Miracle-dz/UniFAR-FTUs huggingface dataset:
The Effectiveness of the Task-Based Language Teaching Approach to Improve Students’ Speaking Skills

This study examines the effectiveness of the Task-Based Language Teaching (TBLT) approach in improving students’ speaking skills and students’ perceptions towards integrating TBLT. The Sri Lankan English as a Second Language (ESL) learning-teaching context was chosen as the research context for this study and comprised a sample of stratified-randomly selected 60, Grade 10 students, at the Katharina Shirani Frauenfeld College in Moragoda, whose proficiency in English was at the elementary level. This sample was divided into two groups as a Control group and an Experimental group, in which, traditional teaching practices and TBLT treatment were applied respectively. Accordingly, data was collected using a pre-test and post-test design and additionally, an attitudinal questionnaire was given to the students to be filled. For data collection, a Mixed-Method Research paradigm was utilized. A Paired Samples T-test was used to determine the statistical significance of students’ scores in the pre-test as compared to the post-test of both groups. Moreover, the thematic analysis was integrated to analyze the open-ended items in the questionnaire. The findings suggested that the mean scores of the post-test of the Experimental group have surpassed the mean scores of the Control group, indicating that the integration of TBLT was effective in improving the students’ speaking ability as opposed to the traditional teaching practices utilized. The questionnaire analysis on the students’ perceptions revealed that they were highly satisfied with the integration of the TBLT approach. It is recommended that ESL teaching practitioners can refer to the TBLT methodology in their classrooms since this has inspired students significantly in learning speaking skills.
Real-time wearable-device based activity recognition using machine learning methods

: Classification of activities from body-worn accelerometer data to help monitor and take care of health attracts much attention from the research community. This paper proposed to design a real-time monitoring device that can identify people’s actions from the accelerometer’s data worn on the waist with five activities, including lying, sitting, standing, walking, and jogging. From the collected acceleration data, it is necessary to extract suitable features for real-time classification with high performance. These features are trained with machine learning algorithms that improve the e ffi ciency of action classification. Consequently, a decision tree algorithm was embedded in the microcontroller. This programmed waist-mounted device was connected to the monitoring system via WiFi protocol. Users could monitor activities and managed data on a computer, a website, or a smartphone. The results were optimistic when the overall accuracy for the activities dataset reached 99.3% when training and classifying the activities on the computer. When experimenting with real-time wearable devices, the overall accuracy when classifying activities decreased but was still very good, reaching over 90%.
DIMENSIONS OF ENVIRONMENTAL PERCEPTION

175 Ss were administered a 23 item semantic differential which was used to rate large city, small town, forest, desert, and beach. The ratings of each environ ment were factor analyzed. The factors obtained were compared across environments to determine common dimensions used to rate the environments. Four common dimensions were obtained: evaluative, spiritual, activity, and aesthetic appeal. The factor loadings of the rating scales were then analyzed to determine ideosyncratic applications of the dimensions to particular environ ments.
A Comparative Study of LDL-C Levels in Polycystic Ovary Syndrome Women with Different Cardiovascular Risks According to American Heart Association Criteria

Objective: To compare metabolic parameters including low-density lipoprotein cholesterol (LDL-C) levels between polycystic ovary syndrome (PCOS) women with the different cardiovascular disease risks (CVD), and to study the frequency of PCOS women in each risk category who need treatment for the target LDL-C levels according to the recommendation by the Androgen Excess and Polycystic Ovary Syndrome Society (AE-PCOS). Material and Method: This was a retrospective comparative study. Data from the medical records and electronic forms of the 208 PCOS women, who consecutively attended the Reproductive between February 2004 and December 2010 were analyzed. According to the recommendation of the AE-PCOS society, the subjects were classi ﬁ ed into three groups of CVD risk. Results: There were 22, 129, and 57 women being at optimal risk, at risk, and at high-risk groups, respectively. There were signi ﬁ cant differences in all metabolic factors between the different CVD classi ﬁ cations according to the AE-PCOS society. Furthermore, the PCOS women at high-risk had higher rates of the most signi ﬁ cant parameters than those at risk. However, the LDL-C levels were not different between PCOS women at high-risk and at risk. Interestingly, 56%, 90.7%, and 100% of PCOS women at risk, at high-risk, and at high-risk plus other risks, respectively, had LDL-C levels above the target goals, which needed to be lower for the primary CVD prevention according to the AE- PCOS society recommendation. Conclusion: There were differences in all metabolic factors between the different CVD classi ﬁ cations, but LDL-C levels were not different between PCOS women at high-risk and at risk. To get target goal for the primary CVD prevention, 91% and 100% of PCOS women at high-risk and at high-risk plus other risks need to lower LDL-C levels.
Electrical Stimulation and Electromagnetic Therapy for Wounds (for North Carolina Only)

subgroup analysis of the efficacy of ES for different DFU subtypes (such as different ulcer sites) This meta-analysis indicates that ES may accelerate DFU healing and may be an effective adjunctive therapy for these complex More RCTs with larger sample sizes are needed to expansively evaluate the value of ES for DFUs. at the of two the lower in lower the hemodynamic changes in the lower with the use of two neuromuscular electrical stimulation Twelve two neuromuscular stimulation interventions. Geko TM randomized between dominant and Hemodynamic measurements of peak venous velocity (cm/s), the time average mean velocity (TAMEAN) (cm/s), and ejected volume (mL) of were recorded. Peak venous velocity was significantly increased by the Geko TM and the NUI Galway device compared to baseline blood flow (p < 0.0001), while only the voluntary contraction produced significant increases in TAMEAN and ejected volume (both p < 0.05). Both devices tested in this study require further examination to determine their effectiveness at altering hemodynamics to beneficial levels in terms of VLU This study is limited by the number of participants in these under study
Mathematical Modelling of Reactive Inks for Additive Manufacturing of Charged Membranes

Patterned charged membranes with engendered useful characteristics can offer selective transport of electrolytes. Chemical patterning across the membrane surface via a physical inkjet deposition process requires precise control of the reactive-ink formulation, which enables the introduction of charged functionality to the membrane. This study develops a new dynamic mathematical model for the primary step of the batch reactive-ink formulation considering an ink mixture of copper sulphate and ascorbic acid. Nonlinear least squares parameter estimation is performed to infer three kinetic model parameters by analysing data from nine dynamic experiments simultaneously. Global sensitivity and Fisher information matrix (FIM) analyses reveal only one kinetic parameter is identifiable from time-series pH measurements. The fitted model can capture the overall nonlinear dynamics of the batch reaction and works best for initial Cu 2+ concentrations between 30 and 50 mM. Time-series Cu 2+ or Cu + concentration measurements are recommended in future experiments to elucidate the kinetics of reactive-ink formulation.
UMUTextStats: A linguistic feature extraction tool for Spanish

Feature Engineering consists in the application of domain knowledge to select and transform relevant features to build efficient machine learning models. In the Natural Language Processing field, the state of the art concerning automatic document classification tasks relies on word and sentence embeddings built upon deep learning models based on transformers that have outperformed the competition in several tasks. However, the models built from these embeddings are usually difficult to interpret. On the contrary, linguistic features are easy to understand, they result in simpler models, and they usually achieve encouraging results. Moreover, both linguistic features and embeddings can be combined with different strategies which result in more reliable machine-learning models. The de facto tool for extracting linguistic features in Spanish is LIWC. However, this software does not consider specific linguistic phenomena of Spanish such as grammatical gender and lacks certain verb tenses. In order to solve these drawbacks, we have developed UMUTextStats, a linguistic extraction tool designed from scratch for Spanish. Furthermore, this tool has been validated to conduct different experiments in areas such as infodemiology, hate-speech detection, author profiling, authorship verification, humour or irony detection, among others. The results indicate that the combination of linguistic features and embeddings based on transformers are beneficial in automatic document classification.
FORMATION OF MARKET-ORIENTED MANAGEMENT SYSTEM OF AGRO-INDUSTRIAL ENTERPRISES IN THE DIRECTION OF INNOVATIVE PROCESSES INTENSIFICATION

The purpose of the work is to identify the main strategic goals of Ukrainian agro-industrial enterprises in the long run taking into account the significant impact of information technology on the socio-economic processes globalization. Taking into account the scientific and methodological approach to the intensification and automation processes of business management, in the article has been proposed implementation the market-oriented management system of the enterprise in order to implement the strategic goals of the enterprises’ innovation activity. The research was conducted on the materials of large agro-industrial enterprises of Ukraine. The methodical basis of the research were general and special methods, namely: methods of synthesis and comparison were used during the research, which allowed the authors to identify conceptual macro- and micro- benchmarks of the agro-industrial enterprises’ management system formation in the direction of innovative processes intensification. Also, the methods of systematization and generalization for agro-industrial enterprises’ conceptual-individual analysis - in the process of formation market-oriented management system of the enterprises. In the research were used the Delphi method of expert forecasting which is most often used in long-term forecasting for making planned strategic decisions of enterprises economic activity. The qualitative Delphi method allows entrepreneurs to assess the characteristics of each situation over a period of time in order to study carefully the various specific elements that determine the current and future business situation in the direction of making the effective strategic decisions. The results of substantiating the priority of strategic benchmarks of agro-industrial enterprises for the effective management of economic activity are outlined taking into account the conceptual and individual analysis of strategic goals of agro-industrial enterprises in the direction of innovation processes. In the research has been improved scientific and methodological approach to the analysis of forecast factors that directly or indirectly affect on the formation and implementation the effective strategic guidelines for enterprises’ innovation activity. As a result of the market-oriented management system development and implementation in the ukrainien agro-industrial enterprise in order to implement the strategic goals of the enterprises’ innovation activity it is concluded that key priorities and directions of agro-industrial enterprises’ market-oriented management system formation are based on the sequence and systematic implementation of forecast situations’ accurate analysis in the direction of innovation processes intensification.
Design and Development of an Online Repository System for Thesis and Special Problem Manuscripts

It is vital to keep track of all the scholarly works undertaken by the students in research universities. As a premier research university,The University of the Philippines (UP) Mindanao has produced several scholarly works made by its students, such as thesis or special problem manuscripts. Similar to typical residential universities in the Philippines, UP Mindanao students submit both hardbound and digital copies of their thesis and special problem manuscripts to their respective departments. Problems such as duplication of work and missing manuscripts are rampant with the current method.This is because printed and electronic copies stored in optical discs can easily be misplaced and are difficult to track when borrowed. This paper describes the development of the UP Mindanao Manuscript e-library and Repository System (UPMERS), which is a web-based information system that provides administrators a tool to monitor and manage records of manuscripts by graduates of the University. This paper also describes the system’s user acceptance using the Technology Acceptance Model (TAM). Furthermore, the study also discusses the result of the System Usability Scale survey.
IJSRD-International Journal for Scientific Research & Development| Vol. 6, Issue 06, 2018 | ISSN (online): 2321-0613

Snow avalanches are natural hazards in snow covered mountains regions and can cause loss of human life and damage to infrastructure such as buildings and roads. Avalanche release depends on many different parameters i.e. terrain, ground cover and meteorological conditions of the region. Many areas of Indian Himalaya were affected by snow and avalanches due to high average annual snow accumulation and severe weather. In Himalaya, Knowledge of when and where avalanches are occur and how large they are, is not easy to obtain due to the inaccessible terrain, harsh climatic conditions and vast region. Observations of past and present avalanche activity are of great importance for avalanche forecasting as they are strong and reliable indicators of snow instability. To limit the number of future fatalities, it is desirable to develop a technique that detect the avalanches. Remote sensing satellite data has a potential to detect the avalanches. In the present study, we present a method to identify the avalanche debris using optical remote sensing data on the basis of change in reflectance after avalanche occurrence.
THE MOBILE APPLICATION DEVELOPMENT PROCESS: A SYSTEMATIC MAPPING STUDY

The rate of mobile applications developed in the overall software development arena is getting bigger and bigger over recent years. However, the information available about the software development processes applied is scarce. To identify evidence of mobile application development processes, we have conducted a Systematic Mapping Study (SMS) to identify existing knowledge on the software development process applied to develop mobile applications and its specific components. We identified 16 studies, of which seven applied agile and nine applied plan-driven methods to develop mobile applications. The proposals differ widely in terms of the activities into which the software process is divided. Usability-related activities are popular across different proposals, since usability and user experience are key quality attributes of mobile applications. Some activity groups like verification and validation are missing. Mobile app development has specific characteristics that call for attention to certain key issues in process enactment. There is no agreed-upon process for developing mobile applications, and further research is required in this area.
Using Graphene Oxide to Efficiently Filter Particulate Matter at High Concentrations

Air pollution is currently one of the biggest environmental challenges around the world. Air pollution not only causes detrimental effects to the human body but also endangers the natural environment. Many have proposed and created various methods to solve this problem, but none of them have worked very effectively. Following the discovery of graphene, scientists began to apply the use of this carbon material to address different kinds of problems. Using graphene oxide to combat air pollution, we prepared graphene oxide, which we then spread onto a commercial air filter, and used this air filter to purify air polluted with particulate matter (PM) of various diameters (PM10, PM2.5, or PM1.0). We observed that filters with the addition of graphene oxide were able to purify polluted air containing PM at concentrations above 3000\ \mu g/m3, a concentration above which commercially available filters cannot. The efficiency of graphene oxide-treated filters was much greater than commercially available filters. Based on our data, we proposed a mechanism by which graphene oxide can effectively diminish the amount of PM in the air.
Trust in Automation: An On-Road Study of Trust in Advanced Driver Assistance Systems

—Appropriate user trust is critical in ensuring the acceptance and safe use of Advanced Driver Assistance Systems (ADAS). Despite the prevalence of ADAS on-road today, there is a limited understanding of how trust is affected by a user’s first contact with the system on-road. Ten participants without prior experience were introduced to a level 2 system and completed an on-road test drive session. Utilizing a mixed-methods approach including the Trust in Automation (TiA) questionnaire, verbal trust scores, and Facial Emotion Recognition (FER), trust in the system was measured at key milestones. TiA scores increased in a majority of participants, and a significant shift in the factor Reliability/Competence (p<0.05) was observed post-drive. According to FER scores, participants with a gain in TiA post-drive and those with a loss in TiA post-drive, more frequently displayed the emotions happy and angry , respectively. Results indicate that trust increases after a user’s first experience with ADAS and further that FER may be predictive of user trust in automation.
Estimating Age Patterns of Net-Migration across European Municipalities

(250) Migration plays a key role in the development of local areas challenged by demographic change and ageing. However, the lack of age-specific data and long time series limit the assessment of migration impacts, even in the European Union (EU) with a common data collection system since 2007. The paper explores patterns in population and net-migration age structures in more than 100,000 EU Local Administrative Units (LAUs) over the period 2011-2019. We harmonize EU population datasets using (2011) population censuses and the latest (indicatively 2019) available statistics provided by National Statistical Institutes. Exploiting these datasets, we estimate age-specific net migration by adapting census survival methods to handle the large EU coverage and variations in data collections coming from different national sources. Specifically, we apply life-table models to assess changes by cohort and derive age-specific net-migration rates applying a residual method. The estimated age-specific net migration rates are validated using pooled official statistics available for small samples of LAUs with limited territorial and temporal coverages. The resulting net migration rates by age at finer geographical resolution could contribute to enhancing research on the determinants of territorial disparities across the EU during intercensal periods. Covering the whole EU territories, the new datasets at LAU level complement the figures available from the 2011 censuses, anticipating the release of gridded census statistics foreseen for 2023.
Animal Companions During Covid-19 Animal Companions During Covid-19

Pets can act as an intervention for the mental declines associated with the COVID pandemic because of the attachment persons hold for the companion animal. Furthermore, it is possible that service dogs may be able to provide support that distinguishes them from regular pets. I evaluated the perceptions of human-dog profiles to determine the influence of pets. I hypothesized that descriptive profiles of human-dog relationships with pictures manipulated for dog size and dog purpose would generate main effects both for both factors on ratings of loneliness and attachment. I also tested correlations between loneliness and comfort from pets in the participants ( N = 60) who were pet owners. Finally, I tested whether comfort was associated with a greater frequency of dog size. I used a 2 x 3 (Dog Purpose by Dog Size) within-subjects design with a human with a laptop as an external control. I found a main effect of size and an interaction such that pets and service dogs were only distinguished in the medium size for ratings of loneliness; however, the presence of a dog was consistently rated as less lonely than a human with a laptop. Service dogs were rated with greater attachment unless they were medium size. No significant correlation was found between self-reports of participants of loneliness and comfort from animals. My results indicate that the presence of service dogs may mitigate feelings of loneliness related to COVID-19. The human-animal attachment is stronger for service animals who spend more time with owners than pets.
Rethinking Engineering Education: Lessons from the Learning Experiences of Early-career Engineers

More and more scholars have attempted to explore the real-world scenarios from the industry view so as to deepen the understanding of the engineering practice and improve engineering education accordingly. Currently, relevant international research has been increasing on this topic, but empirical research from industry perspective in China is still scarce. This work-in-progress focuses on understanding early-career engineers’ perspective in China using the theoretical framework of Cognitive Apprenticeship (CA), which describes the learning environment in four aspects: the content taught/learnt, the teaching/learning methods employed, the sequencing of learning activities, and the sociology of learning. Moreover, this study employs semi-structured interview to collect data from different industries. Preliminary findings suggested that the content that new engineers learnt included both the technical and non-technical aspects. The engineers learned through multiple venues, including such as learning by themselves and learning from their mentors. This study will provide practical suggestions for improving engineering education in both China and other similar contexts.
THE EFFECT OF CHITOSAN-BASED COATING ON THE QUALITY OF FRESH REDFISH ( Sebastes marinus ) FILLET DURING COLD STORAGE

The main aim of this study was to evaluate the improvement of quality obtained through use of chitosan-based coating on redfish fillets under cold storage. For this purpose, four different skinned fillet groups treated with different solutions (water, ascorbic acid 1%, chitosan 1.5 %, chitosan 1.5% combined with gelatine 2%) was stored at 2 -3 o C for 12 days to determine the changes (physical, chemical, microbiological, sensory) during preservation. The results revealed that chitosan-based coating significantly increased the whiteness as well as significantly limited the lipid oxidation of redfish fillets during cold storage (p<0.05). It slowed down the increase in TVC in the first 2 days of cold storage. Chitosan combined with gelatine also showed a delay on TVC and Pseudomonas spp. during cold storage. Chitosan-based coating could significantly prolong the freshness of redfish fillets, as well as increase the shelf life and quality of redfish fillet during cold storage (p<0.05).