Search results for: learning outcomes assessment

Authors: Caroline Lavratti, Pedro Dal Lago, Gustavo Reinaldo, Gilson Dorneles, Andreia Bard, Laira Fuhr, Daniela Pochmann, Alessandra Peres, Luciane Wagner, Viviane Elsner

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Approximately 1% of the world's population is affected by schizophrenia (SZ), a chronic and debilitating neurodevelopmental disorder. Among possible factors, reduced levels of Brain-derived neurotrophic factor (BDNF) has been recognized in physiopathogenesis and course of SZ. In this context, peripheral BDNF levels have been used as a biomarker in several clinical studies, since this neurotrophin is able to cross the blood-brain barrier in a bi-directional manner and seems to present a strong correlation with the central nervous system fluid levels. The patients with SZ usually adopts a sedentary lifestyle, which has been partly associated with the increase in obesity incidence rates, metabolic syndrome, type 2 diabetes and coronary heart disease. On the other hand, exercise, a non-invasive and low cost intervention, has been considered an important additional therapeutic option for this population, promoting benefits to physical and mental health. To our knowledge, few studies have been pointed out that the positive effects of exercise in SZ patients are mediated, at least in part, to enhanced levels of BDNF after training. However, these studies are focused on evaluating the effect of single bouts of exercise of chronic interventions, data concerning the short- and long-term exercise outcomes on BDNF are scarce. Therefore, this study aimed to evaluate the effect of a concurrent exercise protocol (CEP) on plasma BDNF levels of SZ patients in different time-points. Material and Methods: This study was approved by the Research Ethics Committee of the Centro Universitário Metodista do IPA (no 1.243.680/2015). The participants (n=15) were subbmited to the CEP during 90 days, 3 times a week for 60 minutes each session. In order to evaluate the short and long-term effects of exercise, blood samples were collected pre, 30, 60 and 90 days after the intervention began. Plasma BDNF levels were determined with the ELISA method, from Sigma-Aldrich commercial kit (catalog number RAB0026) according to manufacturer's instructions. Results: A remarkable increase on plasma BDNF levels at 90 days after training compared to baseline (p=0.006) and 30 days (p=0.007) values were observed. Conclusion: Our data are in agreement with several studies that show significant enhancement on BDNF levels in response to different exercise protocols in SZ individuals. We might suggest that BDNF upregulation after training in SZ patients acts in a dose-dependent manner, being more pronounced in response to chronic exposure. Acknowledgments: This work was supported by Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS)/Brazil.

Keywords: exercise, BDNF, schizophrenia, time-points

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Authors: Jianhong Xiang, Rui Sun, Linyu Wang

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In recent years, due to the continuous improvement of deep learning theory, Convolutional Neural Network (CNN) has played a great superior performance in the research of Hyperspectral Image (HSI) classification. Since HSI has rich spatial-spectral information, only utilizing a single dimensional or single size convolutional kernel will limit the detailed feature information received by CNN, which limits the classification accuracy of HSI. In this paper, we design a multi-scale CNN with MLP based on residual and attention modules (INRAM-3DCNN) for the HSI classification task. We propose to use multiple 3D convolutional kernels to extract the packet feature information and fully learn the spatial-spectral features of HSI while designing residual 3D convolutional branches to avoid the decline of classification accuracy due to network degradation. Secondly, we also design the 2D Inception module with a joint channel attention mechanism to quickly extract key spatial feature information at different scales of HSI and reduce the complexity of the 3D model. Due to the high parallel processing capability and nonlinear global action of the Multilayer Perceptron (MLP), we use it in combination with the previous CNN structure for the final classification process. The experimental results on two HSI datasets show that the proposed INRAM-3DCNN method has superior classification performance and can perform the classification task excellently.

Keywords: INRAM-3DCNN, residual, channel attention, hyperspectral image classification

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Authors: Adi Sharabi, Dafna Marom-Golan

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Parents’ involvement in the care of their children with Autism Spectrum Disorder (ASD) and its beneficial effect on the children’s developmental and educational outcomes is well documented. At the same time, parents of children with ASD tend to experience greater psychological distress than parents of children with other developmental disabilities or with typical development. Positive social support is an important resource used by parents to reduce their psychological distress. The goal of the current research was to examine the contribution of formal and informal social support in explaining mothers’ and fathers’ involvement with their young children with ASD. The sample consisted of 107 parents who live in Israel (61 mothers and 46 fathers) of children aged between 2 and 7, all diagnosed with ASD and attending special kindergartens or special day care for children with ASD. Parental involvement and social support perception were assessed. Initial analysis focused on the relations between involvement, support, and demographic variables. In addition, analysis of variance (ANOVA) was conducted to test differences between mothers and fathers. Two hierarchical multiple regression analyses were performed to examine the predicted factors in the involvement model while controlling for group (mothers/fathers). Results indicate that mothers reported significantly higher levels of parenting involvement than fathers. Mothers reported higher levels of general involvement and all sub-types of involvement. For example, mothers reported that they were more interested in and have higher levels of attendance in their child’s educational program. They were also more collaborative in their child’s educational therapeutic program, and socialized with other parents of children from their child’s kindergarten than fathers. Mothers’ involvement was found to be related to their informal support (non-formal relatives). Findings also reveal significant differences between mothers and fathers on the formal support subscale measure of specializes services. Fathers, more than mothers, reported more specializes services support such as social workers or professional therapists. Separate hierarchical multiple regression analyses revealed a unique gender difference in the factors that explained parental involvement. Specifically, informal support only had a unique positive contribution in explaining mothers’, but not fathers’ involvement. This study highlights the central role of mothers in maintaining constant contact with the educational system and the professionals who help care for their child with ASD. At the same time, this research emphasizes the crucial role of both mothers and fathers in their child's development and well-being at every development stage, particularly in early development. Further, different kinds of social support seem to relate to the different kinds of parental involvement. It is in the best interest of educators and family therapists who work with families with children with ASD to support the cohesiveness of the family and the collaboration of the parents by understanding and respecting the way each member addresses the responsibilities of parenting a child with ASD, and her or his need for different types of social support.

Keywords: parental differences, parental involvement, social support, specialized support services

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Authors: Hui Zhang, Ye Tian, Fang Ye, Ziming Guo

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Communication signal modulation recognition technology is one of the key technologies in the field of modern information warfare. At present, communication signal automatic modulation recognition methods are mainly divided into two major categories. One is the maximum likelihood hypothesis testing method based on decision theory, the other is a statistical pattern recognition method based on feature extraction. Now, the most commonly used is a statistical pattern recognition method, which includes feature extraction and classifier design. With the increasingly complex electromagnetic environment of communications, how to effectively extract the features of various signals at low signal-to-noise ratio (SNR) is a hot topic for scholars in various countries. To solve this problem, this paper proposes a feature extraction algorithm for the communication signal based on the improved Holder cloud feature. And the extreme learning machine (ELM) is used which aims at the problem of the real-time in the modern warfare to classify the extracted features. The algorithm extracts the digital features of the improved cloud model without deterministic information in a low SNR environment, and uses the improved cloud model to obtain more stable Holder cloud features and the performance of the algorithm is improved. This algorithm addresses the problem that a simple feature extraction algorithm based on Holder coefficient feature is difficult to recognize at low SNR, and it also has a better recognition accuracy. The results of simulations show that the approach in this paper still has a good classification result at low SNR, even when the SNR is -15dB, the recognition accuracy still reaches 76%.

Keywords: communication signal, feature extraction, Holder coefficient, improved cloud model

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Authors: Iris W. A. Boot, Anke Wesselius, Maurice P. Zeegers

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Diet may play an essential role in the aetiology of bladder cancer (BC). Vitamin D is involved in various biological functions which have the potential to prevent BC development. Besides, vitamin D also influences the uptake of calcium and phosphorus , thereby possibly indirectly influencing the risk of BC. The aim of the present study was to investigate the relation between vitamin D intake and BC risk. Individual dietary data were pooled from three cohort studies. Food item intake was converted to daily intakes of vitamin D, calcium and phosphorus. Pooled multivariate hazard ratios (HRs), with corresponding 95% confidence intervals (CIs) were obtained using Cox-regression models. Analyses were adjusted for gender, age and smoking status (Model 1), and additionally for the food groups fruit, vegetables and meat (Model 2). Dose–response relationships (Model 1) were examined using a nonparametric test for trend. In total, 2,871 cases and 522,364 non-cases were included in the analyses. The present study showed an overall increased BC risk for high dietary vitamin D intake (HR: 1.14, 95% CI: 1.03-1.26). A similar increase BC risk with high vitamin D intake was observed among women and for the non-muscle invasive BC subtype, (HR: 1.41, 95% CI: 1.15-1.72, HR: 1.13, 95% CI: 1.01-1.27, respectively). High calcium intake decreased the BC risk among women (HR: 0.81, 95% CI: 0.67-0.97). A combined inverse effect on BC risk was observed for low vitamin D intake and high calcium intake (HR: 0.67, 95% CI: 0.48-0.93), while a positive effect was observed for high vitamin D intake in combination with low, moderate and high phosphorus (HR: 1.31, 95% CI: 1.09-1.59, HR: 1.17, 95% CI: 1.01-1.36, HR: 1.16, 95% CI: 1.03-1.31, respectively). Combining all nutrients showed a decreased BC risk for low vitamin D intake, high calcium and moderate phosphor intake (HR: 0.37, 95% CI: 0.18-0.75), and an increased BC risk for moderate intake of all the nutrients (HR: 1.18, 95% CI: 1.02-1.38), for high vitamin D and low calcium and phosphor intake (HR: 1.28, 95% CI: 1.01-1.62), and for moderate vitamin D and calcium and high phosphorus intake (HR: 1.27, 95% CI: 1.01-1.59). No significant dose-response analyses were observed. The findings of this study show an increased BC risk for high dietary vitamin D intake and a decreased risk for high calcium intake. Besides, the study highlights the importance of examining the effect of a nutrient in combination with complementary nutrients for risk assessment. Future research should focus on nutrients in a wider context and in nutritional patterns.

Keywords: bladder cancer, nutritional oncology, pooled cohort analysis, vitamin D

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Authors: Nina Leila Mussa

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Background: The devastating effects of the war on Syria’s educational infrastructure has been widely reported, with millions of children denied access. However, among those who resettled in Lebanon, the impact of receiving educational assistance on their abilities to pass the English entrance exam is not well described. The aim of this study was to identify predictors of success among Syrian refugees receiving English language courses in a Lebanese university. Methods: The database of Syrian refugee girls matriculated in English courses at the American University of Beirut (AUB) was reviewed. The study period was 7/2018-09/2020. Variables compared included: family size and income, welfare status, parents’ education, English proficiency, access to the internet, and need for external help with homework. Results: For the study period, there were 28 girls enrolled. The average family size was 6 (range 4-9), with eight having completed primary, 14 secondary education, and 6 graduated high school. Eighteen were single-income families. After 12 weeks of English courses, 16 passed the Test of English as Foreign Language (TOEFL) from the first attempt, and 12 failed. Out of the 12, 8 received external help, and 6 passed on the second attempt, which brings the total number of successful passing to 22. Conclusion: Despite the tragedy of war, girls receiving assistance in learning English in Lebanon are able to pass the basic language test. Investment in enhancing those educational experiences will be determinantal in achieving widespread progress among those at-risk children.

Keywords: refugee girls, TOEFL, education, success

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Authors: A. R. Shokoohi, E. A. M. Bulder, C. Th. van Alphen, D. F. den Hertog, E. J. Hin

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The Northern region of the Netherlands has beautiful landscapes, a nice diversity of green and blue areas, and dispersed settlements. However, some recent population changes can become threats to health and wellbeing in these areas. The rural areas in the three northern provinces -Groningen, Friesland, and Drenthe, see youngsters leave the region for which reason they are aging faster than other regions in the Netherlands. As a result, some villages have faced major population decline that is leading to loss of facilities/amenities and a decrease in accessibility and social cohesion. Those who still live in these villages are relatively old, low educated and have low-income. To develop a deeper understanding of the health status of the people living in these areas, and help them to improve their living environment, the GO!-Method is being applied in this study. This method has been developed by the National Institute for Public Health and the Environment (RIVM) of the Netherlands and is inspired by the broad definition of health by Machteld Huber: the ability to adapt and direct control, in terms of the physical, emotional and social challenges of life, while paying extra attention to vulnerable groups. A healthy living environment is defined as an environment that residents find it pleasant and encourages and supports healthy behavior. The GO!-method integrates six domains that constitute a healthy living environment: health and lifestyle, facilities and development, safety and hygiene, social cohesion and active citizens, green areas, and air and noise pollution. First of all, this method will identify opportunities for a healthier living environment using existing information and perceptions of residents and other local stakeholders in order to strengthen social participation and quality of life in these rural areas. Second, this approach will connect identified opportunities with available and effective evidence-based interventions in order to develop an action plan from the residents and local authorities perspective which will help them to design their municipalities healthier and more resilient. This method is being used for the first time in rural areas to our best knowledge, in close collaboration with the residents and local authorities of the three provinces to create a sustainable process and stimulate social participation. Our paper will present the outcomes of the first phase of this project in collaboration with the municipality of Westerkwartier, located in the northwest of the province of Groningen. And will describe the current situation, and identify local assets, opportunities, and policies relating to healthier environment; as well as needs and challenges to achieve goals. The preliminary results show that rural demographic changes in the northern Netherlands have negative impacts on service provisions and social cohesion, and there is a need to understand this complicated situation and improve the quality of life in those areas.

Keywords: population decline, rural areas, healthy environment, Netherlands

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Authors: Sujeet Kumar

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Education has greater importance particularly in terms of increasing human capital and economic competitiveness. It plays a crucial role in terms of cognitive and skill development. Its plays a vital role in process of socialization, fostering social justice, and enhancing social cohesion. Policy related to education has been always a priority for developed countries, which is later adopted by developing countries also. The government of India has also brought change in education polices in line with recognizing change at national and supranational level. However, quality education is still not become an open door for every child in India and several reports are produced year to year about level of school education in India. This paper is concerned with schooling in India. Particularly, it focuses on two government and two private schools in Bihar, but reference has made to schools in Delhi especially around slum communities. The paper presents brief historical context and an overview of current school systems in India. Later, it focuses on analysis of current development in policy in reference with field observation, which is anchored around choice, diversity, market – orientation and gap between different groups of pupils. There is greater degree of difference observed at private and government school levels in terms of quality of teachers, method of teaching and overall environment of learning. The paper concludes that the recent policy development in education particularly Sarva Siksha Abhiyaan (SAA) and Right to Education Act (2009) has required renovating new approach to bridge the gap through broader consultation at grassroots and participatory approach with different stakeholders.

Keywords: education, public policy, participatory approach

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Authors: Rana Alaa El-Deen Ahmed, M. Elemam Shehab, Shereen Morsy, Nermeen Mekawie

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With the growing popularity and acceptance of e-commerce platforms, users face an ever increasing burden in actually choosing the right product from the large number of online offers. Thus, techniques for personalization and shopping guides are needed by users. For a pleasant and successful shopping experience, users need to know easily which products to buy with high confidence. Since selling a wide variety of products has become easier due to the popularity of online stores, online retailers are able to sell more products than a physical store. The disadvantage is that the customers might not find products they need. In this research the customer will be able to find the products he is searching for, because recommender systems are used in some ecommerce web sites. Recommender system learns from the information about customers and products and provides appropriate personalized recommendations to customers to find the needed product. In this paper eleven classification algorithms are comparatively tested to find the best classifier fit for consumer online shopping attitudes and behavior in the experimented dataset. The WEKA knowledge analysis tool, which is an open source data mining workbench software used in comparing conventional classifiers to get the best classifier was used in this research. In this research by using the data mining tool (WEKA) with the experimented classifiers the results show that decision table and filtered classifier gives the highest accuracy and the lowest accuracy classification via clustering and simple cart.

Keywords: classification, data mining, machine learning, online shopping, WEKA

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Authors: Massimiliano Panella, Sara Bortoluzzi, Sophia Russotto, Daniele Nicolini, Carmela Rinaldi

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Osteoporosis affects more than 200 million people worldwide. About 75% of osteoporosis cases are undiagnosed or diagnosed only when a bone fracture occurs. Since osteoporosis related fractures are significant determinants of the burden of disease and health and social costs of aging populations, we believe that this is the early identification and treatment of high-risk patients should be a priority in actual healthcare systems. Screening for osteoporosis by dual energy x-ray absorptiometry (DEXA) is not cost-effective for general population. An alternative is pulse-echo ultrasound (PEUS) because of the minor costs. To this end, we developed an early detection program for osteoporosis with PEUS, and we evaluated is possible impact and sustainability. We conducted a cross-sectional study including 1,050 people in Italy. Subjects with >1 major or >2 minor risk factors for osteoporosis were invited to PEUS bone mass density (BMD) measurement at the proximal tibia. Based on BMD values, subjects were classified as healthy subjects (BMD>0.783 g/cm²) and pathological including subjects with suspected osteopenia (0.783≤BMD>0.719 g/cm²) or osteoporosis (BMD ≤ 0.719 g/cm²). The responder rate was 60.4% (634/1050). According to the risk, PEUS scan was recommended to 436 people, of whom 300 (mean age 45.2, 81% women) accepted to participate. We identified 240 (80%) healthy and 60 (20%) pathological subjects (47 osteopenic and 13 osteoporotic). We observed a significant association between high risk people and reduced bone density (p=0.043) with increased risks for female gender, older ages, and menopause (p<0.01). The yearly cost of the screening program was 8,242 euros. With actual Italian fracture incidence rates in osteoporotic patients, we can reasonably expect in 20 years that at least 6 fractures will occur in our sample. If we consider that the mean costs per fracture in Italy is today 16,785 euros, we can estimate a theoretical cost of 100,710 euros. According to literature, we can assume that the early treatment of osteoporosis could avoid 24,170 euros of such costs. If we add the actual yearly cost of the treatments to the cost of our program and we compare this final amount of 11,682 euros to the avoidable costs of fractures (24,170 euros) we can measure a possible positive benefits/costs ratio of 2.07. As a major outcome, our study let us to early identify 60 people with a significant bone loss that were not aware of their condition. This diagnostic anticipation constitutes an important element of value for the project, both for the patients, for the preventable negative outcomes caused by the fractures, and for the society in general, because of the related avoidable costs. Therefore, based on our finding, we believe that the PEUS based screening performed could be a cost-effective approach to early identify osteoporosis. However, our study has some major limitations. In fact, in our study the economic analysis is based on theoretical scenarios, thus specific studies are needed for a better estimation of the possible benefits and costs of our program.

Keywords: osteoporosis, prevention, public health, screening

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Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

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Authors: Mustapha Ouardouz, Mina Amharref, Abdessamed Bernoussi

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The future energy, for limited energy resources countries, goes through renewable energies (solar, wind etc.). The renewable energies constitute a major component of the energy strategy to cover a substantial part of the growing needs and contribute to environmental protection by replacing fossil fuels. Indeed, sustainable development involves the promotion of renewable energy and the preservation of the environment by the use of clean energy technologies to limit emissions of greenhouse gases and reducing the pressure exerted on the forest cover. So the impact studies, of the energy use on the environment and farm-related risks are necessary. For that, a global approach integrating all the various sectors involved in such project seems to be the best approach. In this paper we present an approach based on the multi criteria analysis and the realization of one pilot to achieve the development of an innovative geo-intelligent environmental platform. An implementation of this platform will collect, process, analyze and manage environmental data in connection with the nature of used energy in the studied region. As an application we consider a region in the north of Morocco characterized by intense agricultural and industrials activities and using diverse renewable energy. The strategic goals of this platform are; the decision support for better governance, improving the responsiveness of public and private companies connected by providing them in real time with reliable data, modeling and simulation possibilities of energy scenarios, the identification of socio-technical solutions to introduce renewable energies and estimate technical and implantable potential by socio-economic analyzes and the assessment of infrastructure for the region and the communities, the preservation and enhancement of natural resources for better citizenship governance through democratization of access to environmental information, the tool will also perform simulations integrating environmental impacts of natural disasters, particularly those linked to climate change. Indeed extreme cases such as floods, droughts and storms will be no longer rare and therefore should be integrated into such projects.

Keywords: renewable energies, decision aided tool, environment, simulation

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Authors: Muhammad Zafarullah Khan, Sumeera Abbasi

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The Farmers Field School (FFS) learning approach aims to improve knowledge of the farmers through integrated crop management and provide leadership in their decision making process. The study was conducted to assess the impact of FFS on vegetables production before and after FFS intervention in four villages of district Peshawar in cropping season 2012, by interviewing 80 FFS respondents, twenty from each selected village. It was observed from the study results that all the respondents were satisfied from the impact of FFS and they informed an increased in production in vegetables. It was further observed that after the implementation of FFS the sowing seed rate of tomato and cucumber were decreased from 0.185kg/kanal to 0.100 kg/ kanal and 0.120kg/kanal to 0.010kg/kanal where as the production of tomato and cucumber were increased from 8158.75kgs/kanal to 10302. 5kgs/kanal and 3230kgs/kanal to 5340kgs/kanal, respectively. The cost of agriculture inputs per kanal including seed cost, crop management, Farm Yard Manure, and weedicides in case of tomato were reduced by Rs.28, Rs. 3170, Rs.658and Rs 205 whereas in cucumber reduced by Rs.35, Rs.570, Rs 80 and Rs.430 respectively. Only fertilizers cost was increased by Rs. 2200 in case of tomato and Rs 465 in case of cucumber. Overall the cost was reduced to Rs 545 in tomato and Rs 490 in cucumber production.FFS provided a healthy vegetables and also reduced input cost by adopting integrated crop management. Therefore the promotion of FFS is needed to be planned for farmers to reduce cost of production, so that the more farmers should be benefited.

Keywords: impact, farmer field schools, vegetable production, Peshawar Khyber Pakhtunkhwa

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Authors: Jameela Ali, Hamid A. Jalab, Loay E. George, Abdul Rahim Ahmad, Azizah Suliman, Karim Al-Jashamy

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Red blood cells (RBC) are the most common types of blood cells and are the most intensively studied in cell biology. The lack of RBCs is a condition in which the amount of hemoglobin level is lower than normal and is referred to as “anemia”. Abnormalities in RBCs will affect the exchange of oxygen. This paper presents a comparative study for various techniques for classifying the RBCs as normal, or abnormal (anemic) using WEKA. WEKA is an open source consists of different machine learning algorithms for data mining applications. The algorithm tested are Radial Basis Function neural network, Support vector machine, and K-Nearest Neighbors algorithm. Two sets of combined features were utilized for classification of blood cells images. The first set, exclusively consist of geometrical features, was used to identify whether the tested blood cell has a spherical shape or non-spherical cells. While the second set, consist mainly of textural features was used to recognize the types of the spherical cells. We have provided an evaluation based on applying these classification methods to our RBCs image dataset which were obtained from Serdang Hospital-alaysia, and measuring the accuracy of test results. The best achieved classification rates are 97%, 98%, and 79% for Support vector machines, Radial Basis Function neural network, and K-Nearest Neighbors algorithm respectively.

Keywords: K-nearest neighbors algorithm, radial basis function neural network, red blood cells, support vector machine

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Authors: Hanadi Khadawardi

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The current study contributes to research on foreign/second language (L2) academic reading by presenting a significant case study, which seeks to investigate specific groups of international (Arab) postgraduate students’ L2 academic reading practices in the UK educational context. In particular, the study scrutinises postgraduate students’ L2 paper-based and digital-based academic reading strategies, and their use of digital aids while engaged in L2 academic reading. To this end, the study investigates Arab readers’ attitudes toward digital L2 academic reading. The study aims to compare between paper and digital L2 academic reading strategies that the students employ and which reading formats they prefer. This study tracks Masters-level students and examines the way in which their reading strategies and attitudes change throughout their Masters programme in the UK educational context. The academic reading strategies and attitudes of five students from four different disciplines (Health Science, Psychology, Management, and Education) are investigated at two points during their one-year Masters programmes. In addition, the study investigates the same phenomenon with 15 Saudi PhD students drawn from seven different disciplines (Computer Science, Engineering, Psychology, Management, Marketing, Health Science, and Applied Linguistics) at one period of their study in the same context. The study uses think-aloud protocol, field notes, stimulated recall, and semi-structured interviews to collect data. The data is analysed qualitatively. The results of the study will explain the process of learning in terms of reading L2 paper and digital academic texts in the L2 context.

Keywords: EFL: English as a foreign language, ESL: English as a second language, L: Language

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Authors: Derjew Ayele Ejigu, Houde Song, Xiaojing Liu

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This work presents the particle swarm optimization trained neural network (PSO-NN) supervisory proportional integral derivative (PID) control method to monitor the pressurized water reactor (PWR) core power for safe operation. The proposed control approach is implemented on the transfer function of the PWR core, which is computed from the state-space model. The PWR core state-space model is designed from the neutronics, thermal-hydraulics, and reactivity models using perturbation around the equilibrium value. The proposed control approach computes the control rod speed to maneuver the core power to track the reference in a closed-loop scheme. The particle swarm optimization (PSO) algorithm is used to train the neural network (NN) and to tune the PID simultaneously. The controller performance is examined using integral absolute error, integral time absolute error, integral square error, and integral time square error functions, and the stability of the system is analyzed by using the Bode diagram. The simulation results indicated that the controller shows satisfactory performance to control and track the load power effectively and smoothly as compared to the PSO-PID control technique. This study will give benefit to design a supervisory controller for nuclear engineering research fields for control application.

Keywords: machine learning, neural network, pressurized water reactor, supervisory controller

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Authors: A. H. Nanher, N. P. Singh, Shashidhar Yadav, Sachin Tyagi

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Agricultural production system is an outcome of a complex interaction of seed, soil, water and agro-chemicals (including fertilizers). Therefore, judicious management of all the inputs is essential for the sustainability of such a complex system. Precision agriculture gives farmers the ability to use crop inputs more effectively including fertilizers, pesticides, tillage and irrigation water. More effective use of inputs means greater crop yield and/or quality, without polluting the environment the focus on enhancing the productivity during the Green Revolution coupled with total disregard of proper management of inputs and without considering the ecological impacts, has resulted into environmental degradation. To evaluate a new approach for site-specific nutrient management (SSNM). Large variation in initial soil fertility characteristics and indigenous supply of N, P, and K was observed among Field- and season-specific NPK applications were calculated by accounting for the indigenous nutrient supply, yield targets, and nutrient demand as a function of the interactions between N, P, and K. Nitrogen applications were fine-tuned based on season-specific rules and field-specific monitoring of crop N status. The performance of SSNM did not differ significantly between high-yielding and low-yielding climatic seasons, but improved over time with larger benefits observed in the second year Future, strategies for nutrient management in intensive rice systems must become more site-specific and dynamic to manage spatially and temporally variable resources based on a quantitative understanding of the congruence between nutrient supply and crop demand. The SSNM concept has demonstrated promising agronomic and economic potential. It can be used for managing plant nutrients at any scale, i.e., ranging from a general recommendation for homogenous management of a larger domain to true management of between-field variability. Assessment of pest profiles in FFP and SSNM plots suggests that SSNM may also reduce pest incidence, particularly diseases that are often associated with excessive N use or unbalanced plant nutrition.

Keywords: nutrient, pesticide, crop, yield

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Authors: Arunima Verma, Padmabati Mondal

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Serotonin-receptor binding plays a key role in several neurological and biological processes, including mood, sleep, hunger, cognition, learning, and memory. In this article, we performed molecular dynamics simulation to examine the key residues that play an essential role in the binding of serotonin to the G-protein-coupled 5-HT₁ᴮ receptor (5-HT₁ᴮ R) via electrostatic interactions. An end-point free energy calculation method (MM-PBSA) determines the stability of the 5-HT1B R due to serotonin binding. The single-point mutation of the polar or charged amino acid residues (Asp129, Thr134) on the binding sites and the calculation of binding free energy validate the importance of these residues in the stability of the serotonin-receptor complex. Principal component analysis indicates the serotonin-bound 5-HT1BR is more stabilized than the apo-receptor in terms of dynamical changes. The difference dynamic cross-correlations map shows the correlation between the transmembrane and mini-Go, which indicates signal transduction happening between mini-Go and the receptor. Allosteric communication reveals the key nodes for signal transduction in 5-HT1BR. These results provide useful insights into the signal transduction pathways and mutagenesis study to regulate the functionality of the complex. The developed protocols can be applied to study local non-covalent interactions and long-range allosteric communications in any protein-ligand system for computer-aided drug design.

Keywords: allostery, CADD, MD simulations, MM-PBSA

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Authors: Stacey Herzog, Mitchell McCleary, Istvan Kovacs

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Purpose: Maximal strength and maximal power are key athletic abilities in many sports disciplines. In recent years, velocity-based training (VBT) with a relatively high 75-85% 1RM resistance has been popularized in preparation for powerlifting and various other sports. The purpose of this study was to discover differences between beginner/intermediate and advanced lifters’ push/press performances after a heavy resistance-based BP training program. Methods: A six-week, three-workouts per week program was administered to 52 young, physically active adults (age: 22.4±5.1; 12 female). The majority of the participants (84.6%) had prior experience in bench pressing. Typical workouts began with BP using 75-95% 1RM in the 1-5 repetition range. The sets in the lower part of the range (75-80% 1RM) were performed with velocity-focus as well. The BP sets were followed by seated dumbbell presses and six additional upper-body assistance exercises. Pre- and post-tests were conducted on five test exercises: one-repetition maximum BP (1RM), calculated relative strength index: BP/BW (RSI), four-repetition maximal-effort dynamic BP for peak concentric velocity with 80% 1RM (4RV), 4-repetition ballistic pushups (BPU) for height (4PU), and seated medicine ball toss for distance (MBT). For analytic purposes, the participant group was divided into two subgroups: self-indicated beginner or intermediate initial resistance training status (BITS) [n=21, age: 21.9±3.6; 10 female] and advanced initial resistance training status (ATS) [n=31, age: 22.7±5.9; 2 female]. Pre- and post-test results were compared within subgroups. Results: Paired-sample t-tests indicated significant within-group improvements in all five test exercises in both groups (p < 0.05). BITS improved 18.1 lbs. (13.0%) in 1RM, 0.099 (12.8%) in RSI, 0.133 m/s (23.3%) in 4RV, 1.55 in. (27.1%) in BPU, and 1.00 ft. (5.8%) in MBT, while the ATS group improved 13.2 lbs. (5.7%) in 1RM, 0.071 (5.8%) in RSI, 0.051 m/s (9.1%) in 4RV, 1.20 in. (13.7%) in BPU, and 1.15 ft. (5.5%) in MBT. Conclusion: While the two training groups had different initial resistance training backgrounds, both showed significant improvements in all test exercises. As expected, the beginner/intermediate group displayed better relative improvements in four of the five test exercises. However, the medicine ball toss, which had the lightest resistance among the tests, showed similar relative improvements between the two groups. These findings relate to two important training principles: specificity and transfer. The ATS group had more specific experiences with heavy-resistance BP. Therefore, fewer improvements were detected in their test performances with heavy resistances. On the other hand, while the heavy resistance-based training transferred to increased power outcomes in light-resistance power exercises, the difference in the rate of improvement between the two groups disappeared. Practical applications: Based on initial training status, S&C coaches should expect different performance gains in maximal strength training-specific test exercises. However, the transfer from maximal strength to a non-training-specific performance category along the F-v curve continuum (i.e., light resistance and high velocity) might not depend on initial training status.

Keywords: exercise, power, resistance training, strength

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Authors: Kyu Jin Kim, Byung Kwan Oh, Hyo Seon Park

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The seismic responses-based structural health monitoring system has been performed to prevent seismic damage. Structural seismic damage of building is caused by the instantaneous stress concentration which is related with dynamic characteristic of earthquake. Meanwhile, seismic response analysis to estimate the dynamic responses of building demands significantly high computational cost. To prevent the failure of structural members from the characteristic of the earthquake and the significantly high computational cost for seismic response analysis, this paper presents an artificial neural network (ANN) based prediction model for dynamic responses of building considering specific time length. Through the measured dynamic responses, input and output node of the ANN are formed by the length of specific time, and adopted for the training. In the model, evolutionary radial basis function neural network (ERBFNN), that radial basis function network (RBFN) is integrated with evolutionary optimization algorithm to find variables in RBF, is implemented. The effectiveness of the proposed model is verified through an analytical study applying responses from dynamic analysis for multi-degree of freedom system to training data in ERBFNN.

Keywords: structural health monitoring, dynamic response, artificial neural network, radial basis function network, genetic algorithm

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Authors: Qaisara Parveen, M. Imran Yousuf

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The purpose of this study was to find the effectiveness of the use of advance organizer model for enhancing meta-cognition of students in the subject of science. It was hypothesized that the students of experimental group taught through advance organizer model would show the better cognition than the students of control group taught through traditional teaching. The population of the study consisted of all secondary school students studying in government high school located in Rawalpindi. The sample of the study consisted of 50 students of 9th class of humanities group. The sample was selected on the basis of their pretest scores through matching, and the groups were randomly assigned for the treatment. The experimental group was taught through advance organizer model while the control group was taught through traditional teaching. The self-developed achievement test was used for the purpose of pretest and posttest. After collecting the pre-test score and post-test score, the data was analyzed and interpreted by use of descriptive statistics as mean and standard deviation and inferential statistics t-test. The findings indicate that students taught using advance organizers had a higher level of meta-cognition as compared to control group. Further, meta cognition level of boys was found higher than that of girls students. This study also revealed the fact that though the students at different meta-cognition level approached learning situations in a different manner, Advance organizer model is far superior to Traditional method of teaching.

Keywords: descriptive, experimental, humanities, meta-cognition, statistics, science

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Authors: Ewa M. Laskowska, Jorn Vatn

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Industry 4.0 focuses on digital optimization of industrial processes. The idea is to use extracted data in order to build a decision support model enabling use of those data for real time decision making. In terms of predictive maintenance, the desired decision support tool would be a model enabling prognostics of system's health based on the current condition of considered equipment. Within area of system prognostics and health management, a commonly used health indicator is Remaining Useful Lifetime (RUL) of a system. Because the RUL is a random variable, it has to be estimated based on available health indicators. Health indicators can be of different types and come from different sources. They can be process variables, equipment performance variables, data related to number of experienced failures, etc. The aim of this study is the analysis of performance variables of emergency shutdown valves (ESV) used in oil and gas industry. ESV is inspected periodically, and at each inspection torque and time of valve operation are registered. The data will be analyzed by means of machine learning or statistical analysis. The purpose is to investigate whether the available data could be used as a health indicator for a prognostic purpose. The second objective is to examine what is the most efficient way to incorporate the data into predictive model. The idea is to check whether the data can be applied in form of explanatory variables in Markov process or whether other stochastic processes would be a more convenient to build an RUL model based on the information coming from registered data.

Keywords: emergency shutdown valves, health indicator, prognostics, remaining useful lifetime, RUL

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Authors: Israel Ropo Orimoloye, Johanes A. Belle, Olusola Adeyemi, Olusola O. Ololade

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With more than 100,000 orbits during the past 20 years, Terra has significantly improved our knowledge of the Earth's climate and its implications on societies and ecosystems of human activity and natural disasters, including drought events. With Terra instrument's performance and the free distribution of its products, this study utilised Terra MOD13Q1 satellite data to assess drought disaster events and its spatiotemporal patterns over the Free State Province of South Africa between 2001 and 2019 for summer, autumn, winter, and spring seasons. The study also used high-resolution downscaled climate change projections under three representative concentration pathways (RCP). Three future periods comprising the short (the 2030s), medium (2040s), and long term (2050s) compared to the current period are analysed to understand the potential magnitude of projected climate change-related drought. The study revealed that the year 2001 and 2016 witnessed extreme drought conditions where the drought index is between 0 and 20% across the entire province during summer, while the year 2003, 2004, 2007, and 2015 observed severe drought conditions across the region with variation from one part to the another. The result shows that from -24.5 to -25.5 latitude, the area witnessed a decrease in precipitation (80 to 120mm) across the time slice and an increase in the latitude -26° to -28° S for summer seasons, which is more prominent in the year 2041 to 2050. This study emphasizes the strong spatio-environmental impacts within the province and highlights the associated factors that characterise high drought stress risk, especially on the environment and ecosystems. This study contributes to a disaster risk framework to identify areas for specific research and adaptation activities on drought disaster risk and for environmental planning in the study area, which is characterised by both rural and urban contexts, to address climate change-related drought impacts.

Keywords: remote sensing, drought disaster, climate scenario, assessment

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Authors: Shiva Naidoo, Kristena Yossef, Grimm Jimm, Mirza Wasique, Eric Kemmerer, Joshua Obuch, Anand Mahadevan

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Background: Fiducial markers effectively enhance tumor target visibility prior to Stereotactic Body Radiation Therapy or Proton therapy. To streamline clinical practice, fiducial placement guidelines from a robotic radiosurgery vendor were examined with the goals of optimizing and standardizing feasible geometries for each treatment indication. Clinical examples of prostate, renal, and pancreatic cases are presented. Methods: Vendor guidelines (Accuray, Sunnyvale, Ca) suggest implantation of 4–6 fiducials at least 20 mm apart, with at least a 15-degree angular difference between fiducials, within 50 mm or less from the target centroid, to ensure that any potential fiducial motion (e.g., from respiration or abdominal/pelvic pressures) will mimic target motion. Also recommended is that all fiducials can be seen in 45-degree oblique views with no overlap to coincide with the robotic radiosurgery imaging planes. For the prostate, a standardized geometry that meets all these objectives is a 2 cm-by-2 cm square in the coronal plane. The transperineal implant of two pairs of preloaded tandem fiducials makes the 2 cm-by-2 cm square geometry clinically feasible. This technique may be applied for renal cancer, except repositioned in a sagittal plane, with the retroperitoneal placement of the fiducials into the tumor. Pancreatic fiducial placement via endoscopic ultrasound (EUS) is technically more challenging, as fiducial placement is operator-dependent, and lesion access may be limited by adjacent vasculature, tumor location, or restricted mobility of the EUS probe in the duodenum. Fluoroscopically assisted fiducial placement during EUS can help ensure fiducial markers are deployed with optimal geometry and visualization. Results: Among the first 22 fiducial cases on a newly installed robotic radiosurgery system, live x-ray images for all nine prostatic cases had excellent fiducial visualization at the treatment console. Renal and pancreatic fiducials were not as clearly visible due to difficult target access and smaller caliber insertion needle/fiducial usage. The geometry of the first prostate case was used to ensure accurate geometric marker placement for the remaining 8 cases. Initially, some of the renal and pancreatic fiducials were closer than the 20 mm recommendation, and interactive feedback with the proceduralists led to subsequent fiducials being too far to the edge of the tumor. Further feedback and discussion of all cases are being used to help guide standardized geometries and achieve ideal fiducial placement. Conclusion: The ideal tradeoffs of fiducial visibility versus the thinnest possible gauge needle to avoid complications needs to be systematically optimized among all patients, particularly in regards to body habitus. Multidisciplinary collaboration among proceduralists and radiation oncologists can lead to improved outcomes.

Keywords: fiducial, prostate cancer, renal cancer, pancreatic cancer, radiotherapy

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Authors: Marcus Lee

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The energy sector is the largest contributor of greenhouse gas emissions in Malaysia, which induces climate change. The climate change problem is therefore an energy sector problem. Tackling climate change issues successfully is contingent on actions taken in the energy sector. The researcher propounds that ‘Climate Policy Integration’ (CPI) into energy policy is a viable and insufficiently developed strategy in Malaysia that promotes the synergies between climate change and energy objectives, in order to achieve the targets found in both climate change and energy policies. In exploring this hypothesis, this paper presentation will focus on two particular aspects. Firstly, the meaning of CPI as an approach and as a concept will be explored. As an approach, CPI into energy policy means the integration of climate change objectives into the energy policy area. Its subject matter focuses on establishing the functional interrelations between climate change and energy objectives, by promoting their synergies and minimising their contradictions. However, its conceptual underpinnings are less than straightforward. Drawing from the ‘principle of integration’ found in international treaties and declarations such as the Stockholm Declaration 1972, the Rio Declaration 1992 and the United Nations Framework on Climate Change 1992 (‘UNFCCC’), this paper presentation will explore the contradictions in international standards on how the sustainable development tenets of environmental sustainability, social development and economic development are to be balanced and its relevance to CPI. Further, the researcher will consider whether authority may be derived from international treaties and declarations in order to argue for the prioritisation of environmental sustainability over the other sustainable development tenets through CPI. Secondly, this paper presentation will also explore the degree to which CPI into energy policy has been achieved and pursued in Malaysia. In particular, the strength of the conceptual framework with regard to CPI in Malaysian governance will be considered by assessing Malaysia’s National Policy on Climate Change (2009) (‘NPCC 2009’). The development (or the lack of) of CPI as an approach since the publication of the NPCC 2009 will also be assessed based on official government documents and policies that may have a climate change and/or energy agenda. Malaysia’s National Renewable Energy Policy and Action Plan (2010), draft National Energy Efficiency Action Plan (2014), Intended Nationally Determined Contributions (2015) in relation to the Paris Agreement, 11th Malaysia Plan (2015) and Biennial Update Report to the UNFCCC (2015) will be discussed. These documents will be assessed for the presence of CPI based on the language/drafting of the documents as well as the degree of subject matter regarding CPI expressed in the documents. Based on the analysis, the researcher will propose solutions on how to improve Malaysia’s climate change and energy governance. The theory of reflexive governance will be applied to CPI. The concluding remarks will be about whether CPI reflects reflexive governance by demonstrating how the governance process can be the object of shaping outcomes.

Keywords: climate policy integration, mainstreaming, policy coherence, Malaysian energy governance

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Authors: Vadim Allheily, Rudiger Schmitt, Lionel Merlat, Gildas L'Hostis

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Inflight devices are nowadays major actors in both military and civilian landscapes. Among others, missiles, mortars, rockets or even drones this last decade are increasingly sophisticated, and it is today of prior manner to develop always more efficient defensive systems from all these potential threats. In this frame, recent High Energy Laser weapon prototypes (HEL) have demonstrated some extremely good operational abilities to shot down within seconds flying targets several kilometers off. Whereas test outcomes are promising from both experimental and cost-related perspectives, the deterioration process still needs to be explored to be able to closely predict the effects of a high-energy laser irradiation on typical structures, heading finally to an effective design of laser sources and protective countermeasures. Laser matter interaction researches have a long history of more than 40 years at the French-German Research Institute (ISL). Those studies were tied with laser sources development in the mid-60s, mainly for specific metrology of fast phenomena. Nowadays, laser matter interaction can be viewed as the terminal ballistics of conventional weapons, with the unique capability of laser beams to carry energy at light velocity over large ranges. In the last years, a strong focus was made at ISL on the interaction process of laser radiation with metal targets such as artillery shells. Due to the absorbed laser radiation and the resulting heating process, an encased explosive charge can be initiated resulting in deflagration or even detonation of the projectile in flight. Drones and Unmanned Air Vehicles (UAVs) are of outmost interests in modern warfare. Those aerial systems are usually made up of polymer-based composite materials, whose complexity involves new scientific challenges. Aside this main laser-matter interaction activity, a lot of experimental and numerical knowledge has been gathered at ISL within domains like spectrometry, thermodynamics or mechanics. Techniques and devices were developed to study separately each aspect concerned by this topic; optical characterization, thermal investigations, chemical reactions analysis or mechanical examinations are beyond carried out to neatly estimate essential key values. Results from these diverse tasks are then incorporated into analytic or FE numerical models that were elaborated, for example, to predict thermal repercussion on explosive charges or mechanical failures of structures. These simulations highlight the influence of each phenomenon during the laser irradiation and forecast experimental observations with good accuracy.

Keywords: composite materials, countermeasure, experimental work, high-energy laser, laser-matter interaction, modeling

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Authors: Sylvanus N. Wosu

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Research shows that factors, such as low motivation, preparation, resources, emotional and social integration, and fears of risk-taking, are the most common barriers to access, matriculation, and retention into science, technology, engineering, and mathematics (STEM) disciplines for underrepresented (URM) students. These factors have been shown to impact students’ attraction and success in STEM fields. Standardized tests such as the SAT and ACT often used as predictor of success, are not always true predictors of success for African and Hispanic American students. Without an adequate academic support environment, even a high SAT score does not guarantee academic success in science and engineering. This paper proposes a model for Academic Coaching for building success and inclusive excellence in STEM education. Academic coaching is framed as a process of motivating students to be independent learners through relational mentorship, facilitating learning supports inside and outside of the classroom or school environment, and developing problem-solving skills and success attitudes that lead to higher performance in the specific subjects. The model is formulated based on best strategies and practices for enriching Academic Performance Impact skills and motivating students’ interests in STEM. A scaled model for measuring the Academic Performance Impact (API) index and STEM is discussed. The study correlates API with state standardized test and shows that the average impact of those skills can be predicted by the Academic Performance Impact (API) index or Academic Preparedness Index.

Keywords: diversity, equity, graduate education, inclusion, inclusive excellence, model

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Authors: Shanker Kalakotla, Krishna Mohan Gottumukkala

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Diabetes is a metabolic disorder characterized by hyperglycemia, carbohydrates, altered lipids and proteins metabolism. In recent research nanotechnology is a blazing field for the researchers; latterly there has been prodigious excitement in the nanomedicine and nano pharmacological area for the study of silver nanoparticles synthesis using natural products. Biological methods have been used to synthesize silver nanoparticles in presence of medicinally active antidiabetic plants, and this intention made us assess the biologically synthesized silver nanoparticles from the seed extract of Psoralea corylfolia using 1 mM silver nitrate solution. The synthesized herbal mediated silver nanoparticles (HMSNP’s) then subjected to various characterization techniques such as XRD, SEM, EDX, TEM, DLS, UV and FT-IR respectively. In current study, the silver nanoparticles tested for in-vitro anti-diabetic activity and possible toxic effects in healthy female albino mice by following OECD guidelines-425. Herbal mediated silver nanoparticles were successfully obtained from bioreduction of silver nitrate using Psoralea corylifolia plant extract. Silver nanoparticles have been appropriately characterized and confirmed using different types of equipment viz., UV-vis spectroscopy, XRD, FTIR, DLS, SEM and EDX analysis. From the behavioral observations of the study, the female albino mice did not show sedation, respiratory arrest, and convulsions. Test compounds did not cause any mortality at the dose level tested (i.e., 2000 mg/kg body weight) doses till the end of 14 days of observation and were considered safe. It may be concluded that LD50 of the HMSNPs was 2000mg/kg body weight. Since LD50 of the HMSNPs was 2000mg/kg body weight, so the preferred dose range for HMSNPs falls between the levels of 200 and 400 mg/kg. Further In-vivo pharmacological models and biochemical investigations will clearly elucidate the mechanism of action and will be helpful in projecting the currently synthesized silver nanoparticles as a therapeutic target in treating chronic ailments.

Keywords: herbal mediated silver nanoparticles, HMSNPs, toxicity of silver nanoparticles, PTP1B in-vitro anti-diabetic assay female albino mice, 425 OECD guidelines

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Authors: Gianluca Castelnuovo, Giada Pietrabissa, Gian Mauro Manzoni, Margherita Novelli, Emanuele Maria Giusti, Roberto Cattivelli, Enrico Molinari

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Diabesity could be defined as a new global epidemic of obesity and being overweight with many complications and chronic conditions. Such conditions include not only type 2 diabetes, but also cardiovascular diseases, hypertension, dyslipidemia, hypercholesterolemia, cancer, and various psychosocial and psychopathological disorders. The financial direct and indirect burden (considering also the clinical resources involved and the loss of productivity) is a real challenge in many Western health-care systems. Recently the Lancet journal defined diabetes as a 21st-century challenge. In order to promote patient compliance in diabesity treatment reducing costs, evidence-based interventions to improve weight-loss, maintain a healthy weight, and reduce related comorbidities combine different treatment approaches: dietetic, nutritional, physical, behavioral, psychological, and, in some situations, pharmacological and surgical. Moreover, new technologies can provide useful solutions in this multidisciplinary approach, above all in maintaining long-term compliance and adherence in order to ensure clinical efficacy. Psychological therapies with diet and exercise plans could better help patients in achieving weight loss outcomes, both inside hospitals and clinical centers and during out-patient follow-up sessions. In the management of chronic diseases clinical psychology play a key role due to the need of working on psychological conditions of patients, their families and their caregivers. mHealth approach could overcome limitations linked with the traditional, restricted and highly expensive in-patient treatment of many chronic pathologies: one of the best up-to-date application is the management of obesity with type 2 diabetes, where mHealth solutions can provide remote opportunities for enhancing weight reduction and reducing complications from clinical, organizational and economic perspectives. A stepped care mHealth-based approach is an interesting perspective in chronic care management of obesity with type 2 diabetes. One promising future direction could be treating obesity, considered as a chronic multifactorial disease, using a stepped-care approach: -mhealth or traditional based lifestyle psychoeducational and nutritional approach. -health professionals-driven multidisciplinary protocols tailored for each patient. -inpatient approach with the inclusion of drug therapies and other multidisciplinary treatments. -bariatric surgery with psychological and medical follow-up In the chronic care management of globesity mhealth solutions cannot substitute traditional approaches, but they can supplement some steps in clinical psychology and medicine both for obesity prevention and for weight loss management.

Keywords: clinical health psychology, mhealth, obesity, type 2 diabetes, stepped care, chronic care management

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Authors: Lola Vivour-Adeniyi, Oluwatoyosi Abikoye

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Gender-Based Violence has persistently been an enduring epidemic rooted in various homes and cultures worldwide throughout history. Despite numerous multifaceted approaches aiming to eliminate gender disparity by empowering girls with independent, survival, or entrepreneurial skills, positive transformation efforts for boys are often neglected. This gap, particularly in West Africa, hinders the reshaping of generational perceptions of men's roles in eradicating all forms of violence against women, creating an imbalance in providing holistic, sustainable solutions in our society. In response to this, the Lagos State Domestic and Sexual Violence Agency established the Kings Club initiative in 2018 in Lagos, Nigeria, aligning with UN SDG 5 on 'gender equality and the empowerment of all women and girls.' This initiative aims to debunk sociocultural misconceptions, educate on sexual and Gender Violence issues, and promote positive masculinity among schoolboys. The objective is to foster a balance in the behavioral and attitudinal mindsets of young boys, contributing to the success of an equitable society for all in the eradication of gender disparity. The ongoing project demonstrates an annual 89% success rate in behavioral and attitudinal mindset shifts among schoolboys by employing qualitative assessments of participants aged 10–17. The paper uses data from 161 secondary school students drawn from 15 schools in Lagos State Education District 6, exposed to modules on Sexual and Reproductive Rights and Health, Masculinity, Sexual and Gender-Based Violence, Anger Management, and transitioning from boy to man in a 6-week program, with observations and feedback analyses. The paper evaluates changes in attitudes, behaviors, and gender role perceptions among the boys who are now pioneers of the Kings' Club in their respective schools, serving as positive influencers among their peers in schools and at home. The paper's findings provide a nuanced understanding of cultural dynamics shaping male masculinity in West Africa and offer valuable insights into the effectiveness of innovative interventions to end all forms of violence against women. In conclusion, by intentionally reshaping young boys alongside girls, who represent the future, we stand a chance to create culturally sensitive societal norms that bridge the gap of gender inequity and end all forms of violence against women. The paper posits that its outcomes hold research significance, providing targeted interventions in male masculinity in patriarchal societies.

Keywords: cultural dynamics, gender equality, gender-based violence, positive masculinity

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