Miracle-dz/UniFAR-FTUs huggingface dataset:
EVALUATION OF CARDIAC FUNCTION IN SYSTEMIC SCLEROSIS WITH NOVEL ECHOCARDIOGRAPHIC TECHNOLOGIES

Systemic sclerosis (SSc) is a connective tissue disease characterized by widespread vascular lesions. Primary myocardial involvement is common in SSc and, when clinically evident, appears as a poor prognostic factor. Using conventional methods, myocardial perfusion impairment and biventricular dysfunction have been reported in SSc. Recently, Tissue Doppler Imaging (TDI) echocardiography and magnetic resonance imaging have confirmed these results. TDI, with its derived strain, strain rate and myocardial performance index, is a powerful new echocardiographic tool that is now becoming the standard for assessing ventricular function in a variety of situations and diseases. In the present article we review the main published data on the application of TDI in the evaluation of cardiac function and prediction of prognosis in patients with SSc.
Development of Tissue-Specific Age Predictors Using DNA Methylation Data

DNA methylation patterns have been shown to change throughout the normal aging process. Several studies have found epigenetic aging markers using age predictors, but these studies only focused on blood-specific or tissue-common methylation patterns. Here, we constructed nine tissue-specific age prediction models using methylation array data from normal samples. The constructed models predict the chronological age with good performance (mean absolute error of 5.11 years on average) and show better performance in the independent test than previous multi-tissue age predictors. We also compared tissue-common and tissue-specific aging markers and found that they had different characteristics. Firstly, the tissue-common group tended to contain more positive aging markers with methylation values that increased during the aging process, whereas the tissue-specific group tended to contain more negative aging markers. Secondly, many of the tissue-common markers were located in Cytosine-phosphate-Guanine (CpG) island regions, whereas the tissue-specific markers were located in CpG shore regions. Lastly, the tissue-common CpG markers tended to be located in more evolutionarily conserved regions. In conclusion, our prediction models identified CpG markers that capture both tissue-common and tissue-specific characteristics during the aging process.
Selection of Features and Classifiers for EMG-EEG-Based Upper Limb Assistive Devices—A Review

Bio-signals are distinctive factors in the design of human-machine interface, essentially useful for prosthesis, orthosis, and exoskeletons. Despite the progress in the analysis of pattern recognition based devices; the acceptance of these devices is still questionable. One reason is the lack of information to identify the possible combinations of features and classifiers. Besides; there is also a need for optimal selection of various sensors for sensations such as touch, force, texture, along with EMGs/EEGs. This article reviews the two bio-signal techniques, named as electromyography and electroencephalography. The details of the features and the classifiers used in the data processing for upper limb assist devices are summarised here. Various features and their sets are surveyed and different classifiers for feature sets are discussed on the basis of the classification rate. The review was carried out on the basis of the last 10–12 years of published research in this area. This article also outlines the influence of modality of EMGs and EEGs with other sensors on classifications. Also, other bio-signals used in upper limb devices and future aspects are considered.
Traffic Flow Prediction with Compact Neural Networks

As a crucial role of the Intelligent Transportation Systems, real-time traffic flow prediction technology is increasingly required for society. Traffic flow prediction is based on historical traffic data to predict upcoming traffic flow. In this paper, we employ Recurrent Residual Network and Temporal Convolutional Network to predict the traffic flow and compared it with several deep learning models such as Gated Recurrent Units and Long Short Term Memory in traffic flow prediction tasks. To the best of our knowledge, this is the first time that Recurrent Residual Network and Temporal Convolutional Network are applied to traffic flow prediction. We show that Temporal Convolutional Network has similar performance to other models with less computing parameters, which is simpler and clearer.
Yoga and Cardiovascular Health Trial (YACHT): a UK-based randomised mechanistic study of a yoga intervention plus usual care versus usual care alone following an acute coronary event

Objective To determine the effects of yoga practice on subclinical cardiovascular measures, risk factors and neuro-endocrine pathways in patients undergoing cardiac rehabilitation (CR) following acute coronary events. Design 3-month, two-arm (yoga +usual care vs usual care alone) parallel randomised mechanistic study. Setting One general hospital and two primary care CR centres in London. Assessments were conducted at Imperial College London. Participants 80 participants, aged 35–80 years (68% men, 60% South Asian) referred to CR programmes 2012–2014. Intervention A certified yoga teacher conducted yoga classes which included exercises in stretching, breathing, healing imagery and deep relaxation. It was pre-specified that at least 18 yoga classes were attended for inclusion in analysis. Participants and partners in both groups were invited to attend weekly a 6- to 12-week local standard UK National Health Service CR programme. Main outcome measures (i) Estimated left ventricular filling pressure (E/e′), (ii) distance walked, fatigue and breathlessness in a 6 min walk test, (iii) blood pressure, heart rate and estimated peak VO2 following a 3 min step-test. Effects on the hypothalamus–pituitary–adrenal axis, autonomic function, body fat, blood lipids and glucose, stress and general health were also explored. Results 25 participants in the yoga + usual care group and 35 participants in the usual care group completed the study. Following the 3-month intervention period, E/e′ was not improved by yoga (E/e′: between-group difference: yoga minus usual care:−0.40 (−1.38, 0.58). Exercise testing and secondary outcomes also showed no benefits of yoga. Conclusions In this small UK-based randomised mechanistic study, with 60 completing participants (of whom 25 were in the yoga + usual care group), we found no discernible improvement associated with the addition of a structured 3-month yoga intervention to usual CR care in key cardiovascular and neuroendocrine measures shown to be responsive to yoga in previous mechanistic studies. Trial registration number NCT01597960; Pre-results.
A Review of Fog Computing and Machine Learning: Concepts, Applications, Challenges, and Open Issues

Systems based on fog computing produce massive amounts of data; accordingly, an increasing number of fog computing apps and services are emerging. In addition, machine learning (ML), which is an essential area, has gained considerable progress in various research domains, including robotics, neuromorphic computing, computer graphics, natural language processing (NLP), decision-making, and speech recognition. Several researches have been proposed that study how to employ ML to settle fog computing problems. In recent years, an increasing trend has been observed in adopting ML to enhance fog computing applications and provide fog services, like efficient resource management, security, mitigating latency and energy consumption, and traffic modeling. Based on our understanding and knowledge, there is no study has yet investigated the role of ML in the fog computing paradigm. Accordingly, the current research shed light on presenting an overview of the ML functions in fog computing area. The ML application for fog computing become strong end-user and high layers services to gain profound analytics and more smart responses for needed tasks. We present a comprehensive review to underline the latest improvements in ML techniques that are associated with three aspects of fog computing: management of resource, accuracy, and security. The role of ML in edge computing is also highlighted. Moreover, other perspectives related to the ML domain, such as types of application support, technique, and dataset are provided. Lastly, research challenges and open issues are discussed.
Preparing career development model of Iranian nurses using Delphi technique

Introduction: Nowadays, nurses play a key role in all health-care systems in the world. To improve the professional conditions and increase the quality of care, nurses' career development should be fulfilled in a specific and targeted way. The aim of this study was to investigate the factors affecting career development of nurses in Iran. Method: This prospective study was conducted using Delphi technique in three stages with the presence of 60 nurses and nursing faculty members. In the first round, four questions were used to assess the participants' perspective career development, how to evaluate it, its effective factors and suggestions to design a career development model. In the second and third rounds, to collect participants' opinion and create consensus, a researcher-made questionnaire based on the results obtained during the first round was used. Results: According to the results, 25 effective factors were extracted and classified into 6 categories of professional development, increasing organizational influence, individual development, specialization, improvement of career dignity, and psychological acceptance. The nurses' career development model was also developed and validated with the results. Conclusion: According to the obtained results, it can be concluded that career development in the nursing area is achieved by realizing six factors of professional development, specialization, increasing organizational influence, individual development, psychological acceptance, improvement of career dignity, and psychological acceptance.
Acceptance of mobile phone return programs : a case study based analysis

The need of recycling obsolete mobile phones has significantly increased with the worldwide propagation of mobile phones and their inherent rapid turnover. In this article, we examine the acceptance of mobile phone return programs by using the Technology Acceptance Model and multiple case studies. Our findings can provide valuable recommendations for the setup of future mobile phone return programs.
Developing Action Research for Developing Teachers in Kosovo

Bu arastirma, ogrenci ogretmenlerin arastirmaci rollerini algilamalarini ve profesyonel gelisimlerinde eylem arastirmalarin etkisini incelemektedir. Arastirma, Pristine Universitesi Egitim Fakultesi’nde yuksek lisans duzeyinde olan ogrenci ogretmenleri kapsamaktadir. Ogrenciler tarafindan yapilan yansitici yazilar, acik uclu soru formlari ve arastirma raporlarindan toplanan nitel veriler tumevarim yontemi araciligiyla incelenmistir. Arastirma, ogretmen arastirmalarinin, sadece ogretim uygulamalarinin gelistirilmesine ve ogrenci basarisinin yukseltilmesine degil, ayni zamanda yasitlar arasi isbirligini arttirma ve ogretmenlerin sinifi donusturme araci olarak kendi siniflarinda eylem arastirmasini uygulama sorumlulugunu yeni bir kultur olarak tesvik etmeye onemli bir etkisinin oldugunu ortaya koymaktadir. Bu konuda ogretmen egitimi ve okul baglami cok onemlidir ve ogretmen arastirmalarinin yalitilmis bir etkinlik olarak gorulmesini onler. Anahtar kelimeler: Eylem arastirmasi, profesyonel gelisim, ogrenci ogretmen Abstract This study explores the perception of student teachers on their role as researchers and the impact of action research in their professional development. The study involves the master level student teachers of the Faculty of Education at the University of Prishtina in Kosovo. Qualitative data collected through reflective writing, open-ended questionnaires and research reports developed by student teachers were analyzed through the inductive method. The study suggests that teacher research has a major impact not only on the improvement of teaching practices and increasing student achievement, but also on increasing the collaboration among peers and fostering a new culture where teachers assume their responsibilities to apply action research in their own classrooms as a tool for classroom transformation. Teacher education and school context are crucial in this matter and the need arises to avoid viewing the development of teacher research as an isolated activity. Keywords: Action research, professional development, student teacher, environment
In vitro shoot regeneration through anther culture of Brassica spp.

The experiment was conducted to investigate the performance of three different genotypes (BARI Sarisha-6, BARI Sarisha-8, and BARI Sarisha-11) in two different media viz., MS and B5 with different concentrations of phytohormone (2, 4-D) for callus induction from uninucleate stage anthers of Brassica and subsequent plant regeneration in MS media with different concentrations of phytohormone (BAP and NAA). Among the genotypes, BARI Sarisha-8 showed the best performance for all the parameters of callus induction. The performance of BARI Sarisha-6 was poor compared to others. Maximum rate of callus induction (%) was observed in MS + 0.5 mg/L 2, 4-D followed by B5 + 0.5 mg/L 2,4-D. The media combination MS + 1.0 mg/L BAP 0.3 mg/L 2,4-D showed the best performance for maintenance of calli. Significant variations were observed among the genotypes and media composition for shoot regeneration. Among the genotypes, BARI Sarisha-8 showed the best performance for shoot regeneration followed by BARJ Sarisha-l1. The genotype BARI Sarisha-8 produced higher percent of shoots/calli and required minimum days for shoot initiation. Higher percent calli without shoot were produced by the genotype BARI Sarisha-6. The media combination MS + 2.0 mg/L BAP + 0.5 mg/L NAA showed the best performance for shoot regeneration and required maximum days for shoot initiation. Keywords: Regeneration; BARI Sarisha-6; BARI Sarisha-8; BARI Sarisha-11; anther culture; phytohormone DOI: 10.3329/bjar.v35i2. 5896 Bangladesh J. Agril. Res. 35(2) : 331-341, June 2010
The Role of Performance Measure Characteristics in the Design of Incentive Systems: An Empirical Analysis

In this paper, I derive three general performance measure characteristics from the literature that are theoretically related to the use of performance measures, i.e., (1) the impact that a manager has on performance (sensitivity), (2) the impact that uncontrollable factors have on performance (controllability), and (3) the degree to which the performance measure is objective and verifiable (measurement accuracy). I empirically examine the effect of information asymmetry and the performance measure characteristics on the use of three types of performance measures. Furthermore, I examine how two types of uncertainty, i.e., task uncertainty and environmental uncertainty, affect information asymmetry and performance measure characteristics. The major finding of this study is that sensitivity is positively related to the use of all three types of performance measures, while measurement accuracy is positively related to two out of these three types of performance measures. Controllability, on the other hand, does not have the proposed positive effect on any of the performance measures examined. These results suggest that sensitivity and measurement accuracy play an important role in designing incentive systems, while controllability seems to play no role.
Bayesian power equivalence in latent growth curve models

Longitudinal studies are the gold standard for research on time‐dependent phenomena in the social sciences. However, they often entail high costs due to multiple measurement occasions and a long overall study duration. It is therefore useful to optimize these design factors while maintaining a high informativeness of the design. Von Oertzen and Brandmaier (2013,Psychology and Aging, 28, 414) applied power equivalence to show that Latent Growth Curve Models (LGCMs) with different design factors can have the same power for likelihood‐ratio tests on the latent structure. In this paper, we show that the notion of power equivalence can be extended to Bayesian hypothesis tests of the latent structure constants. Specifically, we show that the results of a Bayes factor design analysis (BFDA; Schönbrodt & Wagenmakers (2018,Psychonomic Bulletin and Review, 25, 128) of two power equivalent LGCMs are equivalent. This will be useful for researchers who aim to plan for compelling evidence instead of frequentist power and provides a contribution towards more efficient procedures for BFDA.
Appraising the neuroprotective competence of nitrogen-enriched Arthrospira platensis in comparison to commercial resource in depressed mice models

Back ground: Arthrospira platensis encompasses vital nutrients and is commercialized globally. It comprises of imperative resource that has the potential to combat neurological deformities caused due to stress and anxiety. Objective: This work evaluates the neuroprotective effect of spirulina cultivated on nitrogen enriched medium and compares it with the commercial samples. Materials and Methods: The study is authenticated with an antioxidant assay and the vital compounds are relatively profiled by GC-MS study. Furthermore, a molecular docking analysis is implemented to investigate the therapeutic potentials of the phytocompounds against Monoamine Oxidase –A and establish them as inhibitors. The ethanolic extract of spirullina as are fed on depressed mice models to assay its neuroprotective effect and rehabilitation of brain cells by a histopathological study. Results: The antioxidant content of the augmented sample was consistent on par with the commercial sample. The in silico assay was performed with 10 extricated compounds of both the samples where, Butanoic acid, 3-hydroxy- furnished a minimum binding affinity energy value of -56.24 kcal/mol and dodecanamide was efficient to bind with the active site of the amino acid residue TYR 69 with a minimum energy of -87.8 kcal/mol. The histopathological examination upholds the refurbished parameter of vital phytocompounds with placid cellular edema and perivascular infiltration. Conclusion: There is a wide range of need to develop research against stress and anxiety and the study fortifies the restorative efficacy of the phytocompounds as a neuroprotective drug.
Grain Boundary Dynamics: A Novel Tool for Microstructure Control

The reaction of grain boundaries to a wide spectrum of forces is reviewed. Curvature, volume energy and mechanical forces are considered. The boundary mobility is strongly dependent on misorientation, which is attributed to both grain boundary structure and segregation. In magnetically anisotropic materials grain boundaries can be moved by magnetic forces. For the first time a directionality of boundary mobility is reported. Flat boundaries can also be moved by mechanical forces, which sheds new light on microstructure evolution during elevated temperature deformation. Curvature driven and mechanically moved boundaries can behave differently. A sharp transition between the small and large angle boundary regime is observed. It is shown that grain boundary triple junctions have a finite mobility and thus, may have a serious impact on grain growth in fine grained materials. The various dependencies can be utilized to influence grain boundary motion and thus, microstructure evolution during recrystallization and grain growth.
Engagement Detection in e-Learning Environments using Convolutional Neural Networks

Learning institutions are responsible for ensuring effective learning environments for online learners. An effective learning environment engages learners in educational activities. In this paper, we investigate the suitability of using three different popular models and a proposed model of convolutional neural network (CNN) for the engagement detection of online learners in their educational activities. By using the models, engagement detection has been done in a scalable and accessible manner by using facial expressions. The three models include all convolutional network (All-CNN), network-in-network (NiN-CNN), and very deep convolutional network (VD-CNN). These models are popular as they have simple network architectures and these have shown efficiency in different pattern classification applications. Each model has some unique features that made it different from the traditional as well as the other architectures. The All-CNN model replaces the max-pooling layer by a convolutional layer with increased stride. The NiN-CNN model replaces the linear convolutional layer with a multilayer perceptron and the fully connected layer by the same number of activation maps of the target classes. The VD-CNN model increases the depth by using small (3×3) convolutional filters. In the proposed model, we accumulate a number of advantageous features from the above three base models and achieved promising improvement. In the proposed model, the linear convolutional layer is replaced with a multilayer perceptron, the depth of the network is increased by using small (3×3) convolutional filters, and some max-pooling layers are replaced by convolutional layer with an increased stride. The three base models and the proposed model are applied on Dataset for the Affective States in E-Environments (DAiSEE) and analyzed their performances in a learner's engagement detection, where the proposed model outperforms the others.
Design of robot finger based on flexible tactile sensor

In this article, a flexible tactile sensor that made of conductive silicone rubber for dexterous robot hand is designed. The tactile sensor is made up of four microsensors. The maximum gripping force is simulated when the degree of a robot finger joint is 138. Meanwhile, a control system to analyze the creep and hysteresis characteristics and a processing system of the tactile sensor is designed. We also demonstrated an experiment for the application of robot grasp object, showing the finger’s flexibility and sensitivity. Then the feedback data is sent to control system to provide precise grasp action changes for the robot hand.
The haemostasis traffic light: a pragmatic tool for bleeding management

Major bleeding is a critical scenario which can quickly develop into a crisis, thereby compromising patient safety. Controversy still exists regarding the best approach for haemostatic management with different initial coagulation resuscitation strategies receiving the same level of recommendation [1, 2]. From a logistical point of view, the availability of certain resources does not necessarily imply their timelyapplication. Turnaround timesof localdiagnostic and therapeutic resourcesareoftenneglectedwhendrafting coagulationmanagementprotocols. Suchuncertaintiesmay impede the much needed shared mental model by the medical team resulting in great variability during crisis management negatively impacting treatment delivery [3]. In this context, checklists andcognitiveaidshavebeen found to be useful in assembling the team under one conceptual umbrella. Hence, the integration of logistic and human factors is fundamental to translate knowledge into actions andbenefitbothpatientsandhealthprofessionals [4]. In order to simplify the initial assessment and management of a bleeding situation, we have developed the ‘haemostasis traffic light’ (Fig. 1). Firstly, we obtained our actual turnaround times over 4 months which enabled us to see which diagnostic and therapeutic resources are readily available at different time-points during the first 45 min from the onset of bleeding [5–8]. Subsequently, we developed an early clinical classification system including haemodynamic stability, requirement for and response to vasopressors and fluids, and the surgical possibility to limit bleeding over time. This aligns with the recommendation that during the initial evaluation, the physician should clinically assess the extent of the haemorrhage using a combination of patient physiology, the anatomic pattern of