Search results for: disaster training

Authors: Robin Ferguson

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The context which this paper explores is the provision of Technical Vocational Education and Training (TVET) of southern African workers at sea on local and foreign vessels using a blended learning approach. The pedagogical challenge of providing quality education in this context is that multiple African and foreign languages and cultural norms are found amongst the all-male crew; and there are widely differing levels of education, low levels of digital literacy and limited connectivity. The methodology used is a nested case study. The study describes the mechanisms used to provide ongoing, real-time workplace TVET on two foreign vessels. Some training was done in person when the vessels came into port, however, the majority of the TVET was achieved from shore to ship using a combination of commonly available Google and Microsoft Apps and WhatsApp. Voice, video and text in multiple languages were used to accommodate different learning styles. The learning was supported by the development of learning networks using social media. This paper also reflects on the shore-based organisational change processes required to support sea learning. The conceptual framework used is the Theory of Practice Architectures (TPA) as is provides a site-ontological perspective of the sayings/thinkings, doings and relatings of this workplace training which is multiplanar as it plays out at sea and ashore, in-person and on-line. Using TPA, the overarching practice architectures and supporting structures which confound or enable these learning practices are revealed. The contribution which this paper makes is an insight into an innovative vocational pedagogy which promotes ICT-mediated learning amongst workers who suffer from low levels of literacies and limited ICT-access and who work and live in remote places. It is a pedagogy of possibility which crosses the digital divide.

Keywords: theory of practice architecture, microsoft, google, whatsapp, vocational pedagogy, mariners, distributed workplaces

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Authors: Rajni Singh, Rakesh Mishra, Mukunda Upadhyay

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India is among the nations born out of partition that led to one of the greatest forced migrations that marked the past century. The Indian subcontinent got partitioned into two nation-states, namely India and Pakistan. This led to an unexampled mass displacement of people accounting for about 20 million in the subcontinent as a whole. This exemplifies the socio-political version of displacement, but there are other identified reasons leading to human displacement viz., natural calamities, development projects and people-trafficking and smuggling. Although forced migrations are rare in incidence, they are mostly region-specific and a very less percentage of population appears to be affected by it. However, when this percentage is transcripted in terms of volume, the real impact created by such migration can be realized. Forced migration is thus an issue related to the lives of many people and requires to be addressed with proper intervention. Forced or involuntary migration decimates peoples' assets while taking from them their most basic resources and makes them migrate without planning and intention. This in most cases proves to be a burden on the destination resources. Thus, the question related to their security concerns arise profoundly with regard to the protection and safeguards to these migrants who need help at the place of destination. This brings the human security dimension of forced migration into picture. The present study is an analysis of a sample of 1501 persons by NSSO in India (National Sample Survey Organisation), which identifies three reasons for forced migration- natural disaster, social/political problem and displacement by development projects. It was observed that, of the total forced migrants, about 4/5th comprised of the internally displaced persons. However, there was a huge inflow of such migrants to the country from across the borders also, the major contributing countries being Bangladesh, Pakistan, Sri Lanka, Gulf countries and Nepal. Among the three reasons for involuntary migration, social and political problem is the most prominent in displacing huge masses of population; it is also the reason where the share of international migrants to that of internally displaced is higher compared to the other two factors /reasons. Second to political and social problems, natural calamities displaced a high portion of the involuntary migrants. The present paper examines the factors which increase people's vulnerability to forced migration. On perusing the background characteristics of the migrants it was seen that those who were economically weak and socially fragile are more susceptible to migration. Therefore, getting an insight about this fragile group of society is required so that government policies can benefit these in the most efficient and targeted manner.

Keywords: involuntary migration, displacement, natural disaster, social and political problem

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Authors: Abhisek Dash

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This study had three neural networks, one for number segmentation, one for number detection and one for number recognition all of which are coupled to one another. All networks were trained on the MNIST dataset and were convolutional. It was assumed that the images had lighter background and darker foreground. The segmentation network took 28x28 images as input and had sixteen outputs. Segmentation training starts when a dark pixel is encountered. Taking a window(7x7) over that pixel as focus, the eight neighborhood of the focus was checked for further dark pixels. The segmentation network was then trained to move in those directions which had dark pixels. To this end the segmentation network had 16 outputs. They were arranged as “go east”, ”don’t go east ”, “go south east”, “don’t go south east”, “go south”, “don’t go south” and so on w.r.t focus window. The focus window was resized into a 28x28 image and the network was trained to consider those neighborhoods which had dark pixels. The neighborhoods which had dark pixels were pushed into a queue in a particular order. The neighborhoods were then popped one at a time stitched to the existing partial image of the number one at a time and trained on which neighborhoods to consider when the new partial image was presented. The above process was repeated until the image was fully covered by the 7x7 neighborhoods and there were no more uncovered black pixels. During testing the network scans and looks for the first dark pixel. From here on the network predicts which neighborhoods to consider and segments the image. After this step the group of neighborhoods are passed into the detection network. The detection network took 28x28 images as input and had two outputs denoting whether a number was detected or not. Since the ground truth of the bounds of a number was known during training the detection network outputted in favor of number not found until the bounds were not met and vice versa. The recognition network was a standard CNN that also took 28x28 images and had 10 outputs for recognition of numbers from 0 to 9. This network was activated only when the detection network votes in favor of number detected. The above methodology could segment connected and overlapping numbers. Additionally the recognition unit was only invoked when a number was detected which minimized false positives. It also eliminated the need for rules of thumb as segmentation is learned. The strategy can also be extended to other characters as well.

Keywords: convolutional neural networks, OCR, text detection, text segmentation

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Authors: Jeremy Jenrette, Z. Y. C. Liu, Pranav Chimote, Edward Fox, Trevor Hastie, Francesco Ferretti

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Suitable shark conservation depends on well-informed population assessments. Direct methods such as scientific surveys and fisheries monitoring are adequate for defining population statuses, but species-specific indices of abundance and distribution coming from these sources are rare for most shark species. We can rapidly fill these information gaps by boosting media-based remote monitoring efforts with machine learning and automation. We created a database of shark images by sourcing 24,546 images covering 219 species of sharks from the web application spark pulse and the social network Instagram. We used object detection to extract shark features and inflate this database to 53,345 images. We packaged object-detection and image classification models into a Shark Detector bundle. We developed the Shark Detector to recognize and classify sharks from videos and images using transfer learning and convolutional neural networks (CNNs). We applied these models to common data-generation approaches of sharks: boosting training datasets, processing baited remote camera footage and online videos, and data-mining Instagram. We examined the accuracy of each model and tested genus and species prediction correctness as a result of training data quantity. The Shark Detector located sharks in baited remote footage and YouTube videos with an average accuracy of 89\%, and classified located subjects to the species level with 69\% accuracy (n =\ eight species). The Shark Detector sorted heterogeneous datasets of images sourced from Instagram with 91\% accuracy and classified species with 70\% accuracy (n =\ 17 species). Data-mining Instagram can inflate training datasets and increase the Shark Detector’s accuracy as well as facilitate archiving of historical and novel shark observations. Base accuracy of genus prediction was 68\% across 25 genera. The average base accuracy of species prediction within each genus class was 85\%. The Shark Detector can classify 45 species. All data-generation methods were processed without manual interaction. As media-based remote monitoring strives to dominate methods for observing sharks in nature, we developed an open-source Shark Detector to facilitate common identification applications. Prediction accuracy of the software pipeline increases as more images are added to the training dataset. We provide public access to the software on our GitHub page.

Keywords: classification, data mining, Instagram, remote monitoring, sharks

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Authors: Gheorghe Braniste

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The management of the training process in sports is closely related to the awareness of the close connection between performance and the morphological, functional and psychological characteristics of the athlete's body. Achieving high results in Olympic sports is influenced, on the one hand, by the genetically determined characteristics of the body and, on the other hand, by the morphological, functional and motor abilities of the athlete. Taking into account the importance of properly understanding the evolutionary specificity of athletes to assess their competitive potential, this study provides a comparative analysis of the parameters that characterize the growth and development of the level of adaptation of sweeping rowers, considering the growth interval between 12 and 20 years. The study established that, in the multi-annual training process, the bodies of the targeted athletes register significant adaptive changes while analyzing parameters of the morphological, functional, psychomotor and sports-technical spheres. As a result of the influence of physical efforts, both specific and non-specific, there is an increase in the adaptability of the body, its transfer to a much higher level of functionality within the parameters, useful and economical adaptive reactions influenced by environmental factors, be they internal or external. The research was carried out for 7 years, on a group of 28 athletes, following their evolution and recording the specific parameters of each age stage. In order to determine the level of physical, morpho-functional, psychomotor development and technical training of rowers, the screening data were applied at the State University of Physical Education and Sports in the Republic of Moldova. During the research, measurements were made on the waist, in the standing and sitting position, arm span, weight, circumference and chest perimeter, vital capacity of the lungs, with the subsequent determination of the vital index (tolerance level to oxygen deficiency in venous blood in Stange and Genchi breath-taking tests that characterize the level of oxygen saturation, absolute and relative strength of the hand and back, calculation of body mass and morphological maturity indices (Kettle index), body surface area (body gait), psychomotor tests (Romberg test), test-tepping 10 s., reaction to a moving object, visual and auditory-motor reaction, recording of technical parameters of rowing on a competitive distance of 200 m. At the end of the study it was found that highly performance is sports is to be associated on the one hand with the genetically determined characteristics of the body and, on the other hand, with favorable adaptive reactions and energy saving, as well as morphofunctional changes influenced by internal and external environmental factors. The importance of the results obtained at the end of the study was positively reflected in obtaining the maximum level of training of athletes in order to demonstrate performance in large-scale competitions and mostly in the Olympic Games.

Keywords: olympics, parameters, performance, peak

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Authors: Santhani M. Selveindran

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Background: Natural disasters are traumatic global events that are becoming increasing more common, with significant psychosocial impact on survivors. This impact results not only in psychosocial distress but, for many, can lead to psychosocial disorders and chronic psychopathology. While there are currently available interventions that seek to prevent and treat these psychosocial sequelae, their effectiveness is uncertain. The evidence-base is emerging with more primary studies evaluating the effectiveness of various psychosocial interventions for survivors of natural disasters, which remains to be synthesized. Aim of Review: To identify, critically appraise and synthesize the current evidence-base on the effectiveness of psychosocial interventions in preventing or treating Post-Traumatic Stress Disorder (PTSD), Major Depressive Disorder (MDD) and/or Generalized Anxiety Disorder (GAD) in adults and children who are survivors of natural disasters. Methods: A protocol was developed as a guide to carry out this review. A systematic search was conducted in eight international electronic databases, three grey literature databases, one dissertation and thesis repository, websites of six humanitarian and non-governmental organizations renowned for their work on natural disasters, as well as bibliographic and citation searching for eligible articles. Papers meeting the specific inclusion criteria underwent quality assessment using the Downs and Black checklist. Data were extracted from the included papers and analysed by way of narrative synthesis. Results: Database and website searching returned 3777 papers where 31 met the criteria for inclusion. Additional 2 papers were obtained through bibliographic and citation searching. Methodological quality of most papers was fair. Twenty-five studies evaluated psychological interventions, five, social interventions whereas three studies evaluated ‘mixed’ psychological and social interventions. All studies, irrespective of methodological quality, reported post-intervention reductions in symptom scores for PTSD, depression and/or anxiety and where assessed, reduced diagnosis of PTSD and MDD, and produced improvements in self-efficacy and quality of life. Statistically significant results were seen in 27 studies. However, three studies demonstrated that the evaluated interventions may not have been very beneficial. Conclusions: The overall positive results suggest that any psychosocial interventions are favourable and should be delivered to all natural disaster survivors, irrespective of age, country, and phase of disaster. Yet, heterogeneity and methodological shortcomings of the current evidence-base makes it difficult to draw definite conclusions needed to formulate categorical guidance or frameworks. Further, rigorously conducted research is needed in this area, although the feasibility of such, given the context and nature of the problem, is also recognized.

Keywords: psychosocial interventions, natural disasters, survivors, effectiveness

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Authors: P. Kavuma, J. Mukasa, M. Lusunku

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Background and Problem: The widespread poverty in Africa- together with the negative impacts of climate change-are two great global challenges that call for everyone’s involvement including Architects. This in particular places serious challenges on architects to have additional skills in both Entrepreneurship and Environmental Design (SEED). Regrettably, while Architectural Training in most African Universities including those from Uganda lack comprehensive implementation of SEED in their curricula, regulatory bodies have not contributed towards the effective integration of SEED in their professional practice. In response to these challenges, Nkumba University (NU) under Architect Kavuma Paul supported by the Uganda Chambers of Architects– initiated the SEED integration in the undergraduate Architectural curricula to cultivate SEED know-how and examples of best practices. Main activities: Initiated in 2007, going beyond the traditional Architectural degree curriculum, the NU Architect department offers SEED courses including provoking passions for creating desirable positive changes in communities. Learning outcomes are assessed theoretically and practically through field projects. The first set of SEED graduates came out in 2012. As part of the NU post-graduation and alumni survey, in October 2014, the pioneer SEED graduates were contacted through automated reminder emails followed by individual, repeated personal follow-ups via email and phone. Out of the 36 graduates who responded to the survey, 24 have formed four (4) private consortium agencies of 5-7 graduates all of whom have pioneered Ugandan-own-cultivated Architectural social projects that include: fishing farming in shipping containers; solar powered mobile homes in shipping containers, solar powered retail kiosks in rural and fishing communities, and floating homes in the flood-prone areas. Primary outcomes: include being business self –reliant in creating the social change the architects desired in the communities. Examples of the SEED project returns to communities reported by the graduates include; employment creation via fabrication, retail business, marketing, improved diets, safety of life and property, decent shelter in the remote mining and oil exploration areas. Negative outcomes-though not yet evaluated include the disposal of used-up materials. Conclusion: The integration of SEED in Architectural Training has established a baseline benchmark and a replicable model based on best practice projects.

Keywords: architectural training, entrepreneurship, environment, integration

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Authors: Lateefat Amao, Misagh Faezipour

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Implementing electronic health records (EHR) in healthcare is a pivotal transition aimed at digitizing and optimizing patient health information management. The expectations associated with this transition are high, even towards other health information systems (HIS) and health technology. This multifaceted process involves careful planning and execution to improve the quality and efficiency of patient care, especially as healthcare technology is a sensitive niche. Key considerations include a thorough needs assessment, judicious vendor selection, robust infrastructure development, and training and adaptation of healthcare professionals. Comprehensive training programs, data migration from legacy systems and models, interoperability, as well as security and regulatory compliance are imperative for healthcare staff to navigate EHR systems adeptly. The purpose of this work is to offer a comprehensive review of the literature on EHR implementation. It explores the impact of this health technology on health practices, highlights challenges and barriers to its successful utility, and offers practical strategies that can impact its success in healthcare. This paper provides a thorough review of studies on the adoption of EHRs, emphasizing the wide range of experiences and results connected to EHR use in the medical field, especially across different types of healthcare organizations.

Keywords: healthcare, electronic health records, EHR implementation, patient care, interoperability

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Authors: Bianca W. O. Silva, Adriana C. M. Andrade, Gustavo C. M. Lucena, Virna M. S. Lima

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Introduction: Basic life support (BLS) involves the immediate recognition of cardiopulmonary arrest. Each year, 359.400 and 275.000 individuals with cardiac arrest are attended in emergency departments in USA and Europe. Brazilian data shows that 200.000 cardiac arrests occur every year, and half of them out of the hospital. Medical schools around the world teach BLS in the first years of the course, but studies show that there is a decline of the knowledge as the years go by, affecting the chain of survival. The objective was to analyze the knowledge of medical students about BLS and the retention of this learning throughout the course. Methods: This study included 150 students who were at the second and third year of a medical school in Salvador, Bahia, Brazil. The instrument of data collection was a structured questionnaire composed of 20 questions based on the 2015 American Heart Association guideline. The Pearson Chi-square test was used in order to study the association between previous training, sex and semester with the degree of knowledge of the students. The Kruskal-Wallis test was used to evaluate the different yields obtained between the various semesters. The number of correct answers was described by average and quartiles. Results: Regarding the degree of knowledge, 19.6% of the female students reached the optimal classification, a better outcome than the achieved by the male participants. Of those with previous training, 33.33% were classified as good and optimal, none of the students reached the optimal classification and only 2.2% of them were classified as bad (those who did not have 52.6% of correct answers). The analysis of the degree of knowledge related to each semester revealed that the 5th semester had the highest outcome: 30.5%. However, the acquaintance presented by the semesters was generally unsatisfactory, since 50% of the students, or more, demonstrated knowledge levels classified as bad or regular. When confronting the different semesters and the achieved scores, the value of p was 0.831. Conclusion: It is important to focus on the training of medical professionals that are capable of facing emergency situations, improving the systematization of care, and thereby increasing the victims' possibility of survival.

Keywords: basic life support, cardiopulmonary ressucitacion, education, medical students

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Authors: Raman Sharma, Ashok Kumar, Vipin Koushal

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Healthcare facilities are always seen as places of haven and protection for managing the external incidents, but the situation becomes more difficult and challenging when such facilities themselves are affected from internal hazards. Such internal hazards are arguably more disruptive than external incidents affecting vulnerable ones, as patients are always dependent on supportive measures and are neither in a position to respond to such crisis situation nor do they know how to respond. The situation becomes more arduous and exigent to manage if, in case critical care areas like Intensive Care Units (ICUs) and Operating Rooms (OR) are convoluted. And, due to these complexities of patients’ in-housed there, it becomes difficult to move such critically ill patients on immediate basis. Healthcare organisations use different types of electrical equipment, inflammable liquids, and medical gases often at a single point of use, hence, any sort of error can spark the fire. Even though healthcare facilities face many fire hazards, damage caused by smoke rather than flames is often more severe. Besides burns, smoke inhalation is primary cause of fatality in fire-related incidents. The greatest cause of illness and mortality in fire victims, particularly in enclosed places, appears to be the inhalation of fire smoke, which contains a complex mixture of gases in addition to carbon monoxide. Therefore, healthcare organizations are required to have a well-planned disaster mitigation strategy, proactive and well prepared manpower to cater all types of exigencies resulting from internal as well as external hazards. This case report delineates a true OR fire incident in Emergency Operation Theatre (OT) of a tertiary care multispecialty hospital and details the real life evidence of the challenges encountered by OR staff in preserving both life and property. No adverse event was reported during or after this fire commotion, yet, this case report aimed to congregate the lessons identified of the incident in a sequential and logical manner. Also, timely smoke evacuation and preventing the spread of smoke to adjoining patient care areas by opting appropriate measures, viz. compartmentation, pressurisation, dilution, ventilation, buoyancy, and airflow, helped to reduce smoke-related fatalities. Henceforth, precautionary measures may be implemented to mitigate such incidents. Careful coordination, continuous training, and fire drill exercises can improve the overall outcomes and minimize the possibility of these potentially fatal problems, thereby making a safer healthcare environment for every worker and patient.

Keywords: healthcare, fires, smoke, management, strategies

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Authors: Hervice Roméo Fogno Fotsoa, Germaine Djuidje Kenmoe, Claude Vidal Aloyem Kazé

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One of the main applications of machine learning is the prediction of time series. But a more accurate prediction requires a more optimal model of machine learning. Several optimization techniques have been developed, but without considering the input variables disposition of the system. Thus, this work aims to present a new machine learning architecture optimization technique based on their optimal input variables disposition. The validations are done on the prediction of wind time series, using data collected in Cameroon. The number of possible dispositions with four input variables is determined, i.e., twenty-four. Each of the dispositions is used to perform the prediction, with the main criteria being the training and prediction performances. The results obtained from a static architecture and a dynamic architecture of neural networks have shown that these performances are a function of the input variable's disposition, and this is in a different way from the architectures. This analysis revealed that it is necessary to take into account the input variable's disposition for the development of a more optimal neural network model. Thus, a new neural network training algorithm is proposed by introducing the search for the optimal input variables disposition in the traditional back-propagation algorithm. The results of the application of this new optimization approach on the two single neural network architectures are compared with the previously obtained results step by step. Moreover, this proposed approach is validated in a collaborative optimization method with a single objective optimization technique, i.e., genetic algorithm back-propagation neural networks. From these comparisons, it is concluded that each proposed model outperforms its traditional model in terms of training and prediction performance of time series. Thus the proposed optimization approach can be useful in improving the accuracy of time series forecasts. This proves that the proposed optimization approach can be useful in improving the accuracy of time series prediction based on machine learning.

Keywords: input variable disposition, machine learning, optimization, performance, time series prediction

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Authors: Paula Andrea Segura Delgado, Martha Helena Ramírez-Bahena

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Research on how the brain learns has a transcendental application in the educational context. It is crucial for teacher training to understand the nature of brain changes and their direct influence on learning processes. This communication is based on a literature review focused on neuroscience, neuroeducation, and the impact of digital technology on the human brain. Information was gathered from both English and Spanish language sources, using online journals, books and reports. The general objective was to analyze the role of neuroscience knowledge in enriching our understanding of the learning process. In fact, the authors have focused on the impact of digital technology on the human brain as well as its influence in the field of education..Neuroscience knowledge can contribute significantly to improving the training of educators and therefore educational practices. Education as an instrument of change and school as an agent of socialization, it is necessary to understand what it aims to transform: the human brain. Understanding the functioning of the human brain has important repercussions on education: this elucidates cognitive skills, psychological processes and elements that influence the learning process (memory, executive functions, emotions and the circadian cycle); helps identify psychological and neurological deficits that can impede learning processes (dyslexia, autism, hyperactivity); It allows creating environments that promote brain development and contribute to the advancement of brain capabilities in alignment with the stages of neurobiological development. The digital age presents diverse opportunities to every social environment. The frequent use of digital technology (DT) has had a significant and abrupt impact on both the cognitive abilities and physico-chemical properties of the brain, significantly influencing educational processes. Hence, educational community, with the insights from advances in neuroscience, aspire to identify the positive and negative effects of digital technology on the human brain. This knowledge helps ensure the alignment of teacher training and practices with these findings. The knowledge of neuroscience enables teachers to develop teaching methods that are aligned with the way the brain works. For example, neuroscience research has shown that digital technology is having a significant impact on the human brain (addition, anxiety, high levels of dopamine, circadian cycle disorder, decrease in attention, memory, concentration, problems with their social relationships). Therefore, it is important to understand the nature of these changes, their impact on the learning process, and how educators should effectively adapt their approaches based on these brain's changes.

Keywords: digital technology, learn process, neuroscience knowledge, neuroeducation, training proffesors

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Authors: A. Qayed Al-Emadi, C. Schwabenland, Q. Wei, B. Czarnecka

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In 2009, the new HRM policy was implemented in Qatar for public sector organisations. The purpose of this research is to examine how Qatar’s 2009 HRM policy was significant in influencing employee retention in public organisations. The conducted study utilised quantitative methodology to analyse the data on employees’ perceptions of such HRM practices as performance çanagement, rewards and promotion, training and development associated with the HRM policy in public organisations in comparison to semi-private organisations. Employees of seven public and semi-private organisations filled in the questionnaire based on the 5-point likert scale to present quantitative results. The data was analysed with the correlation and multiple regression statistical analyses. It was found that Performance Management had the relationship with Employee Retention, and Rewards and Promotion influenced Job Satisfaction in public organisations. The relationship between Job Satisfaction and Employee Retention was also observed. However, no significant differences were observed in the role of HRM practices in public and semi-private organisations.

Keywords: performance management, rewards and promotion, training and development, job satisfaction, employee retention, SHRM, configurational perspective

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Authors: Tamara Thompson

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This presentation will address the vocality of American soprano Sibyl Sanderson (1865–1903) in Massenet’s Manon and Esclarmonde as discernible from documentary sources such as vocal treatises, annotated scores, and correspondence. These sources will then be compared and contrasted with Sanderson’s reception in French press. Sanderson sang Manon in 1888, which Massenet revised for her. She then created the role of Esclarmonde for the 1889 l'Exposition Universelle in Paris. The soprano appeared as the Byzantine Empress more than 100 times in the nine months following the premiere, which secured her fame and an international operatic career frought with controversy and criticism as well as adulation. Before her débuts as Manon and Esclarmonde, Sanderson received musical training in California and Paris from multiple teachers with varied and opposing methods. There will be an exploration of the ways in which the disparate pedagogic influences such as those taught by Giovanni Sbriglia and Jean de Reszké may have guided Sanderson’s vocal strategies, and possibly caused or promoted the severe vocal pathologies she battled in subsequent years. In addition, there is interrogation of the vocal writing and revisions made to the titular roles for Sanderson in order to assess how these factors may have affected her technique and vocal health.

Keywords: French, nineteenth-century, opera, pedagogy, vocality

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Authors: Adavbiele Airewe Stephen

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Sustainable energy use is exploiting energy resources within acceptable levels of global resource depletion without destroying the ecological balance of an area. In the context of sustainability, the rush to quell the energy crisis of the fossil fuels of the 1970's by embarking on nuclear energy technology has now been seen as a disaster. In the circumstance, action (policy) suggested in this study to avoid future occurrence is exergy maximization/entropy generation minimization and the use is renewable energy technologies that are hybrid based. Thirty-two (32) selected hybrid renewable energy technologies were assessed with respect to their energetic efficiencies and entropy generation. The results indicated that determining which of the hybrid technologies is the most efficient process and sustainable is a matter of defining efficiency and knowing which of them possesses the minimum entropy generation.

Keywords: entropy, exergy, hybrid renewable energy technologies, sustainability

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Authors: Aliya Grig, Konstantin Sokolov, Igor Shatalin

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The report describes the methodology for collecting data and building an ML model for determining the personality psychotype using profiling and personality traits methods based on several short messages of a user communicating on an arbitrary topic with a chitchat bot. In the course of the experiments, the minimum amount of text was revealed to confidently determine aspects of personality. Model accuracy - 85%. Users' language of communication is English. AI for a personalized communication with a user based on his mood, personality, and current emotional state. Features investigated during the research: personalized communication; providing empathy; adaptation to a user; predictive analytics. In the report, we describe the processes that captures both structured and unstructured data pertaining to a user in large quantities and diverse forms. This data is then effectively processed through ML tools to construct a knowledge graph and draw inferences regarding users of text messages in a comprehensive manner. Specifically, the system analyzes users' behavioral patterns and predicts future scenarios based on this analysis. As a result of the experiments, we provide for further research on training AI models to be empathetic, creating personalized communication for a user

Keywords: AI, empathetic, chatbot, AI models

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Authors: Najim Akorede Babalola

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Adult Education is a multi and interdisciplinary in nature that cut across different fields of study which includes education, social sciences, engineering even information technologies that dominate the contemporary world among others. In the past, Adult Education has been used as an instrument of civilization by teaching people how to read and write as well as earning a better living. The present world has witnessed a transition from industrial age to information age which is also known as knowledge society needs Adult Education for knowledge acquisition and update of existing knowledge. An individual needs Adult Education in either of its various forms (on-the-job-training, in-service training, extramural classes, vocational education, continuing education among others) in order to develop towards the information society trends; this is because Adult Education is a process of transforming an individual through acquisition of relevant skills and knowledge for personal as well as societal development. Evidence abounds in the literature that Adult Education has not only assisted people in the medieval period but still assisting people in this modern society in changing and transforming their lives for a better living. This study, therefore, raised a salient question that with different ideas and innovations brought by the contemporary world, is Adult Education relevant? It is on this basis that this study intends to examine the relevance of Adult Education in the past and present in order to determine its future relevance.

Keywords: adult education, multi and inter-disciplinary, knowledge society, skill acquisition

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Authors: Nathaniel Deboever, Malcolm Breeze, Adrian Sheen

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Basic surgical and suturing techniques are a fundamental requirement for all doctors. In order to gain confidence and competence, doctors in training need to obtain sufficient teaching and just as importantly: practice. Young doctors with an apt level of expertise on these simple surgical skills, which are often used in the Emergency Department, can help alleviate some pressure during a busy evening. Unfortunately, learning these skills can be quite difficult during medical school or even during junior doctor years. The aim of this project was to adequately train medical students attending University of Sydney’s Nepean Clinical School through a series of workshops highlighting practical skills, with hopes to further extend this program to junior doctors in the hospital. The sessions instructed basic skills via tutorials, demonstrations, and lastly, the sessions cemented these proficiencies with practical sessions. During such an endeavor, it is fundamental to employ models that appropriately resemble what students will encounter in the clinical setting. The sustainability of workshops is similarly important to the continuity of such a program. To address both these challenges, the authors have developed models including suturing platforms, knot tying, and vessel ligation stations, as well as a shave and punch biopsy models and ophthalmologic foreign body device. The unique aspect of this work is that we utilized hands-on teaching sessions, to address a gap in doctors-in-training and junior doctor curriculum. Presented to you through this poster are our approaches to creating models that do not employ animal products and therefore do not necessitate particular facilities or discarding requirements. Covering numerous skills that would be beneficial to all young doctors, these models are easily replicable and affordable. This exciting work allows for countless sessions at low cost, providing enough practice for students to perform these skills confidently as it has been shown through attendee questionnaires.

Keywords: medical education, surgical models, surgical simulation, surgical skills education

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Authors: Muazzam A. Khan

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In order to avoid dangerous environmental disasters, robots are being recognized as good entrants to step in as human rescuers. Robots has been gaining interest of many researchers in rescue matters especially which are furnished with advanced sensors. In distributed wireless robot system main objective for a rescue system is to track the location of the object continuously. This paper provides a novel idea to track and locate human in disaster area using stereo vision system and ZigBee technology. This system recursively predict and updates 3D coordinates in a robot coordinate camera system of a human which makes the system cost effective. This system is comprised of ZigBee network which has many advantages such as low power consumption, self-healing low data rates and low cost.

Keywords: stereo vision, segmentation, classification, human tracking, ZigBee module

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Authors: Pariya Sheykhmaleki, Debajyoti Pati

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Rural areas in the United States face numerous challenges in providing quality and assessable primary healthcare services, especially during emergencies such as natural disasters or pandemics. This study showcases a cutting-edge flexible module that aims to overcome these challenges by offering adaptable healthcare facilities capable of providing comprehensive health services in remote and disaster-prone regions. According to the Health Resources and Services Administration (HRSA), approximately 62 million Americans, or 1 in 5 individuals, live in areas designated as Health Professional Shortage Areas (HPSAs) for primary care. These areas are characterized by limited access to healthcare facilities, shortage of healthcare professionals, transportation barriers, inadequate healthcare infrastructure, higher rates of chronic diseases, mental health disparities, and limited availability of specialized care, including urgent circumstances like pandemics that can exacerbate this issue. To address these challenges, the literature study began by examining primary health solutions in very remote areas, e.g., spaceships, to identify the state-of-the-art technologies and the methods used to facilitate primary care needs. The literature study on flexibility in architecture and interior design was also adapted to develop a conceptual design for rural areas. The designed flexible module provides an innovative solution. This module can be prefabricated as all parts are standardized. The flexibility of the module allows the structure to be modified based on local and geographical requirements as well as the ability to expand as required. It has been designed to stand either by itself or work in tandem with public buildings. By utilizing sustainable approaches and flexible spatial configurations, the module optimizes the utilization of limited resources while ensuring efficient and effective healthcare delivery. Furthermore, the poster highlights the key features of this flexible module, including its ability to support telemedicine and telehealth services for all five levels of urgent care conditions, i.e., from facilitating fast tracks to supporting emergency room services, in two divided zones. The module's versatility enables its deployment in rural areas located far from urban centers and disaster-stricken regions, ensuring access to critical healthcare services in times of need. This module is also capable of responding in urban areas when the need for primary health becomes vastly urgent, e.g., during a pandemic. It emphasizes the module's potential to bridge the healthcare gap between rural and urban areas and mitigate the impact of rural health challenges.

Keywords: rural health, healthcare challenges, flexible modular design, telemedicine, telehealth

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Authors: Dacheng Li, Bo Huang, Qinjin Han, Ming Li

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Remotely sensed imagery with the high spatial and temporal characteristics, which it is hard to acquire under the current land observation satellites, has been considered as a key factor for monitoring environmental changes over both global and local scales. On a basis of the limited high spatial-resolution observations, challenged studies called spatiotemporal fusion have been developed for generating high spatiotemporal images through employing other auxiliary low spatial-resolution data while with high-frequency observations. However, a majority of spatiotemporal fusion approaches yield to satisfactory assumption, empirical but unstable parameters, low accuracy or inefficient performance. Although the spatiotemporal fusion methodology via sparse representation theory has advantage in capturing reflectance changes, stability and execution efficiency (even more efficient when overcomplete dictionaries have been pre-trained), the retrieval of high-accuracy dictionary and its response to fusion results are still pending issues. In this paper, we employ additional image pairs (here each image-pair includes a Landsat Operational Land Imager and a Moderate Resolution Imaging Spectroradiometer acquisitions covering the partial area of Baotou, China) only into the coupled dictionary training process based on K-SVD (K-means Singular Value Decomposition) algorithm, and attempt to improve the fusion results of two existing sparse representation based fusion models (respectively utilizing one and two available image-pair). The results show that more eligible image pairs are probably related to a more accurate overcomplete dictionary, which generally indicates a better image representation, and is then contribute to an effective fusion performance in case that the added image-pair has similar seasonal aspects and image spatial structure features to the original image-pair. It is, therefore, reasonable to construct multi-dictionary training pattern for generating a series of high spatial resolution images based on limited acquisitions.

Keywords: spatiotemporal fusion, sparse representation, K-SVD algorithm, dictionary learning

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Authors: Mohd Faridz Ahmad, Amirul Hakim Hasbullah

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Electrical Muscle Stimulation (EMS) has been introduced to the world in the 19th and 20th centuries and has globally gained increasing attention on its usefulness. EMS is known as the application of electrical current transcutaneous to muscles through electrodes to induce involuntary contractions that can lead to the increment of muscle mass and strength. This study can be used as an alternative to help people especially those living a sedentary lifestyle to improve their muscle activity without having to go through a heavy workout session. Therefore, this study intended to investigate the effectiveness of EMS training in 5 weeks interventions towards male body composition. It was a quasi-experimental design, held at the Impulse Studio Bangsar, which examined the effects of EMS training towards skeletal muscle mass among the subjects. Fifteen subjects (n = 15) were selected to assist in this study. The demographic data showed that, the average age of the subjects was 43.07 years old ± 9.90, height (173.4 cm ± 9.09) and weight was (85.79 kg ± 18.07). Results showed that there was a significant difference on the skeletal muscle mass (p = 0.01 < 0.05), upper body (p = 0.01 < 0.05) and lower body (p = 0.00 < 0.05). Therefore, the null hypothesis has been rejected in this study. As a conclusion, the application of EMS towards body composition can increase the muscle size and strength. This method has been proven to be able to improve athlete strength and thus, may be implemented in the sports science area of knowledge.

Keywords: body composition, EMS, skeletal muscle mass, strength

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Authors: Cyrus Addy, Venkata Sriram Siddhardh Nadendla, Kwame Awuah-Offei

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Surface robots in modern underground mine rescue operations suffer from several limitations in enabling a prompt self-rescue. Therefore, the possibility of designing and deploying in-mine robots to expedite miner self-rescue can have a transformative impact on miner safety. These in-mine robots for miner self-rescue can be envisioned to carry out diverse tasks such as object detection, autonomous navigation, and payload delivery. Specifically, this paper investigates the challenges in the design of object detection algorithms for in-mine robots using thermal images, especially to detect people in real-time. A total of 125 thermal images were collected in the Missouri S&T Experimental Mine with the help of student volunteers using the FLIR TG 297 infrared camera, which were pre-processed into training and validation datasets with 100 and 25 images, respectively. Three state-of-the-art, pre-trained real-time object detection models, namely YOLOv5, YOLO-FIRI, and YOLOv8, were considered and re-trained using transfer learning techniques on the training dataset. On the validation dataset, the re-trained YOLOv8 outperforms the re-trained versions of both YOLOv5, and YOLO-FIRI.

Keywords: miner self-rescue, object detection, underground mine, YOLO

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Authors: Leonard Tiong, Sonit Singh, Kevin Ho Shon, Sarah Lewis

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Investigation into the use of artificial intelligence in radiology continues to develop at a rapid rate. During the coronavirus pandemic, the combination of an exponential increase in chest x-rays and unpredictable staff shortages resulted in a huge strain on the department's workload. There is a World Health Organisation estimate that two-thirds of the global population does not have access to diagnostic radiology. Therefore, there could be demand for a program that could detect acute changes in imaging compatible with infection to assist with screening. We generated a conventional neural network and tested its efficacy in recognizing changes compatible with coronavirus infection. Following ethics approval, a deidentified set of 77 normal and 77 abnormal chest x-rays in patients with confirmed coronavirus infection were used to generate an algorithm that could train, validate and then test itself. DICOM and PNG image formats were selected due to their lossless file format. The model was trained with 100 images (50 positive, 50 negative), validated against 28 samples (14 positive, 14 negative), and tested against 26 samples (13 positive, 13 negative). The initial training of the model involved training a conventional neural network in what constituted a normal study and changes on the x-rays compatible with coronavirus infection. The weightings were then modified, and the model was executed again. The training samples were in batch sizes of 8 and underwent 25 epochs of training. The results trended towards an 85.71% true positive/true negative detection rate and an area under the curve trending towards 0.95, indicating approximately 95% accuracy in detecting changes on chest X-rays compatible with coronavirus infection. Study limitations include access to only a small dataset and no specificity in the diagnosis. Following a discussion with our programmer, there are areas where modifications in the weighting of the algorithm can be made in order to improve the detection rates. Given the high detection rate of the program, and the potential ease of implementation, this would be effective in assisting staff that is not trained in radiology in detecting otherwise subtle changes that might not be appreciated on imaging. Limitations include the lack of a differential diagnosis and application of the appropriate clinical history, although this may be less of a problem in day-to-day clinical practice. It is nonetheless our belief that implementing this program and widening its scope to detecting multiple pathologies such as lung masses will greatly assist both the radiology department and our colleagues in increasing workflow and detection rate.

Keywords: artificial intelligence, COVID, neural network, machine learning

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Authors: Chayanika Hazarika Bordoloi

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This paper is an attempt to work on a highly inflectional language called Assamese. This is also one of the national languages of India and very little has been achieved in terms of computational research. Building a language processing tool for a natural language is not very smooth as the standard and language representation change at various levels. This paper presents inflectional suffixes of Assamese verbs and how the statistical tools, along with linguistic features, can improve the tagging accuracy. Conditional random fields (CRF tool) was used to automatically tag and train the text data; however, accuracy was improved after linguistic featured were fed into the training data. Assamese is a highly inflectional language; hence, it is challenging to standardizing its morphology. Inflectional suffixes are used as a feature of the text data. In order to analyze the inflections of Assamese word forms, a list of suffixes is prepared. This list comprises suffixes, comprising of all possible suffixes that various categories can take is prepared. Assamese words can be classified into inflected classes (noun, pronoun, adjective and verb) and un-inflected classes (adverb and particle). The corpus used for this morphological analysis has huge tokens. The corpus is a mixed corpus and it has given satisfactory accuracy. The accuracy rate of the tagger has gradually improved with the modified training data.

Keywords: CRF, morphology, tagging, tagset

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Authors: Peiming Li

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This paper presents an innovative blockchain-based approach to enhance the reliability and efficiency of federated learning systems. By integrating a decentralized oracle consensus mechanism into the federated learning framework, we address key challenges of data and model integrity. Our approach utilizes a network of redundant oracles, functioning as independent validators within an epoch-based training system in the federated learning model. In federated learning, data is decentralized, residing on various participants' devices. This scenario often leads to concerns about data integrity and model quality. Our solution employs blockchain technology to establish a transparent and tamper-proof environment, ensuring secure data sharing and aggregation. The decentralized oracles, a concept borrowed from blockchain systems, act as unbiased validators. They assess the contributions of each participant using a Hidden Markov Model (HMM), which is crucial for evaluating the consistency of participant inputs and safeguarding against model poisoning and malicious activities. Our methodology's distinct feature is its epoch-based training. An epoch here refers to a specific training phase where data is updated and assessed for quality and relevance. The redundant oracles work in concert to validate data updates during these epochs, enhancing the system's resilience to security threats and data corruption. The effectiveness of this system was tested using the Mnist dataset, a standard in machine learning for benchmarking. Results demonstrate that our blockchain-oriented federated learning approach significantly boosts system resilience, addressing the common challenges of federated environments. This paper aims to make these advanced concepts accessible, even to those with a limited background in blockchain or federated learning. We provide a foundational understanding of how blockchain technology can revolutionize data integrity in decentralized systems and explain the role of oracles in maintaining model accuracy and reliability.

Keywords: federated learning system, block chain, decentralized oracles, hidden markov model

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Authors: E. Verduci, I. Aguglia, A. Filocamo, I. Macrì, R. Scala, A. Vinci

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One of the first indicator of autism spectrum disorder (ASD) is a decreasing tendency or failure to respond to name (RTN) call. Despite RTN is important for social and language developmentand it’s a common target for early interventions for children with ASD, research on specific treatments is insufficient and does not consider the importance of the discrimination between the own name and other names. The purpose of the current study was to replicate an assessment and treatment model proposed by Conine et al. (2020) to teach children with ASD to respond to their own name and to not respond to other names (RTO). The model includes three different phases (baseline/screening, treatment, and generalization), and itgradually introduces the different treatment components, starting with the most naturalistic ones (such as social interaction) and adding more intrusive components (such as tangible reinforcements, prompt and fading procedures) if necessary. The participants of this study were three children with ASD diagnosis: D. (5 years old) with a low frequency of RTN, M. (7 years old) with a RTN unstable and no ability of discrimination between his name and other names, S. (3 years old) with a strong RTN but a constant response to other names. Moreover, the treatment for D. and M. consisted of social and tangible reinforcements (treatment T1), for S. the purpose of the treatment was to teach the discrimination between his name and the others. For all participants, results suggest the efficacy of the model to acquire the ability to selectively respond to the own name and the generalization of the behavior with other people and settings.

Keywords: response to name, autism spectrum disorder, progressive training, ABA

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Authors: Julaine Allan, Katarzyna Olcon, Padmini Pai, Lynne Keevers, Mim Fox, Maria Mackay, Ruth Everingham

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Effective local responses to community needs are grounded in contextual knowledge and build on existing resources. The Stability, Encompassing, Endurance & Direction (SEED) Wellbeing Program was created in 2020 in response to cumulative disasters, bushfires, floods and COVID, experienced by healthcare staff in the Illawarra Shoalhaven Local Health District, NSW Australia. SEED used a participatory action methodology to bring healthcare staff teams together to engage in restorative activities in the workplace. Guided by Practice Theory, this study identified the practices that supported the recovery of healthcare staff.

Keywords: mental health and wellbeing, workplace wellness, healthcare providers, natural disasters, COVID-19, burnout, occupational trauma

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Authors: Kohkan Shamsi

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Background: MRI and PET are now extensively utilized in Alzheimer's disease (AD) trials for patient eligibility, efficacy assessment, and safety evaluations but including imaging in AD trials impacts site selection process, patient recruitment, and patient retention. Methods: PET/MRI are performed at baseline and at multiple follow-up timepoints. This requires prospective site imaging qualification, evaluation of phantom data, training and continuous monitoring of machines for acquisition of standardized and consistent data. This also requires prospective patient/caregiver training as patients must go to multiple facilities for imaging examinations. We will share our experience form one of the largest AD programs. Lesson learned: Many neurological diseases have a similar presentation as AD or could confound the assessment of drug therapy. The inclusion of wrong patients has ethical and legal issues, and data could be excluded from the analysis. Centralized eligibility evaluation read process will be discussed. Amyloid related imaging abnormalities (ARIA) were observed in amyloid-β trials. FDA recommended regular monitoring of ARIA. Our experience in ARIA evaluations in large phase III study at > 350 sites will be presented. Efficacy evaluation: MRI is utilized to evaluate various volumes of the brain. FDG PET or amyloid PET agents has been used in AD trials. We will share our experience about site and central independent reads. Imaging logistic issues that need to be handled in the planning phase will also be discussed as it can impact patient compliance thereby increasing missing data and affecting study results. Conclusion: imaging must be prospectively planned to include standardizing imaging methodologies, site selection process and selecting assessment criteria. Training should be transparently conducted and documented. Prospective patient/caregiver awareness of imaging requirement is essential for patient compliance and reduction in missing imaging data.

Keywords: Alzheimer's disease, ARIA, MRI, PET, patient recruitment, retention

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Authors: Samar Mousa, Ryan Kerstein, Mohanad Adam

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Regional anaesthesia has been associated with favourable outcomes in patients undergoing a wide range of surgeries. Beneficial effects have been demonstrated in terms of postoperative respiratory and cardiovascular endpoints, 7-day survival, time to ambulation and hospital discharge, and postoperative analgesia. Our project aimed at assessing the regional anaesthesia practice in the plastic surgery department of Buckinghamshire trust and finding out ways to improve the service in collaboration with the anaesthesia team. It is a retrospective study associated with a questionnaire filled out by plastic surgeons and anaesthetists to get the full picture behind the numbers. The study period was between 1/3/2022 and 23/5/2022 (12 weeks). The operative notes of all patients who had an operation under plastic surgery, whether emergency or elective, were reviewed. The criteria of suitable candidates for the regional block were put by the consultant anaesthetists as follows: age above 16, single surgical site (arm, forearm, leg, foot), no drug allergy, no pre-existing neuropathy, no bleeding disorders, not on ant-coagulation, no infection to the site of the block. For 12 weeks, 1061 operations were performed by plastic surgeons. Local cases were excluded leaving 319 cases. Of the 319, 102 patients were suitable candidates for regional block after applying the previously mentioned criteria. However, only seven patients had their operations under the regional block, and the rest had general anaesthesia that could have been easily avoided. An online questionnaire was filled out by both plastic surgeons and anaesthetists of different training levels to find out the reasons behind the obvious preference for general over regional anaesthesia, even if this was against the patients’ interest. The questionnaire included the following points: training level, time taken to give GA or RA, factors that influence the decision, percentage of RA candidates that had GA, reasons behind this percentage, recommendations. Forty-four clinicians filled out the questionnaire, among which were 23 plastic surgeons and 21 anaesthetists. As regards the training level, there were 21 consultants, 4 associate specialists, 9 registrars, and 10 senior house officers. The actual percentage of patients who were good candidates for RA but had GA instead is 93%. The replies estimated this percentage as between 10-30%. 29% of the respondents thought that this percentage is because of surgeons’ preference to have GA rather than RA for their operations without medical support for the decision. 37% of the replies thought that anaesthetists prefer giving GA even if the patient is a suitable candidate for RA. 22.6% of the replies thought that patients refused to have RA, and 11.3% had other causes. The recommendations were in 5 main accesses, which are protocols and pathways for regional blocks, more training opportunities for anaesthetists on regional blocks, providing a separate block room in the hospital, better communication between surgeons and anaesthetists, patient education about the benefits of regional blocks.

Keywords: regional anaesthesia, regional block, plastic surgery, general anaesthesia

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