Miracle-dz/UniFAR-FTUs huggingface dataset:
Yoga and Multiple Sclerosis: Maintaining engagement in physical activity

Background Physical activity is encouraged for people with Multiple Sclerosis. Yoga is a popular form of physical activity and is chosen by some people with Multiple Sclerosis. However, little is known about the impact of yoga for this population, alongside what influences ongoing engagement. Aim The aim of this study is to qualitatively explore the impact of online home-based yoga on people with Multiple Sclerosis and to explore factors that influence engagement. Methods A qualitative study using semi-structured interviews and focus groups with people with Multiple Sclerosis and a yoga teacher. Thematic analysis was used to analyse the data. Ethical Approval was gained from Northumbria University. Findings Three overarching themes emerged from the analysis. ‘Yoga as engagement in physical activity’ captured the reasoning for participating in yoga and how this method of physical activity was an alternative to physical activity done prior to diagnosis. Frustration was apparent within this theme that some individuals were unable to engage in the range of physical activity that they wished to. ‘Yoga is a personalised approach’ demonstrated the flexibility and inclusivity of yoga, for individuals with varying symptoms to be able to engage with. Finally, ‘yoga impacts individuals both physically and psychologically’ captured the focus on the psychological impact of yoga, improving wellbeing and control. Conclusions Yoga gives people with Multiple Sclerosis the feeling of control over their symptoms and a means to engage with meaningful physical activity. Prior involvement in physical activity influenced engagement in yoga and wanting to push themselves. There was reluctance among this group to engage with aerobic activity, which warrants future investigation and support from health and exercise professionals.
Detection d'objets en mouvement

Nous presentons ici un algorithme de detection d'objets en mouvement a partir de n images successives de segments de contour et de la mesure de leur mouvement 2D. Cet algorithme est base sur la detection de structures rigides qui seront rangees dans des classes selon leur mouvement affine 2D.
Exploring parents’ views of the use of narratives to promote childhood vaccination online

Background Negative information about vaccines that spreads online may contribute to parents’ vaccine hesitancy or refusal. Studies have shown that false claims about vaccines that use emotive personal narratives are more likely to be shared and engaged with on social media than factual evidence-based public health messages. The aim of this study was to explore parents’ views regarding the use of positive narratives to promote childhood vaccination. Methods We identified three ∼4-minute video narratives from social media that counter frequent parental concerns about childhood vaccination: parents and informed decision-making (online misinformation about vaccines); a paediatrician’s clinical experience with vaccine-preventable diseases (prevention of still existing diseases); and a mother’s experience with vaccine-preventable disease (risks of the disease). Focus group discussions were held with parents of children aged 0 to 5 years to assess their views on these three narratives and their general opinion on the use of narratives as a vaccine promotion intervention. Results Four focus groups discussions were virtually held with 15 parents in December 2021. In general, parents trusted both health care provider’s and parent’s narratives, but participants identified more with stories having a parent as the main character. Both narratives featuring personal stories with vaccine-preventable diseases were preferred by parents, while the story about informed decision-making was perceived as less influential. Parents expressed the need for reliable and nuanced information about vaccines and diseases and felt that a short video format featuring a story was an efficient vaccine promotion intervention. However, many mentioned that they generally are not watching such videos while navigating the Web. Conclusion While vaccine-critical stories are widely shared online, evidence on how best public health could counter these messages remains scarce. The use of narratives to promote vaccination was well-perceived by parents. Future studies are needed to assess reach and impact of such an intervention.
Instantaneous high-resolution focus tracking and a vibrometery system using parallel phase shift interferometry

High resolution fast focus tracking and vibrometery system based on parallel phase shift polarization interferometry using three detectors is presented. The basic design and algorithm are described, followed by an experimental demonstration showing sub nm resolution of different controlled motion profiles instantaneously monitored at a feedback rate of 100 kHz. The fact that the method does not rely on active optical components, potentially allows extremely high vibration rates to be measured; limited only by the detector bandwidth and sampling rate. In addition, the relatively simple design relies only on standard optical equipment, combined with the simple algorithm, makes the task of setting up a high performance vibrometry system cheap and readily available.
Credibility of vaccine-related content on Twitter during COVID-19 pandemic

During national COVID-19 vaccine campaigns, people continuously engaged on Twitter to receive updates on the latest public health information, and to discuss and share their experiences. During this time, the spread of misinformation was widespread, which threatened the uptake of vaccines. It is therefore critical to understand the reasons behind vaccine misinformation and strategies to mitigate it. The current research aimed to understand the content of misleading tweets and the characteristics of their corresponding accounts. We performed a machine learning approach to identify misinformation in vaccine-related tweets, and calculated the demographic, engagement metrics and bot-like activities of corresponding accounts. We found critical periods where high amounts of misinformation coincided with important vaccine announcements, such as emergency approvals of vaccines. Moreover, we found Asian countries had a lower percentage of misinformation shared compared to Europe and North America. Our results showed accounts spreading misinformation had an overall 10% greater likelihood of bot activity and 15% more astroturf bot activity than accounts spreading accurate information. Furthermore, we found that accounts spreading misinformation had five times fewer followers and three times fewer verified badges than fact-sharing accounts. The findings of this study may help authorities to develop strategies to fight COVID-19 vaccine misinformation and improve vaccine uptake.
The effects of silica nanoparticles on blue-phase liquid crystals

ABSTRACT We investigate the effects of hydrophobic silica nanoparticles (SNs) on blue-phase liquid crystals (BPLCs). The optical microscope and reflection spectra observation reveal that a tiny hydrophobic SN dopant stabilises the BPLC phase, and widens the temperature range of the BP I phase. Furthermore, the doped dilute SNs can fine-tune their positions to relax the formation stress of the BPLC lattices, and slightly increase the platelet sizes of the BPLCs. The doped SNs also decrease the driving voltage and response time of the BPLC cell, because the added SNs decrease the elastic constant of the LC host and the relaxation time constant of the BPLC mixture. Graphical Abstract
Dual-Cross-Polarized Antenna Decoupling for 43 GHz Planar Massive MIMO in Full Duplex Single Channel Communications

Massive Multiple Input Multiple Output (MIMO) and Full Duplex Single Channel (FDSC) at mm-Wave are key technology of future advanced wireless communications. Self-interference is the main problem in this technique because big number of antennas. This paper proposes dual-cross-polarized configuration to reduce self-interference between antennas. We conduct some computer simulations to design the antenna and to verify self-interference effect of the designed antenna. Computer simulation shows that the proposed design has lower Envelope Correlation Coefficient (ECC). This result is achieved because dual-cross-polarized technique can reduce coupling between an-tennas. We found that bit-error-rate (BER) performances of the proposed antenna is better than single polarized antenna indicating that the designed antenna is well design to reduce self-interference effect between antennas.
Effectors of the Type VI Secretion System Have the Potential to Be Modified into Antimicrobial Peptides

This study first identified and modified the T6SS effector into positively charged alpha-helical peptides. These peptides have good antibacterial and bactericidal effects on G+ bacteria and G− bacteria. This study broadens the source of AMPs and makes T6SS effectors more useful. ABSTRACT The use of antibiotics has led to the emergence of multidrug-resistant (MDR) bacteria, and there is an urgent need to find alternative treatments to alleviate this pressure. The type VI secretion system (T6SS) is a protein delivery system present in bacterial cells that secretes effectors that participate in bacterial virulence. Given the potential for the transformation of these effectors into antimicrobial peptides (AMPs), we designed T6SS effectors into AMPs that have a membrane-disrupting effect. These effectors kill bacteria by altering the membrane potential and increasing the intracellular reactive oxygen species (ROS) content. Moreover, AMPs also have a significant therapeutic effect both in vivo and in vitro. This finding suggests that it is possible to modify bacterial components of bacteria themselves to create compounds that fight bacteria. IMPORTANCE This study first identified and modified the T6SS effector into positively charged alpha-helical peptides. These peptides have good antibacterial and bactericidal effects on G+ bacteria and G− bacteria. This study broadens the source of AMPs and makes T6SS effectors more useful.
Design and fabrication of an inverted hat antenna paired with a filled cavity for radio-navigation applications

Abstract Antennas installed on aircraft are used for communications as well as for various radio navigation systems such as direction finders, distance measuring systems, and altimeters. Generally, these systems use blade antennas operating in the L frequency band. Recently, inverted-hat empty section monopole antennas have been found to be good candidates for such uses. In this study, we propose a new design of inverted-hat antenna based on optimized elliptical shapes and a filled cavity. The dielectric material added in the cavity helps to improve the monopole stability and to adjust the resonant frequency of the antenna. The proposed antenna meets the distance measuring equipment requirements, namely an omnidirectional radiation pattern in the H-plane, a vertical polarization, a frequency band from 960 MHz to 1.22 GHz, and a gain better than 1 dB. This antenna is entirely made of aluminum in order to obtain a homogeneity with the aluminum fuselage. In addition, the solution proposed brings a better protection against weather conditions. The antenna performance is analyzed on the basis of simulation and measurement results.
Interpreting Arrhythmia Classification Using Deep Neural Network and CAM-Based Approach

Arrhythmia is a type of heart condition in which the rate or rhythm of the heartbeat is abnormal. Machine learning is increasingly being researched for automated computer-aided ECG diagnosis of arrhythmia detection. Previous works have shown that using Deep CNNs for time series classification has several significant advantages over other methods, since they are highly noise-resistant models, and they can extract very informative, deep features, which are independent of time. However, in using deep learning for arrhythmia detection, the interpretation of how the model learns from the ECG data is limited. In this paper, we propose an extension of CNN-based learning in detecting arrhythmia using recurrence plots from ECG signal data with accuracy within 95.8%, then we conduct the visualization using the Grad-CAM approach on the recurrence plot data to have a better interpretation of the classification process. We summarize our results by drawing comparisons between traditional diagnosis by clinicians and AI-based diagnosis using our classification model.
Investigating the effect of visual realism on empathic responses to emotionally expressive virtual humans

With the remarkable improvement in technical systems for generating realistic virtual humans, there comes a requirement to quantify the effects that different aspects of realism can have on users. The study outlined here sought to advance research on emotion perception and virtual humans by assessing basic empathic responses to high fidelity emotionally expressive characters. We report findings on participants experiences of cognitive, affective and compassionate empathy, as well as measurements for the uncanny valley at two levels of visual realism. We find that the levels of emotion expressed by virtual humans within our study influenced ratings of empathy and the uncanny valley, with positive and negative valence having significant effects on empathic responses and perceived appeal. We discuss these findings in relation to studies which have measured empathy responses to animated characters, as well as notable differences in uncanny valley measurements.
Evaluating the Benefits: Quantifying the Effects of TCP Options, QUIC, and CDNs on Throughput

To keep up with increasing demands on quality of experience, assessing and understanding the performance of network connections is crucial for web service providers. While different measures, like TCP options, alternative transport layer protocols like QUIC, or the hosting of services in CDNs, are expected to improve connection performance, no studies are quantifying such impacts on connections on the Internet. This paper introduces an active Internet measurement approach to assess the impacts of mentioned measures on connection performance. We conduct downloads from public web servers considering different vantage points, extract performance indicators like throughput, RTT, and retransmission rate, and survey speed-ups due to TCP option usage. Further, we compare the performance of QUIC-based downloads to TCP-based downloads considering different option configurations. Next to significant throughput improvements due to TCP option usage, in particular TCP window scaling, and QUIC, our study shows significantly increased performance for connections to domains hosted by different giant CDNs.
ICP-RL: Identifying Critical Paths for Fault Diagnosis Using Reinforcement Learning

Identifying the critical paths is crucial to reducing the complexity of performance analysis and reliability calculation for logic circuits. In this article, we propose a method for identifying the critical path in a combination circuit using a reinforcement learning framework to enhance its applicability and compatibility. Initially, we configured the learning environment of the model based on circuit structure information to provide valuable information for decision-making on time. Subsequently, the upper confidence bound applied to trees (UCT) algorithm is employed to construct the behavior decision strategy of the model, which avoids invalid traversal and reduces computing costs. Then, a goal-oriented reward and punishment function is constructed based on the distance from the circuit primary outputs. Finally, based on the parallel computing strategy, we construct an adaptive training method to improve the model’s prediction accuracy by using finite sampling, which speeds up the convergence speed and enhances the quality of the model. Experimental results on benchmark circuits show that, with the functional timing analysis method as the reference, the average accuracy of the proposed method is as high as 99.39% and the single average calculation speed is 18.07 times faster than that of the reference method. Compared with the Monte Carlo model, the proposed method has a higher critical path hit rate, and the average calculation speed is 928.75 times faster.
Postoperative analgesia for Kasai portoenterostomy using external oblique intercostal blocks

Background External Oblique Intercostal (EOI) fascial plane blockade is a relatively new regional anesthetic technique used for a variety of upper abdominal surgical procedures. Proponents of this block praise its simple sonoanatomy, extensive local anesthetic (LA) spread, and ease of catheter placement, while avoiding encroachment into the surgical field or dressing sites; nevertheless, it is underutilized in pediatric surgery. Kasai portoenterostomy is a common pediatric surgical procedure for biliary atresia typically done via an open abdominal approach with an extended subcostal incision. Postoperative analgesic management with epidural anesthetic techniques are considered but may be limited by periprocedural coagulopathy concerns. Case Presentation We present a case of a neonate who underwent successful analgesic management of Kasai portoenterostomy with bilateral EOI block catheters. Opioid consumption and other postoperative outcomes were comparative to previously reported literature of epidural analgesia in this patient population. Conclusions The purpose of this report is to describe the outcomes and technical approach in a neonate who received EOI blocks as an alternative to epidural anesthetic management. Further studies are needed to compare the efficacy and complication rate of EOI blockade to epidural analgesia for Kasai portoenterostomy surgery.
Machine learning based churn analysis for sellers on the e-commerce marketplace

Abstract The goal of this study is to develop churn models for sellers on the e-commerce marketplace by using machine learning methods. In order to develop these models, three approaches are applied for developing the models. The dataset used in this study includes ten features, which are maturity type, maturity interval, city of the seller, total revenue of the seller, total transaction of the seller, sector type of the seller, business type of the seller, sales channel, installment option and discount type. Random Forest (RF) and Logistic Regression (LR) are used for churn analysis in all of the approaches. In the first approach, models are developed without applying preprocessing operations on the dataset. In the second and third approaches, under sampling and oversampling methods are used respectively to balance the data set. By using stratified cross validation on the dataset, F-Scores of the churn models are obtained. The results show that F-Scores were 0.76, 0.71 and 0.92 for the three approaches developed with RF, and 0.84, 0.68 and 0.69 for the three approaches developed with LR, respectively.
Antioxidant, sensory, and functional properties of low-alcoholic IPA beer with Pinus sylvestris L. shoots addition fermented using unconventional yeast

Abstract The study investigated the potential use of pine (Pinus sylvestris L.) shoots and standard and unconventional yeast strains for the production of low-alcohol IPA beer. For this purpose, control worts without added shoots, and worts enriched with pine shoots at 10 g/L were prepared. Worts were inoculated with yeast: Saccharomyces cerevisiae var. chevalieri SafBrew™ LA-01, Saccharomyces cerevisiae var. boulardii, Pichia kluyveri NEER™, and Saccharomyces cerevisiae Safale US-05. The final beer was tested for yeast cell number, basic physicochemical properties, effects on cell line and antioxidant properties. Pine shoots and the tested yeast strains were shown to be suitable for developing low-alcohol beers with potential functional effects. Pine shoots have no negative impact on the beer-making process and may increase the antioxidant potential of beer. Beers supplemented with pine shoots were shown to increase the ability to quench DPPH free radicals in vitro, while all low-alcohol beers tested were found to have the ability to reduce nitrite production by lipopolysaccharide-induced RAW264.7. Depending on the yeast used, pine shoots affect the flavour and aroma profile differently, possibly masking foreign aftertastes and odours resulting from the use of unconventional yeast.
Mode-constrained estimation of a log-concave density

Abstract: We study nonparametric maximum likelihood estimation of a log-concave density function f with a known mode m. We develop asymptotic theory for the mode-constrained estimator, including, consistency, global rates of convergence, and local rates of convergence both in neighborhoods of the specified mode and away from the known mode. In local neighborhoods of the specified mode the constrained and unconstrained estimators differ, but away from the specified mode the constrained and unconstrained estimators are asymptotically equivalent. Software to compute the mode-constrained estimator is available in the R package logcondens.mode. In a companion paper we use the mode-constrained MLE to develop a likelihood ratio test of the null hypothesis that the mode of f equals a specified valued m versus the alternative hypothesis that the mode of f differs from the specified valued m. We show that under the null hypothesis (and strict curvature of log f at the mode) the natural likelihood ratio statistic is asymptotically pivotal: that is, it converges in distribution to a limiting distribution which is free of nuisance parameters, thus playing the role of the χ1 distribution in classical parametric statistical problems. The test can be inverted to form confidence intervals.
Transistor-Based Biomolecule Sensors: Recent Technological Advancements and Future Prospects

Abstract Transistor-based sensors have been widely recognized to be highly sensitive and reliable for point-of-care/bed-side diagnosis. In this line, a range of cutting-edge technologies has been generated to elevate the role of transistors for biomolecule detection. Detection of a wide range of clinical biomarkers has been reported using various configurations of transistors. The inordinate sensitivity of transistors to the field-effect imparts high sensitivity toward wide range of biomolecules. This overview has gleaned the present achievements with the technological advancements using high performance transistor-based sensors. This review encloses transistors incorporated with a variety of functional nanomaterials and organic elements for their excellence in selectivity and sensitivity. In addition, the technological advancements in fabrication of these microdevices or nanodevices and functionalization of the sensing elements have also been discussed. The technological gap in the realization of sensors in transistor platforms and the resulted scope for research has been discussed. Finally, foreseen technological advancements and future research perspectives are described.