Search results for: online teaching and learning

Authors: Divvela Vishnu Sai Kumar, Deepak Arora, Sheenu Rizvi

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Forecasting a cyclone through satellite images consists of the estimation of the intensity of the cyclone and predicting it before a cyclone comes. This research work can help people to take safety measures before the cyclone comes. The prediction of the intensity of a cyclone is very important to save lives and minimize the damage caused by cyclones. These cyclones are very costliest natural disasters that cause a lot of damage globally due to a lot of hazards. Authors have proposed five different CNN (Convolutional Neural Network) models that estimate the intensity of cyclones through INSAT-3D IR images. There are a lot of techniques that are used to estimate the intensity; the best model proposed by authors estimates intensity with a root mean squared error (RMSE) of 10.02 kts.

Keywords: estimating cyclone intensity, deep learning, convolution neural network, prediction models

Procedia PDF Downloads 119

Authors: Amer Momani, Tarek Al-Hawari, Abdallah Alakayleh

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This study explains how to construct an actionable activity-based costing and management system to accurately track and account the total costs of architectural aluminum projects. Two ABC models were proposed to accomplish this purpose. First, the learning and development model was introduced to examine how to apply an ABC model in an architectural aluminum firm for the first time and to be familiar with ABC concepts. Second, an actual ABC model was built on the basis of the results of the previous model to accurately trace the actual costs incurred on each project in a year, and to be able to provide a quote with the best trade-off between competitiveness and profitability. The validity of the proposed model was verified on a local architectural aluminum company.

Keywords: activity-based costing, activity-based management, construction, architectural aluminum

Procedia PDF Downloads 91

Authors: Abrar Al-Hasan

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Objectives: There is ongoing discourse regarding the potential risks to health professionals' reputations and credibility arising from their personal social media use. However, the specific impacts on professional credibility and the health professional-client relationship remain largely unexplored. This study aims to investigate the type and frequency of the content posted by health professionals on their Instagram accounts and its influence on their credibility and the professional-client relationship. Methodology: In a controlled field study, participants reviewed randomly assigned mock Instagram profiles of health professionals. Mock profiles were constructed according to gender (female/male), social media usage (high/low), and social media richness (high/ low), with richness increasing from posts to stories to reels and personal content type (high /low). Participants then rated the profile owners’ credibility on a visual analog scale. An analysis of variance compared these ratings, and mediation analyses assessed the influence of credibility ratings on participants' willingness to become clients of the mock health professional. Results: Results from 315 participants showed that health professionals with personal Instagram profiles displaying high social media richness were perceived as more credible than those with lower social media richness. Low social media usage is perceived as more credible than high social media usage. Personal content type is perceived as less credible as compared to those with low personal content type. Contributions: These findings provide initial evidence of the impact of health professionals' personal online disclosures on credibility and the health professional-client relationship. Understanding public perceptions of professionalism and credibility is essential for informing e-professionalism guidelines and promoting best practices in social media use among health professionals.

Keywords: credibility, consumer behavior, social media, media richness, healthcare professionals

Procedia PDF Downloads 35

Authors: Hassan Ali Abood Nassrullah, Jabbar Fadeel Mahdi, Mohammed Salih Mahdi Alkurdi, Ali Al Mousawi, Saad Ibrahim Al-Ghabban, Abdul Amir H. Kadhum, Ahmed Al-Amiery

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Background: Q fever is a zoonotic disease characterized by its clinical polymorphism and can present acutely as fever, pneumonia, hepatitis, and chronically as infective endocarditis, arthritis, osteomyelitis, or hepatitis. Objective: The aim of this study is To estimate the prevalence of cases of Q fever in hospitalized febrile patients in Imam Al Hussain Teaching Medical City in Karbala. Methods: One hundred patients with pyrexia were admitted to the medical ward from 1st August to 31st December 2019. Serological procedures fortified by Enzyme-linked Immunosorbent Assay test. Patients were considered to have acute Q fever when the specific antibodies (IgM and IgG) of phase II of Coxiella burnetii were positive. Results: The mean age of the patients was 35.05±12.93 years; females constituted 60% of them. Eighteen patients (18%) showed positive results for IgM, a lower proportion (13% n=13) had positive IgG levels, and 9% showed equivocal results. Statistical analysis revealed a significant association between positive IgM levels of the female gender and in patients consuming unpasteurized milk. One patient (female aged 60 years) died in the hospital, while all other patients were discharged well. Two female patients were pregnant, and one of them had an abortion. Conclusions: Q fever is more common in febrile patients. The study indicates that this disease should not be overlooked in the differential diagnosis of acute fever. Serological testing should be performed in all patients with acute febrile illness with an unsettling diagnosis.

Keywords: antibodies, frequency, immunoglobulin IgM, Q fever

Procedia PDF Downloads 117

Authors: Mohammed Nasser Al-Suqri

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Former information-seeking models are proposed more than two decades ago. These already existed models were given prior to the evolution of digital information era and Artificial Intelligence (AI) technologies. Lack of current information seeking models within Library and Information Studies resulted in fewer advancements for teaching students about information-seeking behaviors, design of library tools and services. In order to better facilitate the aforementioned concerns, this study aims to propose state-of-the-art model while focusing on the information seeking behavior of library users in the Sultanate of Oman. This study aims for the development, designing and contextualizing the real-time user-centric information seeking model capable of enhancing information needs and information usage along with incorporating critical insights for the digital library practices. Another aim is to establish far-sighted and state-of-the-art frame of reference covering Artificial Intelligence (AI) while synthesizing digital resources and information for optimizing information-seeking behavior. The proposed study is empirically designed based on a mix-method process flow, technical surveys, in-depth interviews, focus groups evaluations and stakeholder investigations. The study data pool is consist of users and specialist LIS staff at 4 public libraries and 26 academic libraries in Oman. The designed research model is expected to facilitate LIS by assisting multi-dimensional insights with AI integration for redefining the information-seeking process, and developing a technology rich model.

Keywords: artificial intelligence, information seeking, information behavior, information seeking models, libraries, Sultanate of Oman

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Authors: M. S. M. Anjalee Umesha Bandara

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Hela Veda Shastra is a science that is an endowment from generation to generation. There is also an individualistic science and indigenous practice of traditional herbs. There are many effective cures for snakes, fractures, head cancer, cuts, lunatics, reflexology, etc. Hela physicians who rescued them from infections caused by snakes have recognized poems to remember the medicines they used to cure the patients. Due to the harmony of the Hela Osu and Hela Knowledge poetry collection, it has become easy for the juniors of the Hela Veda generation to gain medical knowledge. It is a research problem whether it is possible to arrive at a correct conclusion about the patient form of the snake information thread through the existing Dhuta characteristics of Hela Serpa Vedakam. This research was done with the assumption that snake venom can be successfully treated according to its characteristics. In this research, two generations related to the Ratmalavatiya Vedaparamparava and the Vannihatpattu of the Kalla Veda generation have been identified as Veda Paramparas who treat and created Dutha Kavya, including the form of the Serpent Dasthana. They have collected ancient books, documents and interviews related to qualitative research on snake disease treatment. In addition, collecting data by referring to books related to Hela medicine. The ancient indigenous lineage methods that are superior to modern Western science's snake therapy should save the Hela's amazing wealth of wisdom for the future, leaving aside the selfishness of keeping the teaching to themselves.

Keywords: snake venom medicine, vedic genealogy, Dhuta characteristic, snake

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Authors: Eleanor Nel

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Research output is an indicator of institutional performance (and quality), resulting in increased pressure on academic institutions to perform in the research arena. Research output is further utilised to obtain research funding. Resultantly, academic institutions face significant pressure from governing bodies to provide evidence on the return for research investments. Research output has thus become a substantial discourse within institutions, mainly due to the processes linked to evaluating research output and the associated allocation of research funding. This focus on research outputs often surpasses the development of robust, widely accepted tools to additionally measure research impact at institutions. A publication writing programme, for enhancing research output, was launched at a South African university in 2011. Significant amounts of time, money, and energy have since been invested in the programme. Although participants provided feedback after each session, no formal review was conducted to evaluate the research output directly associated with the programme. Concerns in higher education about training costs, learning results, and the effect on society have increased the focus on value for money and the need to improve training, research performance, and productivity. Furthermore, universities rely on efficient and reliable monitoring and evaluation systems, in addition to the need to demonstrate accountability. While publishing does not occur immediately, achieving a return on investment from the intervention is critical. A multi-method study, guided by the New World Kirkpatrick Model (NWKM), was conducted to determine the impact of the publication writing programme for the period of 2011 to 2018. Quantitative results indicated a total of 314 academics participating in 72 workshops over the study period. To better understand the quantitative results, an open-ended questionnaire and semi-structured interviews were conducted with nine participants from a particular faculty as a convenience sample. The purpose of the research was to collect information to develop a comprehensive framework for impact evaluation that could be used to enhance the current design and delivery of the programme. The qualitative findings highlighted the critical role of a multi-stakeholder strategy in strengthening support before, during, and after a publication writing programme to improve the impact and research outputs. Furthermore, monitoring on-the-job learning is critical to ingrain the new skills academics have learned during the writing workshops and to encourage them to be accountable and empowered. The NWKM additionally provided essential pointers on how to link the results more effectively from publication writing programmes to institutional strategic objectives to improve research performance and quality, as well as what should be included in a comprehensive evaluation framework.

Keywords: evaluation, framework, impact, research output

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Authors: Sozon, Pok Wei Fong, Sia Bee Chuan, Omar Hamdan Mohammad

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Owing to the widespread phenomenon of plagiarism and cheating in higher education institutions (HEIs), it is now difficult to ensure academic integrity and quality education. Moreover, the COVID-19 pandemic has intensified the issue by shifting educational institutions into virtual teaching and assessment mode. Thus, there is a need to carry out an extensive and holistic systematic review of the literature to highlight plagiarism and cheating in both prevalence and form among HEIs. This paper systematically reviews the literature concerning academic plagiarism and cheating in HEIs to determine the most common forms and suggest strategies for resolution and boosting the academic integrity of students. The review included 45 articles and publications for the period from February 12, 2018, to September 12, 2022, in the Scopus database aligned with the Systematic Review and Meta-Analysis (PRISMA) guidelines in the selection, filtering, and reporting of the papers for review from which a conclusion can be drawn. Based on the results, out of the studies reviewed, 48% of the quantitative results of students were plagiarized and obtained through cheating, with 84% coming from the fields of Humanities. Moreover, Psychology and Social Sciences studies accumulated 9% and 7% articles respectively. Based on the results, individual factors, institutional factors, and social and cultural factors have contributed to plagiarism and cheating cases in HEIs. The resolution of this issue can be the establishment of ethical and moral development initiatives and modern academic policies and guidelines supported by technological strategies of testing.

Keywords: plagiarism, cheating, systematic review, academic integrity

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Authors: Marc Sader, Michiel Stock, Bernard De Baets

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Considering a large amount of adsorption data of adsorbate gases on adsorbent materials in literature, it is interesting to predict such adsorption data without experimentation. A quantitative structure-activity relationship (QSAR) is developed to correlate molecular characteristics of gases and existing knowledge of materials with their respective adsorption properties. The application of Random Forest, a machine learning method, on a set of adsorption isotherms at a wide range of partial pressures and concentrations is studied. The predicted adsorption isotherms are fitted to several adsorption equations to estimate the adsorption properties. To impute the adsorption properties of desired gases on desired materials, leave-one-out cross-validation is employed. Extensive experimental results for a range of settings are reported.

Keywords: adsorption, predictive modeling, QSAR, random forest

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Authors: Judit Vilarmau

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Generative AI is emerging rapidly and transforming higher education in many ways, occasioning new challenges and disrupting traditional models and methods. The studies and authors explored remark on the impact on the ethics, curriculum, and pedagogical methods. Students are increasingly using generative AI for study, as a virtual tutor, and as a resource for generating works and doing assignments. This point is crucial for educators to make sure that students are using generative AI with ethical considerations. Generative AI also has relevant benefits for educators and can help them personalize learning experiences and promote self-regulation. Educators must seek and explore tools like ChatGPT to innovate without forgetting an ethical and pedagogical perspective. Eighteen studies were systematically reviewed, and the findings provide implementation guidelines with pedagogical and ethical considerations.

Keywords: ethics, generative artificial intelligence, guidelines, higher education, pedagogy

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Authors: Hee Soo Kim, Ha Young Kyung

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This paper examines the role of positive emotions in human psychology. By understanding Fredrickson and Lyubomirsky et al.’s on positive emotions, one can better understand people’s intuitive understanding, mental health and well-being. Fredrickson asserts that positive emotions create positive affects and personal resources, and Lyubomirsky et al. relate such positive resources to the creation of happiness and personal development. This paper finds that positive emotions play a significant role in the learning process, and they are instrumental in creating a long-lasting repertoire of personal resources and play an essential role in the development of the intuitive understanding of life variables, resilience in coping with life challenges, and ability to build more successful lives.

Keywords: Positive emotions, positive affects, personal resources, negative emotions, development

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Authors: Bersufekad Wubie

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Introduction: Near visual impairment is presenting near visual acuity of the eye worse than N6 at a 40 cm distance. Teachers' regular duties, such as reading books, writing on the blackboard, and recognizing students' faces, need good near vision. If a teacher has near-visual impairment, the work output is unsatisfactory. Objective: The study was aimed to assess the prevalence and associated factors near vision impairment among school teachers at Gondar city Northwest Ethiopia, August 2022. Methods: To select 567 teachers in Gondar city schools, an institutional-based cross-sectional study design with a multistage sampling technique were used. The study was conducted in selected schools from May 1 to May 30, 2022. Trained data collectors used well-structured Amharic and English language questionnaires and ophthalmic instruments for examination. The collected data were checked for completeness and entered into Epi data version 4.6, then exported to SPSS version 26 for further analysis. A binary and multivariate logistic regression model was fitted. And associated factors of the outcome variable. Result: The prevalence of near visual impairment was 64.6%, with a confidence interval of 60.3%–68.4%. Near visual impairment was significantly associated with age >= 35 years (AOR: 4.90 at 95% CI: 3.15, 7.65), having prolonged years of teaching experience (AOR: 3.29 at 95% CI: 1.70, 4.62), having a history of ocular surgery (AOR: 1.96 at 95% CI: 1.10, 4.62), smokers (AOR: 2.21 at 95% CI: 1.22, 4.07), history of ocular trauma (AOR : 1.80 at 95%CI:1.11,3.18 and uncorrected refractive error (AOR:2.01 at 95%CI:1.13,4.03). Conclusion and recommendations: This study showed the prevalence of near vision impairment among school teachers was high, and it is not a problem of the presbyopia age group alone; it also happens at a young age. So teachers' ocular health should be well accommodated in the school's eye health.

Keywords: Gondar, near visual impairment, school, teachers

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Authors: Slimane Ouhmad, Abdellah Halimi

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In this work, we have applied neural networks method MLP type to a database from an array of six sensors for the detection of three toxic gases. As the choice of the number of hidden layers and the weight values has a great influence on the convergence of the learning algorithm, we proposed, in this article, a mathematical formulation to determine the optimal number of hidden layers and good weight values based on the method of back propagation of errors. The results of this modeling have improved discrimination of these gases on the one hand, and optimize the computation time on the other hand, the comparison to other results achieved in this case.

Keywords: MLP Neural Network, back-propagation, number of neurons in the hidden layer, identification, computing time

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Authors: Shatha Khuzaee

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The study develops multimodal stylistic approach to analyse a number of BBC online news articles reporting some key events from the so called ‘Arab Uprisings’. Critical stylistics (CS) and visual grammar (VG) provide insightful arguments to the ways ideology is projected through different verbal and visual modes, yet they are mode specific because they examine how each mode projects its meaning separately and do not attempt to clarify what happens intersemiotically when the two modes co-occur. Therefore, it is the task undertaken in this research to propose multimodal stylistic approach that addresses the issue of ideology construction when the two modes co-occur. Informed by functional grammar and social semiotics, the analysis attempts to integrate three linguistic models developed in critical stylistics, namely, transitivity choices, prioritizing and hypothesizing along with their visual equivalents adopted from visual grammar to investigate the way ideology is constructed, in multimodal text, when text/image participate and interrelate in the process of meaning making on the textual level of analysis. The analysis provides comprehensive theoretical and analytical elaborations on the different points of integration between CS linguistic models and VG equivalents which operate on the textual level of analysis to better account for ideology construction in news as non-literary multimodal texts. It is argued that the analysis well thought out a plan that would remark the first step towards the integration between the well-established linguistic models of critical stylistics and that of visual analysis to analyse multimodal texts on the textual level. Both approaches are compatible to produce multimodal stylistic approach because they intend to analyse text and image depending on whatever textual evidence is available. This supports the analysis maintain the rigor and replicability needed for a stylistic analysis like the one undertaken in this study.

Keywords: multimodality, stylistics, visual grammar, social semiotics, functional grammar

Procedia PDF Downloads 218

Authors: Ngabilas Salama

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Background: Indonesia has the 2nd highest number of incidents of tuberculosis (TB). It accounts for 1.020.000 new cases per year, only 30% of which has been reported. To find the lost 70%, a massive active case discovery was conducted through two programs: Ketuk Pintu Layani Dengan Hati (KPLDH) and Kader Juru Pemantau Batuk (Jumantuk cadres), who also plays a role in child TB screening. Methods: Data was collected and analyzed through Tuberculosis Integrated Online System from 2014 to 2017 involving 129 DOTS facility with 86 primary health centers in East Jakarta. Results: East Jakarta consists of 2.900.722 people. KPLDH program started in February 2016 consisting of 84 teams (310 people). Jumantuk cadres was formed 4 months later (218 orang). The number of new TB cases in East Jakarta (primary health center) from 2014 to June 2017 respectively is as follows: 6.499 (2.637), 7.438 (2.651), 8.948 (3.211), 5.701 (1.830). Meanwhile, the percentage of child TB case discovery in primary health center was 8,5%, 9,8%, 12,1% from 2014 to 2016 respectively. In 2017, child TB case discovery was 13,1% for the first 3 months and 16,5% for the next 3 months. Discussion: Increased TB incidence rate from 2014 to 2017 was 14,4%, 20,3%, and 27,4% respectively in East Jakarta, and 0,5%, 21,1%, and 14% in primary health center. This reveals the positive role of KPLDH and Jumantuk in TB detection and reporting. Likewise, these programs were responsible for the increase in child TB case discovery, especially in the first 3 months of 2017 (Ketuk Pintu TB Day program) and the next 3 months (active TB screening). Conclusion: KPLDH dan Jumantuk are actively involved in increasing TB case discovery in both adults and children.

Keywords: tuberculosis, case discovery program, primary health center, cadre

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Authors: Debjit Ray

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Horizontal gene transfer (HGT) and recombination leads to the emergence of bacterial antibiotic resistance and pathogenic traits. HGT events can be identified by comparing a large number of fully sequenced genomes across a species or genus, define the phylogenetic range of HGT, and find potential sources of new resistance genes. In-depth comparative phylogenomics can also identify subtle genome or plasmid structural changes or mutations associated with phenotypic changes. Comparative phylogenomics requires that accurately sequenced, complete and properly annotated genomes of the organism. Assembling closed genomes requires additional mate-pair reads or “long read” sequencing data to accompany short-read paired-end data. To bring down the cost and time required of producing assembled genomes and annotating genome features that inform drug resistance and pathogenicity, we are analyzing the performance for genome assembly of data from the Illumina NextSeq, which has faster throughput than the Illumina HiSeq (~1-2 days versus ~1 week), and shorter reads (150bp paired-end versus 300bp paired end) but higher capacity (150-400M reads per run versus ~5-15M) compared to the Illumina MiSeq. Bioinformatics improvements are also needed to make rapid, routine production of complete genomes a reality. Modern assemblers such as SPAdes 3.6.0 running on a standard Linux blade are capable in a few hours of converting mixes of reads from different library preps into high-quality assemblies with only a few gaps. Remaining breaks in scaffolds are generally due to repeats (e.g., rRNA genes) are addressed by our software for gap closure techniques, that avoid custom PCR or targeted sequencing. Our goal is to improve the understanding of emergence of pathogenesis using sequencing, comparative genomics, and machine learning analysis of ~1000 pathogen genomes. Machine learning algorithms will be used to digest the diverse features (change in virulence genes, recombination, horizontal gene transfer, patient diagnostics). Temporal data and evolutionary models can thus determine whether the origin of a particular isolate is likely to have been from the environment (could it have evolved from previous isolates). It can be useful for comparing differences in virulence along or across the tree. More intriguing, it can test whether there is a direction to virulence strength. This would open new avenues in the prediction of uncharacterized clinical bugs and multidrug resistance evolution and pathogen emergence.

Keywords: genomics, pathogens, genome assembly, superbugs

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Authors: Gabriel Chavira, Jorge Orozco, Salvador Nava, Eduardo Álvarez, Julio Rolón, Roberto Pichardo

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Ambient Intelligence promotes a shift in computing which involves fitting-out the environments with devices to support context-aware applications. One of main objectives is the reduction to a minimum of the user’s interactive effort, the diversity and quantity of devices with which people are surrounded with, in existing environments; increase the level of difficulty to achieve this goal. The mobile phones and their amazing global penetration, makes it an excellent device for delivering new services to the user, without requiring a learning effort. The environment will have to be able to perceive all of the interaction techniques. In this paper, we present the PICTAC model (Perceiving touch Interaction through TAgging Context), which similarly delivers service to members of a research group.

Keywords: ambient intelligence, tagging context, touch interaction, touching services

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Authors: Guofan Shao, Lina Tang, Hao Zhang

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The values of image classification accuracy are affected by class size distributions and classification schemes, making it difficult to compare the performance of classification algorithms across different remote sensing data sources and classification systems. Based on the term efficacy from medicine and pharmacology, we have developed the metrics of image classification efficacy at the map and class levels. The novelty of this approach is that a baseline classification is involved in computing image classification efficacies so that the effects of class statistics are reduced. Furthermore, the image classification efficacies are interpretable and comparable, and thus, strengthen the assessment of image data classification methods. We use real-world and hypothetical examples to explain the use of image classification efficacies. The metrics of image classification efficacy meet the critical need to rectify the strategy for the assessment of image classification performance as image classification methods are becoming more diversified.

Keywords: accuracy assessment, efficacy, image classification, machine learning, uncertainty

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Authors: Mark Salisbury

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The arrival of artificially intelligent avatars and the automation they bring is worrying many of us, not only for our livelihood but for the jobs that may be lost to our kids. We worry about what our place will be as human beings in this new economy where much of it will be conducted online in the metaverse – in a network of 3D virtual worlds – working with intelligent machines. The Future of Leadership was written to address these fears and show what our place will be – the right place – in this new economy of AI avatars, automation, and 3D virtual worlds. But to be successful in this new economy, our job will be to bring wisdom to our workplace and the marketplace. And we will use AI avatars and 3D virtual worlds to do it. However, this book is about more than AI and the avatars that we will work with in the metaverse. It’s about building Organizational intelligence (OI) -- the capability of an organization to comprehend and create knowledge relevant to its purpose; in other words, it is the intellectual capacity of the entire organization. To increase organizational intelligence requires a new kind of knowledge worker, a wisdom worker, that requires a new kind of leadership. This book begins your story for how to become a leader of wisdom workers and be successful in the emerging wisdom economy. After this presentation, conference participants will be able to do the following: Recognize the characteristics of the new generation of wisdom workers and how they differ from their predecessors. Recognize that new leadership methods and techniques are needed to lead this new generation of wisdom workers. Apply personal and professional values – personal integrity, belief in something larger than yourself, and keeping the best interest of others in mind – to improve your work performance and lead others. Exhibit an attitude of confidence, courage, and reciprocity of sharing knowledge to increase your productivity and influence others. Leverage artificial intelligence to accelerate your ability to learn, augment your decision-making, and influence others.Utilize new technologies to communicate with human colleagues and intelligent machines to develop better solutions more quickly.

Keywords: metaverse, generative artificial intelligence, automation, leadership, organizational intelligence, wisdom worker

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Authors: Narayana Murthy Akurathi, Vijaya Lakshmi Marella

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The literature is reviewed with regard to online Super critical fluid extraction (SFE) coupled directly with supercritical fluid chromatography (SFC) -mass spectrometry that have typically more sensitive than conventional LC-MS/MS and GC-MS/MS. It is becoming increasingly interesting to use on-line techniques that combine sample preparation, separation and detection in one analytical set up. This provides less human intervention, uses small amount of sample and organic solvent and yields enhanced analyte enrichment in a shorter time. The sample extraction is performed under light shielding and anaerobic conditions, preventing the degradation of thermo labile analytes. It may be able to analyze compounds over a wide polarity range as SFC generally uses carbon dioxide which was collected as a by-product of other chemical reactions or is collected from the atmosphere as it contributes no new chemicals to the environment. The diffusion of solutes in supercritical fluids is about ten times greater than that in liquids and about three times less than in gases which results in a decrease in resistance to mass transfer in the column and allows for fast high resolution separations. The drawback of SFC when using carbon dioxide as mobile phase is that the direct introduction of water samples poses a series of problems, water must therefore be eliminated before it reaches the analytical column. Hundreds of compounds analysed simultaneously by simple enclosing in an extraction vessel. This is mainly applicable for pharmaceutical industry where it can analyse fatty acids and phospholipids that have many analogues as their UV spectrum is very similar, trace additives in polymers, cleaning validation can be conducted by putting swab sample in an extraction vessel, analysing hundreds of pesticides with good resolution.

Keywords: super critical fluid extraction (SFE), super critical fluid chromatography (SFC), LCMS/MS, GCMS/MS

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Authors: Omar Mayyas

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Clinical decision-making (CDM) is essential to community health nursing (CHN) education. For this reason, nursing educators are responsible for developing these skills among nursing students because nursing students are exposed to highly critical conditions after graduation. However, due to limited exposure to real-world situations, many nursing students need help developing clinical decision-making skills in this area. Therefore, the impact of Virtual Simulation (VS) on community health nursing students' clinical decision-making in nursing education has to be investigated. This study aims to examine the difference in CDM ability among CHN students who received traditional education compared to those who received VS classes, to identify the factors that may influence CDM ability differences between CHN students who received a traditional education and VS classes, and to provide recommendations for educational programs that can enhance the CDM ability of CHN students and improve the quality of care provided in community settings. A mixed-method study will conduct. A randomized controlled trial will compare the CDM ability of CHN students who received 1hr traditional class with another group who received 1hr VS scenario about diabetic patient nursing care. Sixty-four students in each group will randomly select to be exposed to the intervention from undergraduate nursing students who completed the CHN course at York University. The participants will receive the same Clinical Decision Making in Nursing Scale (CDMNS) questionnaire. The study intervention will follow the Medical Research Council (MRC) approach. SPSS and content analysis will use for data analysis.

Keywords: clinical decision-making, virtual simulation, community health nursing students, community health nursing education

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Authors: Chonlakorn Wiboonsiriruk, Ekachai Phaisangittisagul, Chadchai Srisurangkul, Itsuo Kumazawa

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Public transportation is a crucial aspect of passenger transportation, with buses playing a vital role in the transportation service. Passenger counting is an essential tool for organizing and managing transportation services. However, manual counting is a tedious and time-consuming task, which is why computer vision algorithms are being utilized to make the process more efficient. In this study, different object detection algorithms combined with passenger tracking are investigated to compare passenger counting performance. The system employs the EfficientDet algorithm, which has demonstrated superior performance in terms of speed and accuracy. Our results show that the proposed system can accurately count passengers in varying conditions with an accuracy of 94%.

Keywords: computer vision, object detection, passenger counting, public transportation

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Authors: Stephanie Cheng, Benjamin Poh, Vivyan Tay, Sachin Mathur

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Aim: Diabetes mellitus (DM) is a worldwide pandemic affecting 500 million people. It is known to be associated with increased susceptibility to soft tissue infections (STI). Despite being a major public health burden, the literature relating the effects of DM and the presentation, severity and healing of STIs in general surgical patients remain limited. Methods: We conducted a retrospective review of all patients admitted with STI in a tertiary teaching hospital over a 12-month period. Patient demographics and surgical outcomes were collected and analyzed. Results: During the study period, 1059 patients were admitted for STIs, of which 936 (88%) required surgical intervention. Diabetic patients were presented with a higher body-mass index (BMI) (28 vs 26), larger abscess size (24 vs 14 cm²) and a longer length of stay (LOS)(4.4 days vs 2.9 days). They also underwent a higher proportion of wide debridement as well as application of negative pressure wound therapy (NPWT) (42% vs 35%). More diabetic patients underwent subsequent re-operation within the same sitting (8 vs 4). There were no differences in re-admission rates within 30 days nor subsequent abscess formation in those followed for 6 months. Conclusion: The incidence of STIs among DM patients represents a significant disease burden; surgeons should consider intensive patient counseling and partnering with primary care providers in order to help reduce the incidence of future STI admissions based on lifestyle modification and glucose control.

Keywords: general surgery, emergency general surgery, acute care surgery, soft tissue infections, diabetes mellitus

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Authors: Khaled Mohammed

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This paper presents novel face recognition and identification approaches for the real-time attendance management problem in large companies/factories and government institutions. The proposed uses the Minimum Ratio (MR) approach for employee identification. Capturing the authentic face variability from a sequence of video frames has been considered for the recognition of faces and resulted in system robustness against the variability of facial features. Experimental results indicated an improvement in the performance of the proposed system compared to the Previous approaches at a rate between 2% to 5%. In addition, it decreased the time two times if compared with the Previous techniques, such as Extreme Learning Machine (ELM) & Multi-Scale Structural Similarity index (MS-SSIM). Finally, it achieved an accuracy of 99%.

Keywords: attendance management system, face detection and recognition, live face recognition, minimum ratio

Procedia PDF Downloads 152

Authors: Karish Thavabalan, Aman Sutaria, Alistair Ovenell, Annabelle Parkhouse, Numan Baydemir, Theodore Lally

Abstract:

Background: Open discussions surrounding menopause are often associated with stigma, with many women feeling uncomfortable engaging in them with friends, colleagues, and healthcare professionals. Though the barriers regarding safe discussions of symptom management experienced by menopausal women are well documented, existing research offers little insight into whether these were shared by pre-menopausal women. This study aimed to explore the barriers regarding safe discussions about menopausal symptom management as perceived or experienced by pre-menopausal and menopausal women. Methods: This qualitative study was conducted over a 2-month period (March 2022 - April 2022) under the auspices of Imperial College Business School, London, UK. Snowball sampling was used to recruit both menopausal (age 45-70) and pre-menopausal participants (age <45) and sampling continued until data saturation was achieved. Sixteen semi-structured online interviews were conducted, and transcripts were thematically analyzed following Braun and Clarke’s six-step methodology. Results: A total of 7 higher themes regarding safe discussion of menopausal symptom management were identified by both pre-menopausal and menopausal women, including: “ineffective coping mechanisms,” “perceived onus to self-endure,” “lack of workplace support,” “poor knowledge of management approaches,” “poor healthcare infrastructure,” “poor support from friends and family,” “lack of knowledge and interest from a young age.” Conclusion: Identifying the barriers regarding safe discussion helped to highlight which areas require the most significant intervention. Alongside tackling the barriers, menopausal women face, ultimately, there is a pertinent need to address the lack of insight into menopause from a younger age and to encourage earlier discussions so as to not propagate the cycle of stigma.

Keywords: menopause, safe discussion, symptom management, stigma

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Authors: Aparna Datta, Mahabub Alam

Abstract:

Animal welfare is comparatively new area of research in Bangladesh and welfare concern for animal is increasing day by day. The study was conducted to investigate the animal welfare violations during treatment at different level of hospitals in Bangladesh and India. This study was conducted between January and May, 2017. The recorded data (N=180) were categorized into eight major types of violation like - delay in starting treatment, non-specific treatment, surgery without anesthesia, use of unsterilized needle, rough and painful handling, fearful approach, multiple pricking during injection and use of blunt needle. Categorized groups were analyzed according to different hospitals like Upazila Veterinary Hospitals, Bangladesh (UVHs), SAQ-Teaching Veterinary Hospital, Bangladesh (SAQTVH) and Veterinary College and Research Institute, India (VCRI). Among all hospitals, violation during treatment more frequently occurred in UVH. Among all violations, surgery without anesthesia was only found in UVH (80%) and it was belong to considerable number of cases (80%). In the view of other major violations like - non-specific treatment was 69% in UVHs, 13% in SAQTVH and 5% in VCRI. Use of unsterilized instruments during treatment was also higher in UVHs (65%) than SAQTVH (5%) and VCRI (1%). But delay in starting treatment varied insignificantly and it was 26-42% across the different levels of hospitals. Although multiple pricking during injection was found 30% cases in UVH, but statistical variations with other level of hospitals were unnoticed (p>0.05). The findings of this study will help to take necessary steps to control violation against animal welfare during treatment. A comprehensive study considering all levels of hospitals including field treatment is also recommended to find out the welfare violations during treatment.

Keywords: animal welfare, treatment, veterinary hospitals, violations

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Authors: Michal Kozubík, Svetlana Síthová

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Aim: Aim of this study is to introduce the method of instrumental enrichment to people who works in the helping professions, and show further possibilities of its realization with children from socially disadvantaged backgrounds into society. Methods: We focused on Feuerstein’s Instrumental Enrichment method, its theoretical grounds and practical implementation. We carried out questionnaires and directly observed children from the disadvantaged background in Partizánske district. Results: We outlined the issues of children from disadvantaged social environment and their opportunity of social integration using the method. The findings showed the utility of Feuerstein method. Conclusions: We conclude that Feuerstein methods are very suitable for children from socially disadvantaged background and importance of social workers and special educator co-operation.

Keywords: Feuerstein, inclusion, education, socially disadvantaged background

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Authors: Rania A. Aboalela, Javed I. Khan

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This research presents a learning assessment theory of Cognitive Skill in Concept Space (CS2) to measure the assessed knowledge in terms of cognitive skill levels of the concepts. The cognitive skill levels refer to levels such as if a student has acquired the state at the level of understanding, or applying, or analyzing, etc. The theory is comprised of three constructions: Graph paradigm of a semantic/ ontological scheme, the concept states of the theory and the assessment analytics which is the process to estimate the sets of concept state at a certain skill level. Concept state means if a student has already learned, or is ready to learn, or is not ready to learn a certain skill level. The experiment is conducted to prove the validation of the theory CS2.

Keywords: cognitive skill levels, concept states, concept space, knowledge assessment theory

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Authors: Sarah Barnard, John Arnold, Fehmidah Munir, Sara Bosley

Abstract:

Gender issues in higher education continue to represent a complex issue as institutions grapple with the role that organisations can play in combatting inequality. Schemes like Athena SWAN and the Aurora leadership programme in the UK context are attempting to tackle some of the issues around representation and the recognition of women in the sector. This paper is the first of its kind in reporting findings from a mixed-methods longitudinal study on both professional services and academic women in higher education in the UK. Online surveys have been completed by over 2,000 women in the sector. The qualitative elements include interviews with women and their mentors, and diaries with a select group of women. So far results have shown that contrary to the stereotype of women lacking leadership skills or having no desire to go into higher roles, women in the sector consistently assessed their leadership abilities positively, especially but not only regarding interpersonal interaction and facilitation. Over 80% of women agreed that they felt confident about putting themselves forward for positions of responsibility at work. However, qualitative data shows that confidence remains a salient term for how women talk about the challenges they have faced at work. This suggests that the work needed to challenge systemic gender issues requires action to be driven above the individual level. Overall, academics reported more negative experiences than professional services staff. Similarly BAME women’s responses are more negative. Therefore, the study offers some information on the differential experiences of women. In conclusion, women in higher education are undertaking considerable ‘below the radar’ leadership activities in what they perceive to be a somewhat inhospitable hostile workplace culture. The significant amount of effort expended in the sector is affecting slow, partial impacts on gender inequalities.

Keywords: gender, higher education, leadership, longitudinal research

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Authors: Abdelrhman Elagez, Rolla Monib

Abstract:

This empirical study investigates the observed risks associated with adopting Artificial Intelligence (AI) technologies in the construction industry and proposes potential mitigation strategies. While AI has transformed several industries, the construction industry is slowly adopting advanced technologies like AI, introducing new risks that lack critical analysis in the current literature. A comprehensive literature review identified a research gap, highlighting the lack of critical analysis of risks and the need for a framework to measure and mitigate the risks of AI implementation in the construction industry. Consequently, an online survey was conducted with 24 project managers and construction professionals, possessing experience ranging from 1 to 30 years (with an average of 6.38 years), to gather industry perspectives and concerns relating to AI integration. The survey results yielded several significant findings. Firstly, respondents exhibited a moderate level of familiarity (66.67%) with AI technologies, while the industry's readiness for AI deployment and current usage rates remained low at 2.72 out of 5. Secondly, the top-ranked barriers to AI adoption were identified as lack of awareness, insufficient knowledge and skills, data quality concerns, high implementation costs, absence of prior case studies, and the uncertainty of outcomes. Thirdly, the most significant risks associated with AI use in construction were perceived to be a lack of human control (decision-making), accountability, algorithm bias, data security/privacy, and lack of legislation and regulations. Additionally, the participants acknowledged the value of factors such as education, training, organizational support, and communication in facilitating AI integration within the industry. These findings emphasize the necessity for tailored risk assessment frameworks, guidelines, and governance principles to address the identified risks and promote the responsible adoption of AI technologies in the construction sector.

Keywords: risk management, construction, artificial intelligence, technology

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