bluuebunny/crossref_metadata_2025_split huggingface dataset:
EXAMINING SCHOOL AND COMMUNITY PREPAREDNESS FOR TRANSITION OF YOUNG ADULTS WITH INTELLECTUAL DISABILITY FOR INDEPENDENT LIVING IN SELECTED COUNTIES, KENYA
Authors: Makumi Mary Wangui , Karugu Geoffrey , Runo Mary , Muthee Jessina 
Published: March 2023

<p>Despite the Government of Kenya’s commitment to provide education for all its citizens including those with disability, those with mild and moderate ID seem not to proceed for further education, training, employment and settling in their community after special or primary school. The main goal of the study was to examine the school and community preparedness for transition of young adults with mild and moderate intellectual disability for independent living in Kiambu and Murang´a counties, Kenya. The specific objectives of the study were investigated barriers to successful transition of young adults with intellectual disability for independent living and to establish the levels of independent living among young adult graduates with intellectual disability within the last five years in Kiambu and Murang’a counties. The study adopted descriptive research design which utilized both quantitative and qualitative research approaches. Target population was 239 headteachers, 405 SNE teachers, 1,200 young adults in school, 600 young adult graduates and 199 opinion leaders in 9 special schools and 230 special units. Purposive sampling method was used to select 120 young adult learners, 60 graduate, 30 headteachers and 20 opinion leaders while simple random sampling method was used to select 48 SNE teachers to participate in the study. This gave a total of 278 study respondents. Questionnaires and interview guides were used to collect data. The study established that dual diagnosis, assistance of taking medication independently, medical condition, running or wandering from home and loneliness were the main barriers to independent living for adult learners with ID. The study found out that the level of independent living was low among individuals with intellectual disabilities in the schools and units of study. After graduating from special schools and units, majority of learners with ID went back home and they were not engaging in any activity after school. Among the few who were engaging in activities after school, most of them were doing beadwork and weaving, hairdressing, farming, carpentry and dressmaking. Others were employed in their former schools/units as security guards. Graduation of the learners with intellectual disability was not considered as a very relevant process and hence most schools were not given any certification to learners with ID. This is despite most of the teachers and headteachers reporting that learners with ID graduated after learning the required skills. The study recommends that; the government through the Ministry of Education should create a special class in secondary schools for those in the borderline; provide policy that Ministries, NGOs, private sectors among others should employ young adults with ID to promote independent living.</p><p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0466/a.php" alt="Hit counter" /></p>
MACROSTRUCTURES AND RHETORICAL MOVES IN COMPUTER ENGINEERING RESEARCH ARTICLES
Authors: Seiradakis Emmanouela V. 
Published: March 2023

Genre analysis has offered us valuable insights into the rhetorical organization of different genres. Regarding the research article (RA) genre in engineering, previous move-based works have shown that disciplinary variation is evident, especially across computer engineering (CE) sub-disciplines which often showcase unique rhetorical structures and patterns due to the newness of the field. Using Swales’ seminal move analysis approach (1990) this paper presents a synthesis of move-based works that have explored RA textual structures in CE sub-disciplines. The discipline-specific A-IMRD move/step frameworks and patterns presented in this paper can form the foundation of research-based English for Academic Purposes (EAP) pedagogies and teaching materials tailored to the needs of CE students in academic writing courses.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0202/a.php" alt="Hit counter" /></p>
FOSTERING INTERCULTURAL AWARENESS IN ONLINE EAP CLASSROOMS USING THE REALIST SOAP OPERA GENRE
Authors: Seiradakis Emmanouela V. 
Published: March 2023

This qualitative work attempts to investigate the viability of using the British realist soap genre as an authentic cultural resource for raising the intercultural awareness of Greek EFL university students about social issues relevant to their age. Five students participated in this study. Participants watched thirteen online episodes of the British realist soap EastEnders for one academic semester within their EAP courses and completed weekly reflective tasks based on the storylines of each episode. Data were gathered from semi-structured interviews. The findings garnered suggest that British realist soap operas could be exploited in order to foster intercultural awareness processes of Greek EFL university students. This small-scale study highlights the need for further research in the field of intercultural awareness within the EAP context. Additionally, it is proposed that EFL teachers in Greece and elsewhere invest more in intercultural training and use authentic materials more often in the EAP classroom.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0205/a.php" alt="Hit counter" /></p>
BARRIERS TO MENTAL HEALTH SEEKING INTENTIONS AMONG STUDENTS AT MULUNGUSHI UNIVERSITY, KABWE DISTRICT, ZAMBIA
Authors: Milanzi Jamia , Makukula Marjory Kabinga , Lyambai Kestone , Kabungo Clement John , Matipa Royda , Changwe Gilbert 
Published: March 2023

Increasing rates of mental health problems among students and the reluctance of students to seek help in times of crisis are of great concern. This study aimed at determining the mental health seeking intentions and the barriers to mental health seeking intentions among students at Mulungushi University. An analytical cross-sectional quantitative design was used as a research design. A self-administered questionnaire adapted from 3 validated tools namely; General Help-Seeking Questionnaire (GHSQ), Perception of Stigmatization of Others for Seeking Help Scale (PSOSH) and Barriers to Access Care Evaluation (BACE-3) was used to collect data. 137 nursing and pharmacy students were randomly selected after meeting the inclusion criteria. SPSS version 23 and binary logistic regression were used for data analysis. The results of the study showed that mental health seeking intentions were weak, (69%) of students indicated that they were unlikely to seek help. Further, university sources of help were the least preferred by students, (60 %) of students indicated that they were unlikely to seek help from university-based sources. The study also showed that stigma was associated with poor mental health seeking intentions (OR=3.190, CI=0.8-11.339, P=0.041), students who were perceiving stigma were 3.19 times less likely to seek help than those who were not perceiving stigma. Although barriers to care showed no significant effect at a 5% level of significance, the odds showed that those who perceived barriers to access care were 1.41 less likely to seek help compared to those who were not perceiving barriers to access care. (OR=1.418, CI=0.623-3.230, P=0.404). The study also revealed that there was an association between gender and mental health seeking intentions. (OR=0.90, CI=0.3826-2.137, P=0.003), females showed stronger intentions to seek help seeking than males. Based on these findings, the study recommends that efforts should be channeled towards overcoming stigma and site-specific barriers to access care which are contributing to poor mental health seeking intentions among students. Further, special consideration must be placed on males through these interventions since they showed lower health seeking intentions than females.<p> </p><p><strong> Article visualizations:</strong></p><p><img src="/-counters-/edu_01/0205/a.php" alt="Hit counter" /></p>
Melanoma Skin Cancer Classification via Region-Aware Hierarchical Feature Aggregation
Authors: Kim Kyungryun 
Published: March 2023

Melanoma is a type of skin cancer with the highest risk of death. It is critical to identify these early, as the chances of survival significantly drop after stage 2 melanoma. The use of many technologies has lessened this risk but is still limited when correctly identifying malignant ones. Classifying malignant skin cancers is difficult for the following reasons: the shapes and sizes of melanomas are irregular. It is also challenging to visually distinguish between melanomas and non-melanoma regions. The performance of the previous research is based on the depth of the networks. This has a significant trade-off as using more layers adds to the computational cost of the process. Also, their methods use melanoma segmentation as an auxiliary input to the classification network. Thus, the performance of the classification significantly drops when the segmentation task fails. To address this issue, I propose a novel melanoma classification network that uses hierarchical feature aggregation with an attention mechanism. The overall architecture of the proposed network is as follows: The melanoma feature extractor takes the melanoma image as input and produces the image feature related to melanoma. The second module, the attention network, takes the same melanoma image and outputs the attention map which provides the melanoma feature extractor with feature-level regions of interest. The proposed network achieves an accuracy of 82.7% on the melanoma detection dataset which is publicly available online. Throughout the experiments, I have shown that the proposed method outperforms the previous state-of-the-art methods.
Testing a Choline Diet in Neuroligin-Deficient C. elegans to Reduce Repetitive Behavior in Autism
Authors: Balasingam Sasha , Messenger Cathy 
Published: March 2023

Autism Spectrum Disorder (ASD) is a neurodevelopmental disorder characterized by obsessive adherence to repetitive motion and behavior. Previously my research tested whether neuroligin, a brain molecule and potential determinant of behavioral traits in autism, could be used in neuroligin-deficient C.elegans (strain NLG-1) to model autism. The experiment studied the sensitivity of neuroligin-deficient C. elegan to sensory stimuli, a core symptom of autism, and the results led to the conclusion that NLG-1 C. elegans could be used as a potential model for autism in worms. This project is a continuation study from last year that tested whether choline supplementation reduced repetitive locomotion in neuroligin-deficient C. elegans. Since people with neurological disorders are often deficient in the neurotransmitter acetylcholine, it was hypothesized that increasing the amount of choline, a building block of acetylcholine, would reduce repetitive behaviors. The C. elegans were age-synched to test three different age groups of worms, and over a 1-minute period, and the repetitive movement of the worms exposed to choline was compared to worms without choline using microscope camera software. The results showed the addition of choline in the diet significantly reduced the amount of repetitive locomotion in NLG-1 C. elegans in all 3 age groups. This study suggests that for people with ASD, adding choline to the diet in their daily schedule may help them reduce repetitive behavior. On a larger scale, scientists can use NLG-1 C. elegans to target neurochemical systems to develop early treatments, and ultimately reduce the significant deficits of autism.
How efficient is Machine Learning in detecting financial fraud using mobile transaction metadata?
Authors: Shah Vedant 
Published: March 2023

Global data has shown that each day the number of fraudulent transactions is exponentially increasing. As digital fraud continues to increase, machine learning and AI are being used to curb this increase. This paper will focus on the development and analysis of a machine learning model used to detect financial fraud in mobile money transactions. The aim of this paper is to discuss how effective a machine learning model can be in detecting fraud. This algorithm makes use of the Naïve Bayes model to detect fraud. To prove this, the results have been provided by using a confusion matrix, precision and recall which indicate the effectiveness of the model. The accuracy of the developed model is 0.996 with a precision and recall of both above 0.99. This work explores and develops a solution to one of the biggest breaches in digital security.
The Attenuation of Alcohol and Nicotine Induced Behaviors in Drosophila melanogaster by Sinomenine
Authors: Federici Jacob 
Published: March 2023

The purpose of this experiment is to determine the efficacy of Sinomenine, an opioid receptor antagonist, in attenuating ethanol and nicotine induced behaviors in Drosophila melanogaster. While ethanol and nicotine are quite different in their direct mechanism of action, current research suggests that opiate reward, CREB-mediated transcription, and long-term potentiation are common pathways implicated in the development of various addictions. Due to its ability to antagonize the mu-opioid receptor, Sinomenine prevents endorphin binding, drug-induced CREB transcription, and consequent increase in dopamine production by tyrosine hydroxylase that leads to euphoria and addiction. Sinomenine may be particularly promising over other opioid receptor antagonists for sedative addictions due to its opioid-receptor independent anxiolytic effects. In a CAFE assay performed to measure preference through consumption of ethanol or nicotine, Sinomenine reverses both naive and conditioned preference for ethanol, as well as conditioned nicotine aversion. Measuring locomotion, Sinomenine attenuated both the ethanol and nicotine induced decreases in negative geotaxis exhibition in Drosophila melanogaster. Quantifying olfactory preference, Sinomenine reversed conditioned odor preference in a Y-maze, but not naive preference. Reflecting its effects on the CREB pathway, Sinomenine inhibited a nicotine-induced increase in cAMP, but did not significantly affect cAMP levels in ethanol treated Drosophila. According to these results, Sinomenine is particularly effective in attenuating conditioned ethanol or nicotine-induced behavior, and produces mixed results in terms of naive exposure. The natural chemosensory preferences or aversion of ethanol and nicotine may be independent of the CREB pathway, while developed preference may be directly dependent on it.
Applying adversarial networks to increase the data efficiency and reliability of Self-Driving Cars
Authors: Kumar Aakash , Kim Joe 
Published: March 2023

Convolutional Neural Networks (CNNs) are vulnerable to misclassifying images when small perturbations are present. With the increasing prevalence of CNNs in self-driving cars, it is vital to ensure these algorithms are robust to prevent collisions from occurring due to failure in recognizing a situation. In the Adversarial Self-Driving framework, a Generative Adversarial Network is implemented to generate realistic perturbations in an image that cause a classifier CNN to misclassify data. This perturbed data is then used to train the classifier CNN further. The Adversarial Self-driving framework is applied to an image classification algorithm to improve the classification accuracy on perturbed images and is later applied to train a self-driving car to drive in a simulation. A small-scale self-driving car is also built to drive around a track and classify signs. The Adversarial Self-driving framework produces perturbed images through learning a dataset, as a result removing the need to train on significant amounts of data. Experiments demonstrate that the Adversarial Self-Driving framework identifies situations where CNNs are vulnerable to perturbations and generates new examples of these situations for the CNN to train on. The additional data generated by the Adversarial Self-driving framework provides sufficient data for the CNN to generalize to the environment. Therefore, it is a viable tool to increase the resilience of CNNs to perturbations. Particularly, in the real-world self-driving car, the application of the Adversarial Self-Driving framework resulted in an 18 % increase in accuracy, and the simulated self-driving model had no collisions in 30 minutes of driving.
The Effects of Genetics and Psychosocial Stress on War-Affected Youth
Authors: Brinson Zaila 
Published: March 2023

The atrocities of war have a negative impact on those who must witness them. Syrian refugees have gone to Jordan to escape the civil war in their home country. The genetics of the youngest among the refugees plays a role in the development of mental illnesses. The importance of studying the effect of psychosocial stress and genetics on war-affected youth can be seen today when many children are being uprooted from their homes in Ukraine. This study was done using a case-control study. To get DNA, a gender-balanced group of 417 Syrian refugees and 306 Jodanian non-refugees provided buccal samples to the researchers. The male participants were exposed to more traumatic events, but the female participants had higher rates of perceived stress, insecurity, depression and anxiety, and post-traumatic stress disorder symptoms. The serotonin transporter 5-HTTLPR and the protein-coding gene COMT were found to play a role in the response to trauma. 5-HTTLPR was found to be associated with human insecurity and perceived stress. COMT was found to be associated with CRIES-8, also known as PTSD. The results of this study can be used in many ways. They can be used to know which mental health resources should be allocated to those uprooted by wars and the results can be used to advance the study of human behavior.
How is the vestibular system affected by microgravity, and what implications could this have on elderly patients with ageing-related vestibular dysfunction?
Authors: Hoogewerf Louisa 
Published: March 2023

Microgravity likely affects all body systems to some degree, this review will concentrate primarily on the vestibular system, as it is the organ which detects gravity and controls balance. It will also focus on how the body adapts to attenuate microgravity-induced physiological problems. Understanding the function of the vestibular system will lead to improved adaptation strategies for astronauts entering a microgravity environment and returning to Earth. It will also help aviation pilots, who experience changing gravity levels, and people on Earth, specifically the elderly, prone to dizziness and disorientation.
The impact of ageing on the vestibular system is profound medically and economically. An evaluated $10-$20 billion annual expense is associated with fall-related injuries. Data from the National Center for Health Statistics states that 75.3% of the US population over the age of 70 have issues with balance. The number of balance disordered patients due to vestibular dysfunction may reach epidemic proportions. To improve the management of elderly patients with balance disorders, dizziness and vestibular disease, we must research the vestibular system more; expanding this research to the realms of microgravity will offer new perspectives and explanations of the physiology and mechanisms of the vestibular system.
Comparing Skin Cancer Diagnosis between manual, 4 classical, and 1 deep machine learning algorithms
Authors: Kasbekar Maya , Phillips Catherine 
Published: March 2023

Skin cancer incidence has increased significantly with approximately 1.2 million melanoma cases diagnosed each year globally. An experienced dermatologist’s visual inspection has only been able to achieve a maximum accuracy of 78%. Recently, convolution neural networks algorithms have successfully outperformed human detection. The study aim was to compare the accuracy and computational time of one deep and four shallow machine learning classification algorithms and demonstrate their statistical superiority over human detection. One deep and four shallow learning algorithms were used to compare the detection accuracy and computational time. Using verified skin lesion images from the ISIC Database, the deep and shallow machine learning algorithms were trained, validated, classified and tested to detect a cancerous skin mole. The results showed that the accuracy of the deep algorithm (VGG-16- 98.86%) was superior to both the shallow algorithms (SVMs – 88.29%, decision trees-88.62%, logistic regression -88.26%, neural network -88.59%) as well as human detection (78.3%). The shallow algorithms were also superior to human detection. Deep Learning algorithms, specifically convolution neural networks, can reduce the false negatives and false positives during melanoma detection and could also be used for early detection of skin cancer at home, saving lives and significant costs.
PREVALENCE OF PULMONARY ASPERGILLOSIS AND DRUG SUSCEPTIBILITY TESTING AMONGST ADULT HIV PATIENTS AT KISUGU HEALTH CENTRE III, MAKINDYE DIVISION, IN UGANDA
Authors:   , Buyi Jonah , Mwambi Bashir , Mugisha Taremwa Ivan 
Published: March 2023

Background: Pulmonary aspergillosis is one of the respiratory conditions that are missed to be diagnosed and neglected in Uganda and it has been reported to be common worldwide, especially among people living with HIV. With azole resistance common to aspergillus fumigatus becoming a global concern; a species that causes invasive disease, epidemiological data are necessary to guide clinical practice. This study determined the prevalence of pulmonary aspergillosis and drug susceptibility testing amongst adult HIV patients at Kisugu Health Centre III Methods: A cross-sectional study was carried out at Kisugu Health Centre III among 254 HIV clients. Early morning sputa were cultured on Sabroud Dextrose Agar at 300C for 7 days using a high-volume culture technique. Microscopy using lactophenol cotton blue stain was carried out to identify the species. Antifungal susceptibility testing was done using the agar-based disc diffusion method, and data were analyzed as proportion. Results: There were 79.5%(N=202) female participants. Participants' mean age was 34.20±10.27 (range: 18-67) years. The prevalence of pulmonary aspergillosis was 42.5% (N=108) with the commonest species being aspergillus niger (28%, N=71), fumigatus (9%, N=23), terreus(4.7%, N=12), co-species infection at 1.2% (N=3). 98.6% of aspergillus niger isolates were sensitive to voriconazole,87.5% of aspergillus fumigatus isolates were sensitive to voriconazole and 100% of the aspergillus terreus isolates were sensitive to voriconazole. Major resistance in this study was seen in aspergillus fumigatus with 12.5% resistance to itraconazole, resistance was also noticed amongst 8.3% of aspergillus terreus isolates on amphotericin B. Conclusion: The prevalence of Pulmonary aspergillosis was high, and different species were isolated. Moreover, a varied antifungal susceptibility pattern was established Recommendation: Routine testing for pulmonary aspergillosis should be included in HIV clinics.