Miracle-dz/UniFAR-FTUs huggingface dataset:
BCG Artifact Removal Using Improved Independent Component Analysis Approach

Recent advancement in bio-medical field has attracted researchers toward BCG signal processing for monitoring the health activities. There have been various techniques for monitoring physical activities such as (SCG) Seismocardiography, Electrocardiography (ECG) etc. BCG signal is a measurement of reaction force applied for cardiac ejection of blood. Various measurement schemes and systems have been developed for BCG detection and measurement such as tables, beds, weighing scale and chairs. Weighing scales have been promising method for measurement of BCG signal because of less cost of implementation, smaller size etc. but these devices still suffer from the artifact which are induced due to subject movement or motion during signal acquisition or it can be caused due to floor vibrations. Artifact removal is necessary for efficient analysis and health monitoring. In this work we address the issue of artifact removal in BCG signal by proposing a novel method of signal processing. According to proposed approach raw signal is pre-processed and parsed to independent component analysis which provides the decomposed components and later k-means is applied to detect the components which are responsible for artifact and removed. Proposed approach is compared with existing method and shows better performance in terms of artifact removal.
On twin edge colorings in m-ary trees

Let k ≥ 2 be an integer and G be a connected graph of order at least 3 . A twin k -edge coloring of G is a proper edge coloring of G that uses colors from Z k and that induces a proper vertex coloring on G where the color of a vertex v is the sum (in Z k ) of the colors of the edges incident with v . The smallest integer k for which G has a twin k -edge coloring is the twin chromatic index of G and is denoted by χ (cid:48) t ( G ) . In this paper, we study the twin edge colorings in m -ary trees for m ≥ 2 ; in particular, the twin chromatic indices of full m -ary trees that are not stars, r -regular trees for even r ≥ 2 , and generalized star graphs that are not paths nor stars are completely determined. Moreover, our results conﬁrm the conjecture that χ (cid:48) t ( G ) ≤ ∆( G ) + 2 for every connected graph G (except C 5 ) of order at least 3 , for all trees of order at least 3 .
Emotional Self-Regulation in Primary Education: A Heart Rate-Variability Biofeedback Intervention Programme

This study investigated the benefits of using a biofeedback intervention programme to train children in controlling their heart rate variability (HRV) through slow-paced breathing in real time. HRV biofeedback interventions focused on showing subjects to breathe such that their HRV numbers rise, improving their self-regulation. The HRV biofeedback intervention, focused on breathing, was conducted with primary education students aged between 7 and 11 years. The programme consisted of five biofeedback sessions, where students were taught to breathe six long and slow pairs of breaths per minute, to increase their HRV. After participation in the programme, students, regardless of gender, increased their HRV in a statistically significant fashion with a large effect, but this effect was not the same for all ages. HRV biofeedback interventions are rarely applied in schools and given the effectiveness of the intervention to improve HRV in children, the applied implications of our results in educational settings are discussed, especially taking into account the children’s ages.
Evaluation of the Effect of the COVID-19 Pandemic on the Use of Food Supplements in Individuals Aged 18 and Over

Evaluation of the Effect of the COVID-19 Pandemic on the Use of Food Supplements in Introduction: Worldwide, the COVID-19 epidemic remains a serious as an important health problem. Vitamins, minerals, probiotics, and other products that we know as nutritional supplements have received a lot of attention since the beginning of the pandemic, considering their anti-inflammatory, antioxidant, and immune system effects. The aims of this study are to evaluate the reasons and prevalence of the take of nutritional supplements in addition to the normal diet during the COVID-19 pandemic and to observe the effects of the COVID-19 pandemic on individuals ’ efforts to live a healthy life. Material-Method: This study is a descriptive cross-sectional study. The data of the study was obtained through an online questionnaire containing 24 questions. The application of the questionnaire was carried out between August 1, 2021 and August 31, 2021. The online survey was delivered to people aged 18 and over living in Turkey by snowball method. Statistical evaluation was done with SPSS (statistical packet for the social science) 20.0 package program. Chi-square analysis was taken in the evaluation of categorical data. Results: The study group was formed with 513 people. While 225 (43.9%) participants in the study received nutritional supplements, 288 (56.1%) individuals reported that they did not take nutritional supplements. The frequency of using nutritional supplements was higher in women (50.9%) than in men (29.6%) (p<0.001). The frequency of taking nutritional supplements in the undergraduate-graduate education group was higher than in the other groups (p<0.05). The frequency of taking nutritional supplements was found to be higher in the high monthly income group (4,001 ₺ and above) than in the other groups (p<0.05). The frequency of taking nutritional supplements (67.0%) was higher in those who had COVID-19 infection (38.9%) than in those who did not (p<0.05). Multivitamin was the most common combination of nutritional supplement taken in those taking nutritional supplements (10, 4.4%). Those who took nutritional supplements believed more than the other group that nutritional supplements taken in appropriate doses protected against COVID-19 infection (p<0.01). Conclusion: For the study group, it is recommended that family physicians provide counseling on the correct take of other nutritional supplements other than multivitamins.
Penalty and Barrier-Based Numerical Optimization for Efficiency and Power Density of Interleaved Buck/Boost Converter

To achieve the global optimization of efficiency and power density of interleaved buck/boost converter (IBBC) for transportation electrification, a numerical optimization method is proposed based on the penalty and barrier theory. In this method, converter volume is the optimization objective, and efficiency and flux density are the constraints. To formulate the optimization objective and constraints, comprehensive modeling for IBBC is conducted, which decouples master–slave relationships among variables in the modeling process. Then, the penalty and barrier theory is performed on the optimization objective and constraints and an integrated optimization function is established, thus converting the constrained numerical optimization problem to an unconstrained one. Hereafter, a modified backtracking search method is employed for the minimization of the integrated optimization function, and Pareto fronts of efficiency and volume are obtained. Finally, a 60-kW IBBC converter was constructed and the power density reaches 9.65 kW/L, whereas the highest efficiency is 98.6%. Compared with an unoptimized converter, efficiency and power density are improved by 0.3% and 0.65 kW/L, respectively.
Emerging roles of circular RNAs in cancer: a narrative review

Abstract Circular RNAs (circRNAs) constitute a novel class of endogenous noncoding RNAs characterized by a covalently closed structure and involved in multiple biological processes. The main biological functions and properties of circRNAs can be defined by five features: a “sponging” effect on other RNA species, post-transcriptional regulation, rolling circle translation, generation of pseudogenes, and splicing interference. Although circRNAs were first detected decades ago, the role of circRNAs and the mechanisms underlying their actions remain incompletely characterized. Recently, circRNAs were reported to play indispensable roles in regulating metabolic and signal transduction events controlling the proliferation, migration, and survival of cells. Importantly, many studies demonstrated that dysregulated circRNA expression is associated with the development of multiple diseases, including cancer. In this review, we summarize current knowledge on the roles and mechanisms of circRNAs in cancer and discuss their functions as oncogenes or tumor suppressors in different tumor types.
Engineering Induced Pluripotent Stem Cells for Cancer Immunotherapy

Simple Summary Induced pluripotent stem cells (iPSCs) that can be genetically engineered and differentiated into different types of immune cells, providing an unlimited resource for developing off-the-shelf cell therapies. Here, we present a comprehensive review that describes the current stages of iPSC-based cell therapies, including iPSC-derived T, nature killer (NK), invariant natural killer T (iNKT), gamma delta T (γδ T), mucosal-associated invariant T (MAIT) cells, and macrophages (Mφs). Abstract Cell-based immunotherapy, such as chimeric antigen receptor (CAR) T cell therapy, has revolutionized the treatment of hematological malignancies, especially in patients who are refractory to other therapies. However, there are critical obstacles that hinder the widespread clinical applications of current autologous therapies, such as high cost, challenging large-scale manufacturing, and inaccessibility to the therapy for lymphopenia patients. Therefore, it is in great demand to generate the universal off-the-shelf cell products with significant scalability. Human induced pluripotent stem cells (iPSCs) provide an “unlimited supply” for cell therapy because of their unique self-renewal properties and the capacity to be genetically engineered. iPSCs can be differentiated into different immune cells, such as T cells, natural killer (NK) cells, invariant natural killer T (iNKT) cells, gamma delta T (γδ T), mucosal-associated invariant T (MAIT) cells, and macrophages (Mφs). In this review, we describe iPSC-based allogeneic cell therapy, the different culture methods of generating iPSC-derived immune cells (e.g., iPSC-T, iPSC-NK, iPSC-iNKT, iPSC-γδT, iPSC-MAIT and iPSC-Mφ), as well as the recent advances in iPSC-T and iPSC-NK cell therapies, particularly in combinations with CAR-engineering. We also discuss the current challenges and the future perspectives in this field towards the foreseeable applications of iPSC-based immune therapy.
The Impact of Dry Yeast Rehydrated in Different Plasma Treated Waters (PTWs) on Fermentation Process and Quality of Beer

Yeast plays a key role in the production of alcoholic beverages. Effective fermentation requires appropriate conditions to ensure the production of high-quality beer. The paper discusses the effect of dry brewing yeast (Saccharomyces cerevisiae and Saccharomyces pastorianus) after rehydration with water exposed to low-temperature, low-pressure glow plasma (PTW) in the atmosphere of air (PTWAir) and nitrogen (PTWN) in the course of the fermentation process, the formation of volatile compounds and other quality parameters of the finished beer. The obtained results show that the lager yeast strain initiated the process of fermentation faster after rehydration in the presence of PTWAir compared to all of the other treatments. It was observed that PTWAir significantly changed the composition of volatile compounds in the finished beer, especially by increasing the number of terpenes, which are compounds that positively shape the aroma of beer. In the case of PTWN samples, lower alcohol content, real extract, apparent extract and amount of biomass were observed in all analyzed strains.
A Cross-Cultural Study of Modality in the Speech Act of Asking for Permission

This paper is concerned with a cross-cultural study of modality expressions in asking for permission by Vietnamese and English speakers. The study involved 209 Canadian and Vietnamese informants with the use of a Discourse Completion Task questionnaire. A total of 3000 utterances were chosen for analysis to gain insights into the frequency and types of lexico-modal markers manifested in the two languages. It is found that hearer-oriented verbal style tends to be dominant in Vietnamese while the speaker-oriented strategy is more favored in English. Vietnamese speakers tend to employ direct strategies with a dominant use of appealers which sounds intimate to the hearer. English speakers, by contrast, incline to conventionally-indirect strategies such as Can I, Could I, etc. It is also evident that Vietnamese speakers frequently use politeness markers when they communicate with the older, but they hardly use them for their peers. English speakers, however, use politeness markers for all partners with a slight variation. Another noteworthy similarity is that both Canadian and Vietnamese women modalize their language than men.
Cloud Forensics: Challenges and Solutions (Blockchain Based Solutions)

Cloud computing is an on-demand service provided for computer resources, data storage and enhancing computing power. Digital forensics is used to help forensic investigators extract evidence against cloud/ cybercriminals and maintain the integrity and security of data stored in a cloud environment. Based on the prior research in this area concerning existing challenges and solutions, this survey focuses on exploring the problems and their proposed solutions on the basis of a detailed literature survey. It critically explores and reviews the prevailing challenges and solutions through an in-depth review of the cloud forensic area. The paper highlights all the current problems in cloud forensics and their solutions previously identified by the researchers to help investigators probe any criminal incident. The three categorization model explores the challenges and solutions of the existing methods and offers directions for future research in this area. Finally, this survey paper can be considered an initiative to carry out research and develop cloud forensic-able services for the cloud environment. INDEX TERMS: blockchain based solution, cloud forensics, cloud forensics challenges, cloud forensics methodologies, cloud forensics solutions
Car Ride Classification for Drive Context Recognition

The automotive domain, with its more and more increasing number of comfort and infotainment functions, offers a field of opportunities for learning and context-sensitive functions. In this respect, personal and frequent trips of drivers provide very promising and interesting contexts. To identify frequent driving contexts in a set of recorded GPS tracks, this paper presents two different clustering algorithms: First, a hierarchical Drive-Clustering, which combines drives based on their number of common GPS points. Second, a Start-Stop-Clustering, which combines trips with the same startand stop-cluster utilizing density based clustering. Especially the Start-Stop-Clustering showed particularly good results, as it does not depend on the concrete routes taken to a stop position and it is able to detect more trip clusters. To predict these trip contexts, a Bayesian network is presented and evaluated, with logged trip data of 21 drivers. The Bayes classifier uses context information such as the time, weekday and the number of persons in the car, to predict the most likely trip-context and thus achieves a good accuracy in the prediction of the different trip contexts. Keywords–Context-aware Vehicle; Spatial Clustering; Drive Context Prediction
International Students and Homemaking in Transition: Locating Home on the Threshold between Ascription and Achievement

Abstract How do young people such as international students understand home, and “where” do they locate it, upon their transition into adulthood? Building on in-depth interviews with forty students in a dorm in Northern Italy, we explore their home-university transition, and the attendant relocations of home, at two levels: in everyday life environments, related to their material and dwelling circumstances, and in their self-narratives, which reveal a fundamental translation of home from spatial into temporal terms. We understand these spatial and temporal relocations of home in terms of continuity, situatedness, in-betweenness and open-endedness. By unpacking the students’ balancing acts between “ascribed” and “achieved” homes, we contribute to the debate on homemaking as a combination between fixed references and temporary or portable affordances, and on the routines and tactics whereby young people mediate the contrasting pressures of family expectations and individual self-realization. Even in transiency, the narrated and lived experience of home illuminates the students’ aspirations and concerns as they struggle to position themselves between different countries, social roles and biographical stages, from a particular accommodation in the here-and-now.
Full Convolution Neural Network Combined with Contextual Feature Representation for Cropland Extraction from High-Resolution Remote Sensing Images

The quantity and quality of cropland are the key to ensuring the sustainable development of national agriculture. Remote sensing technology can accurately and timely detect the surface information, and objectively reflect the state and changes of the ground objects. Using high-resolution remote sensing images to accurately extract cropland is the basic task of precision agriculture. The traditional model of cropland semantic segmentation based on the deep learning network is to down-sample high-resolution feature maps to low resolution, and then restore from low-resolution feature maps to high-resolution ideas; that is, obtain low-resolution feature maps through a network, and then recover to high resolution by up-sampling or deconvolution. This will bring about the loss of features, and the segmented image will be more fragmented, without very clear and smooth boundaries. A new methodology for the effective and accurate semantic segmentation cropland of high spatial resolution remote sensing images is presented in this paper. First, a multi-temporal sub-meter cropland sample dataset is automatically constructed based on the prior result data. Then, a fully convolutional neural network combined with contextual feature representation (HRNet-CFR) is improved to complete the extraction of cropland. Finally, the initial semantic segmentation results are optimized by the morphological post-processing approach, and the broken spots are ablated to obtain the internal homogeneous cropland. The proposed method has been validated on the Jilin-1 data and Gaofen Image Dataset (GID) public datasets, and the experimental results demonstrate that it outperforms the state-of-the-art method in cropland extraction accuracy. We selected the comparison of Deeplabv3+ and UPerNet methods in GID. The overall accuracy of our approach is 92.03%, which is 3.4% higher than Deeplabv3+ and 5.12% higher than UperNet.
Influence of Solvent-Dependent Morphology on Molecular Doping and Charge Transport in Conductive Thiophene Polymer

The utility of a solvent is one of the key factors that impacts resultant film morphology. However, the effect of solvent-dependent morphology on the doping process and electrical conductivity has not been adequately elucidated. In this work, we compared the morphology of chloroform- and chlorobenzene-processed thiophene polymer films and investigated how the choice of solvent influences film morphology, doping level, charge transport properties, and thus electrical conductivity. It was found that the film drop-casted from chloroform exhibits better crystallinity than that drop-casted from chlorobenzene. The crystallinity has negligible impact on the doping level but significant impact on charge transport properties. As a result, the chloroform-processed film shows a higher electrical conductivity of up to 408 S cm−1 due to a high carrier mobility related to the continuously crystalline domains in film. This finding indicates that the choice of solvent for preparation of film, which strongly correlated with molecular orientation, is a new strategy to optimize the electrical conductivity of doped polymers.
Recalibration of existing pedotransfer functions to estimate soil bulk density at a regional scale

Soil bulk density (ρb) is an important indicator of soil quality, productivity, compaction and porosity. Despite its importance, ρb is often omitted from global datasets due to the costs of making many direct ρb measurements and the difficulty of direct measurement on rocky, sandy, very dry, or very wet soils. Pedotransfer functions (PTFs) are deployed to address these limitations. Using readily available soil properties, PTFs employ estimator equations to fit existing datasets to estimate properties like ρb. However, PTF performance often declines when applied to soils outside those in the training dataset. Potentially, recalibrating existing PTFs using new observations would leverage the power of large datasets used in the original PTF derivation, while updating information based on new soil observations. Here, we evaluate such a recalibration approach for ρb estimation, benchmarking its performance against two alternatives: the original, uncalibrated PTFs, and novel, local PTFs derived solely from new soil observations. Using a ρb dataset of N = 360 total observations obtained in West Azerbaijan, Iran, we varied the local dataset size (with N = 15, 30, 60, and 360) and recalibrated four existing PTFs with these data. Local PTFs were generated based on stepwise multiple linear regression for the same datasets. The same PTFs (original, recalibrated, and local) were also applied to the study area, and the resulting ρb estimates were compared with the global SoilGrids dataset. Recalibration of PTFs reduced errors relative to the original uncalibrated PTFs; for instance, the NSE increased from −22.07 to 0.30 (uncalibrated) to 0.20–0.41 (recalibrated), and RMSE decreased from 0.12 to 0.60 Mg m−3 (uncalibrated) to 0.10–0.13 Mg m−3 (recalibrated). The recalibrated PTFs performance was comparable to or better than local PTFs applied to the same data. Recalibration of existing PTFs with local/regional uses provides a viable alternative to the use of global datasets or the development of local PTFs in data‐scarce regions.
Photoacoustic signal measuring based on cross-correlation analysis and chaotic detection

When photoacoustic spectrum is used to measure the concentration of dissolved gases in transformer oil,the weak photoacoustic signals can not be differentiated from the strong field noises and test interferences. Cross-correlation operation and chaotic detection are introduced,based on which a method integrating these two theories to detect weak photoacoustic signals is proposed. A virtual signal detection system is built with LabVIEW and Matlab,and the Lyapunov characteristic exponent chart is programmed to realize the function of chaotic state determination. Experimental results show that this method is very sensitive to the weak photoacoustic signals under strong noise and has higher detection precision.