Miracle-dz/UniFAR-FTUs huggingface dataset:
An Inquiry-Based Learning Approach for Effective Concept An Inquiry-Based Learning Approach for Effective Concept Teaching Teaching

The purpose of this research was to investigate the effect of simulation-supported inquiry-based learning on pre-service teachers’ conceptual understanding of capacitors. The participants consisted of 50 pre-service teachers studying at a state university in Turkey. The participants were divided into two groups of 25 each on the basis of their physics grades in the previous semester. The research was patterned according to a non-equivalent control group design with pretest and posttest. The experimental group used simulation-supported inquiry-based learning, and the control group used lecture-based learning supported by simulations. The research data were collected with the Capacitor Concept Test prepared by the researchers. The findings showed that pre-service teachers had various misconceptions about parallel plate capacitors before the implementation. The research revealed that inquiry-based learning was more effective than lecture-based learning in eliminating these misconceptions.
Where to from Brahmans in the Northern Australian herd? Maintaining the economic benefit of earlier infusions of Bos indicus.

The economic performance of a terminal crossbreeding system based on Brahman cows and a tropically adapted composite herd were compared to a straightbred Brahman herd. All systems were targeted to meet specifications of the grass-finished Japanese market. The production system modelled represented a typical individual central Queensland integrated breeding/finishing enterprise or a northern Australian vertically integrated enterprise with separate breeding and finishing properties. Due mainly to a reduced age of turnoff of Crossbred and Composite sale animals and an improved weaning rate in the Composite herd, Crossbred and Composite herds returned a gross margin of $7 and $24 per Adult Equivalent (AE) respectively above that of the Brahman herd. The benefits of changing 25% of the existing 85% of Brahmans in the northern Australian herd to either Crossbreds or Composites over a 10-year period were also examined. With no premium for carcass quality in Crossbred and Composite sale animals, annual benefits were $16 M and $61 M for Crossbreds and Composites in 2013. The cumulative Present Value (PV) of this shift over the 10-year period was $88 M and $342 M respectively, discounted at 7%. When a 5c per kg premium for carcass quality was included, differences in annual benefits rose to $30 M and $75 M and cumulative PVs to $168 M and $421 M for Crossbreds and Composites respectively.
Mining Node.js Vulnerabilities via Object Dependence Graph and Query

Node.js is a popular non-browser JavaScript platform that provides useful but sometimes also vulnerable packages. On one hand, prior works have proposed many program analysis-based approaches to detect Node.js vulnerabilities, such as command injection and prototype pollution, but they are spe-ciﬁc to individual vulnerability and do not generalize to a wide range of vulnerabilities on Node.js. On the other hand, prior works on C/C++ and PHP have proposed graph query-based approaches, such as Code Property Graph (CPG), to efﬁciently mine vulnerabilities, but they are not directly applicable to JavaScript due to the language’s extensive use of dynamic features. In the paper, we propose ﬂow-and context-sensitive static analysis with hybrid branch-sensitivity and points-to information to generate a novel graph structure, called Object De-pendence Graph (ODG), using abstract interpretation. ODG represents JavaScript objects as nodes and their relations with Abstract Syntax Tree (AST) as edges, and accepts graph queries—especially on object lookups and deﬁnitions—for detecting Node.js vulnerabilities. We implemented an open-source prototype system, called ODG EN , to generate ODG for Node.js programs via abstract interpretation and detect vulnerabilities. Our evaluation of recent Node.js vulnerabilities shows that ODG together with AST and Control Flow Graph (CFG) is capable of modeling 13 out of 16 vulnerability types. We applied ODG EN to detect six types of vulnerabilities using graph queries: ODG EN correctly reported 180 zero-day vulnerabilities, among which we have received 70 Common Vulnerabilities and Exposures (CVE) identiﬁers so far.
TRENDS AND DETERMINANTS OF KNOWLEDGE AND AWARENESS OF HIV/AIDS AMONG MARRIED WOMEN IN BANGLADESH: AN URBAN–RURAL COMPARISON1)

The aim of this study was to examine the trends and determinants of knowledge and awareness of HIV/AIDS among women in urban and rural areas of Bangladesh. This study used data from the 2014 Bangladesh Demographic and Health Survey (BDHS). A binary logistic regression model was employed to detect potential risk factors (covariates) associated the outcome variable. While women’s HIV/AIDS knowledge in rural areas has shown an increasing trend over the years in which the survey has been conducted, a narrow decline in HIV/AIDS knowledge was observed among women in urban areas in recent years. Education and mass media have played the major role in spreading knowledge about HIV/AIDS among women in both urban and rural areas. Since rural women’s HIV/AIDS knowledge score was relatively lower than that of urban women, awareness raising through mass media should focus especially on rural areas.
MEB HİZMETİÇİ EĞİTİMLERİNİN ÖĞRETMENLERİN BİLGİ VE İLETİŞİM TEKNOLOJİLERİNE İLİŞKİN TUTUMLARINA VE BİLGİSAYAR ÖZ-YETERLİKLERİNE ETKİSİ

Bu calisma MEB tarafindan duzenlenen hizmetici egitim calismalarinin ogretmenlerin bilgi ve iletisim teknolojilerine iliskin oz-yeterlilik algilarina ve tutumlarina etkisini ortaya cikarmak amaciyla gerceklestirilmistir. Tarama modelli arastirmanin orneklemini 437 ogretmen olusturmaktadir. Olcme araci olarak ogretmenlerin algilarini olcmek icin Bilgisayar Oz-yeterlik Algi Olcegi, tutumlarini olcmek icin BIT'e Yonelik Tutum Olcegi kullanilmistir. Verilerin analizinde yuzde, aritmetik ortalama, t, Anova, Scheffe ve Pearson r korelasyon testlerinden yararlanilmistir (p < 0,05). Arastirma sonucunda ulasilan bazi sonuclar soyle ozetlenebilir: Ogretmenlerin BIT'e yonelik tutumlari oldukca yuksektir. Ogretmenlerin BIT'in egitim ve ogretime etkisine yonelik tutumlari uzerinde kidemin etkisi vardir. Ote yandan ogretmenlerin BIT kullanilmasina engellere yonelik tutumlari uzerinde kidemin ve mezun olunan okul duzeyinin etkisi yoktur.
Case Report: COVID-19 in the Setting of HCOM

Importance: Viral infections have been discussed as one of the most common causes of myocarditis but a rare encounter of a patient suffering with a condition of Hypertrophic Cardiomyopathy (HCOM) incidentally discovered with COVID-19, has been presented in this case report with lesser known facts about the involvement of cardiomyopathies (CMPs) as a further complication or an alleviating factor against severe COVID-19 Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection. Objective: To describe the presentation of corona virus disease (COVID-19) in a patient with incidental HCOM, who experienced influenza like syndrome at first and then developed fatigue and signs of severe upper respiratory tract symptoms along with dizziness and chest tightness. Design, Setting, and Participant: This case report describes a 39-year-old man who tested positive for COVID-19 by rapid antigen testing and was admitted to the respiratory care unit in April 2021 with typical imaging features of COVID-19 for acute respiratory depression syndrome, confirmed on HRCT thorax scanning, 8-10 days after a rapid onset of fever, severe headache, myalgia and dry coughing due toa viral COVID-19 infection. Exposure: COVID-19 aggression with stable yet severe cardiac involvement. Main Outcomes and Measures: Monitoring COVID-19 pulmonary effects along the detection of cardiac involvement with normal ECG but increased changes in systolic ejection fraction levels on echocardiography and gradual recovery and resolution by treatment shown through repeat HRCTthoraximaging and echocardiography. Results: An otherwise healthy 39-year-old man presented to the emergency department with fatigue, fever, myalgia, dry cough and signs of severe respiratory depression. He described weakness, recurrent fever, severe headache, body ache, loss of appetite and constant dry coughing that got worse in 8-10 days. His vitals at admission were documented to be febrile with a feeling of anxiety and suffocation, low in oxygen saturation but with normal blood pressure and based on the second wave of the COVID-19 outbreak, a nasopharyngeal swab was performed with a rapid COVID-19 antigen test resulting positive. Typical findings for COVID-19 infection were apparent on HRCT chest imaging. After the patient’s inability of laying in the prone position as a practice to improve oxygen status along with dizziness and chest tightness, a thorough history inclined the consulting physician towards an associated cardiogenic cause. Bedside ECG revealed no serious abnormalities but on echocardiography, left ventricular wall thickness abnormality was evident. The findings were all consistent with HCOM and the patient was placed on 24 hour Holter monitoring which recorded no arrhythmias. He was treated with medications for COVID-19 and its pulmonary and hematological complications till the end of the course of the disease and finally managed with beta-blockers for HCOM. Conclusions and Relevance: This case highlights a progression of cardiac involvement as a complication associated with severe symptoms and signs of COVID-19 sequelae.
Correlation between psychological problems, self- esteem and quality of life among vitiligo patients

Background: Vitiligo is a chronic skin disorder that may have a negative impact on patients’ self-esteem, quality of life and ultimately results in psychological problems. Aim of the study: This study aimed to assess correlation between psychological problems, selfesteem and quality of life among vitiligo patients. Research question: What is the correlation between psychological problems, self-esteem and quality of life among vitiligo patients? Design: A descriptive correlational research design was utilized in this study. Setting: The study was conducted at Benha dermatology hospital and dermatological outpatients at Benha University Hospital, Kaluobia Governorate. Sample: A random sample of 100 vitiligo patients was recruited consecutively from the above mentioned settings. Tools: Tool (1): A structured Interviewing Questionnaire Sheet, Tool (11): Depression Anxiety Stress Scale (DASS), Tool (111): Rosenberg' Self-Esteem Scale and Tool (V): Quality of Life Scale. Results: More than two thirds of the studied vitiligo patients had severe level of depression anxiety stress and more than half of them had low level of self-esteem and quality of life. Conclusion: There is a highly statistically significant negative correlation between mean scores of total depression anxiety stress, self-esteem and quality of life scales among the studied vitiligo patients. Recommendation: Stress management and assertiveness training program should be given to vitiligo patients to relieve their psychological problems and enhance their coping patterns.
Denotative and connotative management of uncertainty: A computational dual-process model

The interplay between intuitive and deliberative processing is known to be important for human decision making. As independent modes, intuitive processes can take on many forms from associative to constructive, while deliberative processes often rely on some notion of decision theoretic rationality or pattern matching. Dual process models attempt to unify these two modes based on parallel constraint networks or on socially or emotionally oriented adjustments to utility functions. This paper presents a new kind of dual process model that unifies decision theoretic deliberative reasoning with intuitive reasoning based on shared cultural affective meanings in a single Bayesian sequential model. Agents constructed according to this unified model are motivated by a combination of affective alignment (intuitive) and decision theoretic reasoning (deliberative), trading the two off as a function of the uncertainty or unpredictability of the situation. The model also provides a theoretical bridge between decision-making research and sociological symbolic interactionism. Starting with a high-level view of existing models, we advance Bayesian Affect Control Theory (BayesACT) as a promising new type of dual process model that explicitly and optimally (in the Bayesian sense) trades off motivation, action, beliefs and utility. We demonstrate a key component of the model as being sufficient to account for some aspects of classic cognitive biases about fairness and dissonance, and outline how this new theory relates to parallel constraint satisfaction models.
International Journal of Electrical and Computer Engineering (IJECE)

Received Jun 5, 2021 Revised Jun 5, 2022 Accepted Jul 1, 2022 Radio frequency identification (RFID) is an important internet of things (IoT) enabling technology. In RFIDs collision occur among tags because tags share communication channel. This is called tag collision problem. The problem becomes catastrophic when dense population of tags are deployed like in IoT. Hence, the need to enhance existing dynamic frame slotted ALOHA (DFSA) based electronic product code (EPC) C1G2 media access control (MAC) protocol. Firstly, this paper validates through simulation the DFSA theory that efficiency of the RFID system is maximum when the number tags is approximately equal to the frame size. Furthermore, literature review shows tag grouping is becoming popular to improving the efficiency of the RFID system. This paper analyzes selected grouping-based algorithms. Their underlining principles are discussed including their tag estimation methods. The algorithms were implemented in MATLAB while extensive Monte Carlo simulation was performed to evaluate their strengths and weaknesses. Results show that with higher tag density, fuzzy C-means based algorithm (FCMBG) outperformed traditional DFSA by over 40% in terms of throughput rate. The results also demonstrate FCMBG bettered other grouping-based algorithms (GB-DFSA and GBSA) whose tag estimation method are based on collision slots in terms slot efficiency by over 10% and also in terms of identification time.
Few-Shot Learning with Siamese Networks and Label Tuning

We study the problem of building text classifiers with little or no training data, commonly known as zero and few-shot text classification. In recent years, an approach based on neural textual entailment models has been found to give strong results on a diverse range of tasks. In this work, we show that with proper pre-training, Siamese Networks that embed texts and labels offer a competitive alternative. These models allow for a large reduction in inference cost: constant in the number of labels rather than linear. Furthermore, we introduce label tuning, a simple and computationally efficient approach that allows to adapt the models in a few-shot setup by only changing the label embeddings. While giving lower performance than model fine-tuning, this approach has the architectural advantage that a single encoder can be shared by many different tasks.
Efficacy of Marine Bacteria against Pathogenic Bacteria

HOceans cover about 71% of the Earth's surface, and seas and oceans provide a tremendous diversity of microbial organisms [18]. The seas and oceans are an important resource of biodiversity that goes well beyond terrestrial environments. It possesses a unique and large group of diverse natural products produced by marine microorganisms [8], [9]. This marine biodiversity is reflected through the chemical diversity and efficacy of organic extracts against bacterial pathogens [7] The rapid development of marine organisms chemistry over the past fifteen years has led to the discovery of a large number of chemical compounds, which have pharmacokinetic, medicinal and toxicological properties towards previously unknown pathogenic human bacteria [16] ,[20]
International Journal of Electrical and Computer Engineering (IJECE)

Kittikhun Meethongjan, Vinh Truong Hoang, Thongchai Surinwarangkoon Department of Computer Science, Faculty of Science and Technology, Suan Sunandha Rajabhat University, Bangkok, Thailand Department of Computer Vision, Faculty of Information Technology, Ho Chi Minh City Open University, Ho Chi Minh City, Vietnam Department of Business Computer, College of Innovation and Management, Suan Sunandha Rajabhat University, Bangkok, Thailand
Retrofit of reinforced concrete frames with buckling‐restrained braces

Damage to buildings observed in recent earthquakes suggests that many old reinforced concrete structures may be vulnerable to the effects of severe earthquakes. One suitable seismic retrofit solution is the installation of steel braces to increase the strength and ductility of a building. Steel bracings have some compelling advantages such as their comparatively low weight, their suitability for prefabrication, and the possibility of openings for utilities, access, and light. The braces are typically connected to steel frames that are fixed to the concrete structure using post‐installed concrete anchors along the perimeter. However, these framed steel braces are not without some disadvantages such as heavier steel usage and greater difficulties during the installation. Therefore, braces without steel frames appear to be an attractive alternative. In this study, braces were connected to gussets furnished with anchor brackets, which were fixed by means of a few post‐installed concrete anchors. The clear structural system and the increased utilization of the anchors allowed the anchorage to be designed precisely and economically. The use of buckling‐restrained braces (BRBs) provides additional benefits in comparison with conventional braces. BRBs improve the energy dissipation efficiency and allow the limitation of the brace force to be taken up by the highly stressed anchorage. Cyclic loading tests were conducted to investigate the seismic performance of BRBs connected with post‐installed anchors used to retrofit reinforced concrete frames. The tests showed that the proposed design method is feasible and increases strength as well as ductility to an adequate seismic performance level. Copyright © 2014 John Wiley & Sons, Ltd.
Investigation of The Relationship Between High Schools’ Instructional Capacity and Academic Achievement

The purpose of this study was to examine the relationship between high schools’ instructional capacities and students’ achievement gains. Designed using a relational screening model descriptive research method, the study draws upon the statistical data on students’ high school and UPE success scores obtained from Ministry of National Education and Center for Evaluation, Selection, and Placement as well as teachers’ responses to the data collection tool in this study. The Instructional Capacity Scale (ICS) was the data collection instrument the researchers developed. The ICS has six subscales, including instructional management practices, teacher quality, quality and quantity of curriculum and materials, instructional climate, students’ readiness to learn, and financial resources. The data were analyzed using multilevel structural equation modeling (ML-SEM) to determine the direct or indirect relationships between dependent variables and independent variables. The findings show that teacher quality and students’ readiness to learn affect students’ achievement directly, while there is an indirect relationship between instructional climate and instructional management practices. There is also evidence to suggest that the previous achievement level of students is the most effective variable for predicting academic achievement. The findings have confirmed the model presenting that instructional capacity components affect the academic achievement of the students either directly or indirectly. In this sense, the most effective variable on the students’ academic achievement was the previous academic achievement level (SBS base scores). Several limitations and recommendations for future research are provided.
DMSO_A_313838 3541..3553

*These authors contributed equally to this work Objective: A simple type 2 diabetes mellitus (T2DM) screening model was established preciously based on easily available variables for identifying high-risk individuals in western Xinjiang, China. Methods: A total of 458,153 cases participating in the national health examination were recruited. Logistic regression and the least absolute shrinkage and selection operator (LASSO) models were used for univariate analysis, factors selection, and the establishment of prediction model. Receiver operating characteristic (ROC) curve, Hosmer–Lemeshow test and clinical decision curve (CDA) were applied for evaluating the discrimination, calibration and clinical validity, respectively. The optimal threshold for predicting risk factors for T2DM has been estimated as well. Results: The nomogram depicted the risk of T2DM based on different genders, the factors mainly consisted of age, family history of T2DM (FHOT), waist circumference (WC), total cholesterol (TC), triglycerides (TG), low-density lipoprotein cholesterol (LDLc), body mass index (BMI), high-density lipoprotein cholesterol (HDLc), etc. The area under ROC of men and women was 0.864 and 0.816 in the development group, similarly in the validation group, which was 0.865 and 0.815, respectively. The calibration curve showed that the nomogram was accurate for predicting the risk of T2DM, and the CDA proved great clinical application value of the nomogram. Threshold values of the age, WC, TC, TG, HDLc, BMI in different genders were 52.5 years old (men) and 48.5 years old (women), 85.50 cm (men) and 89.9 cm (women), 4.94 mmol/L (men) and 4.94mmol/L (women), 1.26mmol/L (men) and 1.67mmol/ L (women), 1.40mmol/L (men) and 1.40mmol/L (women), 24.70kg/m (men) and 24.95kg/ m (women), respectively. Conclusion: Our results give a clue that the nomogram may be useful for identifying adults who have high risk for diabetes, which is simple, affordable, with high credibility and can be widely implemented. Further studies are needed to evaluate the utility and feasibility of this model in various settings.
Graphire: Novel Intent Discovery with Pretraining on Prior Knowledge using Contrastive Learning

In this paper, we introduce Graphire, an intent discovery system leveraging pretraining on predefined intents to automatically discover novel intents for intelligent personal assistants (IPA). In order to transfer the prior knowledge of predefined intents, Graphire first transforms predefined class memberships into pairwise relationships, and then learns a Siamese Neural Network (SNN) model classifying if two utterances have the same intent. The siamese neural network condenses the prior knowledge of predefined intents in the form of trained neural network weights, and infer pairwise relationships among new utterance pairs. The contribution of the paper is three folds: (1) it proposed a pretraining paradigm based on contrastive learning to distill prior knowledge from existing intents. (2) it proposed a new method to discover novel intent leveraging the prior knowledge (3) it proposed a cluster summarization approach to assign labels for the intents. The experimental results demonstrate the effectiveness of pretraining in Graphire and Graphire’s capability to discover novel intents on a real-world IPA dataset with intents from disparate domains.