Search results for: learning difficulties

Authors: Marcio Freire Cruz, Naoaki Ono, Shigehiko Kanaya, Carlos Arthur Mattos Teixeira Cavalcante

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The systems that record patient care information, known as Electronic Medical Record (EMR) and those that monitor vital signs of patients, such as heart rate, body temperature, and blood pressure have been extremely valuable for the effectiveness of the patient’s treatment. Several kinds of research have been using data from EMRs and vital signs of patients to predict illnesses. Among them, we highlight those that intend to predict, classify, or, at least identify patterns, of sepsis illness in patients under vital signs monitoring. Sepsis is an organic dysfunction caused by a dysregulated patient's response to an infection that affects millions of people worldwide. Early detection of sepsis is expected to provide a significant improvement in its treatment. Preceding works usually combined medical, statistical, mathematical and computational models to develop detection methods for early prediction, getting higher accuracies, and using the smallest number of variables. Among other techniques, we could find researches using survival analysis, specialist systems, machine learning and deep learning that reached great results. In our research, patients are modeled as points moving each hour in an n-dimensional space where n is the number of vital signs (variables). These points can reach a sepsis target point after some time. For now, the sepsis target point was calculated using the median of all patients’ variables on the sepsis onset. From these points, we calculate for each hour the position vector, the first derivative (velocity vector) and the second derivative (acceleration vector) of the variables to evaluate their behavior. And we construct a prediction model based on a Long Short-Term Memory (LSTM) Network, including these derivatives as explanatory variables. The accuracy of the prediction 6 hours before the time of sepsis, considering only the vital signs reached 83.24% and by including the vectors position, speed, and acceleration, we obtained 94.96%. The data are being collected from Medical Information Mart for Intensive Care (MIMIC) Database, a public database that contains vital signs, laboratory test results, observations, notes, and so on, from more than 60.000 patients.

Keywords: dynamic analysis, long short-term memory, prediction, sepsis

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Authors: Zan Azma Nasruddin, Irwan Mazlin, Nor Aziah Daud, Fauziah Redzuan, Fariza Hanis Abdul Razak

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This paper focuses on a machine learning that learn the correct pronunciation of Makhraj Huroofs. Usually, people need to find an expert to pronounce the Huroof accurately. In this study, the researchers have developed a system that is able to learn the selected Huroofs which are ha, tsa, zho, and dza using the Convolutional Neural Network. The researchers present the chosen type of the CNN architecture to make the system that is able to learn the data (Huroofs) as quick as possible and produces high accuracy during the prediction. The researchers have experimented the system to measure the accuracy and the cross entropy in the training process.

Keywords: convolutional neural network, Makhraj recognition, speech recognition, signal processing, tensorflow

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Authors: Liping Li, Wenan Tan, Na Wang

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With the rapid development of information technology, project management has gained more and more attention recently. Based on CDIO, this paper proposes some teaching reform ideas for software project management curriculum. We first change from Teacher-centered classroom to Student-centered and adopt project-driven, scenario animation show, teaching rhythms, case study and team work practice to improve students' learning enthusiasm. Results showed these attempts have been well received and very effective; as well, students prefer to learn with this curriculum more than before the reform.

Keywords: CDIO, teaching reform, engineering education, project-driven, scenario animation simulation

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Authors: Deepa Idnani

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Attendance is an important factor in school success among children. Studies show that better attendance is related to higher academic achievement for students of all backgrounds, but particularly for children with lower socio-economic status. Beginning from the early years, students who attend school regularly score higher on tests than their peers who are frequently absent. The present study in different types of School In Delhi tries to highlight the impact of student absenteeism and the challenges it poses for the students. The study relies on Lewin ‘Model of Exclusion’ and tries to focus on the analysis of children with special needs and the inclusion and exclusion of students in the school.

Keywords: student absenteeism, pedagogy, learning, right to education act, exclusion

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Authors: Wing Chi Wong

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Globally, studying parents caring for children with attention deficit/ hyperactivity disorder (ADHD) is valuable in order to design measures in supporting those parents by health care providers and government. Such parents in Hong Kong seem to encounter detrimental stress and enormous difficulties which are exacerbated by the traditional Chinese culture, exclusion from social members and fiercely competitive educational system. However, seldom studies scrutinize this issue in Hong Kong. This article aims to review the literature regarding parents caring offsprings with ADHD in Hong Kong. Criteria were set for searching among published studies listed in various databases, including MEDLINE, CINCAHL, PsycINFO, ProQuest, Embase, Cochrane Library and Springer Link. Articles with words 'Attention Deficit Hyperactivity Disorder', 'parenting', 'parent', 'family', 'father', 'mother', 'care' in titles and abstracts were identified. Articles with all types of research designs and methods, regardless in English or Chinese, were included. They were limited to years between January 2008 and September 2018. Four relevant studies have resulted. Of them, two were exploratory studies, one was a qualitative study, and one was a survey. Samples were recruited from child psychiatric clinic, Child and Adolescent Mental Health Unit, or multiple family group therapy centres. Authors proclaimed that quality of life of those parents was usually low; particularly mothers perceived a higher stress than fathers; parenting barriers existed; conflicts were commonly raised in parent-child relationship resulting in probable maltreatment to children. Previous studies generally suggested the potential negative outcomes of parents caring children with ADHD. The types and effectiveness of interventions to those parents on relieving their tortures under Hong Kong context had not been explored and systematically evaluated. The scanty studies and existing understanding could not give a promising conclusion pertaining to the appropriate family intervention to parents living with children with ADHD. A stringent research design is necessary to establish evidence on the effectiveness of interventions for those families.

Keywords: attention deficit/ hyperactivity disorder, Hong Kong, parents, interventions

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Authors: Ezgi Şendil

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Information extracted from social media has received great attention since it has become an effective alternative for collecting people’s opinions and emotions based on specific experiences in a faster and easier way. The paper aims to put data in a meaningful way to analyze users’ posts and get a result in terms of the experiences and opinions of the users during and after natural disasters. The posts collected from Reddit are classified into nine different categories, including injured/dead people, infrastructure and utility damage, missing/found people, donation needs/offers, caution/advice, and emotional support, identified by using labelled Twitter data and four different machine learning (ML) classifiers.

Keywords: disaster, NLP, postdisaster management, sentiment analysis

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Authors: Sidong Zhao, Xingping Wang

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Recently, the government of China has provided strong support the developments of overseas industrial parks. The global distribution of China overseas industrial parks has gradually moved from the 'sparks of fire' to the 'prairie fires.' The support and distribution have promoted developing overseas industrial parks to a strategy of constructing a China's new open economic system and a typical representative of the 'Chinese wisdom' and the 'China's plans' that China has contributed to the globalization of the new era under the initiative of the Belt and Road. As the industrial parks are the basis of 'work/employment', a basic function of a city (Athens Constitution), planning for developments of industrial parks has become a long-term focus of urban planning. Based on the research of the planning and the analysis of the present developments of some typical China overseas industrial parks, we found some interesting rules: First, large numbers of the China overseas industrial parks are located in less developed countries. These industrial parks have become significant drives of the developments of the host cities and even the regions in those countries, especially in investment, employment and paid tax fee for the local, etc. so, the planning and development of overseas industrial parks have received extensive attention. Second, there are some problems in the small part of the overseas Park, such as the planning of the park not following the planning of the host city and lack of implementation of the park planning, etc. These problems have led to the difficulties of the implementation of the planning and the sustainable developments of the parks. Third, a unique pattern of space development has been formed. in the dimension of the patterns of regional spatial distribution, there are five characteristics - along with the coast, along the river, along with the main traffic lines and hubs, along with the central urban area and along the connections of regions economic. In the dimension of the spatial relationships between the industrial park and the city, there is a growing and evolving trend as 'separation – integration - union'. In the dimension of spatial mode of the industrial parks, there are different patterns of development, such as a specialized industrial park, complex industrial park, characteristic town and new urban area of industry, etc. From the perspective of the trends of the developments and spatial modes, in the future, the planning of China overseas industrial parks should emphasize the idea of 'building a city based on the industrial park'. In other words, it's making the developments of China overseas industrial parks move from 'driven by policy' to 'driven by the functions of the city', accelerating forming the system of China overseas industrial parks and integrating the industrial parks and the cities.

Keywords: overseas industrial park, spatial mode, planning, China

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Authors: Allaw Kamel, Bazzi Hasan

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Municipal solid waste (MSW) generation is among the most significant sources which threaten the global environmental health. Solid Waste Management has been an important environmental problem in developing countries because of the difficulties in finding sustainable solutions for solid wastes. Therefore, more efforts are needed to be implemented to overcome this problem. Lebanon has suffered a severe solid waste management problem in 2015, and a new landfill site was proposed to solve the existing problem. The study aims to identify and locate the most suitable area to construct a landfill taking into consideration the sustainable development to overcome the present situation and protect the future demands. Throughout the article, a landfill site selection methodology was discussed using Geographic Information System (GIS) and Multi Criteria Decision Analysis (MCDA). Several environmental, economic and social factors were taken as criterion for selection of a landfill. Soil, geology, and LUC (Land Use and Land Cover) indices with the Sustainable Development Index were main inputs to create the final map of Environmentally Sensitive Area (ESA) for landfill site. Different factors were determined to define each index. Input data of each factor was managed, visualized and analyzed using GIS. GIS was used as an important tool to identify suitable areas for landfill. Spatial Analysis (SA), Analysis and Management GIS tools were implemented to produce input maps capable of identifying suitable areas related to each index. Weight has been assigned to each factor in the same index, and the main weights were assigned to each index used. The combination of the different indices map generates the final output map of ESA. The output map was reclassified into three suitability classes of low, moderate, and high suitability. Results showed different locations suitable for the construction of a landfill. Results also reflected the importance of GIS and MCDA in helping decision makers finding a solution of solid wastes by a sanitary landfill.

Keywords: sustainable development, landfill, municipal solid waste (MSW), geographic information system (GIS), multi criteria decision analysis (MCDA), environmentally sensitive area (ESA)

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Authors: Guezouli Larbi, Belkacem Soundes

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The automation of the text detection process can help the human in his driving task. Its application can be very useful to help drivers to have more information about their environment by facilitating the reading of road signs such as directional signs, events, stores, etc. In this paper, a system consisting of two stages has been proposed. In the first one, we used pseudo-Zernike moments to pinpoint areas of the image that may contain text. The architecture of this part is based on three main steps, region of interest (ROI) detection, text localization, and non-text region filtering. Then, in the second step, we present a convolutional neural network architecture (On-Road Text Detection Network - ORTDN) which is considered a classification phase. The results show that the proposed framework achieved ≈ 35 fps and an mAP of ≈ 90%, thus a low computational time with competitive accuracy.

Keywords: text detection, CNN, PZM, deep learning

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Authors: Kazuhisa Takagi

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This paper is about movies and dynamic objects for mobile phones. Dynamic objects are the software programmed by JavaScript. They consist of geometric figures and work on HTML5-compliant browsers. Mobile phones are very popular among teenagers. They like watching movies and playing games on them. So, mathematics movies and dynamic objects would enhance teaching and learning processes. In the movies, manga characters speak with artificially synchronized voices. They teach trigonometry together with dynamic mathematical objects. Many movies are created. They are Windows Media files or MP4 movies. These movies and dynamic objects are not only used in the classroom but also distributed to students. By watching movies, students can study trigonometry before or after class.

Keywords: dynamic mathematical object, javascript, google drive, transfer jet

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Authors: Maria Korman, Carmit Gal, Ella Gabitov, Avi Karni

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The main question addressed in this study was whether the time-of-day wherein training is afforded is a significant factor for motor skill ('how-to', procedural knowledge) acquisition and consolidation into long term memory in the healthy elderly population. Twenty-nine older adults (60-75 years) practiced an explicitly instructed 5-element key-press sequence by repeatedly generating the sequence ‘as fast and accurately as possible’. Contribution of three parameters to acquisition, 24h post-training consolidation, and 1-week retention gains in motor sequence speed was assessed: (a) time of training (morning vs. evening group) (b) sleep quality (actigraphy) and (c) chronotype. All study participants were moderately morning type, according to the Morningness-Eveningness Questionnaire score. All participants had sleep patterns typical of age, with average sleep efficiency of ~ 82%, and approximately 6 hours of sleep. Speed of motor sequence performance in both groups improved to a similar extent during training session. Nevertheless, evening group expressed small but significant overnight consolidation phase gains, while morning group showed only maintenance of performance level attained at the end of training. By 1-week retention test, both groups showed similar performance levels with no significant gains or losses with respect to 24h test. Changes in the tapping patterns at 24h and 1-week post-training were assessed based on normalized Pearson correlation coefficients using the Fisher’s z-transformation in reference to the tapping pattern attained at the end of the training. Significant differences between the groups were found: the evening group showed larger changes in tapping patterns across the consolidation and retention windows. Our results show that morning-oriented older adults effectively acquired, consolidated, and maintained a new sequence of finger movements, following both morning and evening practice sessions. However, time-of-training affected the time-course of skill evolution in terms of performance speed, as well as the re-organization of tapping patterns during the consolidation period. These results are in line with the notion that motor training preceding a sleep interval may be beneficial for the long-term memory in the elderly. Evening training should be considered an appropriate time window for motor skill learning in older adults, even in individuals with morning chronotype.

Keywords: time-of-day, elderly, motor learning, memory consolidation, chronotype

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Authors: Simon Espigolé, Igor Shalyminov, Helen Hastie

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Deep machine learning techniques including deep neural networks (DNN) have been used to model language and dialogue for conversational agents to perform tasks, such as giving technical support and also for general chit-chat. They have been shown to be capable of generating long, diverse and coherent sentences in end-to-end dialogue systems and natural language generation. However, these systems tend to imitate the training data and will only generate the concepts and language within the scope of what they have been trained on. This work explores how deep neural networks can be used in a task that would normally require human creativity, whereby the human would read the movie description and/or watch the movie and come up with a compelling, interesting movie title. This task differs from simple summarization in that the movie title may not necessarily be derivable from the content or semantics of the movie description. Here, we train a type of DNN called a sequence-to-sequence model (seq2seq) that takes as input a short textual movie description and some information on e.g. genre of the movie. It then learns to output a movie title. The idea is that the DNN will learn certain techniques and approaches that the human movie titler may deploy that may not be immediately obvious to the human-eye. To give an example of a generated movie title, for the movie synopsis: ‘A hitman concludes his legacy with one more job, only to discover he may be the one getting hit.’; the original, true title is ‘The Driver’ and the one generated by the model is ‘The Masquerade’. A human evaluation was conducted where the DNN output was compared to the true human-generated title, as well as a number of baselines, on three 5-point Likert scales: ‘creativity’, ‘naturalness’ and ‘suitability’. Subjects were also asked which of the two systems they preferred. The scores of the DNN model were comparable to the scores of the human-generated movie title, with means m=3.11, m=3.12, respectively. There is room for improvement in these models as they were rated significantly less ‘natural’ and ‘suitable’ when compared to the human title. In addition, the human-generated title was preferred overall 58% of the time when pitted against the DNN model. These results, however, are encouraging given the comparison with a highly-considered, well-crafted human-generated movie title. Movie titles go through a rigorous process of assessment by experts and focus groups, who have watched the movie. This process is in place due to the large amount of money at stake and the importance of creating an effective title that captures the audiences’ attention. Our work shows progress towards automating this process, which in turn may lead to a better understanding of creativity itself.

Keywords: creativity, deep machine learning, natural language generation, movies

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Authors: Smita Mathur

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Play is an innate, player-centric, joyful, fundamental activity of early childhood development that significantly contributes to social, emotional, and academic learning. Leveraging the power of play can enhance these domains by creating engaging, interactive, and developmentally appropriate learning experiences for young children. This research aimed to systematically examine young children’s play behaviors with a focus on four primary objectives: (1) the frequency and duration of on-task behaviors, (2) social interactions and emotional expressions during play, (3) the correlation between academic skills and play, and (4) identifying best practices for integrating play-based curricula. To achieve these objectives, a mixed-method study was conducted among young preschool-aged children in low socio-economic populations in the United States. The children were identified using purposive sampling. The children were observed during structured play in classrooms and unstructured play during outdoor playtime and in their home environments. The study sampled 97 preschool-aged children. A total of 3970 minutes of observations were coded to address the research questions. Thirty-seven percent of children lived in linguistically isolated families, and 76% lived in basic budget poverty. Children lived in overcrowded housing situations (67%), and most families had mixed citizenship status (66%). The observational study was conducted using the observation protocol during the Oxford Study Project. On-task behaviors were measured by tracking the frequency and duration of activities where children maintained focus and engagement. In examining social interactions and emotional expressions, the study recorded social interactions, emotional responses, and teacher involvement during play. The study aimed to identify best practices for integrating play-based curricula into early childhood education. By analyzing the effectiveness of different play-based strategies and their impact on on-task behaviors, social-emotional development, and academic skills, the research sought to provide actionable recommendations for educators and caregivers. The findings from study 1. Highlight play behaviors that increase on-task behaviors and academic, & social skills in young children. 2. Offers insights into teacher preparation and designing play-based curriculum 3. Research critiques observation as a data collection technique.

Keywords: play, early childhood education, social-emotional development, academic development

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Authors: Jihee Jang, Seowon Yoon, Gaeun Son, Minjung Kang, Joon Yeon Choeh, Kee-Hong Choi

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Background: Self-report multiple choice questionnaires have been widely utilized to quantitatively measure one’s personality and psychological constructs. Despite several strengths (e.g., brevity and utility), self-report multiple-choice questionnaires have considerable limitations in nature. With the rise of machine learning (ML) and Natural language processing (NLP), researchers in the field of psychology are widely adopting NLP to assess psychological constructs to predict human behaviors. However, there is a lack of connections between the work being performed in computer science and that psychology due to small data sets and unvalidated modeling practices. Aims: The current article introduces the study method and procedure of phase II, which includes the interview questions for the five-factor model (FFM) of personality developed in phase I. This study aims to develop the interview (semi-structured) and open-ended questions for the FFM-based personality assessments, specifically designed with experts in the field of clinical and personality psychology (phase 1), and to collect the personality-related text data using the interview questions and self-report measures on personality and psychological distress (phase 2). The purpose of the study includes examining the relationship between natural language data obtained from the interview questions, measuring the FFM personality constructs, and psychological distress to demonstrate the validity of the natural language-based personality prediction. Methods: The phase I (pilot) study was conducted on fifty-nine native Korean adults to acquire the personality-related text data from the interview (semi-structured) and open-ended questions based on the FFM of personality. The interview questions were revised and finalized with the feedback from the external expert committee, consisting of personality and clinical psychologists. Based on the established interview questions, a total of 425 Korean adults were recruited using a convenience sampling method via an online survey. The text data collected from interviews were analyzed using natural language processing. The results of the online survey, including demographic data, depression, anxiety, and personality inventories, were analyzed together in the model to predict individuals’ FFM of personality and the level of psychological distress (phase 2).

Keywords: personality prediction, psychological distress prediction, natural language processing, machine learning, the five-factor model of personality

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Authors: Thiago Ribeiro de Alencar, Jacyro Gramulia Junior, Patricia Teixeira Leite

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The area of the electricity sector that deals with energy needs by the hydroelectric in a coordinated manner is called Operation Planning of Hydrothermal Power Systems (OPHPS). The purpose of this is to find a political operative to provide electrical power to the system in a given period, with reliability and minimal cost. Therefore, it is necessary to determine an optimal schedule of generation for each hydroelectric, each range, so that the system meets the demand reliably, avoiding rationing in years of severe drought, and that minimizes the expected cost of operation during the planning, defining an appropriate strategy for thermal complementation. Several optimization algorithms specifically applied to this problem have been developed and are used. Although providing solutions to various problems encountered, these algorithms have some weaknesses, difficulties in convergence, simplification of the original formulation of the problem, or owing to the complexity of the objective function. An alternative to these challenges is the development of techniques for simulation optimization and more sophisticated and reliable, it can assist the planning of the operation. Thus, this paper presents the development of a computational tool, namely Hydro-IA for solving optimization problem identified and to provide the User an easy handling. Adopted as intelligent optimization technique is Genetic Algorithm (GA) and programming language is Java. First made the modeling of the chromosomes, then implemented the function assessment of the problem and the operators involved, and finally the drafting of the graphical interfaces for access to the User. The results with the Genetic Algorithms were compared with the optimization technique nonlinear programming (NLP). Tests were conducted with seven hydroelectric plants interconnected hydraulically with historical stream flow from 1953 to 1955. The results of comparison between the GA and NLP techniques shows that the cost of operating the GA becomes increasingly smaller than the NLP when the number of hydroelectric plants interconnected increases. The program has managed to relate a coherent performance in problem resolution without the need for simplification of the calculations together with the ease of manipulating the parameters of simulation and visualization of output results.

Keywords: energy, optimization, hydrothermal power systems, artificial intelligence and genetic algorithms

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Authors: Mohammad Khatib, Salam Hadid

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Introduction: Developing nurses' cultural competence begins in their basic training and requires them to participate in an array of activities which raise their awareness and stimulate their interest, desire and curiosity about different cultures, by creating opportunities for intercultural meetings promoting the concept of 'culture' and its components, including recognition of cultural diversity and the legitimacy of the other. Importantly, professionals need to acquire specific cultural knowledge and thorough understanding of the values, norms, customs, beliefs and symbols of different cultures. Similarly, they need to be given opportunities to practice the verbal and non-verbal communication skills of other cultures according to their cultural codes. Such a system is being implemented as part of nursing studies at Zefat Academic College in two study frameworks; firstly, a course integrating nursing theory and practice in multicultural nursing; secondly, a course in learning the languages spoken in Israel focusing on medical and nursing terminology. Methods: Students participating in the 'Transcultural Nursing' course come from a variety of backgrounds: Jews, or Arabs, religious, or secular; Muslim, Christian, new immigrants, Ethiopians or from other cultural affiliations. They are required to present and discuss cultural practices that affect health. In addition, as part of the language course, students learn and teach their friends 5 spoken languages (Arabic, Russian, Amharian, Yidish, and Sign language) focusing on therapeutic interaction and communication using the vocabulary and concepts necessary for the therapeutic encounter. An evaluation of the process and the results was done using a structured questionnaire which includes series of questions relating to the contributions of the courses to their cultural knowledge, awareness and skills. 155 students completed the questionnaire. Results: A preliminary assessment of this educational system points an increase in cultural awareness and knowledge among the students as well as in their willingness to recognize the other's difference. A positive atmosphere of multiculturalism is reflected in students' mutual interest and respect was created. Students showed a deep understanding of cultural issues relating to health and care (consanguinity and genetics, food customs; cultural events, reincarnation, traditional treatments etc.). Most of the students were willing to recommend the courses to others and suggest some changes relating learning methods (more simulations, role playing and activities).

Keywords: cultural competence, nursing education, culture, language

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Authors: Ivana Scidà, Francesco Alotto, Anna Osello

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Due to the importance given today to innovation, the education sector is evolving thanks digital technologies. Virtual Reality (VR) can be a potential teaching tool offering many advantages in the field of training and education, as it allows to acquire theoretical knowledge and practical skills using an immersive experience in less time than the traditional educational process. These assumptions allow to lay the foundations for a new educational environment, involving and stimulating for students. Starting from the objective of strengthening the innovative teaching offer and the learning processes, the case study of the research concerns the digitalization of MastrLAB, High Quality Laboratory (HQL) belonging to the Department of Structural, Building and Geotechnical Engineering (DISEG) of the Polytechnic of Turin, a center specialized in experimental mechanical tests on traditional and innovative building materials and on the structures made with them. The MastrLAB-VR has been developed, a revolutionary innovative training tool designed with the aim of educating the class in total safety on the techniques of use of machinery, thus reducing the dangers arising from the performance of potentially dangerous activities. The virtual laboratory, dedicated to the students of the Building and Civil Engineering Courses of the Polytechnic of Turin, has been projected to simulate in an absolutely realistic way the experimental approach to the structural tests foreseen in their courses of study: from the tensile tests to the relaxation tests, from the steel qualification tests to the resilience tests on elements at environmental conditions or at characterizing temperatures. The research work proposes a methodology for the virtualization of technical laboratories through the application of Building Information Modelling (BIM), starting from the creation of a digital model. The process includes the creation of an independent application, which with Oculus Rift technology will allow the user to explore the environment and interact with objects through the use of joypads. The application has been tested in prototype way on volunteers, obtaining results related to the acquisition of the educational notions exposed in the experience through a virtual quiz with multiple answers, achieving an overall evaluation report. The results have shown that MastrLAB-VR is suitable for both beginners and experts and will be adopted experimentally for other laboratories of the University departments.

Keywords: building information modelling, digital learning, education, virtual laboratory, virtual reality

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Authors: Valencia Fernandes, Dharmendra Kumar Khatri, Shashi Bala Singh

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Background and Aim: Epigenetics are the inaudible signatures of several pathological processes in the brain. This study understands the influence of DNA methylation, a major epigenetic modification, in the prefrontal cortex and hippocampus of the diabetic brain and its notable effect on the cellular chaperones and synaptic proteins. Method: Chronic high fat diet and STZ-induced diabetic mice were studied for cognitive dysfunction, and global DNA methylation, as well as DNA methyltransferase (DNMT) activity, were assessed. Further, the cellular chaperones and synaptic proteins were examined using DNMT inhibitor, 5-aza-2′-deoxycytidine (5-aza-dC)-via intracerebroventricular injection. Moreover, % methylation of these synaptic proteins were also studied so as to correlate its epigenetic involvement. Computationally, its interaction with the DNMT enzyme were also studied using bioinformatic tools. Histological studies for morphological alterations and neuronal degeneration were also studied. Neurogenesis, a characteristic marker for new learning and memory formation, was also assessed via the BrdU staining. Finally, the most important behavioral studies, including the Morris water maze, Y maze, passive avoidance, and Novel object recognition test, were performed to study its cognitive functions. Results: Altered global DNA methylation and increased levels of DNMTs within the nucleus were confirmed in the cortex and hippocampus of the diseased mice, suggesting hypermethylation at a genetic level. Treatment with AzadC, a global DNA demethylating agent, ameliorated the protein and gene expression of the cellular chaperones and synaptic fidelity. Furthermore, the methylation analysis profile showed hypermethylation of the hsf1 protein, a master regulator for chaperones and thus, confirmed the epigenetic involvement in the diseased brain. Morphological improvements and decreased neurodegeneration, along with enhanced neurogenesis in the treatment group, suggest that epigenetic modulations do participate in learning and memory. This is supported by the improved behavioral test battery seen in the treatment group. Conclusion: DNA methylation could possibly accord in dysregulating the memory-associated proteins at chronic stages in type 2 diabetes. This could suggest a substantial contribution to the underlying pathophysiology of several metabolic syndromes like insulin resistance, obesity and also participate in transitioning this damage centrally, such as cognitive dysfunction.

Keywords: epigenetics, cognition, chaperones, DNA methylation

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Authors: L. Beltran, S. Solano, T. Lacruz, M. Blanco, M. Rojo, M. Graell, A. R. Sepulveda

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Introduction and Objective: Given the difficulties of assessing Binge Eating Disorder during childhood, episodes of Loss of Control (LOC) eating can be a key symptom. The objective is to know the prevalence of food psychopathology depending on the type of evaluation and find out which psychological characteristics differentiate overweight or obese children who present LOC from those who do not. Material and Methods: 170 children from 8 to 12 years of age with overweight or obesity (P > 85) were evaluated through the Primary Care Centers of Madrid. Sociodemographic data and psychological measures were collected through the Kiddie-Schedule for Affective Disorders & Schizophrenia, Present & Lifetime Version (K-SADS-PL) diagnostic interview and self-applied questionnaires: Children's eating attitudes (ChEAT), depressive symptomatology (CDI), anxiety (STAIC), general self-esteem (LAWSEQ), body self-esteem (BES), perceived teasing (POTS) and perfectionism (CAPS). Results: 15.2% of the sample exceeded the ChEAT cut-off point, presenting a risk of pathological eating; 5.88% presented an Eating Disorder through the diagnostic interview (2.35% Binge Eating disorder), and 33.53% had LOC episodes. No relationship was found between the presence of LOC and clinical diagnosis of eating disorders according to DSM-V; however, the group with LOC presented a higher risk of eating psychopathology using the ChEAT (p < .02). Significant differences were found in the group with LOC (p < .02): higher z-BMI, lower body self-esteem, greater anxious symptomatology, greater frequency of teasing towards weight, and greater effect of teasing both towards weight and competitions; compared to their peers without LOC. Conclusion: According to previous studies in samples with overweight children, in this Spanish sample of children with obesity, we found a prevalence of moderate eating disorder and a high presence of LOC episodes, which is related to both eating and general psychopathology. These findings confirm that the exclusion of LOC episodes as a diagnostic criterion can underestimate the presence of eating psychopathology during this developmental stage. According to these results, it is highly recommended to promote school context programs that approach LOC episodes in order to reduce associated symptoms. This study is included in a Project funded by the Ministry of Innovation and Science (PSI2011-23127).

Keywords: childhood obesity, eating psychopathology, loss-of-control eating, psychological symptomatology

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Authors: Adel Edwar Waheeb Louka

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

Keywords: artificial intelligence, convolutional neural networks, deeplearning, image processing, machine learningSarapin, intraarticular, chronic knee pain, osteoarthritisFNS, trauma, hip, neck femur fracture, minimally invasive surgery

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Authors: Inger Eriksson, Lisbeth Ohlsson, Jeremias Rosenqvist

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Ingress: Focus in the study is directed towards phenomena and concepts of segregation, integration, and inclusion of students attending a special school form in Sweden, namely compulsory school for pupils with learning disabilities (in Swedish 'särskola') as an alternative to mainstream compulsory school. Aim: The aim of the study is to examine the school situation for students attending särskola from a historical perspective focussing the 1980s, 1990s and the 21st century, from an integration perspective, and from a perspective of power. Procedure: Five sub-studies are reported, where integration and inclusion are looked into by observation studies and interviews with school leaders, teachers, special and remedial teachers, psychologists, coordinators, and parents in the special schools/särskola. In brief, the study about special school students attending mainstream classes from 1998 takes its point of departure in the idea that all knowledge development takes place in a social context. A special interest is taken in the school’s role for integration generally, and the role of special education particularly and on whose conditions the integration is taking place – the special school students' or the other students,' or may be equally, in the class. Pedagogical and social conditions for so called individually integrated special school students in elementary school classes were studied in eleven classes. Results: The findings are interpreted in a power perspective supported by Foucault and relationally by Vygotsky. The main part of the data consists of extensive descriptions of the eleven cases, here called integration situations. Conclusions: In summary, this study suggests that the possibilities for a special school student to get into the class community and fellowship and thereby be integrated with the class are to a high degree dependant on to what extent the student can take part in the pedagogical processes. The pedagogical situation for the special school student is affected not only by the class teacher and the support and measures undertaken but also by the other students in the class as they, in turn, are affected by how the special school student is acting. This mutual impact, which constitutes the integration process in itself, might result in a true integration if the special school student attains the status of being accepted on his/her own terms not only being cared for or cherished by some classmates. A special school student who is not accepted even on the terms of the class will often experience severe problems in the contacts with classmates and the school situation might thus be a mere placement.

Keywords: integration/inclusion, mainstream school, power, special school students

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Authors: Carlos Monge López, Patricia Gómez Hernández

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Educational innovation is a quality factor of teaching-learning processes and institutional accreditation. There is an increasing of these change processes, especially after 2000. However, the publications about this topic are more associated with ICTs in currently century. The main aim of the study was to determine the tendency of educational innovations around ICTs. The used method was mixed research design (content analysis, review of scientific literature and descriptive, comparative and correlation study) with 649 papers. In summary, the results indicated that, progressively, the educational innovation is associated with ICTs, in comparison with this type of change processes without ICTs. In conclusion, although this tendency, scientific literature must divulgate more kinds of pedagogical innovation with the aim of deepening in other new resources.

Keywords: descriptive study, knowledge society, pedagogical innovation, technologies

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Authors: Susan Scott, Dina Hanna, Bassel Zebian, Gary Ruiz, Sreena Das

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Although the number of cases of TB in England has fallen over the last 4 years, it remains an important public health burden with 1 in 20 cases dying annually. The vast majority of cases present in non-UK born individuals with social risk factors. We present a case of non-pulmonary TB presenting in a healthy child born in the UK to professional parents. We present a case of a healthy 10 year old boy who developed acute back pain during school PE. Over the next 5 months, he was seen by various health and allied professionals with worsening back pain and kyphosis. He became increasing unsteady and for the 10 days prior to admission to our hospital, he developed fevers. He was admitted to his local hospital for tonsillitis where he suffered two falls on account of his leg weakness. A spinal X-ray revealed a pathological fracture and gibbus formation. He was transferred to our unit for further management. On arrival, the patient had lower motor neurone signs of his left leg. He underwent spinal fixture, laminectomy and decompression. Microbiology samples taken intra-operatively confirmed Mycobacterium Tuberculosis. He had a positive Mantoux and T-spot and treatment were commenced. There was no evidence of immune compromise. The patient was born in the UK, had a BCG scar and his only travel history had been two years prior to presentation when he travelled to the Phillipines for a short holiday. The patient continues to have issues around neuropathic pain, mobility, pill burden and mild liver side effects from treatment. Discussion: There is a paucity of case reports on spinal TB in paediatrics and diagnosis is often difficult due to the non-specific symptomatology. Although prognosis on treatment is good, a delayed diagnosis can have devastating consequences. This case highlights the continued need for higher index of suspicion and diagnosis in a world with changing patterns of migration and increase global travel. Surgical intervention is limited to the most serious cases to minimise further neurological damage and improve prognosis. There remains the need for a multi-disciplinary approach to deal with challenges of treatment and rehabilitation.

Keywords: tuberculosis, non-pulmonary TB, public health burden, diagnostic challenge

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Authors: Merle Talvik, Taimi Tulva, Kristi Puusepp, Ülle Ernits

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Background data. In 1940–1941 and 1945–1991 Estonia was occupied by Soviet Union. Paternalism was a common principle in Soviet social policy, including health care. The Soviet government, not the individuals themselves, decided on achieving a person’s quality of life. With the help of Soviet ideology, the work culture of nurses was constructed and the education system was also reshaped according to the ideology. The “new period of awakening” was initiated under Gorbachev’s perestroika and glasnost (1985–1991), leading to democratization. Aim. The qualitative study aimed to analyze nursing education in Soviet Estonia in the conditions of paternalistic orientation and ideological pressure. Method. The research was conducted in 2021 and 2023. Senior nurses (aged 69–87) who had worked for at least 20 years during the Soviet era were surveyed. Thematic interviews were conducted in written form and orally (13 interviewees), followed by a focus group interview (8 interviewees). A thematic content analysis was performed. Results. Nursing is part of society’s culture and in this sense, in - terviews with nurses provide us with critical information about the functioning of society and cultural identity at a given time. During the Soviet era the training of nurses occured within vocational training institutions. The curricula underwent a shift towards a Soviet-oriented approach. A significant portion of lessons were dedicated to imparting knowledge on the principles and tenets of Communist-Marxist ideology. Therefore, practical subjects and nursing theory were frequently allocated limited space. A paternalistic orientation prevailed in health care: just as the state regulated how to cure, spread hygiene, and healthy lifestyles propaganda, training was also determined by the management of the institution, thereby limiting the person´s autonomy to decide what kind of training was needed. The research is of significant value in the context of the history of nursing, as it helps to understand the difficulties and complexity of the development of nursing on the timeline. The Soviet era still affects Estonian society today and will continue to do so in the future. The same type of developments occurred in other post-Soviet countries.

Keywords: Estonian SSR, nursing education, paternalism, senior nurse, Soviet ideology

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Authors: Xinyu Zhang, Olga Torres-Hostench

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This article presents a comparative study evaluating and comparing the quality of machine translation (MT) output of Chinese gastronomy nomenclature. Chinese gastronomic culture is experiencing an increased international acknowledgment nowadays. The nomenclature of Chinese gastronomy not only reflects a specific aspect of culture, but it is related to other areas of society such as philosophy, traditional medicine, etc. Chinese dish names are composed of several types of cultural references, such as ingredients, colors, flavors, culinary techniques, cooking utensils, toponyms, anthroponyms, metaphors, historical tales, among others. These cultural references act as one of the biggest difficulties in translation, in which the use of translation techniques is usually required. Regarding the lack of Chinese food-related translation studies, especially in Chinese-Spanish translation, and the current massive use of MT, the quality of the MT output of Chinese dish names is questioned. Fifty Chinese dish names with different types of cultural components were selected in order to complete this study. First, all of these dish names were translated by three different MT tools (Google Translate, Baidu Translate and Bing Translator). Second, a questionnaire was designed and completed by 12 Chinese online users (Chinese graduates of a Hispanic Philology major) in order to find out user preferences regarding the collected MT output. Finally, human translation techniques were observed and analyzed to identify what translation techniques would be observed more often in the preferred MT proposals. The result reveals that the MT output of the Chinese gastronomy nomenclature is not of high quality. It would be recommended not to trust the MT in occasions like restaurant menus, TV culinary shows, etc. However, the MT output could be used as an aid for tourists to have a general idea of a dish (the main ingredients, for example). Literal translation turned out to be the most observed technique, followed by borrowing, generalization and adaptation, while amplification, particularization and transposition were infrequently observed. Possibly because that the MT engines at present are limited to relate equivalent terms and offer literal translations without taking into account the whole context meaning of the dish name, which is essential to the application of those less observed techniques. This could give insight into the post-editing of the Chinese dish name translation. By observing and analyzing translation techniques in the proposals of the machine translators, the post-editors could better decide which techniques to apply in each case so as to correct mistakes and improve the quality of the translation.

Keywords: Chinese dish names, cultural references, machine translation, translation techniques

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Ole-Christian Galbo Engstrøm, Erik Schou Dreier, Birthe Møller Jespersen, Kim Steenstrup Pedersen

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Grain quality analysis is a multi-parameterized problem that includes a variety of qualitative and quantitative parameters such as grain type classification, damage type classification, and nutrient regression. Currently, these parameters require human inspection, a multitude of instruments employing a variety of sensor technologies, and predictive model types or destructive and slow chemical analysis. This paper investigates the feasibility of applying near-infrared hyperspectral imaging (NIR-HSI) to grain quality analysis. For this study two datasets of NIR hyperspectral images in the wavelength range of 900 nm - 1700 nm have been used. Both datasets contain images of sparsely and densely packed grain kernels. The first dataset contains ~87,000 image crops of bulk wheat samples from 63 harvests where protein value has been determined by the FOSS Infratec NOVA which is the golden industry standard for protein content estimation in bulk samples of cereal grain. The second dataset consists of ~28,000 image crops of bulk grain kernels from seven different wheat varieties and a single rye variety. In the first dataset, protein regression analysis is the problem to solve while variety classification analysis is the problem to solve in the second dataset. Deep convolutional neural networks (CNNs) have the potential to utilize spatio-spectral correlations within a hyperspectral image to simultaneously estimate the qualitative and quantitative parameters. CNNs can autonomously derive meaningful representations of the input data reducing the need for advanced preprocessing techniques required for classical chemometric model types such as artificial neural networks (ANNs) and partial least-squares regression (PLS-R). A comparison between different CNN architectures utilizing 2D and 3D convolution is conducted. These results are compared to the performance of ANNs and PLS-R. Additionally, a variety of preprocessing techniques from image analysis and chemometrics are tested. These include centering, scaling, standard normal variate (SNV), Savitzky-Golay (SG) filtering, and detrending. The results indicate that the combination of NIR-HSI and CNNs has the potential to be the foundation for an automatic system unifying qualitative and quantitative grain quality analysis within a single sensor technology and predictive model type.

Keywords: deep learning, grain analysis, hyperspectral imaging, preprocessing techniques

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Authors: Kaewmart Pongakkasira

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This study examines whether construction of LEGO reflects affective states and creativity as the clue to develop effective learning resources for classrooms. For this purpose, participants are instructed to complete a hands-on activity by using LEGO. Prior to the experiment, participants’ affective states and creativity are measured by the Positive and Negative Affect Schedule (PANAS) and the Alternate Uses Task (AUT), respectively. Then, subjects are asked to freely combine LEGO as unusual as possible versus constraint LEGO combination and named the LEGO products. Creativity of the LEGO products is scored for originality and abstractness of titles. It is hypothesized that individuals’ mood and creativity may affect product creativity. If so, there might be correlation among the three parameters.

Keywords: affective states, creativity, hands-on activity, LEGO

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Authors: Trupti Diwan

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Predicting student failure at school has changed into a difficult challenge due to both the large number of factors that can affect the reduced performance of students and the imbalanced nature of these kinds of data sets. This paper surveys the two elements needed to make prediction on Students’ Academic Performances which are parameters and methods. This paper also proposes a framework for predicting the performance of engineering students. Genetic programming can be used to predict student failure/success. Ranking algorithm is used to rank students according to their credit points. The framework can be used as a basis for the system implementation & prediction of students’ Academic Performance in Higher Learning Institute.

Keywords: classification, educational data mining, student failure, grammar-based genetic programming

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Authors: Ahmet Ozcan, Onder Alparslan, Anil Sezgin, Omer Cetin

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The use of unmanned systems for different purposes has become very popular over the past decade. Expectations from these systems have also shown an incredible increase in this parallel. But meeting the demands of the tasks are often not possible with the usage of a single unmanned vehicle in a mission, so it is necessary to use multiple autonomous vehicles with different abilities together in coordination. Therefore the usage of the same type of vehicles together as a swarm is helped especially to satisfy the time constraints of the missions effectively. In other words, it allows sharing the workload by the various numbers of homogenous platforms together. Besides, it is possible to say there are many kinds of problems that require the usage of the different capabilities of the heterogeneous platforms together cooperatively to achieve successful results. In this case, cooperative working brings additional problems beyond the homogeneous clusters. In the scenario presented as an example problem, it is expected that an autonomous ground vehicle, which is lack of its position information, manage to perform point-to-point navigation without losing its way in a previously unknown labyrinth. Furthermore, the ground vehicle is equipped with very limited sensors such as ultrasonic sensors that can detect obstacles. It is very hard to plan or complete the mission for the ground vehicle by self without lost its way in the unknown labyrinth. Thus, in order to assist the ground vehicle, the autonomous air drone is also used to solve the problem cooperatively. The autonomous drone also has limited sensors like downward looking camera and IMU, and it also lacks computing its global position. In this context, it is aimed to solve the problem effectively without taking additional support or input from the outside, just benefiting capabilities of two autonomous vehicles. To manage the point-to-point navigation in a previously unknown labyrinth, the platforms have to work together coordinated. In this paper, cooperative work of heterogeneous unmanned systems is handled in an applied sample scenario, and it is mentioned that how to work together with an autonomous ground vehicle and the autonomous flying platform together in a harmony to take advantage of different platform-specific capabilities. The difficulties of using heterogeneous multiple autonomous platforms in a mission are put forward, and the successful solutions are defined and implemented against the problems like spatially distributed tasks planning, simultaneous coordinated motion, effective communication, and sensor fusion.

Keywords: unmanned systems, heterogeneous autonomous vehicles, coordination, task planning

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