Search results for: living & learning

Authors: Maryam Borjali

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Introduction. Mortality due to noncommunicable diseases has increased in the world today with the advent of demographic shifts, growing age, and lifestyle patterns in the world, which have been affected by economic and social crises. Congenital heart defects are one of the forms of diseases that have raised infant mortality worldwide. e objective of present study was to identify nonmedical determinants related to this abnormality from the mother’s perspectives. Methods. is research was a qualitative study and the data collection method was a semistructured interview with mothers who had children with congenital heart diseases referring to the Shahid Rajaei Heart Hospital in Tehran, Iran. A thematic analysis approach was employed to analyze transcribed documents assisted by MAXQDA Plus version 12. Results. Four general themes and ten subthemes including social contexts (social harms, social interactions, and social necessities), psychological contexts (mood disorders and mental well-being), cultural contexts (unhealthy lifestyle, family culture, and poor parental health behaviors), and environmental contexts (living area and polluted air) were extracted from interviews with mothers of children with congenital heart diseases. Conclusions. Results suggest that factors such as childhood poverty, lack of parental awareness of congenital diseases, lack of proper nutrition and health facilities, education, and lack of medical supervision during pregnancy were most related with the birth of children with congenital heart disease from mothers’ prospective. In this regard, targeted and intersectorial collaborations are proposed to address nonmedical determinants related to the incidence of congenital heart diseases.

Keywords: congenital_cou, cultural, social, platform

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Authors: Tesnim Charrad, Kaouther Nouira, Ahmed Ferchichi

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In order to reduce the number of deaths due to heart problems, we propose the use of Hierarchical Temporal Memory Algorithm (HTM) which is a real time anomaly detection algorithm. HTM is a cortical learning algorithm based on neocortex used for anomaly detection. In other words, it is based on a conceptual theory of how the human brain can work. It is powerful in predicting unusual patterns, anomaly detection and classification. In this paper, HTM have been implemented and tested on ECG datasets in order to detect cardiac anomalies. Experiments showed good performance in terms of specificity, sensitivity and execution time.

Keywords: cardiac anomalies, ECG, HTM, real time anomaly detection

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Authors: Yujun Liu, Maosheng Ran

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Background: Chinese rural-to-urban migrant workers’ mental health problems raise attentions from different social sectors. However, situation of present mental health services provided to this population has not been discovered. This study attempts to describe the current mental health service situation, identify the gaps and give the future directions based on the quantitative data. Methods: Questionnaire surveys were conducted among 2017 rural-to-urban migrant workers in 13 cities and 100 social work service organizations in 5 cities in 2014. Data was collected by face-to-face structured interview by trained interviewers. Findings: Migrant workers’ mental health status was not good. Compared to the severity of mental distress, mental health service for this population was lacking and insufficient, which accounted for only 14.4% of all services in our sample. And the group work and case work were the most frequently-used methods. By estimating a series of regression models, we revealed that life experiences and working conditions were significantly associated with migrant workers’ mental health status. Therefore, the macro social work practices aimed at this whole group were advocated to promote their mental wellbeing. That is, practitioners should not only focus on the improvement of migrant workers’ emotion management capacity, but also pay attention to raise awareness and improve their living and working condition; not only concentrate on the solving of individuals’ dilemma, but also promote gradual reformation of present labor regime and hukou system in China.

Keywords: Chinese rural-to-urban migrant workers, macro social work practice, mental health service needs, mental health status

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Authors: Mei Lin, Chang Liu

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Argumentative writing is the most prevalent genre in diverse writing tests. How to construct academic arguments is often regarded as a difficult task by most English as a foreign language (EFL) learners. A failure to generate enough ideas and organise them coherently and logically as well as a lack of competence in supporting their arguments with relevant evidence are frequent problems faced by EFL learners when approaching an English argumentative writing task. Overall, these problems are closely related to planning, and planning an argumentative writing at pre-writing stage plays a vital role in a good academic essay. However, how teachers can effectively guide students to generate ideas is rarely discussed in planning English argumentative writing, apart from brainstorming. Brainstorming has been a common practice used by teachers to help students generate ideas. However, some limitations of brainstorming suggest that it can help students generate many ideas, but ideas might not necessarily be coherent and logic, and could sometimes impede production. It calls for a need to explore effective instructional strategies at pre-writing stage of English argumentative writing. This paper will first examine how a Six Thinking Hats approach can be used to provide a dialogic space for EFL learners to experience and collaboratively generate ideas from multiple perspectives at pre-writing stage. Part of the findings of the impact of a twelve-week intervention (from March to July 2021) on students learning to generate ideas through engaging in group discussions of using Six Thinking Hats will then be reported. The research design is based on the sociocultural theory. The findings present evidence from a mixed-methods approach and fifty-nine participants from two first-year undergraduate natural classes in a Chinese university. Analysis of pre- and post- questionnaires suggests that participants had a positive attitude toward the Six Thinking Hats approach. It fosters their understanding of prewriting and argumentative writing, helps them to generate more ideas not only from multiple perspectives but also in a systematic way. A comparison of participants writing plans confirms an improvement in generating counterarguments and rebuttals to support their arguments. Above all, visual and transcripts data of group discussion collected from different weeks throughout the intervention enable teachers and researchers to ‘see’ the hidden process of learning to generate ideas in action.

Keywords: argumentative writing, innovative pedagogy, six thinking hats, dialogic space, prewriting, higher education

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Authors: Wafa' Slaibi Alsharafat

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Cloud computing is a modern approach in network environment. According to increased number of network users and online systems, there is a need to help these systems to be away from unauthorized resource access and detect any attempts for privacy contravention. For that purpose, Intrusion Detection System is an effective security mechanism to detect any attempts of attacks for cloud resources and their information. In this paper, Cloud Intrusion Detection System has been proposed in term of reducing or eliminating any attacks. This model concerns about achieving high detection rate after conducting a set of experiments using benchmarks dataset called KDD'99.

Keywords: IDS, cloud computing, anticipating classifier system, intrusion detection

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Authors: Hafiz Arbab Sakandar

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Time-restricted intermittent fasting (TRIF) impacts host’s physiology and health. Plenty of health benefits have been reported for TRIF in animal models. However, limited studies have been conducted on humans especially in underdeveloped economies. Here, we designed a study to investigate the impact of TRIF/Ramadan fasting (16:8) on the modulation of gut-microbiome structure, metabolic pathways, and predicted metabolites and explored the correlation among them at different time points (during and after the month of Ramadan) in Pakistani Expats living in China. We observed different trends of Shannon-Wiener index in different subjects; however, all subjects showed substantial change in bacterial diversity with the progression of TRIF. Moreover, the changes in gut microbial structure by the end of TRIF were higher vis-a-vis in the beginning, significant difference was observed among individuals. Additionally, metabolic pathways analysis revealed that amino acid, carbohydrate and energy metabolism, glycan biosynthesis metabolism of cofactors and vitamins were significantly affected by TRIF. Pyridoxamine, glutamate, citrulline, arachidonic acid, and short chain fatty acid showed substantial difference at different time points based on the predicted metabolism. In conclusion, these results contribute to further our understanding about the key relationship among, dietary intervention (TRIF), gut microbiome structure and function. The preliminary results from study demonstrate significant potential for elucidating the mechanisms underlying gut microbiome stability and enhancing the effectiveness of microbiome-tailored interventions among the Pakistani populace. Nonetheless, extensive, and rigorous large-scale research on the Pakistani population is necessary to expound on the association between diet, gut microbiome, and overall health.

Keywords: gut microbiome, health, fasting, functionality

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Authors: Nizar Mansour, Chiha Gaha, Emna Gara

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The present research investigates the relationship among HRM practices, knowledge sharing behavior and a certain number of intermediary variables in the context of Tunisian knowledge-intensive firms. Results suggest that five HR practices influence either directly or indirectly the knowledge sharing behavior through enhancing the value of human capital and fostering a learning-oriented organizational climate. Results have strong theoretical implications for both the fields of knowledge management and strategic human resource management. Managerial implications are also derived.

Keywords: human capital, knowledge intensive firms, knowledge sharing, organizational climate, Tunisia

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Authors: Jacqueline M. Dias, Amina A Khowaja

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Clinical education is one of the most important components of a nursing curriculum as it develops the students’ cognitive, psychomotor and affective skills. Clinical teaching ensures the integration of knowledge into practice. As the numbers of students increase in the field of nursing coupled with the faculty shortage, clinical preceptors are the best choice to ensure student learning in the clinical settings. The clinical preceptor role has been introduced in the undergraduate nursing programme. In Pakistan, this role emerged due to a faculty shortage. Initially, two clinical preceptors were hired. This study will explore clinical preceptors views and experiences of precepting Bachelor of Science in Nursing (BScN) students in an undergraduate program. A case study design was used. As case studies explore a single unit of study such as a person or very small number of subjects; the two clinical preceptors were fundamental to the study and served as a single case. Qualitative data were obtained through an iterative process using in depth interviews and written accounts from reflective journals that were kept by the clinical preceptors. The findings revealed that the clinical preceptors were dedicated to their roles and responsibilities. Another, key finding was that clinical preceptors’ prior knowledge and clinical experience were valuable assets to perform their role effectively. The clinical preceptors found their new role innovative and challenging; it was stressful at the same time. Findings also revealed that in the clinical agencies there were unclear expectations and role ambiguity. Furthermore, clinical preceptors had difficulty integrating theory into practice in the clinical area and they had difficulty in giving feedback to the students. Although this study is localized to one university, generalizations can be drawn from the results. The key findings indicate that the role of a clinical preceptor is demanding and stressful. Clinical preceptors need preparation prior to precepting students on clinicals. Also, institutional support is fundamental for their acceptance. This paper focuses on the views and experiences of clinical preceptors undertaking a newly established role and resonates with the literature. The following recommendations are drawn to strengthen the role of the clinical preceptors: A structured program for clinical preceptors is needed along with mentorship. Clinical preceptors should be provided with formal training in teaching and learning with emphasis on clinical teaching and giving feedback to students. Additionally, for improving integration of theory into practice, clinical modules should be provided ahead of the clinical. In spite of all the challenges, ten more clinical preceptors have been hired as the faculty shortage continues to persist.

Keywords: baccalaureate nursing education, clinical education, clinical preceptors, nursing curriculum

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Authors: Marcelus U. Ajonina, Deodata B. Ngonga, Kenric B. Ware, Carine K. Nfor

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Background: Lack of knowledge of rational use of antimalarial drugs among dispensers is a serious problem, especially in areas of intense transmission, thus increasing the risk of resistance and adverse drug reactions. This study was aimed at assessing the knowledge of malaria as well as perception and dispensing practices of antimalarials among vendors in Buea community. Methods: A community-based cross-sectional survey of a random sample of 140 drug vendors living within the Buea community was conducted between March and June 2017. A questionnaire was designed to obtain information from drug vendors on the general knowledge of malaria as well as dispensing practices. Data were analyzed using SPSS Statistics 20.0 and were considered significant at p ≤ 0.05. Results: Knowledge of malaria symptoms, transmission, and prevention was reasonable among 55.8% (77) of the respondents. Only 33.6% (47) of the respondents could attribute the cause of malaria to protozoan of genus Plasmodium species. Of the 140 vendors, 115 (82.7%) prescribe antimalarial drugs. The knowledge of the national protocol was malaria case management among dispensers was 35.0%. Vendors in hospital/community pharmacies were 2.4 times (OR = 3.14, 95% CI: 4.14 - 8.74, p < 0.001) more knowledgeable about malaria treatment protocol than those of in drugstores. The prevalence of self-prescription of antimalarials was 39.3%. Self-prescription was significantly higher in drugstores than hospital/community pharmacies (p=0.004). In all, 56 (40.6%) of vendors showed good practices regarding antimalarial drug dispensing with the majority (51.7%) from community pharmacies (OR=2.27,95% CI: 1.13-4.56). Conclusion: Findings reveal moderate knowledge of malaria but poor prescription and dispensing practices of antimalarial drugs among vendors, thus indicating a need for routine monitoring and evaluation to prevent the emergence of resistant strains to current efficacious antimalarials.

Keywords: antimalarials, drug retail outlets, dispensing, drug resistance, prescription

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Authors: Bilel Chalghaf, Mathieu Varin

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Forest characterization in Quebec, Canada, is usually assessed based on photo-interpretation at the stand level. For species identification, this often results in a lack of precision. Very high spatial resolution imagery, such as DigitalGlobe, and Light Detection and Ranging (LiDAR), have the potential to overcome the limitations of aerial imagery. To date, few studies have used that data to map a large number of species at the tree level using machine learning techniques. The main objective of this study is to map 11 individual high tree species ( > 17m) at the tree level using an object-based approach in the broadleaf forest of Kenauk Nature, Quebec. For the individual tree crown segmentation, three canopy-height models (CHMs) from LiDAR data were assessed: 1) the original, 2) a filtered, and 3) a corrected model. The corrected CHM gave the best accuracy and was then coupled with imagery to refine tree species crown identification. When compared with photo-interpretation, 90% of the objects represented a single species. For modeling, 313 variables were derived from 16-band WorldView-3 imagery and LiDAR data, using radiance, reflectance, pixel, and object-based calculation techniques. Variable selection procedures were employed to reduce their number from 313 to 16, using only 11 bands to aid reproducibility. For classification, a global approach using all 11 species was compared to a semi-hierarchical hybrid classification approach at two levels: (1) tree type (broadleaf/conifer) and (2) individual broadleaf (five) and conifer (six) species. Five different model techniques were used: (1) support vector machine (SVM), (2) classification and regression tree (CART), (3) random forest (RF), (4) k-nearest neighbors (k-NN), and (5) linear discriminant analysis (LDA). Each model was tuned separately for all approaches and levels. For the global approach, the best model was the SVM using eight variables (overall accuracy (OA): 80%, Kappa: 0.77). With the semi-hierarchical hybrid approach, at the tree type level, the best model was the k-NN using six variables (OA: 100% and Kappa: 1.00). At the level of identifying broadleaf and conifer species, the best model was the SVM, with OA of 80% and 97% and Kappa values of 0.74 and 0.97, respectively, using seven variables for both models. This paper demonstrates that a hybrid classification approach gives better results and that using 16-band WorldView-3 with LiDAR data leads to more precise predictions for tree segmentation and classification, especially when the number of tree species is large.

Keywords: tree species, object-based, classification, multispectral, machine learning, WorldView-3, LiDAR

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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: Parul Johri, Mala Trivedi

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Major Intrinsic proteins (MIPs), actively involved in the passive transport of small polar molecules across the membranes of almost all living organisms. MIPs that specifically transport water molecules are named aquaporins (AQPs). The permeability of membranes is actively controlled by the regulation of the amount of different MIPs present but also in some cases by phosphorylation and dephosphorylation of the channel. Based on sequence similarity, MIPs have been classified into many categories. All of the proteins are made up of the 20 amino acids, the only difference is there in their orientations. Again all the 20 amino acids are made up of the basic five elements namely: carbon, hydrogen, oxygen, sulphur and nitrogen. These elements are responsible for giving the amino acids the properties of hydrophilicity/hydrophobicity which play an important role in protein interactions. The hydrophobic amino acids characteristically have greater number of carbon atoms as carbon is the main element which contributes to hydrophobic interactions in proteins. It is observed that the carbon level of proteins in different species is different. In the present work, we have taken a sample set of 150 aquaporins proteins from Uniprot database and a dynamic programming code was written to calculate the carbon percentage for each sequence. This carbon percentage was further used to barcode the aqauporins of animals and plants. The protein taken from Oryza sativa, Zea mays and Arabidopsis thaliana preferred to have carbon percentage of 31.8 to 35, whereas on the other hand sequences taken from Mus musculus, Saccharomyces cerevisiae, Homo sapiens, Bos Taurus, and Rattus norvegicus preferred to have carbon percentage of 31 to 33.7. This clearly demarks the carbon range in the aquaporin proteins from plant and animal origin. Hence the atom level analysis of protein sequences can provide us with better results as compared to the residue level comparison.

Keywords: aquaporins, carbon, dynamic prgramming, MIPs

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Authors: Lucile Soudani, Hervé Illy, Rémi Bouchié

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Mould growth in living environments has been widely reported for decades all throughout the world. A higher level of moisture in housings can lead to building degradation, chemical component emissions from construction materials as well as enhancing mould growth within the envelope elements or on the internal surfaces. Moreover, a significant number of studies have highlighted the link between mould presence and the prevalence of respiratory diseases. In recent years, the proportion of biobased materials used in construction has been increasing, as seen as an effective lever to reduce the environmental impact of the building sector. Besides, bio-based materials are also hygroscopic materials: when in contact with the wet air of a surrounding environment, their porous structures enable a better capture of water molecules, thus providing a more suitable background for mould growth. Many studies have been conducted to develop reliable models to be able to predict mould appearance, growth, and decay over many building materials and external exposures. Some of them require information about temperature and/or relative humidity, exposure times, material sensitivities, etc. Nevertheless, several studies have highlighted a large disparity between predictions and actual mould growth in experimental settings as well as in occupied buildings. The difficulty of considering the influence of all parameters appears to be the most challenging issue. As many complex phenomena take place simultaneously, a preliminary study has been carried out to evaluate the feasibility to sadopt a reliability approach rather than a deterministic approach. Both epistemic and random uncertainties were identified specifically for the prediction of mould appearance and growth. Several studies published in the literature were selected and analysed, from the agri-food or automotive sectors, as the deployed methodology appeared promising.

Keywords: bio-based materials, mould growth, numerical prediction, reliability approach

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Authors: Man Wai Leung, Margaret O’Donoghue, Lorna K. P. Suen

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Personal and household hygiene measures are important to prevent upper respiratory tract infections (URTIs) and other infectious diseases, including coronavirus disease 2019 (COVID-19). An online survey recruited 414 eligible parents in Hong Kong to study their hygiene knowledge, attitudes, and practices (KAP) in the prevention of URTIs among their children. The average knowledge score was high (10.2/12.0), but some misconceptions were identified. The majority of participants agreed that good personal hygiene (93.5%) and good environmental hygiene (92.8%) can prevent URTIs. The average score for hand hygiene practices was high (3.78/4.00), but only 56.8% of parents always perform hand hygiene before touching their mouth, nose, or eyes. For environmental hygiene, only some household items were disinfected with disinfectants (69.8%: door handles, 60.4%: toilet seats, 42.8%: floor, 24.2%: dining chairs, 20.5%: dining tables). Higher knowledge score was associated with parents having a tertiary educational level or above, working as healthcare professionals, living at private residential flat or staff quarter, and having a household income of $70,000 or above. Hand hygiene practices varied significantly with parents’ age and income. During the 5th wave of the COVID-19 epidemic, misconceptions about hygiene knowledge were found among parents. Health promotion programs should target parents, especially those who are in old age, obtain lower educational levels, live in public housing, or have a lower income. Hand hygiene moments and proper use of disinfectants could be one of the targeted educational topics.

Keywords: hygiene, upper respiratory tract infection, parents, children, COVID-19

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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: Md Tareq Ferdous Khan, Shrabanti Mazumder, MB Rao

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Background: Substance use during pregnancy is a longstanding public health problem that results in severe consequences for pregnant women and fetuses. Methods: Eight (2011-2018) datasets on pregnant women’s admissions are extracted from TEDS-A. Distributions of sociodemographic, substance abuse behaviors, and clinical characteristics are constructed and compared over the years for trends by the Cochran-Armitage test. Market basket analysis is used in mining the association among polysubstance abuse. Results: Over the years, pregnant woman admissions as the percentage of total and female admissions remain stable, where total annual admissions range from 1.54 to about 2 million with the female share of 33.30% to 35.61%. Pregnant women aged 21-29, 12 or more years of education, white race, unemployed, holding independent living status are among the most vulnerable. Concerns prevail on a significant number of polysubstance users, young age at first use, frequency of daily users, and records of prior admissions (60%). Trends of abused primary substances show a significant rise in heroin (66%) and methamphetamine (46%) over the years, although the latest year shows a considerable downturn. On the other hand, significant decreasing patterns are evident for alcohol (43%), marijuana or hashish (24%), cocaine or crack (23%), other opiates or synthetics (36%), and benzodiazepines (29%). Basket analysis reveals some patterns of co-occurrence of substances consistent over the years. Conclusions: This comprehensive study can work as a reference to identify the most vulnerable groups based on their characteristics and deal with the most hazardous substances from their evidence of co-occurrence.

Keywords: basket analysis, pregnant women, substance abuse, trend analysis

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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: Ajay Kumar, Shamily Jaggi

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In developing countries like India development of modern technologies has been a key determinant in accelerating industrialization and urbanization. But in the pursuit of rapid economic growth, development is considered a top priority, while environmental protection is not given the same importance. Thus, a number of industries sited haphazardly have been established, leading to a deterioration of natural resources like water, soil and air. As a result, environmental pollution is tremendously increasing due to industrialization and mechanization that are serving to fulfill the demands of the population. With the increasing population, demand for bricks for construction work is also increasing, establishing the brick industry as a growing industry. Brick production requires two main resources; water as a source of life, and soil, as a living environment. Water and soil conservation is a critical issue in areas facing scarcity of water and soil resources. The purpose of this review paper is to provide a brief overview of the theoretical frameworks used in the analysis of the adoption and/or acceptance of soil and water conservation practices in the brick industry. Different frameworks and models have been used in the analysis of the adoption and/or acceptance of new technologies and practices; these include the technology acceptance model, motivational model, theory of reasoned action, innovation diffusion theory, theory of planned behavior, and the unified theory of acceptance and use of technology. However, every model has some limitations, such as not considering environmental/contextual and economic factors that may affect the individual’s intention to perform a behavior. The paper concludes that in comparing other models, the UTAUT seems a better model for understanding the dynamics of acceptance and adoption of water and soil conservation practices.

Keywords: brick kiln, water conservation, soil conservation, unified theory of acceptance and use of technology, technology adoption

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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: 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: 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: Gong Zhang

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The villages surrounding protected areas of the Sites are a unique vernacular heritage due to their geographical location, long history, and the combination of nature and humanity. With the construction of more and more heritage sites, the villages around them are faced with the conflict between conservation and development. How to carry out sustainable micro-renewal while preserving the authenticity of the vernacular heritage is of great importance for the co-growth of the village residents and the site. This paper focuses on the process of revitalization of the villages nearby the Erlitou Site Park in China, aiming to study how sustainable village regeneration and conservation can be carried out through the activation of public culture. Firstly, through field research and literature review, this paper studies the vernacular morphology and architecture types of more than ten historical villages around the Erlitou site and investigates the traditional vernacular culture and the daily public activities of the local villagers. Secondly, taking the nearest village to the site area, Ranzhuang Village, as an example, the paper studies the role of public cultural activity interventions on the three different stages of vernacular heritage renewal: master planning, architecture group, and acupuncture-style micro-renewal of individual buildings, aiming to summarise its impact on villagers' lives and vernacular heritage. This paper concludes that a living regeneration with a moderate public cultural activity intervention can promote the symbiosis between the heritage site and the life of the villagers and increase the vitality of the village. This study aims to use the example of village regeneration in Henan, China, as a sustainable reference for the co-development of heritage sites and villages in other parts of the world.

Keywords: Erlitou site, public culture intervention, sustainable, vernacular heritage

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Authors: Ehsan Ahmadifar

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In this research, the effects of different levels (control group (T0), (T1)1, (T2)2 and (T3)3 gr Inulin per Kg diet) of prebiotic Inulin as nutritional supplement on Danio rerio were investigated for 4 month. Since the beginning of feeding larvae until adult (average weight: 67.1 g, length: 4.5 cm) were fed with experimental diets. The survival rate of fish had no significant effect on rate survival (P > 0.05). The highest food conversion ratio (FCR) was in control group and the lowest was observed in T3. Treatment of T3 significantly caused the best feed conversion ratio in Zebra fish (P < 0.05). By increasing the inulin diet during the experiment, specific growth rate increased. The highest and the lowest body weight gain and condition factor were observed in T3 and control, respectively (P < 0.05). Adding 3 gr inulin in Zebra fish diet can improve the performance of the growth indices and final biomass, also this prebiotic can be considered as a suitable supplement for Cyprinidae diet. In the first sampling stage for feeding fish, fat and muscle protein was significantly higher than the second sampling stage (P < 0.05). Given that the second stage fish were full sexual maturity, the amount of fat in muscle decreased (P < 0.05). Moisture and ash levels were significantly (P < 0.05) higher in the second stage sampling than the first stage. Overall, different stage of living affected on muscle chemical composition muscle. Reproductive performance in treatment T2 and T3 were significantly higher than other treatments (P < 0.05). According to the results, the prebiotic inulin does not have a significant impact on the sex ratio in zebrafish (P > 0.05). Based on histology of the gonads, the use of dietary inulin accelerates the process of gonad development in zebrafish.

Keywords: inulin, zebrafish, reproduction, histology

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Authors: Naci Büyükkaracığan

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Hydroclimatic observation values ​​are used in the planning of the project of water resources. Climate variables are the first of the values ​​used in planning projects. At the same time, the climate system is a complex and interactive system involving the atmosphere, land surfaces, snow and bubbles, the oceans and other water structures. The amount and distribution of precipitation, which is an important climate parameter, is a limiting environmental factor for dispersed living things. Trend analysis is applied to the detection of the presence of a pattern or trend in the data set. Many trends work in different parts of the world are usually made for the determination of climate change. The detection and attribution of past trends and variability in climatic variables is essential for explaining potential future alteration resulting from anthropogenic activities. Parametric and non-parametric tests are used for determining the trends in climatic variables. In this study, trend tests were applied to annual total precipitation data obtained in period of 1972 and 2012, in the Konya Basin. Non-parametric trend tests, (Sen’s T, Spearman’s Rho, Mann-Kendal, Sen’s T trend, Wald-Wolfowitz) and parametric test (mean square) were applied to annual total precipitations of 15 stations for trend analysis. The linear slopes (change per unit time) of trends are calculated by using a non-parametric estimator developed by Sen. The beginning of trends is determined by using the Mann-Kendall rank correlation test. In addition, homogeneities in precipitation trends are tested by using a method developed by Van Belle and Hughes. As a result of tests, negative linear slopes were found in annual total precipitations in Konya.

Keywords: trend analysis, precipitation, hydroclimatology, Konya

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Authors: Marilena Ianculescu, Adriana Alexandru

Abstract:

Internet of Health Things (IoHT) has already proved to be a persuasive means to support a proper assessment of the living conditions by collecting a huge variety of data. For a customized health management of a senior patient, IoHT provides the capacity to build a dynamic solution for sustaining the shift inside the patient-physician relationship by allowing a real-time and continuous remote monitoring of the health status, well-being, safety and activities of the senior, especially in a non-clinical environment. Thus, is created a win-win solution in which both the patient and the physician enhance their involvement and shared decision-making, with significant outcomes. Health monitoring systems in smart environments are becoming a viable alternative to traditional healthcare solutions. The ongoing “Non-invasive monitoring and health assessment of the elderly in a smart environment (RO-SmartAgeing)” project aims to demonstrate that the existence of complete and accurate information is critical for assessing the health condition of the seniors, improving wellbeing and quality of life in relation to health. The researches performed inside the project aim to highlight how the management of IoHT devices connected to the RO-SmartAgeing platform in a secure way by using a role-based access control system, can allow the physicians to provide health services at a high level of efficiency and accessibility, which were previously only available in hospitals. The project aims to identify deficient aspects in the provision of health services tailored to a senior patient’s specificity and to offer a more comprehensive perspective of proactive and preventive medical acts.

Keywords: health management, internet of health things, remote monitoring, senior patient

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Authors: M. A. Askari, M. A. Nazari, P. Perrier, Y. Payan

Abstract:

Growth and remodeling of biological structures have gained lots of attention over the past decades. Determining the response of the living tissues to the mechanical loads is necessary for a wide range of developing fields such as, designing of prosthetics and optimized surgery operations. It is a well-known fact that biological structures are never stress-free, even when externally unloaded. The exact origin of these residual stresses is not clear, but theoretically growth and remodeling is one of the main sources. Extracting body organs from medical imaging, does not produce any information regarding the existing residual stresses in that organ. The simplest cause of such stresses is the gravity since an organ grows under its influence from its birth. Ignoring such residual stresses might cause erroneous results in numerical simulations. Accounting for residual stresses due to tissue growth can improve the accuracy of mechanical analysis results. In this paper, we have implemented a computational framework based on fixed-point iteration to determine the residual stresses due to growth. Using nonlinear continuum mechanics and the concept of fictitious configuration we find the unknown stress-free reference configuration which is necessary for mechanical analysis. To illustrate the method, we apply it to a finite element model of healthy human face whose geometry has been extracted from medical images. We have computed the distribution of residual stress in facial tissues, which can overcome the effect of gravity and cause that tissues remain firm. Tissue wrinkles caused by aging could be a consequence of decreasing residual stress and not counteracting the gravity. Considering these stresses has important application in maxillofacial surgery. It helps the surgeons to predict the changes after surgical operations and their consequences.

Keywords: growth, soft tissue, residual stress, finite element method

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

Abstract:

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

Procedia PDF Downloads 129

Authors: Belyihun Muchie

Abstract:

This study investigates the low English oral communication skills of 5 second-year English major students at Debark University. It aims to identify the key factors contributing to their weaknesses and propose effective interventions to improve their spoken English proficiency. Mixed-methods research will be employed, utilizing observations, questionnaires, and semi-structured interviews to gather data from the participants. To clearly identify these factors, structured and informal observations will be employed; the former will be used to identify their fluency, pronunciation, vocabulary use, and grammar accuracy, and the later will be suited to observe the natural interactions and communication patterns of learners in the classroom setting. The questionnaires will assess their self-perceptions of their skills, perceived barriers to fluency, and preferred learning styles. Interviews will also delve deeper into their experiences and explore specific obstacles faced in oral communication. Data analysis will involve both quantitative and qualitative responses. The structured observation and questionnaire will be analyzed quantitatively, whereas the informal observation and interview transcripts will be analyzed thematically. Findings will be used to identify the major causes of low oral communication skills, such as limited vocabulary, grammatical errors, pronunciation difficulties, or lack of confidence. They are also helpful to develop targeted solutions addressing these causes, such as intensive pronunciation practice, conversation simulations, personalized feedback, or anxiety-reduction techniques. Finally, the findings will guide designing an intervention plan for implementation during the action research phase. The study's outcomes are expected to provide valuable insights into the challenges faced by English major students in developing oral communication skills, contribute to the development of evidence-based interventions for improving spoken English proficiency in similar contexts, and offer practical recommendations for English language instructors and curriculum developers to enhance student learning outcomes. By addressing the specific needs of these students and implementing tailored interventions, this research aims to bridge the gap between theoretical knowledge and practical speaking ability, equipping them with the confidence and skills to flourish in English communication settings.

Keywords: oral communication skills, mixed-methods, evidence-based interventions, spoken English proficiency

Procedia PDF Downloads 46

Authors: Hyohun Kim, Dongwha Shin, Yeonseok Kim, Ji-Su Ahn, Kensuke Nakamura, Dongeun Choi, Byung-Woo Hong

Abstract:

Artefacts are commonly encountered in the imaging process of clinical computed tomography (CT) where the artefact refers to any systematic discrepancy between the reconstructed observation and the true attenuation coefficient of the object. It is known that CT images are inherently more prone to artefacts due to its image formation process where a large number of independent detectors are involved, and they are assumed to yield consistent measurements. There are a number of different artefact types including noise, beam hardening, scatter, pseudo-enhancement, motion, helical, ring, and metal artefacts, which cause serious difficulties in reading images. Thus, it is desired to remove nuisance factors from the degraded image leaving the fundamental intrinsic information that can provide better interpretation of the anatomical and pathological characteristics. However, it is considered as a difficult task due to the high dimensionality and variability of data to be recovered, which naturally motivates the use of machine learning techniques. We propose an image restoration algorithm based on the deep neural network framework where the denoising auto-encoders are stacked building multiple layers. The denoising auto-encoder is a variant of a classical auto-encoder that takes an input data and maps it to a hidden representation through a deterministic mapping using a non-linear activation function. The latent representation is then mapped back into a reconstruction the size of which is the same as the size of the input data. The reconstruction error can be measured by the traditional squared error assuming the residual follows a normal distribution. In addition to the designed loss function, an effective regularization scheme using residual-driven dropout determined based on the gradient at each layer. The optimal weights are computed by the classical stochastic gradient descent algorithm combined with the back-propagation algorithm. In our algorithm, we initially decompose an input image into its intrinsic representation and the nuisance factors including artefacts based on the classical Total Variation problem that can be efficiently optimized by the convex optimization algorithm such as primal-dual method. The intrinsic forms of the input images are provided to the deep denosing auto-encoders with their original forms in the training phase. In the testing phase, a given image is first decomposed into the intrinsic form and then provided to the trained network to obtain its reconstruction. We apply our algorithm to the restoration of the corrupted CT images by the artefacts. It is shown that our algorithm improves the readability and enhances the anatomical and pathological properties of the object. The quantitative evaluation is performed in terms of the PSNR, and the qualitative evaluation provides significant improvement in reading images despite degrading artefacts. The experimental results indicate the potential of our algorithm as a prior solution to the image interpretation tasks in a variety of medical imaging applications. This work was supported by the MISP(Ministry of Science and ICT), Korea, under the National Program for Excellence in SW (20170001000011001) supervised by the IITP(Institute for Information and Communications Technology Promotion).

Keywords: auto-encoder neural network, CT image artefact, deep learning, intrinsic image representation, noise reduction, total variation

Procedia PDF Downloads 187