Search results for: brain training

Authors: Mahdi Fakheri, Mohammad Mohsen Mahdizadeh Naeini

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This paper addresses an understudied aspect of U.S. foreign aids between the years 2000 and 2020. Despite a growing body of literature on the impacts of U.S. aids, the question about how the United States uses its foreign aids to change developing countries has remained unanswered. As foreign aid is a tool of the United States' foreign policy, answering this very question can reveal the future that the U.S. prefers for developing countries and that secures its national interest. This paper will explore USAID's official dataset, which includes the data of foreign aids to the Middle East, Africa, Latin America, and Southeast Asia from 2000 to 2020. Through an empirical analysis, this paper argues that the focus of U.S. foreign aid is evenly divided between institutional and non-institutional (i.e., slight enhancement of status quo) changes. The former is induced by training and education, funding the initiatives and projects, making capacity and increasing the efficiency of human, operational, and management sectors, and enhancing the living condition of the people. Moreover, it will be demonstrated that the political, military, cultural, economic, and judicial are some of the institutions that the U.S. has planned to change in the aforementioned period and regions.

Keywords: USAID, foreign aid, development, developing countries, Middle East, Africa, Southeast Asia, Latin America

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Authors: Zhiyan Meng, Dan Liu, Jintao Meng

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Dependable encrypted traffic classification is crucial for improving cybersecurity and handling the growing amount of data. Large language models have shown that learning from large datasets can be effective, making pre-trained methods for encrypted traffic classification popular. However, attention-based pre-trained methods face two main issues: their large neural parameters are not suitable for low-computation environments like mobile devices and real-time applications, and they often overfit by getting stuck in local minima. To address these issues, we developed a lightweight transformer model, which reduces the computational parameters through lightweight vocabulary construction and Squeeze-and-Excitation Block. We use sharpness-aware optimization to avoid local minima during pre-training and capture temporal features with relative positional embeddings. Our approach keeps the model's classification accuracy high for downstream tasks. We conducted experiments on four datasets -USTC-TFC2016, VPN 2016, Tor 2016, and CICIOT 2022. Even with fewer than 18 million parameters, our method achieves classification results similar to methods with ten times as many parameters.

Keywords: sharpness-aware optimization, encrypted traffic classification, squeeze-and-excitation block, pretrained model

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Authors: Mitra Savabi-Esfahani, Fariba Taleghani, Mahnaz Noroozi, Maryam Tabatabaeian, Elsebeth Lynge

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Breast cancer is a health concern in worldwide. All women have not adequate information about breast cancer, resulting in undetected some tumors until advanced stages. Therefore awareness of people was recommended as a strategy to control that. The aim of this study was to assess the effect of an educational intervention on women's knowledge and attitude about breast cancer and screening. This study was conducted in 2016 on 191 women. All women living in one of big cities were invited to enroll in training classes. Inclusion criteria consisted women who were 20 - 69 years and not participated in any educational intervention. The lecture with group discussion was used as educational methods. Data collection tool was a structured questionnaire which filled out before and after intervention. The reliability of the questionnaire was determined by Cronbach's alpha. The data were analyzed using SPSS software. The average age was 44/4 ± 11.5 and 42.6% of the women had obtained high school. Of the 191 women, 70(36.6%) and 76(39.8%) had low and medium level of knowledge respectively and half of them, 95(50%) had medium level of attitude in before intervention. There was significant difference between mean scores of knowledge and attitude before and after the intervention by Paired T test (p < 0/001). It seems applying effective educational interventions can increase knowledge and attitude women about breast cancer particularly in community that they have insufficient levels. Moreover, the lecture method along with group discussion can be proposed as effective and conventional methods for this purpose.

Keywords: attitude, breast cancer, educational intervention, knowledge

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Authors: Rosa S. Wong, Keith T.S. Tung, H.W. Tsang, Frederick K. Ho, Patrick Ip

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Attention-deficit hyperactivity disorder (ADHD) is a neurodevelopmental disorder characterized by inattention and hyperactive-impulsive behavior. Evidence shows that ADHD and mood problems such as depression and anxiety often co-occur and yet not everyone with ADHD reported elevated emotional problems. Given that cholesterol is essential for healthy brain development including the regions governing emotion regulation, reports found lower cholesterol levels in patients with major depressive disorder and those with suicide attempt behavior compared to healthy subjects. This study explored whether ADHD adolescents experienced more emotional problems and whether emotional problems correlated with cholesterol levels in these adolescents. This study used a portion of data from the longitudinal cohort study which was designed to investigate the long-term impact of family socioeconomic status on child development. In 2018/19, parents of 300 adolescents (average age: 12.57+/-0.49 years) were asked to rate their children’s emotional problems and report whether their children had doctor-diagnosed psychiatric diseases. We further collected blood samples from 263 children to study their lipid profile (total cholesterol, high-density lipoprotein (HDL)-cholesterol, and low-density lipoprotein (LDL)-cholesterol). Regression analyses were performed to test the relationships between variables of interest. Among 300 children, 27 (9%) had ADHD diagnosis. Analysis based on overall sample found no association between ADHD and emotional problems, but when investigating the relationship by gender, there was a significant interaction effect of ADHD and gender on emotional problems (p=0.037), with ADHD males displaying more emotional problems than ADHD females. Further analyses based on 263 children (21 with ADHD diagnosis) found significant interaction effect of ADHD and gender on total cholesterol (p=0.038) and low LDL-cholesterol levels (p=0.013) after adjusting for the child’s physical disease history. Specifically, ADHD males had significantly lower total cholesterol and low lipoprotein-cholesterol levels than ADHD females. In ADHD males, more emotional problems were associated with lower LDL-cholesterol levels (B = -4.26, 95%CI (-7.46, -1.07), p=0.013). We found preliminary support for the association between more emotional problems and lower cholesterol levels in ADHD children, especially among males. Although larger prospective studies are needed to substantiate these claims, the evidence highlights the importance of healthy lifestyle to keep cholesterol levels in normal range which can have positive effects on physical and mental health.

Keywords: attention-deficit hyperactivity disorder, cholesterol, emotional problems, adolescents

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Authors: Nhung T. Bui

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Part of the dilemma facing educational reforms in Vietnam as in other Asian contexts is how to encourage more independence in students’ learning approaches. Since 2005, the Ministry of Education and Training of Vietnam has included the students’ ability to learn independently in its national education objectives. While learner autonomy has been viewed as a goal in the teaching and learning English as a foreign language (EFL) and there has been a considerable literature on strategies to stimulate autonomy in learners, teachers’ voices have rarely been heard. Given that teachers play a central role in helping their students to be more autonomous, especially in an inherent Confucian heritage culture like Vietnam, their attitudes towards learner autonomy should be investigated before any practical implementations could be undertaken. This paper reports significant findings of a survey questionnaire with 262 lecturers of English from 5 universities in Hanoi, Vietnam giving opinions regarding the practices and prospects of learner autonomy in their classrooms. The study reveals that lecturers perceive they should be more responsible than their students in all class-related activities; they most appreciate their students’ ability to learn cooperatively and that they consider stimulating students’ interest as the most important teaching strategy to promote learner autonomy. Lecturers, then, are strongly suggested to gradually ‘empower’ their students through the application of out-of-classroom activities; of learning activities which requires collaboration and team spirit; and of activities which could boost students’ interest in learning English.

Keywords: English as a foreign language, higher education, learner autonomy, Vietnam

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Authors: Angelis P. Barlampas

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Objective: The confusion of terms is a common practice in many situations of the everyday life. But, in some circumstances, such as in medicine, the precise meaning of a word curries a critical role for the health of the patient. Fahr disease and Fahr syndrome are often falsely used interchangeably, but they are two different conditions with different physical histories of different etiology and different medical management. A case of the seldom Fahr disease is presented, and a comparison with the more common Fahr syndrome follows. Materials and method: A 72 years old patient came to the emergency department, complaining of some kind of non specific medal disturbances, like anxiety, difficulty of concentrating, and tremor. The problems had a long course, but he had the impression of getting worse lately, so he decided to check them. Past history and laboratory tests were unremarkable. Then, a computed tomography examination was ordered. Results: The CT exam showed bilateral, hyperattenuating areas of heavy, dense calcium type deposits in basal ganglia, striatum, pallidum, thalami, the dentate nucleus, and the cerebral white matter of frontal, parietal and iniac lobes, as well as small areas of the pons. Taking into account the absence of any known preexisting illness and the fact that the emergency laboratory tests were without findings, a hypothesis of the rare Fahr disease was supposed. The suspicion was confirmed with further, more specific tests, which showed the lack of any other conditions which could probably share the same radiological image. Differentiating between Fahr disease and Fahr syndrome. Fahr disease: Primarily autosomal dominant Symmetrical and bilateral intracranial calcifications The patient is healthy until the middle age Absence of biochemical abnormalities. Family history consistent with autosomal dominant Fahr syndrome :Earlier between 30 to 40 years old. Symmetrical and bilateral intracranial calcifications Endocrinopathies: Idiopathic hypoparathyroidism, secondary hypoparathyroidism, hyperparathyroidism, pseudohypoparathyroidism ,pseudopseudohypoparathyroidism, e.t.c The disease appears at any age There are abnormal laboratory or imaging findings. Conclusion: Fahr disease and Fahr syndrome are not the same illness, although this is not well known to the inexperienced doctors. As clinical radiologists, we have to inform our colleagues that a radiological image, along with the patient's history, probably implies a rare condition and not something more usual and prompt the investigation to the right route. In our case, a genetic test could be done earlier and reveal the problem, and thus avoiding unnecessary and specific tests which cost in time and are uncomfortable to the patient.

Keywords: fahr disease, fahr syndrome, CT, brain calcifications

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Authors: Fikret Yalcinkaya, Hamza Unsal

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To understand how neurons work, it is required to combine experimental studies on neural science with numerical simulations of neuron models in a computer environment. In this regard, the simplicity and applicability of spiking neuron modeling functions have been of great interest in computational neuron science and numerical neuroscience in recent years. Spiking neuron models can be classified by exhibiting various neuronal behaviors, such as spiking and bursting. These classifications are important for researchers working on theoretical neuroscience. In this paper, three different spiking neuron models; Izhikevich, Adaptive Exponential Integrate Fire (AEIF) and Hindmarsh Rose (HR), which are based on first order differential equations, are discussed and compared. First, the physical meanings, derivatives, and differential equations of each model are provided and simulated in the Matlab environment. Then, by selecting appropriate parameters, the models were visually examined in the Matlab environment and it was aimed to demonstrate which model can simulate well-known biological neuron behaviours such as Tonic Spiking, Tonic Bursting, Mixed Mode Firing, Spike Frequency Adaptation, Resonator and Integrator. As a result, the Izhikevich model has been shown to perform Regular Spiking, Continuous Explosion, Intrinsically Bursting, Thalmo Cortical, Low-Threshold Spiking and Resonator. The Adaptive Exponential Integrate Fire model has been able to produce firing patterns such as Regular Ignition, Adaptive Ignition, Initially Explosive Ignition, Regular Explosive Ignition, Delayed Ignition, Delayed Regular Explosive Ignition, Temporary Ignition and Irregular Ignition. The Hindmarsh Rose model showed three different dynamic neuron behaviours; Spike, Burst and Chaotic. From these results, the Izhikevich cell model may be preferred due to its ability to reflect the true behavior of the nerve cell, the ability to produce different types of spikes, and the suitability for use in larger scale brain models. The most important reason for choosing the Adaptive Exponential Integrate Fire model is that it can create rich ignition patterns with fewer parameters. The chaotic behaviours of the Hindmarsh Rose neuron model, like some chaotic systems, is thought to be used in many scientific and engineering applications such as physics, secure communication and signal processing.

Keywords: Izhikevich, adaptive exponential integrate fire, Hindmarsh Rose, biological neuron behaviours, spiking neuron models

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Authors: Saifalarab A. Mohmmed, Morgana E. Vianna, Jonathan C. Knowles

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The issue of apical periodontitis has received considerable critical attention. Bacteria is integrated into communities, attached to surfaces and consequently form biofilm. The biofilm structure provides bacteria with a series protection skills against, antimicrobial agents and enhances pathogenicity (e.g. apical periodontitis). Sodium hypochlorite (NaOCl) has become the irrigant of choice for elimination of bacteria from the root canal system based on its antimicrobial findings. The aim of the study was to investigate the effect of different agitation techniques on the efficacy of 2.5% NaOCl to eliminate the biofilm from the surface of the lateral canal using the residual biofilm, and removal rate of biofilm as outcome measures. The effect of canal complexity (lateral canal) on the efficacy of the irrigation procedure was also assessed. Forty root canal models (n = 10 per group) were manufactured using 3D printing and resin materials. Each model consisted of two halves of an 18 mm length root canal with apical size 30 and taper 0.06, and a lateral canal of 3 mm length, 0.3 mm diameter located at 3 mm from the apical terminus. E. faecalis biofilms were grown on the apical 3 mm and lateral canal of the models for 10 days in Brain Heart Infusion broth. Biofilms were stained using crystal violet for visualisation. The model halves were reassembled, attached to an apparatus and tested under a fluorescence microscope. Syringe and needle irrigation protocol was performed using 9 mL of 2.5% NaOCl irrigant for 60 seconds. The irrigant was either left stagnant in the canal or activated for 30 seconds using manual (gutta-percha), sonic and ultrasonic methods. Images were then captured every second using an external camera. The percentages of residual biofilm were measured using image analysis software. The data were analysed using generalised linear mixed models. The greatest removal was associated with the ultrasonic group (66.76%) followed by sonic (45.49%), manual (43.97%), and passive irrigation group (control) (38.67%) respectively. No marked reduction in the efficiency of NaOCl to remove biofilm was found between the simple and complex anatomy models (p = 0.098). The removal efficacy of NaOCl on the biofilm was limited to the 1 mm level of the lateral canal. The agitation of NaOCl results in better penetration of the irrigant into the lateral canals. Ultrasonic agitation of NaOCl improved the removal of bacterial biofilm.

Keywords: 3D printing, biofilm, root canal irrigation, sodium hypochlorite

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Authors: Amir Hossein Hejazi, Nima Amjady

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In recent years, due to environmental issues traditional energy sources had been replaced by renewable ones. Wind energy as the fastest growing renewable energy shares a considerable percent of energy in power electricity markets. With this fast growth of wind energy worldwide, owners and operators of wind farms, transmission system operators, and energy traders need reliable and secure forecasts of wind energy production. In this paper, a new forecasting strategy is proposed for short-term wind power prediction based on Echo State Networks (ESN). The forecast engine utilizes state-of-the-art training process including dynamical reservoir with high capability to learn complex dynamics of wind power or wind vector signals. The study becomes more interesting by incorporating prediction of wind direction into forecast strategy. The Big Bang-Big Crunch (BB-BC) evolutionary optimization algorithm is adopted for adjusting free parameters of ESN-based forecaster. The proposed method is tested by real-world hourly data to show the efficiency of the forecasting engine for prediction of both wind vector and wind power output of aggregated wind power production.

Keywords: wind power forecasting, echo state network, big bang-big crunch, evolutionary optimization algorithm

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Authors: Haruka Ando

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Undernutrition of children is a critical issue, especially for people in the remote areas of the Republic of Djibouti, since household food insecurity, inadequate child caring and feeding, unhealthy environment and lack of clean water, as well as insufficient maternal and child healthcare, are underlying causes which affect. Nomadic pastoralists living in the Dikhil region (Dikhil) are socio-economically and geographically more vulnerable due to displacement, which in turn worsens the situation of child stunting. A high prevalence of inappropriate complementary feeding among pastoral mothers might be a significant barrier to child growth. This study aims to identify health promotion intervention strategies that would support an increase in optimal complementary feeding among pastoral mothers of children aged 6-23 months in Dikhil. There are four objectives; to explore and to understand the existing practice of complementary feeding among pastoral mothers in Dikhil; to identify the barriers in appropriate complementary feeding among the mothers; to critically explore and analyse the strategies for an increase in complementary feeding among the mothers; to make pragmatic recommendations to address the barriers in Djibouti. This is an in-depth study utilizing a conceptual framework, the behaviour change wheel, to analyse the determinants of complementary feeding and categorize health promotion interventions for increasing optimal complementary feeding among pastoral mothers living in Dikhil. The analytical tool was utilized to appraise the strategies to mitigate the selected barriers against optimal complementary feeding. The data sources were secondary literature from both published and unpublished sources. The literature was systematically collected. The findings of the determinants including the barriers of optimal complementary feeding were identified: heavy household workload, caring for multiple children under five, lack of education, cultural norms and traditional eating habits, lack of husbands' support, poverty and food insecurity, lack of clean water, low media coverage, insufficient health services on complementary feeding, fear, poor personal hygiene, and mothers' low decision-making ability and lack of motivation for food choice. To mitigate selected barriers of optimal complementary feeding, four intervention strategies based on interpersonal communication at the community-level were chosen: scaling up mothers' support groups, nutrition education, grandmother-inclusive approach, and training for complementary feeding counseling. The strategies were appraised through the criteria of effectiveness and feasibility. Scaling up mothers' support groups could be the best approach. Mid-term and long-term recommendations are suggested based on the situation analysis and appraisal of intervention strategies. Mid-term recommendations include complementary feeding promotion interventions are integrated into the healthcare service providing system in Dikhil, and donor agencies advocate and lobby the Ministry of Health Djibouti (MoHD) to increase budgetary allocation on complementary feeding promotion to implement interventions at a community level. Moreover, the recommendations include a community health management team in Dikhil training healthcare workers and mother support groups by using complementary feeding communication guidelines and monitors behaviour change of pastoral mothers and health outcome of their children. Long-term recommendations are the MoHD develops complementary feeding guidelines to cover sector-wide collaboration for multi-sectoral related barriers.

Keywords: Afar, child food, child nutrition, complementary feeding, complementary food, developing countries, Djibouti, East Africa, hard-to-reach areas, Horn of Africa, nomad, pastoral, rural area, Somali, Sub-Saharan Africa

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Authors: Azzeddine Atibi, Sara Atibi, Salim Ahmed, Khadija El Kabab

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In an educational context where innovation holds a predominant position, political discourses and pedagogical practices are increasingly oriented toward this concept. Innovation has become a benchmark value, gradually replacing the notion of progress. This term is omnipresent in discussions among policymakers, administrators, and academic researchers. The pressure to innovate impacts all levels of education, influencing institutional and educational policies, training objectives, and teachers' pedagogical practices. Higher education and continuing education sectors are not exempt from this trend. These sectors are compelled to transform to attract and retain an audience whose behaviors and expectations have significantly evolved. Indeed, the employability of young graduates has become a crucial issue, prompting us to question the effectiveness of various pedagogical methods in meeting this criterion. In this article, we propose to thoroughly examine the relationship between pedagogical methods employed in different fields of higher education and the acquisition of interpersonal skills, or "soft skills". Our aim is to determine to what extent these methods contribute to better-preparing students for the professional world. We will analyze how innovative pedagogical approaches can enhance the acquisition of soft skills, which are essential for the professional success of young graduates.

Keywords: educational context, innovation, higher education, soft skills, pedagogical practices, pedagogical approaches

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Authors: YingWei Tan, XueFeng Ding

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Attention-based voice activity detection models have gained significant attention in recent years due to their fast training speed and ability to capture a wide contextual range. The inclusion of multi-head style and position embedding in the attention architecture are crucial. Having multiple attention heads allows for differential focus on different parts of the sequence, while position embedding provides guidance for modeling dependencies between elements at various positions in the input sequence. In this work, we propose an approach by considering each head as a node, enabling the application of graph neural networks (GNN) to identify correlations among the different nodes. In addition, we adopt an implementation named rotary position embedding (RoPE), which encodes absolute positional information into the input sequence by a rotation matrix, and naturally incorporates explicit relative position information into a self-attention module. We evaluate the effectiveness of our method on a synthetic dataset, and the results demonstrate its superiority over the baseline CRNN in scenarios with low signal-to-noise ratio and noise, while also exhibiting robustness across different noise types. In summary, our proposed framework effectively combines the strengths of CNN and RNN (LSTM), and further enhances detection performance through the integration of graph neural networks and rotary position embedding.

Keywords: voice activity detection, CRNN, graph neural networks, rotary position embedding

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Authors: Zahra Alipour, Amirreza Moheb Afzali

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In the field of human-computer interaction (HCI), hand gestures play a crucial role in facilitating communication by expressing emotions and intentions. The precise tracking of the index finger and the estimation of joint positions are essential for developing effective gesture recognition systems. However, various challenges, such as anatomical variations, occlusions, and environmental influences, hinder optimal functionality. This study investigates the performance of the YOLOv8m model for hand detection using the EgoHands dataset, which comprises diverse hand gesture images captured in various environments. Over three training processes, the model demonstrated significant improvements in precision (from 88.8% to 96.1%) and recall (from 83.5% to 93.5%), achieving a mean average precision (mAP) of 97.3% at an IoU threshold of 0.7. We also compared YOLOv8m with MediaPipe and an integrated YOLOv8 + MediaPipe approach. The combined method outperformed the individual models, achieving an accuracy of 99% and a recall of 99%. These findings underscore the benefits of model integration in enhancing gesture recognition accuracy and localization for real-time applications. The results suggest promising avenues for future research in HCI, particularly in augmented reality and assistive technologies, where improved gesture recognition can significantly enhance user experience.

Keywords: YOLOv8, mediapipe, finger tracking, joint estimation, human-computer interaction (HCI)

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Authors: Saiakhil Chilaka

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Juvenile recidivism presents a significant challenge to the criminal justice system, impacting both the individuals involved and broader societal safety. This study aims to identify the key factors influencing recidivism and successful rehabilitation outcomes by utilizing a dataset of over 25,000 individuals from the NIJ Recidivism Challenge. We employed machine learning techniques, particularly Random Forest Classification, combined with SHAP (SHapley Additive exPlanations) for model interpretability. Our findings indicate that supervision risk score, percent days employed, and education level are critical factors affecting recidivism, with higher levels of supervision, successful employment, and education contributing to lower recidivism rates. Conversely, Gang Affiliation emerged as a significant risk factor for reoffending. The model achieved an accuracy of 68.8%, highlighting its utility in identifying high-risk individuals and informing targeted interventions. These results suggest that a comprehensive approach involving personalized supervision, vocational training, educational support, and anti-gang initiatives can significantly reduce recidivism and enhance rehabilitation outcomes for juveniles, providing critical insights for policymakers and juvenile justice practitioners.

Keywords: juvenile, justice system, data analysis, SHAP

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Authors: Henry Wilde, Thiravat Hemachudha

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Human rabies deaths are underreported worldwide at 55,000 annual cases; more than of dengue and Japanese encephalitis. Almost half are children. A recent study from the Philippines of nearly 2,000 rabies deaths revealed that none of had received incomplete or no post exposure prophylaxis. Coming from a canine rabies endemic country, this is not unique. There are two major barriers to reducing human rabies deaths: 1) the large number of unvaccinated dogs and 2) post-exposure prophylaxis (PEP) that is not available, incomplete, not affordable, or not within reach for bite victims travel means. Only the first barrier, inadequate vaccination of dogs, is now being seriously addressed. It is also often not done effectively or sustainably. Rabies PEP has evolved as a complex, prolonged process, usually delegated to centers in larger cities. It is virtually unavailable in villages or small communities where most dog bites occur, victims are poor and usually unable to travel a long distance multiple times to receive PEP. Reseacrh that led to better understanding of the pathophysiology of rabies and immune responses to potent vaccines and immunoglobulin have allowed shortening and making PEP more evidence based. This knowledge needs to be adopted and applied so that PEP can be rendered safely and affordably where needed the most: by village health care workers who have long performed more complex services after appropriate training. Recent research makes this an important and long neglected goal that is now within our means to implement.

Keywords: rabies, post-exposure prophylaxis, availability, immunoglobulin

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Authors: Ntokozo Dennis Ndwandwe

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The aim of the study was to explore the barriers facing the implementation of peace education as a strategy to combat violence in selected secondary schools in the Western Cape Province of South Africa. The problem that motivated this enquiry was the absence of stable peace and the increase of incidents of violence in schools. A qualitative approach was followed when conducting the study, and small samples of three case studies of secondary schools were used. Method used in collecting data consisted of semi-structured interviews; focus group interviews and observation. The participants consisted of the program manager for Quaker for Peace Centre (QPC), three principals, nine teachers, and fifteen learners. Data were analysed by transcribing, organising, marking by hand and coding that produced labels that allowed key points to be highlighted. Findings revealed that the effective implementation of peace education was being constrained by factors such as financial constraints, inadequate time allocated, lack of parental involvement, over work-loaded teachers, negative attitude and other societal influences. It is recommended that teachers should receive an ongoing training for peace education. Therefore, the government should prioritise and provide funds for peace education. In addition, parental involvement should be improved in order to enhance the implementation of peace education in selected secondary schools.

Keywords: barriers, implementation, conflict, peace, peace education, conflict resolution, violence

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Authors: Mehdi Montakhabrazlighi, Ercan Balikci

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The neural network (NN) method is applied to alloy development of single crystal Ni-base Superalloys with low density and improved mechanical strength. A set of 1200 dataset which includes chemical composition of the alloys, applied stress and temperature as inputs and density and time to rupture as outputs is used for training and testing the network. Thermodynamic phase diagram modeling of the screened alloys is performed with Thermocalc software to model the equilibrium phases and also microsegregation in solidification processing. The model is first trained by 80% of the data and the 20% rest is used to test it. Comparing the predicted values and the experimental ones showed that a well-trained network is capable of accurately predicting the density and time to rupture strength of the Ni-base superalloys. Modeling results is used to determine the effect of alloying elements, stress, temperature and gamma-prime phase volume fraction on rupture strength of the Ni-base superalloys. This approach is in line with the materials genome initiative and integrated computed materials engineering approaches promoted recently with the aim of reducing the cost and time for development of new alloys for critical aerospace components. This work has been funded by TUBITAK under grant number 112M783.

Keywords: neural network, rupture strength, superalloy, thermocalc

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Authors: Azieva A. M., Yastremsky E. V., Kirillova D. A., Patsaev T. D., Sharikov R. V., Kamyshinsky R. A., Lukanina K. I., Sharikova N. A., Grigoriev T. E., Vasiliev A. L.

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Adhesive properties of scaffolds, which predominantly depend on the chemical and structural features of their surface, play the most important role in tissue engineering. The basic requirements for such scaffolds are biocompatibility, biodegradation, high cell adhesion, which promotes cell proliferation and differentiation. In many cases, synthetic polymers scaffolds have proven advantageous because they are easy to shape, they are tough, and they have high tensile properties. The regeneration of nerve tissue still remains a big challenge for medicine, and neural stem cells provide promising therapeutic potential for cell replacement therapy. However, experiments with stem cells have their limitations, such as low level of cell viability and poor control of cell differentiation. Whereas the study of already differentiated neuronal cell culture obtained from newborn mouse brain is limited only to cell adhesion. The growth and implantation of neuronal culture requires proper scaffolds. Moreover, the polymer scaffolds implants with neuronal cells could demand specific morphology. To date, it has been proposed to use numerous synthetic polymers for these purposes, including polystyrene, polylactic acid (PLA), polyglycolic acid, and polylactide-glycolic acid. Tissue regeneration experiments demonstrated good biocompatibility of PLA scaffolds, despite the hydrophobic nature of the compound. Problem with poor wettability of the PLA scaffold surface could be overcome in several ways: the surface can be pre-treated by poly-D-lysine or polyethyleneimine peptides; roughness and hydrophilicity of PLA surface could be increased by plasma treatment, or PLA could be combined with natural fibers, such as collagen or chitosan. This work presents a study of adhesion of both induced pluripotent stem cells (iPSCs) and mouse primary neuronal cell culture on the polylactide scaffolds of various types: oriented and non-oriented fibrous nonwoven materials and sponges – with and without the effect of plasma treatment and composites with collagen and chitosan. To evaluate the effect of different types of PLA scaffolds on the neuronal differentiation of iPSCs, we assess the expression of NeuN in differentiated cells through immunostaining. iPSCs more effectively differentiate into neurons on PLA scaffolds with high adhesive properties for primary neuronal cells.

Keywords: PLA scaffold, neurons, neuronal differentiation, stem cells, polylactid

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Authors: Gayathri Sadanala, Shibam Pokhrel, Owen Murphy

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In the ever-evolving landscape of education, Virtual Reality (VR) environments offer a promising avenue for enhancing student engagement and learning experiences. However, understanding and predicting student behavior within these immersive settings remain challenging tasks. This paper presents a comprehensive study on the predictive modeling of student behavior in VR using machine learning techniques. We introduce a rich data set capturing student interactions, movements, and progress within a VR orientation program. The dataset is divided into training and testing sets, allowing us to develop and evaluate predictive models for various aspects of student behavior, including engagement levels, task completion, and performance. Our machine learning approach leverages a combination of feature engineering and model selection to reveal hidden patterns in the data. We employ regression and classification models to predict student outcomes, and the results showcase promising accuracy in forecasting behavior within VR environments. Furthermore, we demonstrate the practical implications of our predictive models for personalized VR-based learning experiences and early intervention strategies. By uncovering the intricate relationship between student behavior and VR interactions, we provide valuable insights for educators, designers, and developers seeking to optimize virtual learning environments.

Keywords: interaction, machine learning, predictive modeling, virtual reality

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Authors: Dina Ahmed Alkhodary

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Human Resources are the individual skills, knowledge, attitude, capabilities and experience collected to produce wealth to the company. Human Resource disclosure is the process of involving, reporting, and sharing the Investments made in the Human Resources of an Organization that such as organizations short goals and objectives, employees creation value, training and development plan are presently not accounted for in the conventional accounting practices which is importance nowadays to reduce the employee`s turnover. For the purpose of the study 3 telecommunications companies in Jordan have been selected. Telecommunication industry has been chosen for this study since it is a successful sector in Jordan and Human resource disclosure practices were adopted in all the selected companies and companies was aware to the HR practices. The objective of the study is to find out the HR disclosures practices of the telecommunication Companies in Jordan and to find the relationship between the HR Disclosures practices and employees’ turnover which has been measured by leaver proficiencies, remaining member proficiencies and the new comers proficiencies. The researcher has used the questioner to collect data for the research purpose. Results reveal that There are human resource disclosure practices in telecommunication companies in Jordan but in some areas only and has found There that there is a significant relationship between the human resource disclosure practices of the telecommunication companies in Jordan and Employees turnover. It is important to the companies to disclose more information and it’s important to the researchers to study the HR disclosure in the other industries in Jordan to increase the awareness about it.

Keywords: HR, disclosure, employee, turnover

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Authors: Roseline Oghogho Osaseri, Osaseri E. I.

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Lassa fever is an epidemic hemorrhagic fever caused by the Lassa virus, an extremely virulent arena virus. This highly fatal disorder kills 10% to 50% of its victims, but those who survive its early stages usually recover and acquire immunity to secondary attacks. One of the major challenges in giving proper treatment is lack of fast and accurate diagnosis of the disease due to multiplicity of symptoms associated with the disease which could be similar to other clinical conditions and makes it difficult to diagnose early. This paper proposed an Adaptive Neuro Fuzzy Inference System (ANFIS) for the prediction of Lass Fever. In the design of the diagnostic system, four main attributes were considered as the input parameters and one output parameter for the system. The input parameters are Temperature on admission (TA), White Blood Count (WBC), Proteinuria (P) and Abdominal Pain (AP). Sixty-one percent of the datasets were used in training the system while fifty-nine used in testing. Experimental results from this study gave a reliable and accurate prediction of Lassa fever when compared with clinically confirmed cases. In this study, we have proposed Lassa fever diagnostic system to aid surgeons and medical healthcare practictionals in health care facilities who do not have ready access to Polymerase Chain Reaction (PCR) diagnosis to predict possible Lassa fever infection.

Keywords: anfis, lassa fever, medical diagnosis, soft computing

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Authors: H. Booke, R. Nordmeier

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Sudden cardiac arrest is sometimes eye-witnessed by kids. Mostly, their (grand-)parents are affected by sudden cardiac arrest, putting these kids under enormous psychological pressure: Although they are more than desperate to help, they feel insecure and helpless and are afraid of causing harm rather than realizing their chance to help. Even years later, they may blame themselves for not having helped their beloved ones. However, the absolute majority of school children - at least in Germany - is not educated to provide first aid. Teaching young kids (5th grade) in basic life support thus may help to save lives while washing away the kids' fear from causing harm during cardio-pulmonary resuscitation. A teaching of circulatory and respiratory (patho-)physiology, followed by hands-on training of basic life support for every single child, was offered to each school in our district. The teaching was performed by anesthesiologists, and the program was called 'kids can save lives'. However, before enrollment in this program, the entire class must have had lessons in biology with a special focus on heart and circulation as well as lung and gas exchange. More than 1.000 kids were taught and trained in basic life support, giving them the knowledge and skills to provide basic life support. This may help to reduce the rate of failure to provide first aid. Therefore, educating young kids in basic life support may not only help to save lives, but it also may help to prevent any feelings of guilt because of not having helped in cases of eye-witnessed sudden cardiac arrest.

Keywords: teaching, children, basic life support, cardiac arrest, CPR

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Authors: Ruwan Gunawardane

Abstract:

This paper attempts to emphasize the benefits of conducting an intensive course in English for university entrants. In the Sri Lankan university context, an intensive course in English is usually conducted amidst various obstacles. In the 1970s and 1980s, undergraduates had intensive programmes in English for two to three months. Towards the end of the 1990s, a programme called General English Language Training (GELT) was conducted for the new students, and it was done outside universities before they entered their respective universities. Later it was not conducted, and that also resulted in students’ poor performance in English at university. However, having understood its importance, an eight week long intensive course in English was conducted for the new intake of the Faculty of Science, University of Ruhuna. As the findings show, the students heavily benefited from the programme. More importantly, they had the opportunity to refresh their knowledge of English gained at school and private institutions while gaining new knowledge. Another advantage was that they had plenty of time to enjoy learning English since the learners had adequate opportunities to carry out communicative tasks and the course was not exam-oriented, which reduced their fear of making mistakes in English considerably. The data was collected through an open-ended questionnaire given to 60 students, and their oral feedback was also taken into consideration. In addition, a focus group interview with 6 teachers was also conducted to get an idea about their experience and perception. The data were qualitatively analyzed. The findings suggest that an intensive programme in English undoubtedly lays a good foundation for the students’ academic career at university.

Keywords: intensive course, English, teachers, undergraduates, experience, perception

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Authors: Mohammad Heidari

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In this paper a method for high strength steel is proposed of residual stresses in autofrettaged tubes by combination of artificial neural networks is presented. Many different thick walled cylinders that were subjected to different conditions were studied. At first, the residual stress is calculated by analytical solution. Then by changing of the parameters that influenced in residual stresses such as percentage of autofrettage, internal pressure, wall ratio of cylinder, material property of cylinder, bauschinger and hardening effect factor, a neural network is created. These parameters are the input of network. The output of network is residual stress. Numerical data, employed for training the network and capabilities of the model in predicting the residual stress has been verified. The output obtained from neural network model is compared with numerical results, and the amount of relative error has been calculated. Based on this verification error, it is shown that the radial basis function of neural network has the average error of 2.75% in predicting residual stress of thick wall cylinder. Further analysis of residual stress of thick wall cylinder under different input conditions has been investigated and comparison results of modeling with numerical considerations shows a good agreement, which also proves the feasibility and effectiveness of the adopted approach.

Keywords: thick walled cylinder, residual stress, radial basis, artificial neural network

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Authors: Xueying Li

Abstract:

Different from English, it is common to observe Chinese clauses logically related in an implicit way without any conjunctions. This typological difference has posed a great challenge for Chinese-English translators, as 1) translators may interpret logical meaning in different ways when there are no conjunctions in Chinese Source Text (ST); 2) translators may have questions whether to make Chinese implicit logical meaning explicit or to remain implicit in Target Text (TT), and whether other dimensions of logical meaning (e.g., type of logical meaning) should be shifted or not. Against this background, this study examines a comprehensive arrange of Chinese-English translation choices of logical meaning to deal with this challenge in a systematic way. It compiles several ST-TT passages from a set of translation textbooks in a corpus, namely Ying Yu Bi Yi Shi Wu (Er Ji)) [Translation Practice between Chinese and English: Intermediate Level] and its supportive training book, analyzes how logical meaning in ST are translated in TT in texts across different text types with Systemic Functional Linguistics (SFL) as the theoretical framework, and finally draws a system network of translation choices of logical meaning from Chinese into English. Since translators may probably think about semantic meaning rather than lexico-grammatical resources in translation, this study goes away from traditional lexico-grammatical choices, but rather describing translation choices from the semantic level. The findings in this study can provide some help and support for translation practitioners so that they can understand that besides explicitation, there are a variety of possible linguistic choices available for making informed decisions when translating Chinese logical meaning into English.

Keywords: Chinese-English translation, logical meaning, systemic functional linguistics, translation choices

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Authors: Garry Gorman, Nigel McKelvey, James Connolly

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This study will evaluate a growth mindset intervention with Junior Cycle Coding and Senior Cycle Computer Science students in Ireland, where gamification will be used to incentivise growth mindset behaviour. An artificial intelligence (AI) driven personalised learning system will be developed to present computer programming learning tasks in a manner that is best suited to the individuals’ own learning preferences while incentivising and rewarding growth mindset behaviour of persistence, mastery response to challenge, and challenge seeking. This research endeavours to measure mindset with before and after surveys (conducted nationally) and by recording growth mindset behaviour whilst playing a digital game. This study will harness the capabilities of AI and aims to determine how a personalised learning (PL) experience can impact the mindset of a broad range of students. The focus of this study will be to determine how personalising the learning experience influences female and disadvantaged students' sense of belonging in the computer science classroom when tasks are presented in a manner that is best suited to the individual. Whole Brain Learning will underpin this research and will be used as a framework to guide the research in identifying key areas such as thinking and learning styles, cognitive potential, motivators and fears, and emotional intelligence. This research will be conducted in multiple school types over one academic year. Digital games will be played multiple times over this period, and the data gathered will be used to inform the AI algorithm. The three data sets are described as follows: (i) Before and after survey data to determine the grit scores and mindsets of the participants, (ii) The Growth Mind-Set data from the game, which will measure multiple growth mindset behaviours, such as persistence, response to challenge and use of strategy, (iii) The AI data to guide PL. This study will highlight the effectiveness of an AI-driven personalised learning experience. The data will position AI within the Irish educational landscape, with a specific focus on the teaching of CS. These findings will benefit coding and computer science teachers by providing a clear pedagogy for the effective delivery of personalised learning strategies for computer science education. This pedagogy will help prevent students from developing a fixed mindset while helping pupils to exhibit persistence of effort, use of strategy, and a mastery response to challenges.

Keywords: computer science education, artificial intelligence, growth mindset, pedagogy

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Authors: Aqsa Ali, Aleem Mushtaq, Attaullah Memon, Monna

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In this paper, we present a low cost design for a smart glove that can perform sign language recognition to assist the speech impaired people. Specifically, we have designed and developed an Assistive Hand Gesture Interpreter that recognizes hand movements relevant to the American Sign Language (ASL) and translates them into text for display on a Thin-Film-Transistor Liquid Crystal Display (TFT LCD) screen as well as synthetic speech. Linear Bayes Classifiers and Multilayer Neural Networks have been used to classify 11 feature vectors obtained from the sensors on the glove into one of the 27 ASL alphabets and a predefined gesture for space. Three types of features are used; bending using six bend sensors, orientation in three dimensions using accelerometers and contacts at vital points using contact sensors. To gauge the performance of the presented design, the training database was prepared using five volunteers. The accuracy of the current version on the prepared dataset was found to be up to 99.3% for target user. The solution combines electronics, e-textile technology, sensor technology, embedded system and machine learning techniques to build a low cost wearable glove that is scrupulous, elegant and portable.

Keywords: American sign language, assistive hand gesture interpreter, human-machine interface, machine learning, sensing glove

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Authors: Naheed Malik, Waheed Akhtar

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An introduction to the Islamic move towards the managing human resource is a preliminary attempt to provide managers with a useful way of managing and accepting employees. This knowledge would be helpful to even non-Muslim managers. Muslim managers are required not to know only the Islamic HR but also it is expected from them to apply the Islamic approach in managing the employees. Human resource is considered the most substantial asset of organizations. Studies have recommended that successful human resource management (HRM) leads to positive attitudes and behaviors at the workplace. On the contrary, unproductive use of human resources results in negative penalty in the form of lower job satisfaction, lower commitment, or even high employee turnover and even poor workforce quality.The study examined the Impact of Islamic HR practices on job satisfaction. Islamic HR variables encompass the aspects of performance appraisal, training and development, selection and recruitment. Data was obtained via self –administered questionnaires distributed among the employees of Banks in Pakistan which are practicing Islamic Banking. The sampling method employed was purposive sampling.Based on 240 responses obtained ,the study revealed that Islamic HRM deliberates the 40per cent of the variances in Job satisfaction .All variables excluding recruitment were found to be substantially pertinent to the dependent variable. The study also meditated the implications for future studies.

Keywords: islamic HRM, job satisfaction, islamic and conventional banks, Pakistan

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Authors: Rafiat Bolanle Fasasi-Hammed

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Cassava is an important food crop in rural households of Nigeria. It has a high potential for product diversification, because it can be processed into various products forms for human consumption and can be made into chips for farm animals, and also starch and starch derivatives. However, cassava roots are highly perishable and contain potentially toxic cyanogenic glycosides which necessitate its processing. Therefore, this study was carried out to assess information needs of cassava processors on food safety practices in Oyo State, Nigeria. Simple random sampling technique was used in the selection of 110 respondents for this study. Descriptive statistics and chi-square were used to analyze the data collected. Results of this study showed that the mean age of the respondents was 39.4 years, majority (78.7%) of the respondents was married, 51.9% had secondary education; 45.8% of the respondents have spent more than 12 years in cassava processing. The mean income realized was ₦26,347.50/month from cassava processing. Information on cassava processing got to the respondents through friends, family and relations (73.6%) and fellow cassava processors (58.6%). Serious constraints identified were ineffective extension agents (93.9%), food safety regulatory agencies (88.1%) and inadequate processing and storage facilities (67.8%). Chi-square results showed that significant relationship existed between socio-economic characteristics of the respondents (χ2 = 29.80, df = 2,), knowledge level (χ2 = 9.26, df = 4), constraints (χ2 = 13.11, df = 2) and information needs at p < 0.05 level of significance. The study recommends that there should be regular training on improved cassava processing methods for the cassava processors in the study area.

Keywords: information, needs, cassava, Oyo State, processing

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Authors: Julie A. Baldwin, Robert T. Trotter, Mark Remiker, C. Loren Buck, Amanda Aguirre, Trudie Milner, Emma Torres, Frank A. von Hippel

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Introduction: In the past, there have been concerns about contaminants in the water sources in Yuma, Arizona, including the Colorado River. Prolonged exposure to contaminants, such as perchlorate and heavy metals, can lead to deleterious health effects in humans. This project examined the association between the concentration of environmental contaminants and patient health outcomes in Yuma residents, using a community-engaged approach to data collection. Methods: A community-engaged design allowed community partners and researchers to establish joint research goals, recruit participants, collect data, and formulate strategies for dissemination of findings. Key informant interviews were conducted to evaluate adherence to models of community-based research. Results: The training needs, roles, and expectations of community partners varied based on available resources, prior research experience, and perceived research challenges and ways to address them. Conclusions: Leveraging community-engaged approaches for studies of environmental contamination in marginalized communities can expedite recruitment efforts and stimulate action that can lead to improved community health.

Keywords: community engaged research, environmental contaminants, underserved populations, health equity

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