Search results for: residency training

Authors: Deepa Vasudevan, Thomas Northrup, Angela Stotts, Michelle Klawans

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To date, we have conducted three studies on this subject. The research done to date is through three studies. The initial study was to document that modified criteria independently identified higher numbers of overweight/obese South Asian Indians. The second study was to document physician knowledge of appropriate diagnosis of obesity among South Asian Indians. The final study was an intervention to evaluate the efficacy of a training module on improving physician diagnosis and counseling of overweight/obese Asian patients.

Keywords: South Asian Indians, obesity, physicians, BMI and waist circumference

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Authors: Mitat Uysal, Aynur Uysal

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Artificial Intelligence (AI) is fundamentally driven by mathematics, with many of its core algorithms rooted in mathematical principles such as linear algebra, probability theory, calculus, and optimization techniques. This paper explores the deep connection between AI and mathematics, highlighting the role of mathematical concepts in key AI techniques like machine learning, neural networks, and optimization. To demonstrate this connection, a case study involving the implementation of a neural network using Python is presented. This practical example illustrates the essential role that mathematics plays in training a model and solving real-world problems.

Keywords: AI, mathematics, machine learning, optimization techniques, image processing

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Authors: M. Naresh, Pratik Chaturvedi, Srishti Yadav, Varun Dutt, K. V. Uday

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Changes in the Earth’s climate are likely to increase natural hazards such as drought, floods, earthquakes, landslides, etc. The present study focusing on to early warning systems (EWS) of landslides, major issues in Himalayan region without prominence to deforestation, encroachments and un-engineered cutting of slopes and reforming for infrastructural purposes. EWS can be depicted by conducting a series of flume tests using micro-electro mechanical systems sensors data after reaching threshold values under controlled laboratory conditions. Based on the threshold value database, an alert will be sent via SMS.

Keywords: slope-instability, flume test, sensors, early warning system

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Authors: A. Giani

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The current pedagogical culture recognizes an educational scope within the sports practices. It is widely accepted, in the pedagogical culture, that thanks to the acquisition and development of motor skills, it is also possible to exercise abilities that concern the way of facing and managing the difficulties of everyday life. Sport is a peculiar educational environment: the children have the opportunity to discover the possibilities of their body, to correlate with their peers, and to learn how to manage the rules and the relationship with authorities, such as coaches. Educational aspects of the sport concern both non-formal and formal educational environments. Coaches play a critical role in an agonistic sphere: exactly like the competencies developed by the children, coaches have to work on their skills to properly set up the educational scene. Facing these new educational tasks - which are not new per se, but new because they are brought back to awareness - a few questions arise: does the coach have adequate preparation? Is the training of the coach in this specific area appropriate? This contribution aims to explore the issue in depth by focusing on the reality of the Football School. Starting from a possible sense of pedagogical inadequacy detected during a series of meetings with several football clubs in Piedmont (Italy), there have been highlighted some important educational needs within the professional training of sports coaches. It is indeed necessary for the coach to know the processes underlying the educational relationship in order to better understand the centrality of the assessment during the educational intervention and to be able to manage the asymmetry in the coach-athlete relationship. In order to provide a response to these pedagogical needs, a formative plan has been designed to allow both an in-depth study of educational issues and a correct self-evaluation of certain pedagogical skills’ control levels, led by the coach. This plan has been based on particular practices, the Educational Practices of Pre-test (EPP), a specific version of community practices designed for the extracurricular activities. The above-mentioned practices realized through the use of texts meant as pre-tests, promoted a reflection within the group of coaches: they set up real and plausible sports experiences - in particular football, triggering a reflection about the relationship’s object, spaces, and methods. The characteristic aspect of pre-tests is that it is impossible to anticipate the reflection as it is necessarily connected to the personal experience and sensitivity, requiring a strong interest and involvement by participants: situations must be considered by the coaches as possible settings in which they could be found on the field.

Keywords: relational needs, values, responsibility, self-evaluation

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Authors: Merih Welay Welesilassie

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This research investigated the factors influencing digital inclusion for young students with disabilities in Ethiopian schools. In this context, socio-economic, infrastructural, and cultural challenges amplify educational disparities. In the era of digital technology's pivotal role in education, it is crucial to ensure equitable access for students with disabilities. Nevertheless, obstacles like inadequate infrastructure, insufficient teacher training, and economic constraints impede the incorporation of digital tools in educational environments, especially for marginalised groups. This study employed an explanatory sequential mixed-methods approach involving data collection through a survey administered to 300 students. Subsequently, in-depth interviews were conducted with 30 participants to provide comprehensive insights into their experiences. The quantitative analysis uncovered that students with disabilities have limited support for digital readiness, find digital technologies less accessible, and perceive digital tools as less easy to use. The study revealed that economic barriers, such as the high cost of devices and limited internet access, prevent students from fully utilising digital resources. Furthermore, infrastructural challenges, such as unreliable electricity and poor internet connectivity, exacerbate the issue. The qualitative data provided a more profound understanding by emphasising social and attitudinal obstacles, including a lack of empathy from both peers and educators, exclusion from participatory digital tasks, and enduring negative stereotypes regarding disabilities. The research highlights the importance of implementing interventions to enhance digital accessibility for students with disabilities. Essential suggestions encompass refining teacher training programs to effectively facilitate inclusive education, improving digital infrastructure, and offering financial assistance to procure digital tools. Furthermore, implementing policy reforms and public awareness campaigns is crucial to cultivate a cultural shift and nurture a more inclusive societal atmosphere. This study yields valuable perspectives on the digital inclusion scenario in Ethiopia, laying the groundwork for prospective research endeavours to narrow the digital gap for students with disabilities.

Keywords: digital inclussion, students with disabilities, ethiopian education, barries and access

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Authors: Mohammad Arabi

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Business Process Reengineering (BPR) is a systematic approach to fundamentally redesign organizational processes to achieve significant improvements in organizational performance. At Fars Regional Electric Company, implementing BPR is deemed essential to increase productivity, reduce costs, and improve service quality. This article examines how BPR can help enhance the performance of Fars Regional Electric Company. The objective of this research is to evaluate and analyze the advancements in process modeling and reengineering at Fars Regional Electric Company and to provide solutions for improving the productivity and efficiency of organizational processes. This study aims to demonstrate how BPR can be used to improve organizational processes and enhance the overall performance of the company. This research employs both qualitative and quantitative research methods and includes interviews with senior managers and experts at Fars Regional Electric Company. The analytical tools include process modeling software such as Bizagi and ARIS, and statistical analysis software such as SPSS and Minitab. Data analysis was conducted using advanced statistical methods. The results indicate that the use of BPR techniques can lead to a significant reduction in process execution time and overall improvement in quality. Implementing BPR at Fars Regional Electric Company has led to increased productivity, reduced costs, and improved overall performance of the company. This study shows that with proper implementation of BPR and the use of modeling tools, the company can achieve significant improvements in its processes. Recommendations: (1) Continuous Training for Staff: Invest in continuous training of staff to enhance their skills and knowledge in BPR. (2) Use of Advanced Technologies: Utilize modeling and analysis software to improve processes. (3) Implementation of Effective Management Systems: Employ knowledge and information management systems to enhance organizational performance. (4) Continuous Monitoring and Review of Processes: Regularly review and revise processes to ensure ongoing improvements. This article highlights the importance of improving organizational processes at Fars Regional Electric Company and recommends that managers and decision-makers at the company seriously consider reengineering processes and utilizing modeling technologies to achieve developmental goals and continuous improvement.

Keywords: business process reengineering, electric company, Fars province, process modeling advancements

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Authors: Guler Duru Asiret, Cemile Kutmec Yilmaz, Gulcan Bagcivan, Tugce Turten Kaymaz

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This descriptive study was conducted to determine the relationship between caregiver burden and life satisfaction who give home care to elderly individuals. The sample was recruited from the internal medicine unit and palliative unit of a state hospital located in Turkey on June 2016-2017. The study sample consisted of 231 primary caregiver family member, who met the eligibility criteria and agreed to participate in the study. The inclusion criteria were as follows: inpatient’s caregiver, primary caregiver for at least 3 months, at least 18 years of age, no communication problem or mental disorder. Data were gathered using an Information Form prepared by the researchers based on previous literature, the Zarit Burden Interview (ZBI), and the Satisfaction with Life Scale (SWLS). The data were analyzed using IBM SPSS Statistics software version 20.0 (SPSS, Chicago, IL). The descriptive characteristics of the participant were analyzed using number, percentage, mean and standard deviation. The suitability of normal distribution of scale scores was analyzed using Kolmogorov-Smirnov and Shapiro-Wilk test. Relationships between scales were analyzed using Spearman’s rank-correlation coefficient. P values less than 0.05 were considered to be significant. The average age of the caregivers was 50.11±13.46 (mean±SD) years. Of the caregivers, 76.2% were women, 45% were primary school graduates, 89.2% were married, 38.1% were the daughters of their patients. Among these, 52.4% evaluated their income level to be good. Of them, 53.6% had been giving care less than 2 years. The patients’ average age was 77.1±8.0 years. Of the patients, 55.8% were women, 56.3% were illeterate, 70.6% were married, and 97.4% had at least one chronic disease. The mean Zarit Burden Interview score was 35.4±1.5 and the Satisfaction with Life Scale score was 20.6±6.8. A negative relationship was found between the patients’ score average on the ZBI, and on the SWLS (r= -0.438, p=0.000). The present study determined that the caregivers have a moderate caregiver burden and the life satisfaction. And the life satisfaction of caregivers decreased as their caregiver burden increase. In line with the results obtained from the research, it is recommended that to increase the effectiveness of discharge training, to arrange training and counseling programs for caregivers to cope with the problems they experienced, to monitor the caregivers at regular intervals and to provide necessary institutional support.

Keywords: caregiver burden, family caregivers, nurses, satisfaction

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Authors: Arpit Rai

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In this work, we propose PatchMix, a semi-supervised method for pre-training visual representations. PatchMix mixes patches of two images and then solves an auxiliary task of predicting the label of each patch in the mixed image. Our experiments on the CIFAR-10, 100 and the SVHN dataset show that the representations learned by this method encodes useful information for transfer to new tasks and outperform the baseline Residual Network encoders by on CIFAR 10 by 12% on ResNet 101 and 2% on ResNet-56, by 4% on CIFAR-100 on ResNet101 and by 6% on SVHN dataset on the ResNet-101 baseline model.

Keywords: self-supervised learning, representation learning, computer vision, generalization

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Authors: Elizabeth B. Katana, Brenda N. Simbwa, Josephine Namayanja, Bob O. Amodan, Edirisa J. Nsubuga, Eva A. O. Laker

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Background: Understanding stakeholders’ perceptions towards the implementation of a mass vaccination campaign is important to ensure the design of better strategies to address challenges. We explored stakeholders’ perceptions of the implementation of a nationwide door-to-door mass vaccination campaign for the oral polio vaccine (nOPV2) in Uganda for the two rounds that occurred in January and November 2022. Methods: A qualitative study was conducted among stakeholders who participated in the campaign implementation from 8 districts in Uganda using random sampling. We conducted 46 In-depth interviews lasting 30 – 40 minutes with 6 national/central supervisors, 12 district, 14 sub-county, and 14 parish-level supervisors. Stakeholders were asked about their experiences in the campaign implementation, including challenges faced and their opinions of the campaign impact and use of the door-to-door strategy. Data were analyzed thematically in line with the major campaign activities. Results: Most of the stakeholders were primarily concerned about poor planning, inadequate training of vaccination teams, community resistance including schools, challenges with recruitment and teaming of vaccinators, poor and delayed payments, lack of logistics and motivation for vaccination teams, the timing of the activities and implementing amidst COVID-19 and Ebola. The stakeholders believed that the first round was not well planned and implemented, while the second round was leveraged in their previous experiences. On the other hand, some positive experiences were noted with regard to communication, advocacy and mobilization, vaccine delivery and distribution, district readiness assessments, and cold chain management. Conclusion: This study identified many challenges that were faced in the implementation of the door-to-door mass campaign for nOPV2 in Uganda. This study identified that more needs to be done to improve door-to-door mass campaigns with a focus on motivating the implementers. These findings highlight the need for conducting performance reviews, improved planning, especially routine updates and verification of target populations and training in microplanning, and adequate mapping of community resistance to inform the implementation of future mass campaigns.

Keywords: mass polio vaccination campaigns, door-to-door strategy, stakeholders' perceptions, implementation challenges

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Authors: Cláudia Patrocínio, Beatriz Fernandes, Ana Filipa Pires

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Background: Mirror therapy (MT) is used to improve motor function after stroke. During MT, a mirror is placed between the two upper limbs (UL), thus reflecting movements of the non- affected side as if it were the affected side. Objectives: The aim of this review is to analyze the evidence on the effec.tiveness of MT in the recovery of UL function in population with post chronic stroke. Methods: The literature search was carried out in PubMed, ISI Web of Science, and PEDro database. Inclusion criteria: a) studies that include individuals diagnosed with stroke for at least 6 months; b) intervention with MT in UL or comparing it with other interventions; c) articles published until 2023; d) articles published in English or Portuguese; e) randomized controlled studies. Exclusion criteria: a) animal studies; b) studies that do not provide a detailed description of the intervention; c) Studies using central electrical stimulation. The methodological quality of the included studies was assessed using the Physiotherapy Evidence Database (PEDro) scale. Studies with < 4 on PEDro scale were excluded. Eighteen studies met all the inclusion criteria. Main results and conclusions: The quality of the studies varies between 5 and 8. One article compared muscular strength training (MST) with MT vs without MT and four articles compared the use of MT vs conventional therapy (CT), one study compared extracorporeal shock therapy (EST) with and without MT and another study compared functional electrical stimulation (FES), MT and biofeedback, three studies compared MT with Mesh Glove (MG) or Sham Therapy, five articles compared performing bimanual exercises with and without MT and three studies compared MT with virtual reality (VR) or robot training (RT). The assessment of changes in function and structure (International Classification of Functioning, Disability and Health parameter) was carried out, in each article, mainly using the Fugl Meyer Assessment-Upper Limb scale, activity and participation (International Classification of Functioning, Disability and Health parameter) were evaluated using different scales, in each study. The positive results were seen in these parameters, globally. Results suggest that MT is more effective than other therapies in motor recovery and function of the affected UL, than these techniques alone, although the results have been modest in most of the included studies. There is also a more significant improvement in the distal movements of the affected hand than in the rest of the UL.

Keywords: physical therapy, mirror therapy, chronic stroke, upper limb, hemiplegia

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Authors: Olivera Lupescu, Taina Elena Avramescu, Mihail Nagea, Alexandru Dimitriu

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Acetabular fractures represent a real challenge due to their impact upon the long term function of the hip joint, and due to the risk of intra- and peri-operative complications especially that they affect young, active people. That is why treating these fractures require certain skills which must be exercised, regarding the pre-operative planning, as well as the execution of surgery.The authors retrospectively analyse 38 cases with acetabular fractures operated using the posterior approach in our hospital between 01.01.2013- 01.01.2015 for which complete medical records ensure a follow-up of 24 months, in order to establish the main causes of potential errors and to underline the methods for preventing them. This target is included in the Erasmus + project ‘Collaborative learning for enhancing practical skills for patient-focused interventions in gait rehabilitation after orthopedic surgery COR-skills’. This paper analyses the pitfalls revealed by these cases, as well as the measures necessary to enhance the practical skills of the surgeons who perform acetabular surgery. Pre-op planning matched the intra and post-operative outcome in 88% of the analyzed points, from 72% at the beginning to 94% in the last case, meaning that experience is very important in treating this injury. The main problems detected for the posterior approach were: nervous complications - 3 cases, 1 of them a complete paralysis of the sciatic nerve, which recovered 6 months after surgery, and in other 2 cases intra-articular position of the screws was demonstrated by post-operative CT scans, so secondary screw removal was necessary in these cases. We analysed this incident, too, due to lack of information about the relationship between the screws and the joint secondary to this approach. Septic complications appeared in 3 cases, 2 superficial and 1 profound (requiring implant removal). The most important problems were the reduction of the fractures and the positioning of the screws so as not to interfere with the the articular space. In posterior acetabular fractures, pre-op complex planning is important in order to achieve maximum treatment efficacy with minimum of risk; an optimal training of the surgeons insisting on the main points of potential mistakes ensure the success of the procedure, as well as a favorable outcome for the patient.

Keywords: acetabular fractures, articular congruency, surgical skills, vocational training

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Authors: Reza Shakoori

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This paper presents a structural correction of Arabic and Persian strokes using manipulation of their chain codes in order to improve the rate and performance of Persian and Arabic handwritten word recognition systems. It collects pure and effective features to represent a character with one consolidated feature vector and reduces variations in order to decrease the number of training samples and increase the chance of successful classification. Our results also show that how the proposed approaches can simplify classification and consequently recognition by reducing variations and possible noises on the chain code by keeping orientation of characters and their backbone structures.

Keywords: Arabic, chain code normalization, OCR systems, image processing

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Authors: Vanessa Kummer, Maik Meusel

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Real estate demand estimates are typically derived from transaction data. However, in regions with excess demand, transactions are driven by supply and therefore do not indicate what people are actually looking for. To estimate the demand for housing in Switzerland, search subscriptions from all important Swiss real estate platforms are used. These data do, however, suffer from missing information—for example, many users do not specify how many rooms they would like or what price they would be willing to pay. In economic analyses, it is often the case that only complete data is used. Usually, however, the proportion of complete data is rather small which leads to most information being neglected. Also, the data might have a strong distortion if it is complete. In addition, the reason that data is missing might itself also contain information, which is however ignored with that approach. An interesting issue is, therefore, if for economic analyses such as the one at hand, there is an added value by using the whole data set with the imputed missing values compared to using the usually small percentage of complete data (baseline). Also, it is interesting to see how different algorithms affect that result. The imputation of the missing data is done using unsupervised learning. Out of the numerous unsupervised learning approaches, the most common ones, such as clustering, principal component analysis, or neural networks techniques are applied. By training the model iteratively on the imputed data and, thereby, including the information of all data into the model, the distortion of the first training set—the complete data—vanishes. In a next step, the performances of the algorithms are measured. This is done by randomly creating missing values in subsets of the data, estimating those values with the relevant algorithms and several parameter combinations, and comparing the estimates to the actual data. After having found the optimal parameter set for each algorithm, the missing values are being imputed. Using the resulting data sets, the next step is to estimate the willingness to pay for real estate. This is done by fitting price distributions for real estate properties with certain characteristics, such as the region or the number of rooms. Based on these distributions, survival functions are computed to obtain the functional relationship between characteristics and selling probabilities. Comparing the survival functions shows that estimates which are based on imputed data sets do not differ significantly from each other; however, the demand estimate that is derived from the baseline data does. This indicates that the baseline data set does not include all available information and is therefore not representative for the entire sample. Also, demand estimates derived from the whole data set are much more accurate than the baseline estimation. Thus, in order to obtain optimal results, it is important to make use of all available data, even though it involves additional procedures such as data imputation.

Keywords: demand estimate, missing-data imputation, real estate, unsupervised learning

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Authors: Mareli Fischer, Kevin G. F. Thomas

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Although Attention-Deficit/Hyperactivity Disorder (ADHD) is one of the most prevalent childhood neurobehavioural disorders, little empirical research has been published on its clinical presentation in Africa, and, globally, few studies evaluate ADHD intervention programs that emphasize parent training. Hence, Stage 1 of this research programme aimed to describe the functional impairment of South African children with ADHD, and also sought to investigate the influence of sociodemographic variables (e.g., sex, age, socioeconomic status, family environment) and clinical variables (e.g., ADHD subtype and comorbidity) on the degree of that impairment. We used the Mini International Neuropsychiatric Interview for Children and Adolescents as a diagnostic tool, and the Child Behavior Checklist, the Strengths and Difficulties Questionnaire, and the Impairment Rating Scale as measures of functional impairment. Results from this stage of the research indicated that South African children and adolescents who meet diagnostic criteria for ADHD experience most functional impairment in the school domain, as well as in the area of social functioning. None of the measured sociodemographic variables had a significant detrimental or protective effect on how ADHD symptoms impacted on functioning. In terms of comorbidity, the presence of Major Depressive Disorder, Conduct Disorder, and Oppositional Defiant Disorder were all associated with significantly impaired overall functioning. Stage 2 of the research programme aimed to design, implement, and evaluate a child-specific intervention that targeted the primary areas of impairment identified in Stage 1. Existing literature suggests that a positive parent-training programme, in the group format, is one of the best options for cost-effective and successful ADHD intervention. Hence, the intervention took that form. Parents were taught basic behaviour analysis concepts within a supportive group context. Evaluation of the intervention’s efficacy used many of the same measures as in Stage 1, but also featured semi-structured interviews with participants and naturalistic observation of parent-child interaction. We will discuss preliminary results of that evaluation. Studying functional impairment and designing intervention plans in this way will pave the way for evidence-based treatment plans for children and adolescents diagnosed with ADHD.

Keywords: attention deficit/hyperactivity disorder, children, intervention, parenting groups

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Authors: Qian Wang, Haibo Gu

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To drive practical interpretations and applications of various policies in building the transnational education joint-ventures, middle managers learn to navigate through uncertainties and ambiguities. However, the current literature views very little about those middle managers’ experiences, perceptions, and practices. This paper takes the empirical approach and aims to uncover the middle managers’ experiences by conducting interviews, campus visits, and document analysis. Following the qualitative research method approach, the researchers gathered information from a mixture of fourteen foreign and Chinese managers. Their perceptions of the China-foreign cooperation in higher education and their perceived roles have offered important, valuable insights to this group of people’s attitudes and management performances. The diverse cultural and demographic backgrounds contributed to the significance of the study. There are four key findings. One, middle managers’ immediate micro-contexts and individual attitudes are the top two influential factors in managers’ performances. Two, the foreign middle managers showed a stronger sense of self-identity in risk-taking. Three, the Chinese middle managers preferred to see difficulties as part of their assigned responsibilities. Four, middle managers in independent universities demonstrated a stronger sense of belonging and fewer frustrations than middle managers in secondary institutes. The researchers propose that training for managers in a transnational educational setting should consider these discoveries when select fitting topics and content. In particular, middle managers should be better prepared to anticipate their everyday jobs in the micro-environment; hence, information concerning sponsor organizations’ working culture is as essential as knowing the national and local regulations, and socio-culture. Different case studies can help the managers to recognize and celebrate the diversity in transnational education. Situational stories can help them to become aware of the diverse and wide range of work contexts so that they will not feel to be left alone when facing challenges without relevant previous experience or training. Though this research is a case study based in the Chinese transnational higher education setting, the implications could be relevant and comparable to other transnational higher education situations and help to continue expanding the potential applications in this field.

Keywords: educational management, middle manager performance, transnational higher education

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Authors: YangHui

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Foreign teachers play an important role in the process of internationalization of higher education in China. Based on the method of literature analysis, firstly study the contents about the mechanism of the foreign teachers’ administration in our country, then secondly analyze the main barriers of the foreign teacher’s administration mechanism. Finally, it is suggested that the international exchange department in universities should constantly improve the employment mechanism, training mechanism, appraisal mechanism and incentive mechanism to promote the internationalization of higher education.

Keywords: internationalization of higher education, mechanism, administration of foreign teachers, colleges and universities, China

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Authors: Zeta Dooly, Aidan Duane

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The collaborative European funded research and development landscape provides prime environmental conditions for multi-disciplinary teams to learn and enhance their knowledge beyond the capability of training and learning within their own organisation cocoons. Whilst the emergence of the academic entrepreneur has changed the focus of educational institutions to that of quasi-businesses, the training and professional development of lecturers and academic staff are often not formalised to the same level as industry. This research focuses on industry and academic collaborative research funded by the European Commission. The impact of research is scalable if an optimum research network is created and managed effectively. This paper investigates network embeddedness, the nature of relationships, links, and nodes within a research network, and the enhancement of the network’s knowledge. The contribution of this paper extends our understanding of establishing and maintaining effective collaborative research networks. The effects of network embeddedness are recognized in the literature as pertinent to innovation and the economy. Network theory literature claims that networks are essential to innovative clusters such as Silicon valley and innovation in high tech industries. This research provides evidence to support the impact collaborative research has on the disparate individuals toward their innovative contributions to their organisations and their own professional development. This study adopts a qualitative approach and uncovers some of the challenges of multi-disciplinary research through case study insights. The contribution of this paper recommends the establishment of scaffolding to accommodate cooperation in research networks, role appointment, and addressing contextual complexities early to avoid problem cultivation. Furthermore, it suggests recommendations in relation to network formation, intra-network challenges in relation to open data, competition, friendships, and competency enhancement. The network capability is enhanced by the adoption of the relevant theories; network theory, open innovation, and social exchange, with the understanding that the network structure has an impact on innovation and social exchange in research networks. The research concludes that there is an opportunity to deepen our understanding of the impact of network reuse and network hoping that provides scaffolding for the network members to enhance and build upon their knowledge using a progressive approach.

Keywords: research networks, competency building, network theory, case study

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Authors: Ahmad Alwosheel, Ahmed Alqaraawi

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This work proposes an unsupervised text-independent probabilistic approach to recognize Quran reciter voice. It is an accurate approach that works on real time applications. This approach does not require a prior information about reciter models. It has two phases, where in the training phase the reciters' acoustical features are modeled using Gaussian Mixture Models, while in the testing phase, unlabeled reciter's acoustical features are examined among GMM models. Using this approach, a high accuracy results are achieved with efficient computation time process.

Keywords: Quran, speaker recognition, reciter recognition, Gaussian Mixture Model

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Authors: Jonathan Gong

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Purpose AI-driven solutions are at the forefront of many pathology and medical imaging methods. Using algorithms designed to better the experience of medical professionals within their respective fields, the efficiency and accuracy of diagnosis can improve. In particular, X-rays are a fast and relatively inexpensive test that can diagnose diseases. In recent years, X-rays have not been widely used to detect and diagnose COVID-19. The under use of Xrays is mainly due to the low diagnostic accuracy and confounding with pneumonia, another respiratory disease. However, research in this field has expressed a possibility that artificial neural networks can successfully diagnose COVID-19 with high accuracy. Models and Data The dataset used is the COVID-19 Radiography Database. This dataset includes images and masks of chest X-rays under the labels of COVID-19, normal, and pneumonia. The classification model developed uses an autoencoder and a pre-trained convolutional neural network (DenseNet201) to provide transfer learning to the model. The model then uses a deep neural network to finalize the feature extraction and predict the diagnosis for the input image. This model was trained on 4035 images and validated on 807 separate images from the ones used for training. The images used to train the classification model include an important feature: the pictures are cropped beforehand to eliminate distractions when training the model. The image segmentation model uses an improved U-Net architecture. This model is used to extract the lung mask from the chest X-ray image. The model is trained on 8577 images and validated on a validation split of 20%. These models are calculated using the external dataset for validation. The models’ accuracy, precision, recall, f1-score, IOU, and loss are calculated. Results The classification model achieved an accuracy of 97.65% and a loss of 0.1234 when differentiating COVID19-infected, pneumonia-infected, and normal lung X-rays. The segmentation model achieved an accuracy of 97.31% and an IOU of 0.928. Conclusion The models proposed can detect COVID-19, pneumonia, and normal lungs with high accuracy and derive the lung mask from a chest X-ray with similarly high accuracy. The hope is for these models to elevate the experience of medical professionals and provide insight into the future of the methods used.

Keywords: artificial intelligence, convolutional neural networks, deep learning, image processing, machine learning

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Authors: Maxence Queyrel, Edi Prifti, Alexandre Templier, Jean-Daniel Zucker

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Analysis of the human microbiome using metagenomic sequencing data has demonstrated high ability in discriminating various human diseases. Raw metagenomic sequencing data require multiple complex and computationally heavy bioinformatics steps prior to data analysis. Such data contain millions of short sequences read from the fragmented DNA sequences and stored as fastq files. Conventional processing pipelines consist in multiple steps including quality control, filtering, alignment of sequences against genomic catalogs (genes, species, taxonomic levels, functional pathways, etc.). These pipelines are complex to use, time consuming and rely on a large number of parameters that often provide variability and impact the estimation of the microbiome elements. Training Deep Neural Networks directly from raw sequencing data is a promising approach to bypass some of the challenges associated with mainstream bioinformatics pipelines. Most of these methods use the concept of word and sentence embeddings that create a meaningful and numerical representation of DNA sequences, while extracting features and reducing the dimensionality of the data. In this paper we present an end-to-end approach that classifies patients into disease groups directly from raw metagenomic reads: metagenome2vec. This approach is composed of four steps (i) generating a vocabulary of k-mers and learning their numerical embeddings; (ii) learning DNA sequence (read) embeddings; (iii) identifying the genome from which the sequence is most likely to come and (iv) training a multiple instance learning classifier which predicts the phenotype based on the vector representation of the raw data. An attention mechanism is applied in the network so that the model can be interpreted, assigning a weight to the influence of the prediction for each genome. Using two public real-life data-sets as well a simulated one, we demonstrated that this original approach reaches high performance, comparable with the state-of-the-art methods applied directly on processed data though mainstream bioinformatics workflows. These results are encouraging for this proof of concept work. We believe that with further dedication, the DNN models have the potential to surpass mainstream bioinformatics workflows in disease classification tasks.

Keywords: deep learning, disease prediction, end-to-end machine learning, metagenomics, multiple instance learning, precision medicine

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Authors: Ruchika Goyal, Ashwani Kumar, Sandeep Jain

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The experimental data for anticancer potential of curcumin nanoparticles was calculated by means of eclectic data. The optimal descriptors were examined using Monte Carlo method based CORAL SEA software. The statistical quality of the model is following: n = 14, R² = 0.6809, Q² = 0.5943, s = 0.175, MAE = 0.114, F = 26 (sub-training set), n =5, R²= 0.9529, Q² = 0.7982, s = 0.086, MAE = 0.068, F = 61, Av Rm² = 0.7601, ∆R²m = 0.0840, k = 0.9856 and kk = 1.0146 (test set) and n = 5, R² = 0.6075 (validation set). This data can be used to build predictive QSAR models for anticancer activity.

Keywords: anticancer potential, curcumin, model, nanoparticles, optimal descriptors, QSAR

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Authors: Esha Banerjee, Atul Kumar Ojha, Girish Nath Jha

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This study aims to develop an intonation labeled database for Hindi. Although no single standard for prosody labeling exists in Hindi, researchers in the past have employed perceptual and statistical methods in literature to draw inferences about the behavior of prosody patterns in Hindi. Based on such existing research and largely agreed upon intonational theories in Hindi, this study attempts to develop a manually annotated prosodic corpus of Hindi speech data, which can be used for training speech models for natural-sounding speech in the future. 100 sentences ( 500 words) each for declarative and interrogative types have been labeled using Praat.

Keywords: speech dataset, Hindi, intonation, labeled corpus

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Authors: Sophia El Ouazzani, Amer M. Burhan, Mary Wickenden

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Background: Despite improvements in recent years, the Moroccan mental healthcare system still face disparity between available resources and the current population’sneeds. The societal stigma, and limited economic, political, and human resources are all factors in shaping the psychiatric system, exacerbating the discontinuity of services for users after discharged from the hospital. As a result, limited opportunities for social inclusion and meaningful community engagement undermines human rights and recovery potential for people with mental health problems, especially those with psychiatric disabilities from serious mental illness (SMI). Recovery-oriented practice, such as mental health rehabilitation, addresses the complex needs of patients with SMI and support their community inclusion. The cultural acceptability of recovery-oriented practice is an important notion to consider for a successful implementation. Exploring the extent to which recovery-oriented practices are used in Morocco is a necessary first step to assess the cultural relevance of such a practice model. Aims: This study aims to explore understanding and knowledge, perception, and perspective about core concepts in mental health rehabilitation, including psychiatric disability, recovery, and engagement in meaningful occupations for people with SMI in Morocco. Methods: A pilot qualitative study was undertaken. Data was collected via semi-structured interviews and focusgroup discussions with healthcare professional students. Questions were organised around the following themes: 1) students’ perceptions, understanding, and expectations around concepts such as SMI, mental health disability, and recovery, and 2) changes in their views and expectations after starting their professional training. Further analysis of students’ perspectives on the concept of ‘meaningful occupation’ and how is this viewed within the context of the research questions was done. The data was extracted using an inductive thematic analysis approach. This is a pilot stage of a doctoral project, further data will be collected and analysed until saturation is reached. Results: A total of eight students were included in this study which included occupational therapy and mental health nursing students receiving training in Morocco. The following themes emerged as influencing students’ perceptions and views around the main concepts: 1) Stigma and discrimination, 2) Fatalism and low expectations, 3) Gendered perceptions, 4) Religious causation, 5) Family involvement, 6) Professional background, 7) Inaccessibility of services and treatment. Discussion/Contribution: Preliminary analysis of the data suggests that students’ perceptions changed after gaining more clinical experiences and being exposed to people with psychiatric disabilities. Prior to their training, stigma shaped greatly how they viewed people with SMI. The fear, misunderstanding, and shame around SMI and their functional capacities may contribute to people with SMI being stigmatizedand marginalised from their family and their community. Religious causations associated to SMIsare understood as further deepening the social stigma around psychiatric disability. Perceptions are influenced by gender, with women being doubly discriminated against in relation to recovery opportunities. Therapeutic pessimism seems to persist amongst students and within the mental healthcare system in general and regarding the recovery potential and opportunities for people with SMI. The limited resources, fatalism, and stigma all contribute to the low expectations for recovery and community inclusion. Implications and future directions will be discussed.

Keywords: disability, mental health rehabilitation, recovery, serious mental illness, transcultural psychiatry

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Authors: Ashraf Ibrahim Awad

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It is important to formulate suitable learning environments ca-pable to be customized according to value perceptions of the university. In this paper, light is shed on the concepts of integration between knowledge management (KM), and e-learning (EL) in the higher education sector of the economy in Abu Dhabi Emirate, United Arab Emirates (UAE). A discussion on and how KM and EL can be integrated and leveraged for effective education and training is presented. The results are derived from the literature and interviews with 16 of the academics in eight universities in the Emirate. The conclusion is that KM and EL have much to offer each other, but this is not yet reflected at the implementation level, and their boundaries are not always clear. Interviews have shown that both concepts perceived to be closely related and, responsibilities for these initiatives are practiced by different departments or units.

Keywords: knowledge management, e-learning, learning integration, universities, UAE

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Authors: Mustapha Majid

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Gone were the days when technicians/mechanics will have to spend too much time trying to identify a mechanical fault and rectify the problem. Now the emphasis is on the use of Automobile diagnosing Equipment through the use of computers and special software. An investigation conducted at Tamale Metropolis and Accra in the Northern and Greater Accra regions of Ghana, respectively. Methodology for data gathering were; questionnaires, physical observation, interviews, and newspaper. The study revealed that majority of mechanics lack computer skills which can enable them use diagnosis tools such as Exhaust Gas Analyzer, Scan Tools, Electronic Wheel Balancing machine, etc.

Keywords: diagnosing, local garages and modern garages, lack of knowledge of diagnosing posing an existential threat, training of local mechanics

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Authors: Abdreheman Seid Abdella

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This study examined instructors willingness, self-efficacy beliefs, attitudes and knowledge about provisions of instructional accommodations for students with disabilities in universities. Major concepts used in this study operationally defined and some models of disability were reviewed. Questionnaires were distributed to a total of 181 instructors from four universities and quantitative data was generated. Then to analyze the data, appropriate methods of data analysis were employed. The result indicated that on average instructors had positive willingness, strong self-efficacy beliefs and positive attitudes towards providing instructional accommodations. In addition, the result showed that the majority of participants had moderate level of knowledge about provision of instructional accommodations. Concerning the relationship between instructors background variables and dependent variables, the result revealed that location of university and awareness raising training about Inclusive Education showed statistically significant relationship with all dependent variables (willingness, self-efficacy beliefs, attitudes and knowledge). On the other hand, gender and college/faculty did not show a statistically significant relationship. In addition, it was found that among the inter-correlation of dependent variables, the correlation between attitudes and willingness to provide accommodations was the strongest. Furthermore, using multiple linear regression analysis, this study also indicated that predictor variables like self-efficacy beliefs, attitudes, knowledge and teaching methodology training made statistically significant contribution to predicting the criterion willingness. Predictor variables like willingness and attitudes made statistically significant contribution to predicting self-efficacy beliefs. Predictor variables like willingness, Special Needs Education course and self-efficacy beliefs made statistically significant contribution to predict attitudes. Predictor variables like Special Needs Education courses, the location of university and willingness made statistically significant contribution to predicting knowledge. Finally, using exploratory factor analysis, this study showed that there were four components or factors each that represent the underlying constructs of willingness and self-efficacy beliefs to provide instructional accommodations items, five components for attitudes towards providing accommodations items and three components represent the underlying constructs for knowledge about provisions of instructional accommodations items. Based on the findings, recommendations were made for improving the situation of instructional accommodations in Ethiopian universities.

Keywords: willingness, self-efficacy belief, attitude, knowledge

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Authors: Stella Laletas, Andrea Reupert, Melinda Goodyear, Bradley Morgan

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Background: Many people with a mental illness have young children. Research has shown that early childhood is a particularly vulnerable time for children whose parents have a mental illness. Moreover, repeated research has demonstrated the effectiveness of a multiagency approach to family focused practice for improving parental functioning and preventing adverse outcomes in children whose parents have a mental illness, particularly in the early years of a child’s life. However, there is a paucity of professional development resources for professionals who work with families where a parent has a mental illness and has young children. Significance of the study: This study will make a contribution to addressing knowledge gaps around resource development and workforce needs for early childhood and mental health professionals working with young children where a parent has a mental illness. Objective: This presentation describes a newly developed resource, 'Pathways of Care', specifically designed for early childhood educators and mental health workers, alongside pilot evaluation data regarding its effectiveness. ‘Pathways of Care’ aims to promote collaborative practice and present early identification and referral processes for workers in this sector. The resource was developed by the Children of Parents with a Mental Illness (COPMI) National Initiative which is funded by the Australian Government. Method: Using a mixed method design, the effectiveness of the training resource is also presented. Fifteen workers completed the Family Focus Mental Health Practice Questionnaire pre and post using the resource, to measure confidence and practice change; semi-structured interviews were also conducted with eight of these same workers to further explore the utility of the resource. Findings: The findings indicated the resource was effective in increasing knowledge and confidence, particularly for new and/or inexperienced staff. Examples of how the resource was used in practice by various professions emerged from the interview data. Conclusions: Collaborative practice, early identification and intervention in early childhood can potentially play a key role in altering the life trajectory of children who are at risk. This information has important implications for workforce development and staff training in both the early childhood and mental health sectors. Implications for policy and future research are discussed.

Keywords: parents with mental ilnesses, early intervention, evaluation, preschool children

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Authors: Aaron Yan

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Tennis elbow has been a leading injury and problem among amateur and even professional players. Many factors contribute to tennis elbow. In this research, we apply state of the art sensor-less computer vision and AI technology to study the biomechanics of a player’s tennis movements during training and competition as they relate to the causes of tennis elbow. We provide a framework for the analysis of key biomechanical parameters and their correlations with specific tennis stroke and movements that can lead to tennis elbow or elbow injury. We also devise a method for using AI to automatically detect player’s forms that can lead to tennis elbow development for on-court injury prevention.

Keywords: Tennis Elbow, Computer Vision, AI, 3DAT

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Authors: Archit Yajnik

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Part of Speech Tagging has always been a challenging task in the era of Natural Language Processing. This article presents POS tagging for Nepali text using Hidden Markov Model and Viterbi algorithm. From the Nepali text, annotated corpus training and testing data set are randomly separated. Both methods are employed on the data sets. Viterbi algorithm is found to be computationally faster and accurate as compared to HMM. The accuracy of 95.43% is achieved using Viterbi algorithm. Error analysis where the mismatches took place is elaborately discussed.

Keywords: hidden markov model, natural language processing, POS tagging, viterbi algorithm

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Authors: Hoda Rahmani, Gary Weckman

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The body of research examining the integration of drones into various industries is expanding rapidly. Despite progress made in addressing the cybersecurity concerns for commercial drones, knowledge deficits remain in determining potential occupational hazards and risks of drone use to employees’ well-being and health in the workplace. This creates difficulty in identifying key approaches to risk mitigation strategies and thus reflects the need for raising awareness among employers, safety professionals, and policymakers about workplace drone-related accidents. The purpose of this study is to investigate the prevalence of and possible risk factors for drone-related mishaps by comparing the application of drones in construction with manufacturing industries. The chief reason for considering these specific sectors is to ascertain whether there exists any significant difference between indoor and outdoor flights since most construction sites use drones outside and vice versa. Therefore, the current research seeks to examine the causes and patterns of workplace drone-related mishaps and suggest possible ergonomic interventions through data collection. Potential ergonomic practices to mitigate hazards associated with flying drones could include providing operators with professional pieces of training, conducting a risk analysis, and promoting the use of personal protective equipment. For the purpose of data analysis, two data mining techniques, the random forest and association rule mining algorithms, will be performed to find meaningful associations and trends in data as well as influential features that have an impact on the occurrence of drone-related accidents in construction and manufacturing sectors. In addition, Spearman’s correlation and chi-square tests will be used to measure the possible correlation between different variables. Indeed, by recognizing risks and hazards, occupational safety stakeholders will be able to pursue data-driven and evidence-based policy change with the aim of reducing drone mishaps, increasing productivity, creating a safer work environment, and extending human performance in safe and fulfilling ways. This research study was supported by the National Institute for Occupational Safety and Health through the Pilot Research Project Training Program of the University of Cincinnati Education and Research Center Grant #T42OH008432.

Keywords: commercial drones, ergonomic interventions, occupational safety, pattern recognition

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