of precast concrete pile,including pile diameter,pile length,length accessing earth,weighted average frictional resistance at the side of pile and average frictional resistance at the end of pile.The ANN can be trained using a small set of parameters obtained from test data which may influence vertical ultimate bearing capacity of precast concrete pile.Three layers are adopted in ANN,which the input layer includes parameters mentioned above and the output layer is vertical ultimate bearing capacity of precast concrete pile.The results show that the accuracy of vertical ultimate bearing capacity of precast concrete pile predicted by the presented approach is validated by comparison with test data.The relationship between vertical ultimate bearing capacity of precast concrete pile and parameters can be found through parametric analysis,and the reasonable parameters can be determined.
Curriculum differentiation for masters in product development

Based on the input from industrial competence centres and consultative group meetings with academics and practitioners, the product development department of the faculty of design sciences has developed a new masters programme giving students the possibility to differentiate by concentrating more on specific domains: interaction design, strategic design, advanced product design and systems design. Although the programme maintains the focus on the integrated approach of a development process for products and services, throughout the first masters year students will be able to gain more skills and competences on specific issues, in order to become more proficient and to adapt their skills to their future professional targets. The programme is to be implemented the coming years. This paper describes the new masters programme concept and goes into detail on the theoretical principles behind the programme, the approach, the nexus with the research programme and the consequences of this programme in function of the assessment of students in applied product development courses. Milestones and deliverables have been redefined in order to guide the studentâs applied development or research projects. New sets of criteria have been developed to assess the milestone and final deliverables throughout the projects.
Identification of Environmental Buffer Areas in Urbanized Catchments Based on Synthetic Ecosystem Functions

The Saldan river basin in the northwest of the metropolitan area of Córdoba, Argentina, was defined as a unit for the observation of ecosystem services of water regulation. The capacity of the system to retain excess rainfall through vegetation in areas with slopes was analyzed. The observation area was a wildland-urban interface. From there, a model capable of synthesizing the behavior of the most relevant variables in the event of an extraordinary rainfall event was adjusted. The result was the definition of buffer areas or exosystemic protection in the interface. The testing and adjustment of this model defined a REP indicator that was proposed as an input in the planning of the sector. For validation, soil moisture permanence values from LWI were used. The derivative was calculated for each pixel value in the basin, for both dry and wet periods, and the areas with the lowest loss in both periods were compared with those with the highest retention obtained in the model. The zones identified by both methods show great similarities.