Search results for: trained athletes

Authors: Nynke de Jong, Inge G. P. Duimel-Peeters

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The flipped classroom as an instructional strategy and a type of blended learning that reverses the traditional learning environment by delivering instructional content, off- and online, in- and outside the classroom, has been implemented in a 4-weeks module focusing on ageing in Europe at the Maastricht University. The main aim regarding the organization of this module was implementing flipped classroom-principles in order to create meaningful learning opportunities, while educational technologies are used to deliver content outside of the classroom. Technologies used in this module were an online interactive real time lecture from England, two interactive face-to-face lectures with visual supports, one group session including role plays and team-based learning meetings. The cohort of 2015-2016, using educational technologies, was compared with the cohort of 2014-2015 on module evaluation such as organization and instructiveness of the module, who studied the same content, although conforming the problem-based educational strategy, i.e. educational base of the Maastricht University. The cohort of 2015-2016 with its specific organization, was also more profound evaluated on outcomes as (1) experienced duration of the lecture by students, (2) experienced content of the lecture, (3) experienced the extent of the interaction and (4) format of lecturing. It was important to know how students reflected on duration and content taken into account their background knowledge so far, in order to distinguish between sufficient enough regarding prior knowledge and therefore challenging or not fitting into the course. For the evaluation, a structured online questionnaire was used, whereby above mentioned topics were asked for to evaluate by scoring them on a 4-point Likert scale. At the end, there was room for narrative feedback so that interviewees could express more in detail, if they wanted, what they experienced as good or not regarding the content of the module and its organization parts. Eventually, the response rate of the evaluation was lower than expected (54%), however, due to written feedback and exam scores, we dare to state that it gives a good and reliable overview that encourages to work further on it. Probably, the response rate may be explained by the fact that resit students were included as well, and that there maybe is too much evaluation as some time points in the program. However, overall students were excited about the organization and content of the module, but the level of self-directed behavior, necessary for this kind of educational strategy, was too low. They need to be more trained in self-directness, therefore the module will be simplified in 2016-2017 with more clear and fewer topics and extra guidance (step by step procedure). More specific information regarding the used technologies will be explained at the congress, as well as the outcomes (min and max rankings, mean and standard deviation).

Keywords: blended learning, flipped classroom, public health, self-directness

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Authors: Olga Maksakova

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A work with unconscious patients after acute brain damages besides special knowledge and practical skills of all the participants requires a very specific organization. A lot of said about team approach in neurorehabilitation, usually as for outpatient mode. Rehabilitologists deal with fixed patient problems or deficits (motion, speech, cognitive or emotional disorder). Team-building means superficial paradigm of management psychology. Linear mode of teamwork fits casual relationships there. Cases with deep altered states of consciousness (vegetative states, coma, and confusion) require non-linear mode of teamwork: recovery of consciousness might not be the goal due to phenomenon uncertainty. Rehabilitation team as Semi-open Complex System includes the patient as a part. Patient's response pattern becomes formed not only with brain deficits but questions-stimuli, context, and inquiring person. Teamwork is sourcing of phenomenology knowledge of patient's processes as Third-person approach is replaced with Second- and after First-person approaches. Here is a chance for real-time change. Patient’s contacts with his own body and outward things create a basement for restoration of consciousness. The most important condition is systematic feedbacks to any minimal movement or vegetative signal of the patient. Up to now, recovery work with the most severe contingent is carried out in the mode of passive physical interventions, while an effective rehabilitation team should include specially trained psychologists and psychotherapists. It is they who are able to create a network of feedbacks with the patient and inter-professional ones building up the team. Characteristics of ‘Team-Patient’ system (TPS) are energy, entropy, and complexity. Impairment of consciousness as the absence of linear contact appears together with a loss of essential functions (low energy), vegetative-visceral fits (excessive energy and low order), motor agitation (excessive energy and excessive order), etc. Techniques of teamwork are different in these cases for resulting optimization of the system condition. Directed regulation of the system complexity is one of the recovery tools. Different signs of awareness appear as a result of system self-organization. Joint meetings are an important part of teamwork. Regular or event-related discussions form the language of inter-professional communication, as well as the patient's shared mental model. Analysis of complex communication process in TPS may be useful for creation of the general theory of consciousness.

Keywords: rehabilitation team, urgent rehabilitation, severe brain damage, consciousness disorders, complex system theory

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Authors: Paola Molteni, Luigi d’Alonzo

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Background: Sensory issues are now considered one of the key aspects in defining and diagnosing autism, changing the perspectives on behavioural analysis and intervention in mainstream educational services. However, Italian teachers’ training is yet not specific on the topic of autism and its sensory-related effects and this research investigates the teacher’s capability in understanding the student’s needs and his/her challenging behaviours considering sensory perceptions. Objectives: The research aims to analyse mainstream schools teachers’ awareness on students’ sensory perceptions and how this affects classroom inclusion and learning process. The research questions are: i) Are teachers able to identify student’s sensory issues?; ii) Are trained teachers more able to identify sensory problems then untrained ones?; iii) What is the impact of sensory issues on inclusion in mainstream classrooms?; iv) What should teachers know about autistic sensory dimensions? Methods: This research was designed as a pilot study that involves a multi-methods approach, including action and collaborative research methodology. The designed research allows the researcher to catch the complexity of a province school district (from kindergarten to high school) through a deep detailed analysis of selected aspects. The researcher explored the questions described above through 133 questionnaires and 6 focus groups. The qualitative and quantitative data collected during the research were analysed using the Interpretative Phenomenological Analysis (IPA). Results: Mainstream schools teachers are not able to confidently recognise sensory issues of children included in the classroom. The research underlines: how professionals with no specific training on autism are not able to recognise sensory problems in students on the spectrum; how hearing and sight issues have higher impact on classroom inclusion and student’s learning process; how a lack of understanding is often followed by misinterpretations of the impact of sensory issues and challenging behaviours. Conclusions: As this research has shown, promoting and enhancing the importance of understanding sensory issues related to autism is fundamental to enable mainstream schools teachers to define educational and life-long plans able to properly answer the student’s needs and support his/her real inclusion in the classroom. This study is a good example of how the educational research can meet and help the daily practice in working with people on the autism spectrum and support the training design for mainstream school teachers: the emerging need of designed preparation on sensory issues is fundamental to be considered when planning school district in-service training programmes, specifically declined for inclusive services.

Keywords: autism spectrum condition, scholastic inclusion, sensory overload, teacher's training

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Authors: Rabia Genc, Aysun Eksioglu, Emine Serap Sarican, Sibel Icke

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Today the habit of cigarette smoking is among one of the most important public health concerns because of the health problems it leads to. The most important and hazardous group to use tobacco and tobacco products is adolescents and teenagers. And one of the most effective ways to prevent them from starting to smoke is education. This research is a kind of educational intervention study which was carried out in order to evaluate the effect of peer education on the teenagers' knowledge about smoking. The research was carried out between October 15, 2013 and September 9, 2015 at Ege University Ataturk Vocational Health School. The population of the research comprised of the students that have been studying at Ege University Atatürk Vocational Health School, Midwifery Department (N=390). The peer educator group that would give training on smoking consisted of 10 people, and the peer groups that would be trained were divided into two groups via simple randomization as experimental group (n=185) and control group (n=185). Questionnaire, information evaluation form, and informed consent forms were used as date collection tools. The analysis of the data which were collected in the study was carried out on Statistical Package for Social Science (SPSS 15.0). It was found out that 62.5 % of the students who were in peer educator group had smoked in some period of their lives; however, none of them continued to smoke. When they were asked about their reasons to start smoking, 25% said they just wanted to try it, and 25% of them answered that it was because of their friend groups. When the pre-peer education and post-peer education point averages of peer educator group were evaluated, the results showed that there was a significant difference between the point averages (p < 0.05). When the cigarette use of experimental group and the control group were evaluated, it was clear that 18.2% of the experimental group and 24.2%of the control group still smokes. 9.1% of the experimental group and 14.8% of control group stated that they started smoking because of their friend groups. Among the students who smoke 15.9% of the ones who belongs to the experimental group and 21.9% of the ones who belong to the control group stated they are thinking of quitting smoking. It was clear that there is a significant difference between the pre-education and post-education point averages of experimental group statistically (p ≤ 0.05); however, in terms of control group, there were no significant differences between the pre-test post-test averages statistically. Between the pre-test post-test averages of experimental and control groups there were not any statistically significant differences (p > 0.05). It was found out in the study that the peer education programme is not effective on the smoking habit of Vocational Health School students. When the future studies are being planned in order to evaluate the peer education activity, it can be taken into consideration that the peer education takes a long term and the students in the educator group will be more enthusiastic and a kind of leader in their environment.

Keywords: midwifery, peer, peer education, smoking

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Authors: Natalia Oli, Abhinav Vaidya, Katja Pahkala, Gabriele Eiben, Alexandra Krettek

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The nutritional transition towards a high fat and energy dense diet, decreasing physical activity level, and poor cardiovascular health knowledge contributes to a rising burden of cardiovascular diseases in Nepal. Dietary and physical activity behaviors are formed early in life and influenced by family, particularly by mothers in the social context of Nepal. The purpose of this study was to explore knowledge, attitude and behavior of mothers regarding diet and physical activity of their pre-school children. Cross-sectional study was conducted in the semi-urban area of Duwakot and Jhaukhel communities near the capital Kathmandu. Between August and November 2014, nine trained enumerators interviewed all mothers having children aged 2 to 7 years in their homes. Questionnaire contained information about mothers’ socio-demographic characteristics; their knowledge, attitude, and behavior regarding diet and physical activity as well as their children’s diet and physical activity. Knowledge, attitude and behavior responses were scored. SPSS version 22.0 was used for data analyses. Out of the 1,052 eligible mothers, 962 consented to participate in the study. The mean age was 28.9 ± 4.5 years. The majority of them (73%) were housewives. Mothers with higher education and income had higher knowledge, attitude, and behavior scores (All p < 0.001) whereas housewives and farmers had low knowledge score (p < 0.001). They, along with laborers, also exhibited lower attitude (p<0.001) and behavior scores (p < 0.001). Children’s diet score increased with mothers’ level of education (p <0.001) and income (p=0.041). Their physical activity score, however, declined with increasing level of their mothers’ education (p < 0.001) and income (p < 0.001). Children’s overall behavior score correlated poorly with mothers’ knowledge (r = 0.009, p=0.003), attitude (r =0.012, p=0.001), and behavior (r = 0.007, p= 0.008). Such poor correlation can be due to existence of the barriers among mothers. Mothers reported such barriers as expensive healthy food, difficulty to give up favorite food, taste preference of others family members and lack of knowledge on healthy food. Barriers for physical activity were lack of leisure time, lack of parks and playgrounds, being busy by caring for children and old people, feeling lazy and embarrassed in front of others. Additionally, among the facilitators for healthy lifestyle, mentioned by mothers, were better information, family eating healthy food and supporting physical activity, advice of medical personnel regarding healthy lifestyle and own ill health. The study demonstrated poor correlation of mothers’ knowledge and attitude with children’s behavior regarding diet and physical activity. Hence improving mothers’ knowledge or attitude may not be enough to improve dietary and physical activity habits of their children. Barriers and facilitators that affect mothers’ practices towards their children should also be addressed due to future intervention.

Keywords: attitude, behavior, diet, knowledge, mothers, physical activity

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this study is to evaluate the advantages and disadvantages of various machine learning techniques in forecasting ground-motion intensity measures given source characteristics, source-to-site distance, and local site condition. Intensity measures such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Estimating these variables for future earthquake events is a key step in seismic hazard assessment and potentially subsequent risk assessment of different types of structures. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as a statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The algorithms are adjusted to quantify event-to-event and site-to-site variability of the ground motions by implementing them as random effects in the proposed models to reduce the aleatory uncertainty. All the algorithms are trained using a selected database of 4,528 ground-motions, including 376 seismic events with magnitude 3 to 5.8, recorded over the hypocentral distance range of 4 to 500 km in Oklahoma, Kansas, and Texas since 2005. The main reason of the considered database stems from the recent increase in the seismicity rate of these states attributed to petroleum production and wastewater disposal activities, which necessities further investigation in the ground motion models developed for these states. Accuracy of the models in predicting intensity measures, generalization capability of the models for future data, as well as usability of the models are discussed in the evaluation process. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available.

Keywords: artificial neural network, ground-motion models, machine learning, random forest, support vector machine

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Authors: Md Iqbal Hossain, Tahmeed Ahmed, Kenneth H. Brown

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Objective: To assess the effect of community-based follow up, with or without food-supplementation and/or psychosocial stimulation, as an alternative to current hospital-based follow-up of children with moderate-acute-malnutrition (WHZ < -2 to -3) (MAM). Design/methods: The study was conducted at the ICDDR,B Dhaka Hospital and in four urban primary health care centers of Dhaka, Bangladesh during 2005-2007. The efficacy of five different randomly assigned interventions was compared with respect to the rate of completion of follow-up, growth and morbidity in 227 MAM children aged 6-24 months who were initially treated at ICDDR,B for diarrhea and/or other morbidities. The interventions were: 1) Fortnightly follow-up care (FFC) at the ICDDR,B’s outpatient-unit, including growth monitoring, health education, and micro-nutrient supplementation (H-C, n=49). 2) FFC at community follow-up unit (CNFU) [established in the existing urban primary health-care centers close to the residence of the child] but received the same regimen as H-C (C-C, n=53). 3) As per C-C plus cereal-based supplementary food (SF) (C-SF, n=49). The SF packets were distributed on recruitment and at every visit in CNFU [@1 packet/day for 6–11 and 2 packets/day for 12-24 month old children. Each packet contained 20g toasted rice-powder, 10g toasted lentil-powder, 5g molasses, and 3g soy bean oil, to provide a total of ~ 150kcal with 11% energy from protein]. 4) As per C-C plus psychosocial stimulation (PS) (C-PS, n=43). PS consisted of child-stimulation and parental-counseling conducted by trained health workers. 5) As per C-C plus both SF+PS (C-SF+PS, n=33). Results: A total of 227children (48.5% female), with a mean ± SD age of 12.6 ±3.8 months, and WHZ of - 2.53±0.28 enrolled. Baseline characteristics did not differ by treatment group. The rate of spontaneous attendance at scheduled follow-up visits gradually decreased in all groups. Follow-up attendance and gain in weight and length were greater in groups C-SF, C-SF+PS, and C-PS than C-C, and these indicators were observed least in H-C. Children in the H-C group more often suffered from diarrhea (25 % vs. 4-9%) and fever (28% vs. 8-11%) than other groups (p < 0.05). Children who attended at least five of the total six scheduled follow-up visits gained more in weight (median: 0.86 vs. 0.62 kg, p=0.002), length (median: 2.4 vs. 2.0 cm, p=0.009) than those who attended fewer. Conclusions: Community-based service delivery, especially including supplementary food with or without psychosocial stimulation, permits better rehabilitation of children with MAM compared to current hospital outpatients-based care. By scaling the community-based follow-up including food supplementation with or without psychosocial stimulation, it will be possible to rehabilitate a greater number of MAM children in a better way.

Keywords: community-based management, moderate acute malnutrition, psychosocial stimulation, supplementary food

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Authors: Rafael do Amaral Teodoro, Leandro Augusto da Silva

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Originated from graphite, graphene is a two-dimensional (2D) material that promises to revolutionize technology in many different areas, such as energy, telecommunications, civil construction, aviation, textile, and medicine. This is possible because its structure, formed by carbon bonds, provides desirable optical, thermal, and mechanical characteristics that are interesting to multiple areas of the market. Thus, several research and development centers are studying different manufacturing methods and material applications of graphene, which are often compromised by the scarcity of more agile and accurate methodologies to characterize the material – that is to determine its composition, shape, size, and the number of layers and crystals. To engage in this search, this study proposes a computational methodology that applies deep learning to identify graphene oxide crystals in order to characterize samples by crystal sizes. To achieve this, a fully convolutional neural network called U-net has been trained to segment SEM graphene oxide images. The segmentation generated by the U-net is fine-tuned with a standard deviation technique by classes, which allows crystals to be distinguished with different labels through an object delimitation algorithm. As a next step, the characteristics of the position, area, perimeter, and lateral measures of each detected crystal are extracted from the images. This information generates a database with the dimensions of the crystals that compose the samples. Finally, graphs are automatically created showing the frequency distributions by area size and perimeter of the crystals. This methodological process resulted in a high capacity of segmentation of graphene oxide crystals, presenting accuracy and F-score equal to 95% and 94%, respectively, over the test set. Such performance demonstrates a high generalization capacity of the method in crystal segmentation, since its performance considers significant changes in image extraction quality. The measurement of non-overlapping crystals presented an average error of 6% for the different measurement metrics, thus suggesting that the model provides a high-performance measurement for non-overlapping segmentations. For overlapping crystals, however, a limitation of the model was identified. To overcome this limitation, it is important to ensure that the samples to be analyzed are properly prepared. This will minimize crystal overlap in the SEM image acquisition and guarantee a lower error in the measurements without greater efforts for data handling. All in all, the method developed is a time optimizer with a high measurement value, considering that it is capable of measuring hundreds of graphene oxide crystals in seconds, saving weeks of manual work.

Keywords: characterization, graphene oxide, nanomaterials, U-net, deep learning

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Authors: G. C. Phiri, E. A. Heil, A. A. Kalimbira, E. Muehlhoff, C. Masangano, B. M. Mtimuni, J. Herrmann, M. B. Krawinkel, I. Jordan

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Background and objectives: High prevalence of undernutrition in Malawi is caused by poor complementary foods. Lack of knowledge of age appropriate food within the household might affect utilization of available resources. FAO-Malawi implemented nutrition education (NE) sessions in 200 villages in Kasungu and Mzimba districts from December 2012 to April 2013 targeting 15 caregivers per village of children aged 6-18 months, grandmothers, spouses and community leaders. Two trained volunteers per village facilitated 10 NE sessions on breastfeeding, food safety and hygiene and complementary feeding using locally available resources. This study assessed the reported dietary diversification practices of infant and young child after nutrition education and the factors that influenced adoption of the practice. Methodology: Questionnaire-based interviews with caregivers were conducted in 16 randomly selected villages (n=108) before training-(t1) and seven months after training-(t2). Knowledge score (KS) was calculated on the indicators breastfeeding, hygiene and complementary feeding. Count regression was performed using SPSS 22. Eight focus group discussions (FGDs) were separately conducted among caregivers and grandmothers in 4 villages. Content analysis was used to analyze FGDs data. Results: Following NE, caregivers' KS significantly increased (p<0.001) between t1 and t2 for breastfeeding (7.7 vs. 9.8, max=18), hygiene (3.8 vs. 5.9, max=7) and complementary feeding (10.2 vs. 16.2, max=26). Caregivers indicated that they stopped preparation of plain-refined maize meal porridge after they gained knowledge on dietary diversification of complementary foods. They learnt mushing and pounding of ingredients for enriched porridge. Whole-maize meal or potatoes were often enriched with vegetables, legumes, small fish or eggs and cooking oil. Children liked the taste of enriched porridge. Amount of enriched porridge consumed at each sitting increase among previously fussy-eater children. Meal frequency increased by including fruits as snacks in child’s diet. Grandmothers observed preparation of enriched porridge among the mothers using locally available foods. Grandmothers liked the taste of enriched porridge and not the greenish color of the porridge. Both grandmothers and mothers reported that children were playing independently after consuming enriched porridge and were strong and healthy. These motivated adoption of the practice. Conclusion: Increased knowledge and skill of preparation and utilisation of locally available foods promoted children’s dietary diversification. Children liking the enriched porridge motivated adoption of dietary diversification.

Keywords: behaviour change, complementary feeding, dietary diversification, IYCN

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Authors: Jason Blair, Adeboye Adebayo, Mohamed Saad, Jeannette M. Byrne, Fabien A. Basset

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Running economy is a key physiological parameter of an individual’s running efficacy and a valid tool for predicting performance outcomes. Of the many factors known to influence running economy (RE), footwear certainly plays a role owing to its characteristics that vary substantially from model to model. Although minimalist footwear is believed to enhance RE and thereby endurance performance, conclusive research reports are scarce. Indeed, debates remain as to which footwear characteristics most alter RE. The purposes of this study were, therefore, two-fold: (a) to determine whether wearing minimalist shoes results in better RE compared to shod and to identify relationships with kinematic and muscle activation patterns; (b) to determine whether changes in RE with minimalist shoes are still evident following a fatiguing bout of exercise. Well-trained male distance runners (n=10; 29.0 ± 7.5 yrs; 71.0 ± 4.8 kg; 176.3 ± 6.5 cm) partook first in a maximal O₂ uptake determination test (VO₂ₘₐₓ = 61.6 ± 7.3 ml min⁻¹ kg⁻¹) 7 days prior to the experimental sessions. Second, in a fully randomized fashion, an RE test consisting of three 8-min treadmill runs in shod and minimalist footwear were performed prior to and following exercise induced fatigue (EIF). The minimalist and shod conditions were tested with a minimum of 7-day wash-out period between conditions. The RE bouts, interspaced by 2-min rest periods, were run at 2.79, 3.33, and 3.89 m s⁻¹ with a 1% grade. EIF consisted of 7 times 1000 m at 94-97% VO₂ₘₐₓ interspaced with 3-min recovery. Cardiorespiratory, electromyography (EMG), kinematics, rate of perceived exertion (RPE) and blood lactate were measured throughout the experimental sessions. A significant main speed effect on RE (p=0.001) and stride frequency (SF) (p=0.001) was observed. The pairwise comparisons showed that running at 2.79 m s⁻¹ was less economic compared to 3.33, and 3.89 m s⁻¹ (3.56 ± 0.38, 3.41 ± 0.45, 3.40 ± 0.45 ml O₂ kg⁻¹ km⁻¹; respectively) and that SF increased as a function of speed (79 ± 5, 82 ± 5, 84 ± 5 strides min⁻¹). Further, EMG analyses revealed that root mean square EMG significantly increased as a function of speed for all muscles (Biceps femoris, Gluteus maximus, Gastrocnemius, Tibialis anterior, Vastus lateralis). During EIF, the statistical analysis revealed a significant main effect of time on lactate production (from 2.7 ± 5.7 to 11.2 ± 6.2 mmol L⁻¹), RPE scores (from 7.6 ± 4.0 to 18.4 ± 2.7) and peak HR (from 171 ± 30 to 181 ± 20 bpm), expect for the recovery period. Surprisingly, a significant main footwear effect was observed on running speed during intervals (p=0.041). Participants ran faster with minimalist shoes compared to shod (3:24 ± 0:44 min [95%CI: 3:14-3:34] vs. 3:30 ± 0:47 min [95%CI: 3:19-3:41]). Although EIF altered lactate production and RPE scores, no other effect was noticeable on RE, EMG, and SF pre- and post-EIF, except for the expected speed effect. The significant footwear effect on running speed during EIF was unforeseen but could be due to shoe mass and/or heel-toe-drop differences. We also cannot discard the effect of speed on foot-strike pattern and therefore, running performance.

Keywords: exercise-induced fatigue, interval training, minimalist footwear, running economy

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Authors: Sue S. Feldman

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As clinical and nursing informatics mature, an area that has gotten a lot of attention is the value capstone projects. Capstones are meant to address authentic and complex domain-specific problems. While capstone projects have not always been essential in graduate clinical and nursing informatics education, employers are wanting to see evidence of the prospective employee's knowledge and skills as an indication of employability. Capstones can be organized in many ways: a single course over a single semester, multiple courses over multiple semesters, as a targeted demonstration of skills, as a synthesis of prior knowledge and skills, mentored by one single person or mentored by various people, submitted as an assignment or presented in front of a panel. Because of the potential for capstones to enhance the educational experience, and as a mechanism for application of knowledge and demonstration of skills, a rigorous capstone can accelerate a graduate's potential in the workforce. In 2016, the capstone at the University of Alabama at Birmingham (UAB) could feel the external forces of a maturing Clinical and Nursing Informatics discipline. While the program had a capstone course for many years, it was lacking the depth of knowledge and demonstration of skills being asked for by those hiring in a maturing Informatics field. Since the program is online, all capstones were always in the online environment. While this modality did not change, other contributors to instruction modality changed. Pre-2016, the instruction modality was self-guided. Students checked in with a single instructor, and that instructor monitored progress across all capstones toward a PowerPoint and written paper deliverable. At the time, the enrollment was few, and the maturity had not yet pushed hard enough. By 2017, doubling enrollment and the increased demand of a more rigorously trained workforce led to restructuring the capstone so that graduates would have and retain the skills learned in the capstone process. There were three major changes: the capstone was broken up into a 3-course sequence (meaning it lasted about 10 months instead of 14 weeks), there were many chunks of deliverables, and each faculty had a cadre of about 5 students to advise through the capstone process. Literature suggests that the chunking, breaking up complex projects (i.e., the capstone in one summer) into smaller, more manageable chunks (i.e., chunks of the capstone across 3 semesters), can increase and sustain learning while allowing for increased rigor. By doing this, the teaching responsibility was shared across faculty with each semester course being taught by a different faculty member. This change facilitated delving much deeper in instruction and produced a significantly more rigorous final deliverable. Having students advised across the faculty seemed like the right thing to do. It not only shared the load, but also shared the success of students. Furthermore, it meant that students could be placed with an academic advisor who had expertise in their capstone area, further increasing the rigor of the entire capstone process and project and increasing student knowledge and skills.

Keywords: capstones, clinical informatics, health informatics, informatics

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Authors: Shayla He

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Background and Purpose: According to Chamie (2017), it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. The homeless population in the United States has grown rapidly in the past four decades. In New York City, the sheltered homeless population has increased from 12,830 in 1983 to 62,679 in 2020. Knowing the trend on the homeless population is crucial at helping the states and the cities make affordable housing plans, and other community service plans ahead of time to better prepare for the situation. This study utilized the data from New York City, examined the key factors associated with the homelessness, and developed systematic modeling to predict homeless populations of the future. Using the best model developed, named HP-RNN, an analysis on the homeless population change during the months of 2020 and 2021, which were impacted by the COVID-19 pandemic, was conducted. Moreover, HP-RNN was tested on the data from Seattle. Methods: The methodology involves four phases in developing robust prediction methods. Phase 1 gathered and analyzed raw data of homeless population and demographic conditions from five urban centers. Phase 2 identified the key factors that contribute to the rate of homelessness. In Phase 3, three models were built using Linear Regression, Random Forest, and Recurrent Neural Network (RNN), respectively, to predict the future trend of society's homeless population. Each model was trained and tuned based on the dataset from New York City for its accuracy measured by Mean Squared Error (MSE). In Phase 4, the final phase, the best model from Phase 3 was evaluated using the data from Seattle that was not part of the model training and tuning process in Phase 3. Results: Compared to the Linear Regression based model used by HUD et al (2019), HP-RNN significantly improved the prediction metrics of Coefficient of Determination (R2) from -11.73 to 0.88 and MSE by 99%. HP-RNN was then validated on the data from Seattle, WA, which showed a peak %error of 14.5% between the actual and the predicted count. Finally, the modeling results were collected to predict the trend during the COVID-19 pandemic. It shows a good correlation between the actual and the predicted homeless population, with the peak %error less than 8.6%. Conclusions and Implications: This work is the first work to apply RNN to model the time series of the homeless related data. The Model shows a close correlation between the actual and the predicted homeless population. There are two major implications of this result. First, the model can be used to predict the homeless population for the next several years, and the prediction can help the states and the cities plan ahead on affordable housing allocation and other community service to better prepare for the future. Moreover, this prediction can serve as a reference to policy makers and legislators as they seek to make changes that may impact the factors closely associated with the future homeless population trend.

Keywords: homeless, prediction, model, RNN

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Authors: Itze Coronel Salomon

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The rise of organized crime and violence related to drug cartels in Mexico has created serious challenges for the authorities to provide security to those who live within its borders. However, to achieve a significant improvement in security is absolute respect for fundamental human rights by the authorities. Irregular migrants in Mexico are at serious risk of abuse. Research by Amnesty International as well as reports of the NHRC (National Human Rights) in Mexico, have indicated the major humanitarian crisis faced by thousands of migrants traveling in the shadows. However, the true extent of the problem remains invisible to the general population. The fact that federal and state governments leave no proper record of abuse and do not publish reliable data contributes to ignorance and misinformation, often spread by the media that portray migrants as the source of crime rather than their victims. Discrimination and intolerance against irregular migrants can generate greater hostility and exclusion. According to the modus operandi that has been recorded criminal organizations and criminal groups linked to drug trafficking structures deprive migrants of their liberty for forced labor and illegal activities related to drug trafficking, even some have been kidnapped for be trained as murderers . If the victim or their family cannot pay the ransom, the kidnapped person may suffer torture, mutilation and amputation of limbs or death. Migrant women are victims of sexual abuse during her abduction as well. In 2011, at least 177 bodies were identified in the largest mass grave found in Mexico, located in the town of San Fernando, in the border state of Tamaulipas, most of the victims were killed by blunt instruments, and most seemed to be immigrants and travelers passing through the country. With dozens of small graves discovered in northern Mexico, this may suggest a change in tactics between organized crime groups to the different means of obtaining revenue and reduce murder profile methods. Competition and conflict over territorial control drug trafficking can provide strong incentives for organized crime groups send signals of violence to the authorities and rival groups. However, as some Mexican organized crime groups are increasingly looking to take advantage of income and vulnerable groups, such as Central American migrants seem less interested in advertising his work to authorities and others, and more interested in evading detection and confrontation. This paper pretends to analyze the introduction of this new trend of kidnapping migrants for forced labors by drug cartels in Mexico into the forms of contemporary slavery and its implications.

Keywords: international law, migration, transnational organized crime

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Authors: Laith F. Gulli, Nicole M. Mallory

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We developed a novel measurement instrument to assess Knowledge of Educational Programs in Professional Psychotherapy Programs (KEP-PPP or KEP-Triple P) within the United States. The instrument was designed by a Panel of Experts (PoE) that consisted of Licensed Psychotherapists and Medical Care Providers. Licensure track psychotherapy programs are listed in the databases of the Commission on Accreditation for Marriage and Family Therapy Education (COAMFTE); American Psychological Association (APA); Council on Social Work Education (CSWE); and the Council for Accreditation of Counseling & Related Educational Programs (CACREP). A complete list of psychotherapy programs can be obtained from these professional databases, selecting search fields of (All Programs) in (All States). Each program has a Web link that electronically and directly connects to the institutional program, which can be researched using the KEP-Triple P. The 29-item KEP Triple P was designed to consist of six categorical fields; Institutional Type: Degree: Educational Delivery: Accreditation: Coursework Competency: and Special Program Considerations. The KEP-Triple P was designed to determine whether a specific course(s) is offered in licensure track psychotherapy programs. The KEP-Triple P is designed to be modified to assess any part or the entire curriculum of licensure graduate programs. We utilized the KEP-Triple P instrument to study whether a graduate course in Addictions was offered in Marriage and Family Therapy (MFT) programs. Marriage and Family Therapists are likely to commonly encounter patients with Addiction(s) due to the broad treatment scope providing psychotherapy services to individuals, couples and families of all age groups. Our study of 124 MFT programs which concluded at the end of 2016 found that we were able to assess 61 % of programs (N = 76) since 27 % (N = 34) of programs were inaccessible due to broken Web links. From the total of all MFT programs 11 % (N = 14) did not have a published curriculum on their Institutional Web site. From the sample study, we found that 66 % (N = 50) of curricula did not offer a course in Addiction Treatment and that 34 % (N =26) of curricula did require a mandatory course in Addiction Treatment. From our study sample, we determined that 15 % (N = 11) of MFT doctorate programs did not require an Addictions Treatment course and that 1 % (N = 1) did require such a course. We found that 99 % of our study sample offered a Campus based program and 1 % offered a hybrid program with both online and residential components. From the total sample studied, we determined that 84 % of programs would be able to obtain reaccreditation within a five-year period. We recommend that MFT programs initiate procedures to revise curricula to include a required course in Addiction Treatment prior to their next accreditation cycle, to improve the escalating addiction crisis in the United States. This disparity in MFT curricula raises serious ethical and legal consideration for national and Federal stakeholders as well as for patients seeking a competently trained psychotherapist.

Keywords: addiction, competency, curriculum, psychotherapy

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Authors: Kadhim Alabady

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Background: Autism poses a particularly large public health challenge and an inspiring lifelong challenge for many families; it is a lifelong challenge of a different kind. Purpose: Therefore, it is important to understand what the key challenges are and how to improve the lives of children who are affected with autism in Dubai. Method: In order to carry out this research we have used a qualitative methodology. We performed structured in–depth interviews and focus groups with mental health professionals working at: Al Jalila hospital (AJH), Dubai Autism Centre (DAC), Dubai Rehabilitation Centre for Disabilities, Latifa hospital, Private Sector Healthcare (PSH). In addition to that, we conducted quantitative approach to estimate ASD prevalence or incidence data due to lack of registry. ASD estimates are based on research from national and international documents. This approach was applied to increase the validity of the findings by using a variety of data collection techniques in order to explore issues that might not be highlighted through one method alone. Key findings: Autism is the most common of the Pervasive Developmental Disorders. Dubai Autism Center estimates it affects 1 in 146 births (0.68%). If we apply these estimates to the total number of births in Dubai for 2014, it is predicted there would be approximately 199 children (of which 58 were Nationals and 141 were Non–Nationals) suffering from autism at some stage. 16.4% of children (through their families) seek help for ASD assessment between the age group 6–18+. It is critical to understand and address factors for seeking late–stage diagnosis, as ASD can be diagnosed much earlier and how many of these later presenters are actually diagnosed with ASD. Autism spectrum disorder (ASD) is a public health concern in Dubai. Families do not consult GPs for early diagnosis for a variety of reasons including cultural reasons. Recommendations: Effective school health strategies is needed and implemented by nurses who are qualified and experienced in identifying children with ASD. There is a need for the DAC to identify and develop a closer link with neurologists specializing in Autism, to work alongside and for referrals. Autism can be attributed to many factors, some of those are neurological. Currently, when families need their child to see a neurologist they have to go independently and search through the many that are available in Dubai and who are not necessarily specialists in Autism. Training of GP’s to aid early diagnosis of Autism and increase awareness. Since not all GP’s are trained to make such assessments increasing awareness about where to send families for a complete assessment and the necessary support. There is an urgent need for an adult autism center for when the children leave the safe environment of the school at 18 years. These individuals require a day center or suitable job training/placements where appropriate. There is a need for further studies to cover the needs of people with an Autism Spectrum Disorder (ASD).

Keywords: autism spectrum disorder, autism, pervasive developmental disorders, incidence

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Authors: Emily Moore, Dora Amos, Tracy Ellimah, Natasha Parrott

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Postoperative haematoma is a well-recognised complication of thyroid surgery with an incidence of 1-5%. Haematoma formation causes progressive airway obstruction, necessitating emergency bedside haematoma evacuation in up to ¼ of patients. ENT UK, BAETS and DAS have developed consensus guidelines to improve perioperative care, recommending that all healthcare staff interacting with patients undergoing thyroid surgery should be trained in managing post-thyroidectomy haematoma. The aim was to assess the effectiveness of a hybrid simulation model in improving clinician’s confidence in dealing with this surgical emergency. A hybrid simulation was designed, consisting of a standardised patient wearing a part-task trainer to mimic a post-thyroidectomy haematoma in a real patient. The part-task trainer was an adapted C-spine collar with layers of silicone representing the skin and strap muscles and thickened jelly representing the haematoma. Both the skin and strap muscle layers had to be opened in order to evacuate the haematoma. Boxes have been implemented into the appropriate post operative areas (recovery and surgical wards), which contain a printed algorithm designed to assist in remembering a sequence of steps for haematoma evacuation using the ‘SCOOP’ method (skin exposure, cut sutures, open skin, open muscles, pack wound) along with all the necessary equipment to open the front of the neck. Small-group teaching sessions were delivered by ENT and anaesthetic trainees to members of the multidisciplinary team normally involved in perioperative patient care, which included ENT surgeons, anaesthetists, recovery nurses, HCAs and ODPs. The DESATS acronym of signs and symptoms to recognise (difficulty swallowing, EWS score, swelling, anxiety, tachycardia, stridor) was highlighted. Then participants took part in the hybrid simulation in order to practice this ‘SCOOP’ method of haematoma evacuation. Participants were surveyed using a Likert scale to assess their level of confidence pre- and post teaching session. 30 clinicians took part. Confidence (agreed/strongly agreed) in recognition of post thyroidectomy haematoma improved from 58.6% to 96.5%. Confidence in management improved from 27.5% to 89.7%. All participants successfully decompressed the haematoma. All participants agreed/strongly agreed, that the sessions were useful for their learning. Multidisciplinary team simulation teaching is effective at significantly improving confidence in both the recognition and management of postoperative haematoma. Hybrid simulation sessions are useful and should be incorporated into training for clinicians.

Keywords: thyroid surgery, haematoma, teaching, hybrid simulation

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Authors: Carolina Brandão Piva

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In our visual culture, images play a newly significant role in the basis of a complex dialogue between imagination, creativity, and social practice. Insofar as imagination has broken out of the 'special expressive space of art' to become a part of the quotidian mental work of ordinary people, it is pertinent to recognize that visual representation can no longer be assumed as if in a domain detached from everyday life or exclusively 'centered' within the limited frame of 'art history.' The approach of Visual Culture as a field of study is, in this sense, indispensable to comprehend that not only 'the image,' but also 'the imagined' and 'the imaginary' are produced in the plurality of social interactions; crucial enough, this assertion directs us to something new in contemporary cultural processes, namely both imagination and image production constitute a social practice. This paper starts off with this approach and seeks to examine the artistic practice of two women from the State of Goiás, Brazil, who are ordinary citizens with their daily activities and narratives but also dedicated to visuality production. With no formal training from art schools, branded or otherwise, Maria Aparecida de Souza Pires deploys 'waste disposal' of daily life—from car tires to old work clothes—as a trampoline for art; also adept at sourcing raw materials collected from her surroundings, she manipulates raw hewn wood, tree trunks, plant life, and various other pieces she collects from nature giving them new meaning and possibility. Hilda Freire works with sculptures in clay using different scales and styles; her art focuses on representations of women and pays homage to unprivileged groups such as the practitioners of African-Brazilian religions, blue-collar workers, poor live-in housekeepers, and so forth. Although they have never been acknowledged by any mainstream art institution in Brazil, whose 'criterion of value' still favors formally trained artists, Maria Aparecida de Souza Pires, and Hilda Freire have produced visualities that instigate 'new ways of seeing,' meriting cultural significance in many ways. Their artworks neither descend from a 'traditional' medium nor depend on 'canonical viewing settings' of visual representation; rather, they consist in producing relationships with the world which do not result in 'seeing more,' but 'at least differently.' From this perspective, the paper finally demonstrates that grouping this kind of artistic production under the label of 'mere craft' has much more to do with who is privileged within the fields of power in art system, who we see and who we do not see, and whose imagination of what is fed by which visual images in Brazilian contemporary society.

Keywords: visual culture, artistic practice, women's art in the Brazilian State of Goiás, Maria Aparecida de Souza Pires, Hilda Freire

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Authors: C. M. Sapkota, B. P. Sapkota

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Background: Continuous developments in science and technology are increasing the availability of thousands of medical devices – all of which should be of good quality and used appropriately to address global health challenges. It is obvious that bio medical devices are becoming ever more indispensable in health service delivery and among the key workforce responsible for their design, development, regulation, evaluation and training in their use: biomedical technician (BMET) is the crucial. As a pivotal member of health workforce, biomedical technicians are an essential component of the quality health service delivery mechanism supporting the attainment of the Sustainable Development Goals. Methods: The study was based on cross sectional descriptive design. Indicators measuring the quality of health services were assessed in Mechi Zonal Hospital (MZH) and Sagarmatha Zonal Hospital (SZH). Indicators were calculated based on the data about hospital utilization and performance of 2018 available in Medical record section of both hospitals. MZH had employed the BMET during 2018 but SZH had no BMET in 2018.Focus Group Discussion with health workers in both hospitals was conducted to validate the hospital records. Client exit interview was conducted to assess the level of client satisfaction in both the hospitals. Results: In MZH there was round the clock availability and utilization of Radio diagnostics equipment, Laboratory equipment. Operation Theater was functional throughout the year. Bed Occupancy rate in MZH was 97% but in SZH it was only 63%.In SZH, OT was functional only 54% of the days in 2018. CT scan machine was just installed but not functional. Computerized X-Ray in SZH was functional only in 72% of the days. Level of client satisfaction was 87% in MZH but was just 43% in SZH. MZH performed all (256) the Caesarean Sections but SZH performed only 36% of 210 Caesarean Sections in 2018. In annual performance ranking of Government Hospitals, MZH was placed in 1st rank while as SZH was placed in 19th rank out of 32 referral hospitals nationwide in 2018. Conclusion: Biomedical technicians are the crucial member of the human resource for health team with the pivotal role. Trained and qualified BMET professionals are required within health-care systems in order to design, evaluate, regulate, acquire, maintain, manage and train on safe medical technologies. Applying knowledge of engineering and technology to health-care systems to ensure availability, affordability, accessibility, acceptability and utilization of the safer, higher quality, effective, appropriate and socially acceptable bio medical technology to populations for preventive, promotive, curative, rehabilitative and palliative care across all levels of the health service delivery.

Keywords: biomedical equipment technicians, BMET, human resources for health, HRH, quality health service, rural hospitals

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Authors: Goutam Kumar Ghorai, Sandip Sadhukhan, Arpita Sarkar, Debprasad Sinha, G. Sarkar, Ashis K. Dhara

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Diabetes mellitus is one of the most common chronic diseases in all countries and continues to increase in numbers significantly. Diabetic retinopathy (DR) is damage to the retina that occurs with long-term diabetes. DR is a major cause of blindness in the Indian population. Therefore, its early diagnosis is of utmost importance towards preventing progression towards imminent irreversible loss of vision, particularly in the huge population across rural India. The barriers to eye examination of all diabetic patients are socioeconomic factors, lack of referrals, poor access to the healthcare system, lack of knowledge, insufficient number of ophthalmologists, and lack of networking between physicians, diabetologists and ophthalmologists. A few diabetic patients often visit a healthcare facility for their general checkup, but their eye condition remains largely undetected until the patient is symptomatic. This work aims to focus on the design and development of a fully automated intelligent decision system for screening retinal fundus images towards detection of the pathophysiology caused by microaneurysm in the early stage of the diseases. Automated detection of microaneurysm is a challenging problem due to the variation in color and the variation introduced by the field of view, inhomogeneous illumination, and pathological abnormalities. We have developed aconvolutional neural network for efficient detection of microaneurysm. A loss function is also developed to handle severe class imbalance due to very small size of microaneurysms compared to background. The network is able to locate the salient region containing microaneurysms in case of noisy images captured by non-mydriatic cameras. The ground truth of microaneurysms is created by expert ophthalmologists for MESSIDOR database as well as private database, collected from Indian patients. The network is trained from scratch using the fundus images of MESSIDOR database. The proposed method is evaluated on DIARETDB1 and the private database. The method is successful in detection of microaneurysms for dilated and non-dilated types of fundus images acquired from different medical centres. The proposed algorithm could be used for development of AI based affordable and accessible system, to provide service at grass root-level primary healthcare units spread across the country to cater to the need of the rural people unaware of the severe impact of DR.

Keywords: retinal fundus image, deep convolutional neural network, early detection of microaneurysms, screening of diabetic retinopathy

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Authors: Jane E. Hill

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Quality of life is an important component for many. With that in mind, everyone will experience some type of loss within his or her lifetime. A person can experience loss due to break up, separation, divorce, estrangement, or death. An individual may experience loss of a job, loss of capacity, or loss caused by human or natural-caused disasters. An individual’s response to such a loss is unique to them, and not everyone will seek services to assist them with their grief due to loss. Counseling can promote positive outcomes for clients that are grieving by addressing the client’s personal loss and helping the client process their grief. However, a lack of understanding on the part of counselors of how people grieve may result in negative client outcomes such as poor health, psychological distress, or an increased risk of depression. Education and training in grief counseling can improve counselors’ problem recognition and skills in treatment planning. The purpose of this study was to examine whether the Council for Accreditation of Counseling and Related Educational Programs (CACREP) master’s degree counseling students view themselves as having been adequately trained in grief theories and skills. Many people deal with grief issues that prevent them from having joy or purpose in their lives and that leaves them unable to engage in positive opportunities or relationships. This study examined CACREP-accredited master’s counseling students’ self-reported competency, training, and education in providing grief counseling. The implications for positive social change arising from the research may be to incorporate and promote education and training in grief theories and skills in a majority of counseling programs and to provide motivation to incorporate professional standards for grief training and practice in the mental health counseling field. The theoretical foundation used was modern grief theory based on John Bowlby’s work on Attachment Theory. The overall research question was how competent do master’s-level counselors view themselves regarding the education or training they received in grief theories or counseling skills in their CACREP-accredited studies. The author used a non-experimental, one shot survey comparative quantitative research design. Cicchetti’s Grief Counseling Competency Scale (GCCS) was administered to CACREP master’s-level counseling students enrolled in their practicum or internship experience, which resulted in 153 participants. Using a MANCOVA, there was significance found for relationships between coursework taken and (a) perceived assessment skills (p = .029), (b) perceived treatment skills (p = .025), and (c) perceived conceptual skills and knowledge (p = .003). Results of this study provided insight for CACREP master’s-level counseling programs to explore and discuss curriculum coursework inclusion of education and training in grief theories and skills.

Keywords: counselor education and training, grief education and training, grief and loss, quality of life

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Authors: Klaus Froehlich-Gildhoff, Ullrich Boettinger, Katharina Rauh, Angela Schickler

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The Prevention Network Ortenaukreis, PNO, funded by the German Ministry of Education and Research, aims to promote physical and mental health as well as the social inclusion of 3 to 10 years old children and their families in the Ortenau district. Within a period of four years starting 11/2014 a community network will be established. One regional and five local prevention representatives are building networks with stakeholders of the prevention and health promotion field bridging the health care, educational and youth welfare system in a multidisciplinary approach. The regional prevention representative implements regularly convening prevention and health conferences. On a local level, the 5 local prevention representatives implement round tables in each area as a platform for networking. In the setting approach, educational institutions are playing a vital role when gaining access to children and their families. Thus the project will offer 18 month long organizational development processes with specially trained coaches to 25 kindergarten and 25 primary schools. The process is based on a curriculum of prevention and health promotion which is adapted to the specific needs of the institutions. Also to ensure that the entire region is reached demand oriented advanced education courses are implemented at participating day care centers, kindergartens and schools. Evaluation method: The project is accompanied by an extensive research design to evaluate the outcomes of different project components such as interview data from community prevention agents, interviews and network analysis with families at risk on their support structures, data on community network development and monitoring, as well as data from kindergarten and primary schools. The latter features a waiting-list control group evaluation in kindergarten and primary schools with a mixed methods design using questionnaires and interviews with pedagogues, teachers, parents, and children. Results: By the time of the conference pre and post test data from the kindergarten samples (treatment and control group) will be presented, as well as data from the first project phase, such as qualitative interviews with the prevention coordinators as well as mixed methods data from the community needs assessment. In supporting this project, the Federal Ministry aims to gain insight into efficient components of community prevention and health promotion networks as it is implemented and evaluated. The district will serve as a model region, so that successful components can be transferred to other regions throughout Germany. Accordingly, the transferability to other regions is of high interest in this project.

Keywords: childhood research, health promotion, physical health, prevention network, psychological well-being, social inclusion

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Authors: Tanvi Rajesh Sanghavi

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Introduction: Neurodiversity is a concept which captures the different ways in which everyone's brain functions and is considered as part of normal variation. It is a strength-based approach which focuses on the individual's strengths and capabilities and believes in providing support wherever necessary. In many parts of the world, those diagnosed with autism spectrum disorder have been ostracized and ridiculed due to their sensory and communication differences. Hence, it becomes important for the teachers to have knowledge about autism and understand the needs of children with Autism. Need: India is rich in terms of culture, languages and religious diversity. It is important to study neurodiversity in such a population for better understanding of neurodiverse individuals and appropriate intervention. Aim & objectives: This study seeks teachers' knowledge of the causes, traits and educational requirements of children with autism spectrum disorder (ASD). It also aims to find out whether mainstream schools actually provide training programs to the teachers to manage such children along with the necessary accommodations. Method: The current study was a cross-sectional study conducted among school teachers. A total of 30 school teachers were taken for the study. The participants were enrolled after informed consent. The participants were directed to a google form consisting of objective questions. The first part of the questionnaire elicited information about school, teaching experience, qualification, etc. There were specific questions extracting details on attending/conducting sensitization and professional programs in regard to care for autistic children. The second part of the questionnaire consisted of some basic questions on the teacher’s understanding of diagnosis, traits, causes, road to recovery and understanding the educational and communication needs of autistic children from the teacher’s perspective. The responses were tabulated and analyzed descriptively. Results: Most of the teachers had 5–10 years of teaching experience. The majority of the teachers used the term “special child” for autistic children. Around 54.8% (17 teachers) of the total teachers felt that the parents of autistic children should teach their child to learn adaptive skills and 41.9% of the teachers felt that they should take medical intervention. About 50% of the teachers felt that the cause of autism is related to pre-natal maternal factors and about 40% felt that its cause is genetic. Only a small percentage of teachers felt that they were trained to manage the children with autism. More than 50% of the teachers mentioned that their schools do not conduct training programs for managing these children. Discussion & Conclusion: In this study, the knowledge and perspectives of teachers on children with ASD were studied. The most widely held contemporary belief is that genetic factors play a major part in the development of ASD, although the existing evidence is muddled, with numerous opposing perspectives on the nature of this mechanism. It is worth noting that any culture's level of humanity is mirrored in how that society "treats" its vulnerable population.

Keywords: autism, neurodiversity, awareness, education

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Authors: Purnima Dhall, Rita Kumar

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Introduction: River Yamuna, in the national capital territory (NCT), and also the primary source of drinking water for the city. Delhi discharges about 3,684 MLD of sewage through its 18 drains in to the Yamuna. Water quality monitoring is an important aspect of water management concerning to the pollution control. Public concern and legislation are now a day’s demanding better environmental control. Conventional method for estimating BOD5 has various drawbacks as they are expensive, time-consuming, and require the use of highly trained personnel. Stringent forthcoming regulations on the wastewater have necessitated the urge to develop analytical system, which contribute to greater process efficiency. Biosensors offer the possibility of real time analysis. Methodology: In the present study, a novel rapid method for the determination of biochemical oxygen demand (BOD) has been developed. Using the developed method, the BOD of a sample can be determined within 2 hours as compared to 3-5 days with the standard BOD3-5day assay. Moreover, the test is based on specified consortia instead of undefined seeding material therefore it minimizes the variability among the results. The device is coupled to software which automatically calculates the dilution required, so, the prior dilution of the sample is not required before BOD estimation. The developed BOD-Biosensor makes use of immobilized microorganisms to sense the biochemical oxygen demand of industrial wastewaters having low–moderate–high biodegradability. The method is quick, robust, online and less time consuming. Findings: The results of extensive testing of the developed biosensor on drains demonstrate that the BOD values obtained by the device correlated with conventional BOD values the observed R2 value was 0.995. The reproducibility of the measurements with the BOD biosensor was within a percentage deviation of ±10%. Advantages of developed BOD biosensor • Determines the water pollution quickly in 2 hours of time; • Determines the water pollution of all types of waste water; • Has prolonged shelf life of more than 400 days; • Enhanced repeatability and reproducibility values; • Elimination of COD estimation. Distinctiveness of Technology: • Bio-component: can determine BOD load of all types of waste water; • Immobilization: increased shelf life > 400 days, extended stability and viability; • Software: Reduces manual errors, reduction in estimation time. Conclusion: BiosensorBOD can be used to measure the BOD value of the real wastewater samples. The BOD biosensor showed good reproducibility in the results. This technology is useful in deciding treatment strategies well ahead and so facilitating discharge of properly treated water to common water bodies. The developed technology has been transferred to M/s Forbes Marshall Pvt Ltd, Pune.

Keywords: biosensor, biochemical oxygen demand, immobilized, monitoring, Yamuna

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Authors: Farid Khosravikia, Patricia Clayton

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The main objective of this work is to evaluate the advantages and disadvantages of various machine learning techniques in two key steps of seismic hazard and risk assessment of different types of structures. The first step is the development of ground-motion models, which are used for forecasting ground-motion intensity measures (IM) given source characteristics, source-to-site distance, and local site condition for future events. IMs such as peak ground acceleration and velocity (PGA and PGV, respectively) as well as 5% damped elastic pseudospectral accelerations at different periods (PSA), are indicators of the strength of shaking at the ground surface. Typically, linear regression-based models, with pre-defined equations and coefficients, are used in ground motion prediction. However, due to the restrictions of the linear regression methods, such models may not capture more complex nonlinear behaviors that exist in the data. Thus, this study comparatively investigates potential benefits from employing other machine learning techniques as statistical method in ground motion prediction such as Artificial Neural Network, Random Forest, and Support Vector Machine. The results indicate the algorithms satisfy some physically sound characteristics such as magnitude scaling distance dependency without requiring pre-defined equations or coefficients. Moreover, it is shown that, when sufficient data is available, all the alternative algorithms tend to provide more accurate estimates compared to the conventional linear regression-based method, and particularly, Random Forest outperforms the other algorithms. However, the conventional method is a better tool when limited data is available. Second, it is investigated how machine learning techniques could be beneficial for developing probabilistic seismic demand models (PSDMs), which provide the relationship between the structural demand responses (e.g., component deformations, accelerations, internal forces, etc.) and the ground motion IMs. In the risk framework, such models are used to develop fragility curves estimating exceeding probability of damage for pre-defined limit states, and therefore, control the reliability of the predictions in the risk assessment. In this study, machine learning algorithms like artificial neural network, random forest, and support vector machine are adopted and trained on the demand parameters to derive PSDMs for them. It is observed that such models can provide more accurate estimates of prediction in relatively shorter about of time compared to conventional methods. Moreover, they can be used for sensitivity analysis of fragility curves with respect to many modeling parameters without necessarily requiring more intense numerical response-history analysis.

Keywords: artificial neural network, machine learning, random forest, seismic risk analysis, seismic hazard analysis, support vector machine

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Authors: Shrabani Ghosh

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Automatic offensive language detection in social media has become a stirring task in today's NLP. Manual Offensive language detection is tedious and laborious work where automatic methods based on machine learning are only alternatives. Previous works have done sentiment analysis over social media in different ways such as supervised, semi-supervised, and unsupervised manner. Domain adaptation in a semi-supervised way has also been explored in NLP, where the source domain and the target domain are different. In domain adaptation, the source domain usually has a large amount of labeled data, while only a limited amount of labeled data is available in the target domain. Pretrained transformers like BERT, RoBERTa models are fine-tuned to perform text classification in an unsupervised manner to perform further pre-train masked language modeling (MLM) tasks. In previous work, hate speech detection has been explored in Gab.ai, which is a free speech platform described as a platform of extremist in varying degrees in online social media. In domain adaptation process, Twitter data is used as the source domain, and Gab data is used as the target domain. The performance of domain adaptation also depends on the cross-domain similarity. Different distance measure methods such as L2 distance, cosine distance, Maximum Mean Discrepancy (MMD), Fisher Linear Discriminant (FLD), and CORAL have been used to estimate domain similarity. Certainly, in-domain distances are small, and between-domain distances are expected to be large. The previous work finding shows that pretrain masked language model (MLM) fine-tuned with a mixture of posts of source and target domain gives higher accuracy. However, in-domain performance of the hate classifier on Twitter data accuracy is 71.78%, and out-of-domain performance of the hate classifier on Gab data goes down to 56.53%. Recently self-supervised learning got a lot of attention as it is more applicable when labeled data are scarce. Few works have already been explored to apply self-supervised learning on NLP tasks such as sentiment classification. Self-supervised language representation model ALBERTA focuses on modeling inter-sentence coherence and helps downstream tasks with multi-sentence inputs. Self-supervised attention learning approach shows better performance as it exploits extracted context word in the training process. In this work, a self-supervised attention mechanism has been proposed to detect hate speech on Gab.ai. This framework initially classifies the Gab dataset in an attention-based self-supervised manner. On the next step, a semi-supervised classifier trained on the combination of labeled data from the first step and unlabeled data. The performance of the proposed framework will be compared with the results described earlier and also with optimized outcomes obtained from different optimization techniques.

Keywords: attention learning, language model, offensive language detection, self-supervised learning

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Authors: Jongtae Rhee, Sangkwon Han, Seungbin Ji, Junhyeong Park, Byeonghun Kim, Taekyung Kim, Byeonghyeon Jeon, Jiwoo Yang

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The shared kitchen is a concept that transfers the value of the sharing economy to the kitchen. It is a type of kitchen equipped with cooking facilities that allows multiple companies or chefs to share time and space and use it jointly. These shared kitchens provide economic benefits and convenience, such as reduced investment costs and rent, but also increase the risk of safety management, such as cross-contamination of food ingredients. Therefore, to manage the safety of food ingredients and finished products in a shared kitchen where several entities jointly use the kitchen and handle various types of food ingredients, it is critical to manage followings: the freshness of food ingredients, user hygiene and safety and cross-contamination of cooking equipment and facilities. In this study, it propose a machine learning-based system for hygiene safety and cross-contamination management, which are highly difficult to manage. User clothing management and user access management, which are most relevant to the hygiene and safety of shared kitchens, are solved through machine learning-based methodology, and cutting board usage management, which is most relevant to cross-contamination management, is implemented as an integrated safety management system based on artificial intelligence. First, to prevent cross-contamination of food ingredients, we use images collected through a real-time camera to determine whether the food ingredients match a given cutting board based on a real-time object detection model, YOLO v7. To manage the hygiene of user clothing, we use a camera-based facial recognition model to recognize the user, and real-time object detection model to determine whether a sanitary hat and mask are worn. In addition, to manage access for users qualified to enter the shared kitchen, we utilize machine learning based signature recognition module. By comparing the pairwise distance between the contract signature and the signature at the time of entrance to the shared kitchen, access permission is determined through a pre-trained signature verification model. These machine learning-based safety management tasks are integrated into a single information system, and each result is managed in an integrated database. Through this, users are warned of safety dangers through the tablet PC installed in the shared kitchen, and managers can track the cause of the sanitary and safety accidents. As a result of system integration analysis, real-time safety management services can be continuously provided by artificial intelligence, and machine learning-based methodologies are used for integrated safety management of shared kitchens that allows dynamic contracts among various users. By solving this problem, we were able to secure the feasibility and safety of the shared kitchen business.

Keywords: artificial intelligence, food safety, information system, safety management, shared kitchen

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Authors: Sidika Di̇lşad Kaya, Ahmet Seli̇m Kaya, Ibrahi̇m Eri̇k, Havva Yaldiz, Yalçin Kaya

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The aim of this study was to reveal the effects of right brain development on reading, comprehension, learning and concentration levels and rapid processing skills in students with low vision and students with standard development, and to explore the effects of right and left brain integration on students' academic success and the permanence of the learned knowledge. A total of 68 students with a mean age of 10.01±0.12 were included in the study, 58 of them with standard development, 9 partially visually impaired and 1 totally visually disabled student. The student with a total visual impairment could not participate in the reading speed test due to her total visual impairment. The following data were measured in the participant students before the project; Reading speed measurement in 1 minute, Reading comprehension questions, Burdon attention test, 50 questions of math quiz timed with a stopwatch. Participants were trained for 3 weeks, 5 days a week, for a total of two hours a day. In this study, right-brain developing exercises were carried out with the use of an abacus, and it was aimed to develop both mathematical and attention of students with questions prepared with numerical data taken from fairy tale activities. Among these problems, the study was supported with multiple-choice, 5W (what, where, who, why, when?), 1H (how?) questions along with true-false and fill-in-the-blank activities. By using memory cards, students' short-term memories were strengthened, photographic memory studies were conducted and their visual intelligence was supported. Auditory intelligence was supported by aiming to make calculations by using the abacus in the minds of the students with the numbers given aurally. When calculating the numbers by touching the real abacus, the development of students' tactile intelligence is enhanced. Research findings were analyzed in SPSS program, Kolmogorov Smirnov test was used for normality analysis. Since the variables did not show normal distribution, Wilcoxon test, one of the non-parametric tests, was used to compare the dependent groups. Statistical significance level was accepted as 0.05. The reading speed of the participants was 83.54±33.03 in the pre-test and 116.25±38.49 in the post-test. Narration pre-test 69.71±25.04 post-test 97.06±6.70; BURDON pretest 84.46±14.35 posttest 95.75±5.67; rapid math processing skills pretest 90.65±10.93, posttest 98.18±2.63 (P<0.05). It was determined that the pre-test and post-test averages of students with typical development and students with low vision were also significant for all four values (p<0.05). As a result of the data obtained from the participants, it is seen that the study was effective in terms of measurement parameters, and the findings were statistically significant. Therefore, it is recommended to use the method widely.

Keywords: Abacus, reading speed, multiple intelligences, right brain training, visually impaired

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Authors: Nguyen J. M., Lucas G., Ruan S., Digonnet H., Antonioli D.

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Introduction: Deep Learning (DL) is a very powerful tool for analyzing image data. But for tabular data, it cannot compete with machine learning methods like XGBoost. The research question becomes: can tabular data be transformed into images that can be analyzed by simple CNNs (Convolutional Neuron Networks)? Will DL be the absolute tool for data classification? All current solutions consist in repositioning the variables in a 2x2 matrix using their correlation proximity. In doing so, it obtains an image whose pixels are the variables. We implement a technology, DeepNIC, that offers the possibility of obtaining an image for each variable, which can be analyzed by simple CNNs. Material and method: The 'ROP' (Regression OPtimized) model is a binary and atypical decision tree whose nodes are managed by a new artificial neuron, the Neurop. By positioning an artificial neuron in each node of the decision trees, it is possible to make an adjustment on a theoretically infinite number of variables at each node. From this new decision tree whose nodes are artificial neurons, we created the concept of a 'Random Forest of Perfect Trees' (RFPT), which disobeys Breiman's concepts by assembling very large numbers of small trees with no classification errors. From the results of the RFPT, we developed a family of 10 statistical information criteria, Nguyen Information Criterion (NICs), which evaluates in 3 dimensions the predictive quality of a variable: Performance, Complexity and Multiplicity of solution. A NIC is a probability that can be transformed into a grey level. The value of a NIC depends essentially on 2 super parameters used in Neurops. By varying these 2 super parameters, we obtain a 2x2 matrix of probabilities for each NIC. We can combine these 10 NICs with the functions AND, OR, and XOR. The total number of combinations is greater than 100,000. In total, we obtain for each variable an image of at least 1166x1167 pixels. The intensity of the pixels is proportional to the probability of the associated NIC. The color depends on the associated NIC. This image actually contains considerable information about the ability of the variable to make the prediction of Y, depending on the presence or absence of other variables. A basic CNNs model was trained for supervised classification. Results: The first results are impressive. Using the GSE22513 public data (Omic data set of markers of Taxane Sensitivity in Breast Cancer), DEEPNic outperformed other statistical methods, including XGBoost. We still need to generalize the comparison on several databases. Conclusion: The ability to transform any tabular variable into an image offers the possibility of merging image and tabular information in the same format. This opens up great perspectives in the analysis of metadata.

Keywords: tabular data, CNNs, NICs, DeepNICs, random forest of perfect trees, classification

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Authors: Kenneth Okelo, Esther Waithaka, Maureen Mweru

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Development of oral language skills is a precursor to writing and reading acquisition. Oral skill is a means of communication through which people express their desires, ideas, excitements, amusements, disappointments and exchange information. In addition, oral skills have been found to be an important tool for thinking and concept development in children. Research carried out in industrialised countries have identified some appropriate teaching strategies used to enhance acquisition of oral language skills such as repetition, substitution, explanation, contrast, exemplification and code-switching. However, these studies’ geographical locations do not reflect the diversity of the Kenyan society. In addition, studies conducted in Kenya in the past have not established why pre-primary school teachers are not using appropriate teaching strategies. The purpose of this study was to find out whether teachers’ experience, academic qualification and type of training influences their choice of teaching strategies in the development of oral language skills inside and out of the classroom in selected preschools in Kibra Sub-County, Nairobi County. In addition, this study aimed at finding out the strategies used by teachers in Kibra Sub-County to promote oral skills development among pre-primary school children. The study was guided by Holdaway’s theory of language acquisition. Descriptive survey design was employed during this study. Questionnaires and observation schedules were used to collect data. Eighty-three (83) preschool teachers were sampled using multistage sampling methods for observation. Data was analysed using SPSS version 20. The researcher carried out content analysis on the qualitative data. The main descriptive methods used were tabulation of frequencies and percentages. Chi squire test was the inferential statistic used to test the relationship between variables. The main findings of the study indicate that teaching strategies that were mostly used by pre-primary school teachers were code-switching, examples, repetition, substitution and explanation. While questions, direction, expansion of children words and contrast were the least used teaching strategies when teaching oral language skills. The study revealed that the there is a slight correlation between the type of training of teachers and the teaching strategies as most of DICECE trained teachers used more teaching strategies when teaching oral skills compared to other teachers. The findings also revealed that there was a partial significant correlation between teacher’s academic qualifications and a few teaching strategies. A similar correlation was also observed between teaching experience and a few teaching strategies. Since the strategies used by pre-primary school teachers under the study were less than half of the recommended teaching strategies to promote oral skills, the study recommends that teachers should be encouraged to use more in structural strategies to improve children’s oral language skills.

Keywords: Kenya early childhood education, Kenya education, oral language skills acquisition, teaching methods

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Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores

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This work introduces the use of EMGs (electromyography) from muscle sensors to develop an Artificial Neural Network (ANN) for pattern recognition to control a small unmanned aerial vehicle. The objective of this endeavor exhibits interfacing drone applications beyond manual control directly. MyoWare Muscle sensor contains three EMG electrodes (dual and single type) used to collect signals from the posterior (extensor) and anterior (flexor) forearm and the bicep. Collection of raw voltages from each sensor were connected to an Arduino Uno and a data processing algorithm was developed with the purpose of interpreting the voltage signals given when performing flexing, resting, and motion of the arm. Each sensor collected eight values over a two-second period for the duration of one minute, per assessment. During each two-second interval, the movements were alternating between a resting reference class and an active motion class, resulting in controlling the motion of the drone with left and right movements. This paper further investigated adding up to three sensors to differentiate between hand gestures to control the principal motions of the drone (left, right, up, and land). The hand gestures chosen to execute these movements were: a resting position, a thumbs up, a hand swipe right motion, and a flexing position. The MATLAB software was utilized to collect, process, and analyze the signals from the sensors. The protocol (machine learning tool) was used to classify the hand gestures. To generate the input vector to the ANN, the mean, root means squared, and standard deviation was processed for every two-second interval of the hand gestures. The neuromuscular information was then trained using an artificial neural network with one hidden layer of 10 neurons to categorize the four targets, one for each hand gesture. Once the machine learning training was completed, the resulting network interpreted the processed inputs and returned the probabilities of each class. Based on the resultant probability of the application process, once an output was greater or equal to 80% of matching a specific target class, the drone would perform the motion expected. Afterward, each movement was sent from the computer to the drone through a Wi-Fi network connection. These procedures have been successfully tested and integrated into trial flights, where the drone has responded successfully in real-time to predefined command inputs with the machine learning algorithm through the MyoWare sensor interface. The full paper will describe in detail the database of the hand gestures, the details of the ANN architecture, and confusion matrices results.

Keywords: artificial neural network, biosensors, electromyography, machine learning, MyoWare muscle sensors, Arduino

Procedia PDF Downloads 174