Miracle-dz/UniFAR-FTUs huggingface dataset:
A Novel Data Augmentation-Based Brain Tumor Detection Using Convolutional Neural Network

Brain tumor is a severe cancer and a life-threatening disease. Thus, early detection is crucial in the process of treatment. Recent progress in the field of deep learning has contributed enormously to the health industry medical diagnosis. Convolutional neural networks (CNNs) have been intensively used as a deep learning approach to detect brain tumors using MRI images. Due to the limited dataset, deep learning algorithms and CNNs should be improved to be more efficient. Thus, one of the most known techniques used to improve model performance is Data Augmentation. This paper presents a detailed review of various CNN architectures and highlights the characteristics of particular models such as ResNet, AlexNet, and VGG. After that, we provide an efficient method for detecting brain tumors using magnetic resonance imaging (MRI) datasets based on CNN and data augmentation. Evaluation metrics values of the proposed solution prove that it succeeded in being a contribution to previous studies in terms of both deep architectural design and high detection success.
Developing a brain computer interface control system for robotic movement in two dimensions

TYLER CHRISTOPHER MAJOR. Developing a brain computer interface control system for robotic movement in two dimensions. (Under the direction of DR. JAMES CONRAD) This paper concerns the theory behind developing a brain computer interface (BCI) and the applications of such a system. Signal acquisition methods such as Functional Magnetic Resonance Imaging (fMRI), Near-Infrared Spectroscopy (NIRS), Magnetoencephalography (MEG), Electrocorticography (ECoG), and Electroencephalography (EEG) are discussed. There is also a review of the different types of Event Related Potentials (ERP) and signal extraction methods for generating filters from the captured data to generate a model for a BCI. Finally, this paper covers a review of notable BCIs that are being utilized in a wide range of applications and gives a working example using BCILAB to generate results from a sample data set using the techniques discussed in the paper.
Facing Challenges in Remote Mathematics Education and Support by building a Community of Practice around Student-Staff Partnership

Our active community of practice, developed from a weekly book club, formed through a shared interest in student-centred learning. Inspired by the book The Power of Partnership, this interdisciplinary group fostered and nurtured two practical mathematical applications through a partnership approach. In Munster Technological University academic support, including Mathematics and Statistics support, is offered to students through the Academic Learning Centre (ALC). Project 1 explored, through student-staff partnership, how best to promote the ALC. The project challenged our assumptions of how students would like to interact with the service. Project 2 focused on teaching mathematical analysis to a large cohort of year two students at the University of Southampton during the Covid-19 pandemic, elaborating on how small study groups, facilitated by student partners, were utilised to maintain a sense of connection and belonging, when possibilities for in-person teaching were extremely reduced. This paper discusses how a dispersed community of practice collaborated to enhance learning and teaching of mathematics.
Machine Learning (ML) Methods in Assessing the Intensity of Damage Caused by High-Energy Mining Tremors in Traditional Development of LGOM Mining Area

The paper presents a comparative analysis of Machine Learning (ML) research methods allowing to assess the risk of mining damage occurring in traditional masonry buildings located in the mining area of Legnica-Głogów Copper District (LGOM) as a result of intense mining tremors. The database of reports on damage that occurred after the tremors of 20 February 2002, 16 May 2004 and 21 May 2006 formed the basis for the analysis. Based on these data, classification models were created using the Probabilistic Neural Network (PNN) and the Support Vector Machine (SVM) method. The results of previous research studies allowed to include structural and geometric features of buildings,as well as protective measures against mining tremors in the model. The probabilistic notation of the model makes it possible to effectively assess the probability of damage in the analysis of large groups of building structures located in the area of paraseismic impacts. The results of the conducted analyses confirm the thesis that the proposed methodology may allow to estimate, with the appropriate probability, the financial outlays that the mining plant should secure for the repair of the expected damage to the traditional development of the LGOM mining area.
Content validity of the NECPAL CCOMS-ICO© in Spanish to identify palliative needs in children and adolescents with Cancer*

Abstract Objective. To perform content validation of the NECPAL CCOMS-ICO© instrument to identify palliative needs in Colombian children and adolescents from 8 to 17 years of age with cancer. Methods. Psychometric study, which used the Lawshe model, modified by Tristan, to perform content validity of the NECPAL CCOMS-ICO© instrument through expert consensus. Results. The Surprise Question (SQ) Would it surprise you if this patient died within the next year? and the parameters Demand: Has there been any implicit or explicit expression of limitation of therapeutic effort or demand for palliative care from the patient, family, or team members? , Need: identified by professional members of the team, Checklist symptoms (ESAS) ≥ 2 persistent or refractory symptoms, Emotional Distress Detection (EDD) > 9, Social and family assessment”, Oncological disease (advanced metastatic or locoregional cancer), Oncological disease (in progression (in solid tumors) and Oncological disease (Persistent symptoms poorly controlled or refractory despite optimizing specific treatment), were considered valid by the experts to identify palliative needs in Colombian children and adolescents aged 8 to 17 years with cancer according to the Lawshe criteria, as modified by Tristán. Parameters specifically related to the oncological disease belonging to the dimension Indicators of severity/progression of the disease showed the highest CVR’ per parameter, with indices of agreement between 0.714 and 0.857. Conclusion. The content of the NECPAL CCOMS-ICO © 3.1 instrument is valid to identify palliative needs in Colombian children and adolescents from 8 to 17 years of age with cancer with a CVI of 0.68.
PHENOTYPIC AND GENETIC VARIABILITY INDUCED BY GAMMA IRRADIATION IN WILMAN LOVEGRASS Eragrostis superba (PEYR.)

Wilman lovegrass [Eragrostis superba (Peyr.)] is a species used worldwide to revegetate rangelands; however, it has a low nutritional quality and a poor tolerance to water stress; thus, it can be a candidate for plant breeding. In this study, the morphological, nutritional, stomatal and molecular diversity induced y gamma irradiation was evaluated in Wilman lovegrass. Seed of the common variety was exposed to gamma radiation at doses of 100, 200, 300, 450, 600, 900, 1400 and 2000 Gy; in addition, non-irradiated seed was used as a control. Seed of the different treatments was used to produce plants in a greenhouse, which were evaluated through traits related to forage quality and water stress tolerance. The first generation of plants derived from mutagenic treatments (M1) was selected based on agronomic traits. The selected promissory mutants were then nutritionally, stomatally and molecularly characterized. All the possible selected mutants presented lower lignin content (P ≤ 0.01) compared to the common variety. The 200-3 and 200-7 promissory mutants showed higher protein content (P ≤ 0.01) at maturity stage. Also, significant differences (P ≤ 0.01) were found between putative mutants and control plants in stomatal density and stomatal area. The molecular analysis revealed significant differences (P ≤ 0.05) between the control genotype and the promissory mutants 200-3, 600-3, and 600-6, which presented genetic distances of 0.41, 0.42 and 0.35 (Dice’s coefficient) in relation to the control genotype, respectively. The phenotypic and genetic variability induced by gamma irradiation allowed to select the first generation of putative mutants with outstanding agronomic traits, nutritional value and stomatal characteristics.
Dynamic tensions in the budgetary system of a family business and the duality of structure

ABSTRACT This article aimed to understand the dynamic tensions in the budgetary system of a family business based on the duality of structure perspective. The research on dynamic tensions in the budgetary system of family businesses is inconclusive. Dynamic tensions are explored in light of traditional theories, and few studies use Simons’ (1995) four levers of control. Moreover, the management control systems (MCSs) of family businesses have particularities that need to be explored. These particularities of family businesses provide different explanations for the dynamic tensions in the budgetary system. Duality of structure enables an understanding of the interaction between the structure and agents’ actions in the production and reproduction of the social order of a family business, within a context of dynamic tensions in the budgetary system. The research revealed the dynamic tensions in the budgetary system of a family business in one budgetary cycle. The findings are relevant since, based on a duality of structure perspective, they present indications that these dynamic tensions are associated with positive forces (belief and interactive control systems) in interaction with negative forces (boundary and diagnostic control systems). This is a single case study in a medium-sized family business engaged in the production and sale of foods. The data were collected through semi-structured interviews, observations, and documents, and analyzed based on discursive textual analysis. The evidence suggests that the social order of the family business is produced and reproduced with the dynamic tensions that occur in the planning and control stages of the budgetary system. The study contributes to the literature on dynamic tensions in the budgetary system of a family business by addressing what they are and how these tensions occur based on the duality of structure perspective. It contributes to practice, as it enables an understanding and better management of these tensions and the achievement of budgetary goals and objectives.
Identifying Key Biomarkers and Immune Infiltration in Female Patients with Ischemic Stroke Based on Weighted Gene Co-Expression Network Analysis

Stroke is one of the leading causes of death and disability worldwide. Evidence shows that ischemic stroke (IS) accounts for nearly 80 percent of all strokes and that the etiology, risk factors, and prognosis of this disease differ by gender. Female patients may bear a greater burden than male patients. The immune system may play an important role in the pathophysiology of females with IS. Therefore, it is critical to investigate the key biomarkers and immune infiltration of female IS patients to develop effective treatment methods. Herein, we used weighted gene co-expression network analysis (WGCNA) to determine the key modules and core genes in female IS patients using the GSE22255, GSE37587, and GSE16561 datasets from the GEO database. Subsequently, we performed functional enrichment analysis and built a protein-protein interaction (PPI) network. Ten genes were selected as the true central genes for further investigation. After that, we explored the specific molecular and biological functions of these hub genes to gain a better understanding of the underlying pathogenesis of female IS patients. Moreover, the “Cell type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT)” was used to examine the distribution pattern of immune subtypes in female patients with IS and normal controls, revealing a new potential target for clinical treatment of the disease.
Keywords-Driven Paper Recommendation Based on Mobile Edge Computing Environment Framework

In the cloud computing era, a paper recommender system is usually deployed on the cloud server and return recommendation results to readers directly. However, considering the paper recommender system, processing tremendous paper citation data on the cloud cannot provide fine-grained personalized and real-time recommendations for each reader because these recommended papers from the cloud are far from readers and probably not correlated strongly with each other for helping each reader research further and deeper in the interested field. Recently, the edge-cloud collaboration-based recommender system has been used for releasing parts of the cloud computing task to the edge and provides the recommendation near the client. Based on the edge computing recommender system, a keywords-driven and weight-aware paper recommendation approach is presented, namely, LP-PRk+ w (link prediction-paper recommendation), to enable intelligent, personalized, and efficient paper recommendation services in the mobile edge computing environment. Specifically, the whole paper recommendation process mainly covers two parts: optimizing the existing paper citation graph via introducing a weighted similarity (i.e., building a weighted paper correlation graph) and then recommending a set of correlated papers according to the weighted paper correlation graph and the users’ query keywords. Experiments on a real-world paper correlation dataset, Hep-Th, show the capability of our proposal for improving the paper recommendation performance and its superiority against other related solutions.
MSTDSNet-CD: Multiscale Swin Transformer and Deeply Supervised Network for Change Detection of the Fast-Growing Urban Regions

Deep learning algorithms have recently provided new ideas for various change detection (CD) tasks, which have yielded promising results. However, accurately identifying urban land cover and land use (LCLU) changes remains challenging in the very high-resolution (HR) remote sensing images due to the difficulties in effectively modeling the features from ground objects with different times and different spatial locations. In this letter, a multiscale swin transformer (MST)-based deeply supervised network (MSTDSNet) is proposed for monitoring urban LCLU changes using bi-temporal very HR remote sensing images. The wider and deeper layer aggregation (WDLA) is first introduced to improve the distinguishability of multiscale features. Subsequently, MST is adopted to make the most of the available spatial information in the refined multiscale features. Moreover, channel-wise dependencies (CWDS) are integrated as a soft constraint to directly supervise WDLA. Experiments are conducted on the widely used SYSU-CD and LEVIR-CD datasets. Compared with other state-of-the-art CD methods, MSTDSNet provides favorable performance, with the highest F1 of 80.33% and 88.10%, respectively.
Human Capital and Economic Growth in Sub Saharan Africa Countries: ARDL and ECM Analysis

The long-run relationship between human capital and economic growth in Sub Saharan African (SSA) countries was examined using An ARDL and ECM cointegration analysis approach. The endogenous and human capital theory posits that human capital accumulated acquired from the education system contributes to economic growth in any economy. Despite many years of huge government expenditure in education, SSA countries appears to experience little or no economic growth. Studies on the long term effects of human capital on economic growth in SSA countries remain few despite a growing body of research in literature. Using data from 37 SSA countries for the year 2000- 2018 an Autoregressive Distributed Lag ARDL and Error Correction Model (ECM) cointegration the study estimated a Dynamic Fixed Effects model (DFE) to examine the long run relationship between human capital and economic growth. The findings revealed that a cointegration relationship exist between human capital and economic growth. Secondary education had a significant but weak positive effect on economic growth in the short run and a weak negative effect on economic growth in the long run. Primary education however, had an insignificant contribution to economic growth both in the short and long run. Physical capital hadsignificant and strong positive effect on economic growth in the short run whilst in the long run it has an insignificant effect. SSA countries interested in enhancing long run term economic growth through human capital should consider promoting access to secondary school enrolment and improving the quality of primary education. The study providedempirical evidence on the short and long run contribution of human capital to economic growth in SSA countries.
Symbolic Planning and Control of Robot Motion: State of the Art and Grand Challenges

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Women’s Political Empowerment in India and Bangladesh: Gender Quotas and Socio-economic Obstructions

India and Bangladesh have introduced gender quotas to increase women’s presence in politics and bring the desired substantive inclusionary effects at the level of emergent policy outcomes. This qualitative study analyzes the socio-economic barriers that quota-elected women representatives encounter in affecting policies regarding their political empowerment at the local council level—in the Gram Panchayat in India and the Union Parishad in Bangladesh. I contribute to the comparative literature on gender quotas and policy outcomes. Results reveal that women members in both countries face serious social and economic impediments to political participation at this local level. Social barriers such as household responsibilities, lack of family support, and lack of social safety and physical mobility, are no longer a major hindrance for women members in offering services to their constituents. However, in Bangladesh, a majority of women members fail to function effectively because of lack of education—more so than the women members in India. Women members in both countries lack sufficient political training, which makes it challenging to claim their rights when offering goods and services to voters, particularly to women. I also find that women members in both countries have failed to perform their political activities due to financial incapability, although in different ways. Indian women members receive a small amount of money per month, whereas Bangladeshi women members cannot meet the demands of three times higher constituents with irregular honoraria. Lack of financial incapability further leads to corruption in the cases of some women members in both countries. To ensure women’s political empowerment through reserved seats in both countries, it is vital to make certain that enough government resources are provided for local councils, together with enough training for elected women members.
Effect of Self-Determination on Postsecondary Enrollment of English Learners With Disabilities

We examined the effect of three components of self-determination—autonomy, empowerment, and self-realization—on the postsecondary enrollment of English learners with disabilities, using quasi-experimental propensity score modeling and data from the U.S. National Longitudinal Transition Study-2. Results support the hypothesis that self-determination components affect postsecondary school enrollment. English learners with disabilities with higher autonomy scores were more likely to enroll in 2-year colleges, and those with higher empowerment scores were more likely to enroll in 4-year colleges. However, prior research found that English learners with disabilities are less likely to act autonomously or report empowerment-related behaviors than other students with disabilities or students in the general population. Considering the increasing importance of postsecondary education, the current study’s findings demonstrate the importance of promoting both the self-determined behaviors of this dually identified population of students and the learning environment supports that facilitate the practice of self-determination.
Mental Health of Filipino Seafarers: The Contributory Factors and the Strategies Applied Onboard Ship

: Mental health is equally important with physical health. Many reports found out that poor mental health is one of the major factors of depression among seafarers. This study ascertains the factors affecting mental health and the strategies applied by Filipino seafarers to combat these issues. A survey was used as a research design. A total of 100 seafarers taken through convenience and snowball sampling answered the validated researcher-made questionnaire. The questionnaire was sent in the JBLFMU Alumni Portal, messenger, and Gmail. Frequency, percentage, and rank were used. Themes were used to classify the strategies applied by seafarers to counteract mental health issues onboard ship. The results revealed that the major contributory factor affecting the mental health of Filipino seafarers is stress, while they believed that a positive outlook in life makes one’s self free from mental health problems onboard. The top thre e factors are stress, fatigue, and separation from families and friends. The top three strategies to counteract factors that affect seafarers’ mental health onboard ship are staying positive, socializing with crewmates, and communicating with family.