Search results for: Leonidas Ndayisaba

Authors: Jean Paul Muambangu Milambo, Peter Nyasulu, John Akudugu, Leonidas Ndayisaba, Joyce Tsoka-Gwegweni, Lebwaze Massamba Bienvenu, Mitshindo Mwambangu Chiro

Abstract:

In 2018, non-communicable diseases (NCDs) were responsible for 48% of deaths in the Democratic Republic of Congo (DRC), with cancer contributing to 5% of these deaths. There is a notable absence of cancer registries, capacity-building activities, budgets, and treatment roadmaps in the DRC. Current cancer estimates are primarily based on mathematical modeling with limited data from neighboring countries. This study aimed to assess cancer subtype prevalence in Kinshasa hospitals and compare these findings with WHO model estimates. Methods: A retrospective observational study was conducted from 2018 to 2020 at HJ Hospitals in Kinshasa. Data were collected using American Cancer Society (ACS) questionnaires and physician logs. Descriptive analysis was performed using STATA version 16 to estimate cancer burden and provide evidence-based recommendations. Results: The results from the chart review at HJ Hospitals in Kinshasa (2018-2020) indicate that out of 6,852 samples, approximately 11.16% were diagnosed with cancer. The distribution of cancer subtypes in this cohort was as follows: breast cancer (33.6%), prostate cancer (21.8%), colorectal cancer (9.6%), lymphoma (4.6%), and cervical cancer (4.4%). These figures are based on histopathological confirmation at the facility and may not fully represent the broader population due to potential selection biases related to geographic and financial accessibility to the hospital. In contrast, the World Health Organization (WHO) model estimates for cancer prevalence in the DRC show different proportions. According to WHO data, the distribution of cancer types is as follows: cervical cancer (15.9%), prostate cancer (15.3%), breast cancer (14.9%), liver cancer (6.8%), colorectal cancer (5.9%), and other cancers (41.2%) (WHO, 2020). Conclusion: The data indicate a rising cancer prevalence in DRC but highlight significant gaps in clinical, biomedical, and genetic cancer data. The establishment of a population-based cancer registry (PBCR) and a defined cancer management pathway is crucial. The current estimates are limited due to data scarcity and inconsistencies in clinical practices. There is an urgent need for multidisciplinary cancer management, integration of palliative care, and improvement in care quality based on evidence-based measures.

Keywords: cancer, risk factors, DRC, gene-environment interactions, survivors

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Authors: Jay-R. Hosana Leonidas, Jayson A. Lucilo

Abstract:

The K-12 Curriculum in the Philippines shed light to mathematics education as it recognizes the use of smartphones/mobile phones as appropriate tools necessary in teaching mathematics. However, there were limited pieces of evidence on the use of these devices in teaching and learning process. This descriptive study developed lessons integrating the use of mobile phone applications with basis on low-level competencies of students in Precalculus and determined its effects on students’ conceptual understanding, procedural skills, and attitudes towards Precalculus. Employing Bring Your Own Device (BYOD) scheme in the study, lessons developed were conducted among Grade 11 Science, Technology, Engineering, and Mathematics (STEM) students at Central Bicol State University of Agriculture for the academic year 2018-2019. This study found that there is a significant difference between the competency levels of students along conceptual understanding and procedural skills prior to and after the conduct of lessons developed. Also, it disclosed that the use of mobile phone applications had positive effects on students’ attitudes towards Precalculus. Thus, the use of mobile phone applications in teaching Precalculus can enrich students’ understanding of concepts and procedural skills (solving and graphing skills) and can increase students’ motivation, self-confidence, and enjoyment in dealing with Precalculus.

Keywords: bring your own device, mathematics education, mobile phone applications, senior high school

Procedia PDF Downloads 155

Authors: Panagiotis Karadimos, Leonidas Anthopoulos

Abstract:

Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.

Keywords: actual cost and duration, attribute selection, bridge construction, neural networks, predicting models, FANN TOOL, WEKA

Procedia PDF Downloads 126