Potential Answers Research Articles

Telemedicine in Diabetic Retinal Screening: Pre- and Post-COVID-19 Challenges a New Perspective

Abstract Telemedicine in ophthalmology has been around for decades and has been successful with its use in diabetic retinal screening in countries like the UK (with the introduction of the UK National Diabetic Eye Screening Programme in 2003). However, most telemedicine, in the field of diabetic retinopathy, has largely been reliant on human graders for triage purposes. With the advent of COVID-19, patients with chronic conditions, such as diabetes, were disproportionately affected. The pandemic also caused significant rise in patients on waiting lists. Before the pandemic, there have been studies illustrating the use of artificial intelligence (AI) to analyse images obtained from patients screened for monitoring of their diabetic retinopathy. The image analysis by AI and deep-learning algorithms offers insight into the future of screening in diabetes. The transition, from the use of human graders in teleophthalmology to the use of AI-based image analysis has the potential to screen a wider cohort of patients, thereby tackling waiting lists awaiting screening which has lengthened since after COVID-19. It is therefore vital to understand the role of AI in screening diabetic retinopathy patients, from a patient-acceptability, cost-effectiveness and reliability perspective as, this offers potential answers to streamline the screening process further.

"Why Did My Seizures Start Now?": A Singular Question With Multiple Potential Answers.

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Concern for Quality in Indian Higher Education

Abstract: The nations with the best higher education systems will rule the world in the twenty-first century because, in addition to increasing social and personal wealth, education also has a direct or indirect bearing on all other facets of development, including intellectual, social, cultural, artistic, economic, moral, and human resources. In India, the number of universities, college campuses, and university-level institutions as well as the number of students enrolled has increased dramatically. Rashtriya Uchchatar Shiksha Abhiyan (RUSA), a Centrally Sponsored Scheme launched by the Indian government in 2013, aims to improve the state of higher education. As a result, the current enrolment ratio is at 27.3% The nation has seen a notable gain in enrolment, but as of right now, concerns remain about the quality, which is crucial to meeting objectives and carrying out national policy. Higher education faces a number of challenges, including inadequate facilities, a curriculum focused on exams, memory-based exams, a lack of qualified faculty, subpar teaching techniques, a lack of funding, uneven government policies regarding education, political unrest, vested political interests, high demand from the youth, growing privatization, a lack of access and equity, etc. There exist numerous other comparable difficulties, all of which are rigorously analysed in this work along with potential answers.

Urban Stormwater Management: A Sustainable Approach

Climate change is impacting urban areas, especially through extreme rainfall that stresses conventional water management systems. Rainwater resulting from impervious runoffs, stormwater leads to an increase in the amount of wastewater that requires treatment and an overflow of the combined sewer system. Therefore, untreated wastewater is released into the surroundings or, in some cases, causes damage to urban systems. Nevertheless, many cities in the world are in the process of establishing a sustainable approach to urban water management. Sustainable stormwater management practices are essential for overcoming various environmental challenges and promoting community sustainability and resilience. The purpose of this paper is to communicate Oslo’s success in implementing sustainable stormwater management in urban areas. By analyzing successful cases, researchers may be able to record patterns that offer potential answers to the stormwater management challenge. The present case study presents an approach that can enlighten urban planners and policymakers about the potential outcomes of sustainable stormwater management under the conditions presented.

The Influence of Financing Growth and CAR on ROA with NPF as a Moderating Variable

This research examined whether financing growth and CAR affect ROA with NPF as a moderating variable of Sharia commercial banks. To address various potential answers, this research employed a quantitative methodology by analyzing financial data from Islamic commercial banks registered with the OJK during the period of 2011-2020. Sample selection was conducted using the purposive sampling method, resulting in the selection of 6 Islamic commercial banks as the sample. The data were analyzed using moderated regression analysis (MRA). The results of this research indicated that financing growth has a positive and significant impact on ROA,CAR has a positive and significant impact on ROA, NPF is not capable of moderating the influence of financing growth on ROA, and NPF is capable of moderating by weakening the influence of CAR on ROA. These findings contribute to Islamic commercial banks effectively channelling financing and achieving efficiency in CAR, thereby enhancing financial performance. Keywords: ROA, financing growth, CAR, NPF

Performance of generative pre-trained transformers (GPTs) in Certification Examination of the College of Family Physicians of Canada

IntroductionThe application of large language models such as generative pre-trained transformers (GPTs) has been promising in medical education, and its performance has been tested for different medical exams. This study...

Chatbots, search engines, and the sealing of knowledges

AbstractIn 2023, online search engine provider Microsoft integrated a language model that provides direct answers to search queries into its search engine Bing. Shortly afterwards, Google also introduced a similar feature to its search engine with the launch of Google Gemini. This introduction of direct answers to search queries signals an important and significant change in online search. This article explores the implications of this new search paradigm. Drawing on Donna Haraway’s theory of Situated Knowledges and Rainer Mühlhoff’s concept of Sealed Surfaces, I introduce the term Sealed Knowledges to draw attention to the increasingly difficult access to the plurality of potential answers to search queries through the output of a singular, authoritative, and plausible text paragraph. I argue that the integration of language models for the provision of direct answers into search engines is based on a de-situated and disembodied understanding of knowledge and affects the subjectivities of its users. At the same time, the sealing of knowledges can lead to an increasing spread of misinformation and may make marginalized knowledge increasingly difficult to find. The paper concludes with an outlook on how to resist the increasing sealing of knowledges in online search.

Weight Averaging and re-adjustment ensemble for QRCD

Question Answering (QA) is a prominent task in the field of Natural Language Processing (NLP) with extensive applications. Recently, there has been a notable surge in research interest concerning the development of QA systems for the Holy Qur’an, an Islamic religious text. The Qur’an Reading Comprehension Dataset (QRCD) Malhas and Elsayed (2020) is a highly commendable effort in this respect. It stands as the first benchmark dataset specifically designed for a set of directly answerable questions from the Qur’an. Each question in the dataset is meticulously labeled with all potential answers sourced from the Holy Qur’an. From our perspective, the main challenge in QRCD stems from the limited volume of training data it offers. As a solution we propose an innovative approach to build a Deep Neural Network (DNN) ensemble, centered around Ara-Electra model (Antoun et al., 2021), that we called Weight Averaging and Re-adjustment (WAR) model. The model is constructed by computing running averages of all model states that evolve during a single training session and ensuring that model weights are readjusted prior to each training epoch, in order to hold it back from over fitting the training data. The scheme results in a single standalone model that exhibits the benefits of multi-model ensembles. It is distinguished from other ensembles proposed for QRCD that accumulates outputs from multiple expert models and employs classic techniques like hard voting or score averaging on output probabilities to build unified results. Each expert model costs individual training time and compute resources. The WAR model outperforms existing systems with improved generalization over unseen data. It achieves F1, partial Reciprocal Rank (pRR), and exact-match (EM) scores of 0.567, 0.60 and 0.29 respectively, exceeding best reported QRCD scores by 3%, 1.5% and 0.69% respectively. Notably, we are comparing our results with the top scores from different models, highlighting our model’s consistent performance across all three metrics.

A DECISION SUPPORT SYSTEM FOR THE IDENTIFICATION OF EMOTIONAL INTELLIGENCE

Emotional intelligence is the relationships that directly impact how people relate to themselves and to others within a socio-cultural environment. A person who possesses emotional intelligence is able to communicate effectively while controlling and understanding other individual’s emotional inclinations. Individuals desire to express their state of mind, worries, traumas, and obstacles in life as well as get answers and help for their problems. However, they desire security and protection for the feelings, anxieties, trauma, and life obstacles they express. Therefore, individuals require a support structure where they can discuss their problems and seek potential answers while knowing that their identities would be kept private. A “Decision Support System” (DSS) for emotional intelligence is a electronic program that supports decisions, judgments, courses of action, and the proficiency of deploying artificial intelligence (AI) to regulate both your own and those around you emotionally. In this study we use AI to help in decision making to create confidential space between user and app. The technology uses are JavaScript, HTML, openAI, Replicate, PINECONE, CSS, PHP and MySQL. This research shows how technology has the ability to improve emotional intelligence using decision support system in individual’s daily lives.

Atrial Fibrillation and Early Vascular Aging: Clinical Implications, Methodology Issues and Open Questions-A Review from the VascAgeNet COST Action.

Atrial fibrillation (AF), the most common cardiac arrhythmia, is associated with adverse CV outcomes. Vascular aging (VA), which is defined as the progressive deterioration of arterial function and structure over a lifetime, is an independent predictor of both AF development and CV events. A timing identification and treatment of early VA has therefore the potential to reduce the risk of AF incidence and related CV events. A network of scientists and clinicians from the COST Action VascAgeNet identified five clinically and methodologically relevant questions regarding the relationship between AF and VA and conducted a narrative review of the literature to find potential answers. These are: (1) Are VA biomarkers associated with AF? (2) Does early VA predict AF occurrence better than chronological aging? (3) Is early VA a risk enhancer for the occurrence of CV events in AF patients? (4) Are devices measuring VA suitable to perform subclinical AF detection? (5) Does atrial-fibrillation-related rhythm irregularity have a negative impact on the measurement of vascular age? Results showed that VA is a powerful and independent predictor of AF incidence, however, its role as risk modifier for the occurrence of CV events in patients with AF is debatable. Limited and inconclusive data exist regarding the reliability of VA measurement in the presence of rhythm irregularities associated with AF. To date, no device is equipped with tools capable of detecting AF during VA measurements. This represents a missed opportunity to effectively perform CV prevention in people at high risk. Further advances are needed to fill knowledge gaps in this field.

Applications of Artificial Intelligence Techniques in Healthcare and Medicine

Abstract—Artificial intelligence (AI) is swiftly coming into fitness care and serving foremost roles, from automating drudgery and ordinary responsibilities in scientific exercise to dealing with sufferers and scientific resources. As builders create AI structures to tackle those responsibilities, numerous dangers and demanding situations emerge, along with the hazard of accidents to sufferers from AI gadget errors, the hazard to affected person privateness of information acquisition and AI inference, and more. Potential answers are complicated however contain funding in infrastructure for high-quality, consultant information; collaborative oversight via way of means of each the Food and Drug Administration and different fitness-care actors; and adjustments to scientific schooling with a purpose to put together companies for moving roles in an evolving gadget. Keywords — Dermatology, Radiology, Screening, Psychiatry, Primary care, Disease diagnosis, Telemedicine, Electronic health records.

A Study of Human Resource Management in 21st Century

The topic of this research is 21st-century human resource management. It will concentrate on the problems, obstacles, and solutions to become competitive in the current environment. The HR phenomena in the current environment will be the main topic of this research study, which will also identify the new problems of the twenty-first century and offer the greatest number of potential answers. Everything has changed significantly as a result of technology, including new equipment, hiring procedures, training methods, and production strategies. Now, in order to manage the workforce as effectively as possible, HR staff members must work with their roles as outlined below. We have covered the main problems and difficulties that organizations are currently facing in these essays. HR managers will need to create a standard structure that enables managing all available workforce alternatives in order to solve these problems and challenges. The company keeps its competitive advantages in this way. To finish the investigation, recommendations and findings are also considered.

Choosing Objective and Non-objective Test as Instrument for Assessment of EFL Student Learning Outcomes

This research describes the type of questions both objective and non-objective tests, benefits, and weaknesses of EFL students’ learning outcomes. This research is a descriptive qualitative. The analysis shows that the written test can be used as a measuring instrument for knowing students’ abilities. The form of the written test consists of objective and non-objective. An objective test consists of multiple potential answers or responses for students to choose one including completion, multiple-choice, matching, short answer, and true-false test. Non-objective tests consist of essays/descriptions for students to compose or organize answers in their language including an essay in limited/restricted essay and free essay tests. Teachers/lecturers should consider aspects of making the tests. They have differences in measured learning outcomes, preparation of question items, sampling of learning materials, control of student answers, scoring, influence on the learning process, and reliability. However, both objective and non-objective tests also have several advantages and disadvantages.

What are delusions? Examining the typology problem.

Delusions are a heterogenous transdiagnostic phenomenon with a higher prevalence in schizophrenia. One of the most fundamental debates surrounding the philosophical understanding of delusions concerns the question about the type of mental state in which reports that we label as delusional are grounded, namely, the typology problem. The formulation of potential answers for this problem seems to have important repercussions for experimental research in clinical psychiatry and the development of psychotherapeutic tools for the treatment of delusions in clinical psychology. Problematically, such alternatives are scattered in the literature, making it difficult to follow the current development and state of the target discussion. This paper offers an updated critical examination of the alternatives to the typology problem currently available in the literature. After clarifying the two main philosophical views underlying the dominant formulation of the debate (interpretivism and functionalism), we follow the usual distinction between doxastic (the idea that delusions are a type of belief) and anti-doxastic views. We then introduce two new sub-distinctions; on the doxastic camp, we distinguish between revisionist and non-revisionist proposals; on the anti-doxastic camp, we distinguish between commonsensical and non-commonsensical anti-doxasticisms. After analyzing the main claims of each view, we conclude with some of the most fundamental challenges that remain open within the discussion. This article is categorized under: Philosophy > Foundations of Cognitive Science Philosophy > Consciousness Philosophy > Psychological Capacities Neuroscience > Cognition.

Variation in patient gender and sexual identity recording within ambulance records

Background: Health inequality among lesbian, gay, bisexual and transgender people has been highlighted in several reports. Ambulance services have been advised to record the gender identity and sexuality of all patients treated to support monitoring of health equity. Method: Four NHS ambulance services in England were asked for their data showing whether the completed patient record for each incident included gender identity and sexuality. Results: All services responded; data from three were used in the analysis. Only one service had the means to record a patient's sexual orientation. Gender identity was recorded by all services but the rate of data capture as well as potential responses varied between organisations. Discussion: There was little consistency between the three services regarding rates of data being captured, responses and potential answers. These variations could not be explained, although introduction of electronic patient records may impact data capture and quality.

Unveiling the Strategic Planning of Teachers, Performance Task Outcome and Learning Style of Students

Setting goals, agreeing on steps to accomplish those goals, and mobilizing the resources required to carry out those activities are all parts of strategic planning. Strategic planning plays a big role in students’ performance tasks. This research aims to identify and investigate potential answers to a fantastic strategic plan for teachers that assists students in attaining greatness in their performance assignments despite various learning styles. Researchers focused on investigating the relationship between strategic planning and the performance task results of diverse students. Fifty students from Bula can State University with a Bachelor of Secondary Education major in Mathematics have partaken in this study. Pearson r correlation was used as a correlation statistic and regression analysis to see the significance of the relationship between the variables. The teaching style that is more dominant among teachers is the Learner-Centered style. Implementing teachers’ strategic planning inside the classroom as to students’ learning styles and performance tasks is well implemented. It has been revealed that the majority of the students with a course of Bachelor of Secondary Education major in mathematics are read/write learners. There is no significant relationship between career/future-ready and high-quality instruction, strategic planning, and performance tasks of diverse students. Researchers recommend teachers focusing more on high-quality instruction since career/future-ready is more dominant in the research findings.

Using ICT Tools and Technological Applications in this Era of the COVID-19 Pandemic to Facilitate Learning

Abstract The globe is shaken by the abrupt emergence of the deadly Coronavirus Disease 2019 (COVID-19) pandemic. The global education system has been put to the test by this circumstance, which compelled educators to quickly adopt Information & Communication Technology (ICT) tools and technologies to support instruction. Although, great attempts are made by scholars to study the implications of Coronavirus Disease 2019 on the higher education curricular, there is dearth of previous studies regarding the impact of Information & Communication Technology tools and technological applications to foster learning amid Coronavirus Disease 2019 outbreak. This exploratory study therefore uses a meta-synthesis approach and visited relevant literatures as lens to primarily examine the need to use Information & Communication Technology tools and technology applications to enhance learning in the Coronavirus Disease 2019 age. The research also shed some insight on the difficulties involved with employing Information & Communication Technology tools and technical applications to aid learning in times of pandemic and catastrophes, and it offers potential answers to educational institutions on how to handle these complications. The study’s findings suggest that Information & Communication Technology tools and technologies are the magic bullet for optimizing learning in times of disaster like the Coronavirus Disease 2019 pandemic. The study also shows that by overcoming some technical and financial obstacles like high cost of technology devices, electricity problems, lack of expertise, etc., the lessons learnt from the abrupt and compulsory adoption of Information & Communication Technology tools and technologies to lessen the negative effects of the disaster on learning amid Coronavirus Disease 2019 global emergency will help create new opportunities for the use of blended learning approaches to meet the needs of the Coronavirus Disease 2019 disruption and future learning deliveries.

Automated Assessment System Using Machine Learning Libraries

Assessment and the grading of students is a task that has been done for as long as school has existed. This was previously done by teachers in primary and secondary, lecturers for institutions like JAMB and lecturers in schools. Up until now students’ marks were influenced by other external factors such as bad handwriting, lengthy paragraphs, roundabout way of speaking rather than going straight to the point and the sheer number of assignments the lecturer has to mark. This has resulted in students getting lower or higher marks than they should be awarded. This project is to create an ML (Machine Learning) powered assessment system that will take the assignment questions and the marking scheme and award the student the marks similar to what the ideal lecturer would have given. This will also reduce the time the lecturers spend on marking and ensure the students get their results on time. This project will be made with Python and machine learning and will be tested with a number of potential answers to the questions and their grading’s. This will enable system to be able to grade assignments as soon as they are uploaded. This research will be limited by the fact that the system can only handle the marking of short sentences accurately and not long paragraphs. The system is also limited by the fact that it can only mark with the aid of the marking scheme and not without it so it is not a truly intelligent model in that regard. The research showed that the system is indeed capable of obtaining the similarity between two paragraphed answers provided but it needs extras to produce the most accurate results.

Semmelweis Caring University Model Program: A Holistic Approach to Promoting Healthy Ageing through a Longitudinal Cohort Study, Preventive Services and Workplace Health Promotion

Abstract Hungary is facing an alarming public health situation due to the unhealthy ageing of its population, calling for innovative and effective approaches to promote healthy ageing. This presentation showcases the Semmelweis Caring University Model Program, a unique and holistic program developed by the leading health sciences university in Hungary to address this societal challenge. The program serves as a model for the ongoing primary care reform, which incorporates preventive services, the first study to address determinants of unhealthy ageing in Hungary, and includes a workplace-based health promotion program that could be implemented nationally. The Semmelweis Caring University Model Program aims to promote the health and wellbeing of its 8500 employees and provides potential answers to the unfavorable public health situation in Hungary. The Semmelweis Study, a longitudinal occupational cohort of university employees, investigates the complex relationships between lifestyle, environmental, and occupational risk factors and the development and progression of chronic age-associated diseases. Data is collected through computer-assisted questionnaires, physical health examination, physiological measurements, biomarker assessment and data linkage to national registers. The results of the Semmelweis Study inform the development of the Centre of Preventive Services (CPS), which provides health risk assessment, screening, and counseling services to employees, with medical specialists in preventive medicine and primary care, public health professionals, dieticians, physiotherapists, and psychologists. The CPS units also serve as practical training sites for medical and health sciences students. The Semmelweis Workplace Health Promotion Program is a personalized public health model program that utilizes infrastructure and human resources of Semmelweis University to encourage healthy behaviors and environments in the workplace, with a potential for nationwide implementation.

Ethical Considerations in Using Artificial Intelligence to Improve Teaching and Learning

One way that AI could improve the educational experience is by making it more individualized and efficient. However, there are a number of ethical concerns that have to be addressed as a result of this accomplishment. This study discusses some of the major ethical challenges posed by AI in the classroom, as well as some potential answers to those problems. The use of AI in the classroom raises serious questions about data privacy and security. Massive volumes of student data are collected and analyzed by AI systems, necessitating robust security measures. Clear communication and informed permission regarding data handling are crucial in solving these privacy issues. Another ethical problem with AI is the potential for discrimination and prejudice. As a result of inherent biases in their training data, AI systems may unfairly disadvantage specific demographics of students. Concerns about bias and unfairness in AI-powered learning technologies persist. Decisions made by AI must be explicable and open to scrutiny. Understanding how AI systems create recommendations and assessments is important for students, teachers, and leaders. Both trust and responsibility in the classroom benefit from more openness. The ethical implications of AI for educators and the field as a whole also need to be considered. Technology should complement teachers rather than replace them. The usage of AI and the training of educators are essential to this shift. The goal of this study is to study the impact of ethical issues factors by using AI in T&L & to identify the impact of AI Factors on T&L (Teaching & Learning)..