American Scientific Research Journal for Engineering, Technology, and Sciences

Assessment of Timelines in the Management and Human Papilloma Virus (HPV) Status of Head and Neck Cancer Patients

Agboola Ogunbiyi — 2025-01-25

Background: In Nigeria, many of the patients with Head and Neck cancers (HNCs) usually present late, with advanced disease, thereby reducing the chances of cure and survival. The commonest sites of HNCs among Nigerians are nasopharynx, sinonasal, larynx, oropharynx and oral cavity, with a total prevalence of 3-5%. Human Papilloma Virus (HPV) have been known to be associated with malignant tumours arising from these sites, but with little documentation in literature in Nigeria.

Objectives/Aim: This study is to assess the socio-demographic factors responsible for delay in hospital presentation, diagnosis and treatment and also to determine the presence of HPV which is a risk factor in head and neck cancer, among patients that presented in the Lagos University Teaching Hospital (LUTH), South West Nigeria, during the study period.

Patients/Method: This is a prospective study conducted to determine the timelines, socio-demographic factors responsible for delays in presentation, diagnosis, treatment and HPV status, among patients presenting with Head and Neck cancers in the Ears, Nose and Throat (ENT) and Oral and Maxillofacial Surgery (OMFS) clinics in the Lagos University Teaching Hospital (LUTH), South West Nigeria during the study period.

Consecutive patients with HNC who presented to ENT and OMFS clinics were selected until the required sample size was met. Informed consent was taken from the patient. Semi-structured questionnaires were used to collect data on history, physical examination, investigation and treatment. History entailed details about the presenting complaints, duration, site of malignancy, socio-demographic, socioeconomic, time taken for symptom recognition, fear associated with treatment, hospital related delays and health related behaviour data were documented. The tumour type was confirmed by histopathological diagnosis and Immunohistochemistry was used to detect HPV viral particles in pathological specimen of patients. The tumour was staged according to TNM classification of the American Joint Committee on Cancer (AJCC), Head & Neck cancer sites was coded and classified according to ICD-10 (International statistical classification of diagnosis).

Results: Ninety-eight patients were recruited for this study while ninety-one (91) completed the study. The median timeline for presentation to a health facility on account of HNC symptoms was 34 weeks, timeline from hospital presentation to review by an ENT/OMF specialist was 5 weeks, from specialist review to diagnosis was 5 weeks and treatment was 6 weeks. The distribution of the Primary sites of Head and Neck Cancers includes Larynx (29.7%), Nasopharynx (28.6%), Oral cavity (16.5%), Nose and Paranasal sinuses (13.2%), Oropharynx (7.8%), Parotid (2.2%). The other sites were from the External Auditory Canal and the Mastoid. Only 14.3% of the HNCs were positive for HPV.

Conclusion: There was delay in presentation and management of patients with HNCs, the economic factor being the major cause. HPV-associated HNCs is higher in this study, compared to other studies in the sub-Saharan Africa.

Detection and Interpretation of X-Ray Scans for the Presence of Pneumonia Using Convolutional Neural Network

Peter Oluwasayo Adigun — 2025-02-03

Convolutional neural network’s application is essentially an impactful technology to proffering solutions in medical diagnostics. This research carried out a design and implementation of a medical imaging analysis and classification of X-ray scans of pneumonia images using a convolutional neural network. The CNN system was designed using an algorithm of a convolutional neural network. The designed CNN system was processed by uploading 5,216 data which comprised normal and pneumonic image scans. The CNN system was trained with 5,000 datasets and tested. The findings from the study established that the implemented system based on a convolutional neural network algorithm is 76% accurate. This study is subjected to further studies.

Detection of Rare Events: The Need to Know the Customer

Ayse Humeyra Bilge — 2025-02-12

The prediction of customer complaints based on a time series of invoices is a two-stage process consisting of determining anomalies in the sequence of invoices and assessing the response of the customers to these anomalies. In the telecommunication sector, the average complaint rate is approximately 10?? hence the prediction of customer complaints falls in the realm of rare event detection. Detecting rare events poses a significant challenge when working with unbalanced datasets. In machine learning applications, oversampling of the minority class and under sampling of the majority class in the training set are well-known preprocessing tools for creating a more balanced set. In previous work, [14] we proposed a cluster based under sampling approach as an alternative to random under sampling of the majority class, based on splitting heterogeneous data into homogeneous subsets, using Principal Component Analysis, to reduce variability within clusters. In the present work we propose a method for assessing the response of the customers to anomalies detected in the time series of invoices.

Enhancing Big Data Analytics with Artificial Intelligence Innovative Techniques and Applications in Various Sectors

Rahul Vadisetty — 2025-01-25

Almost every service industry has been ignored by big data analytics in the last decade. A new trend has also arisen as a result of AI's application to big data analytics; this trend includes distinct types of performance, including marketing, sales, innovation, organisational, financial, and operational kinds. For a better understanding of these performances, it is necessary to thoroughly assess the empirical findings from publications that deal with big data analytics in the services industry. Using this line of thinking, the authors of this study conducted a meta-analysis to draw conclusions about big data analytics and evaluate the potential moderating effect of AI on its effects on service efficiency. Big data analytics penetration is driven mostly by factors including resource availability, competitive pressure, and environmental dynamism, according to the findings. Prior to competences and resources, environmental dynamic has the greatest impact on the outcomes of big data analytics implementation. large data analytics with AI improves service performance more than large data analytics without AI, according to the results.

Yield Analysis of Boost vs Non-Boost Base Trader Joe Liquidity Pools

Jinze Hu — 2025-02-12

This comprehensive study presents an extensive quantitative analysis of the impact of Trader Joe’s Boost Incentive Program on Trader Joe’s liquidity pools. The Boost Incentive Program is a liquidity initiative designed to revitalize a specific DeFi ecosystem by enhancing user engagement and competitiveness. Following the success of a previous program from mid-2021 to early 2022, this new initiative aims to reignite growth and innovation by increasing Total Value Locked (TVL), attracting new protocols, and regaining market share within the DeFi space. The ongoing program focuses on supporting both new and existing DeFi protocols through liquidity mining incentives, direct liquidity deployment, and backing for new assets and products. The strategic use of incentives is designed to maximize impact by concentrating on core primitives and top native protocols, thereby driving substantial growth in TVL. By allocating incentives to specific strategies and liquidity pools, Trader Joe aims to offer higher yields to liquidity providers, thereby attracting more participants and increasing TVL on its platform. This approach aligns with the overarching goal of the Boost program to support innovation and new protocol growth. In the below analysis, I examine how these incentives affect yields will provide insights into the effectiveness of such programs in attracting liquidity and enhancing protocol performance. By integrating detailed data from incentive_analysis.xlsx and traderjoe_base_metrics.csv, we examine how incentive allocations, fee structures, and liquidity provider participation influence liquidity provision, trading volume, fees, and yields. The analysis incorporates statistical insights and trends within the dataset, covering rewards allocation, fee structures, liquidity provider participation, and average USD values across various token pairs. The aim is to offer deep insights into the effectiveness of incentive programs in enhancing protocol performance and user engagement within the decentralized finance (DeFi) ecosystem.

Simulation Prediction of Background Radiation Using Machine Learning

Peter Oluwasayo Adigun — 2025-02-03

The simulation of the natural background radiation dataset is research that implemented the application of machine learning in radiation physics. This is achieved by training natural background radiation datasets using different machine learning algorithms. The background radiation dataset is acquired from a field study carried out in the Gwagwalada Area, Abuja, Federal Capital Territory, Nigeria. The different machine learning algorithms applied are Random Forest, Naïve-Bayes, Support Vector Machine, and Kernel Support Vector Machine. Random Forest algorithms have the best test accuracy of 94.0%, a trained score of 98%, a K-fold cross validation score of 96.9%, and efficiently classify the effect of background radiation as harmful or harmless. This result established the integrated application of artificial intelligence and therefore indicates that machine learning has the ability to classify and categorize the effect of background radiation datasets.

Application of Financial Time Series Techniques in Analysing the Volatility of Metical/dollar and Metical/rand Exchange Rates in Mozambique (2010-2020)

Fernando João Nhampossa — 2025-01-25

Exchange rates play an important role in the economic and financial outlook of any country, making it interesting to evaluate and predict their fluctuations. Based on the combination of ARMA (Autoregressive Moving Average) models with ARCH (Autoregressive Conditional Heteroscedasticity) class models, a study was carried out to analyse and predict the volatility of the metical/dollar and metical/rand exchange rates in Mozambique for the period from January 2010 to December 2020. The use of the ARMA-ARCH combination is justified by the fact that ARMA models are not capable of modelling the variation in the variance of financial series over time. During the empirical study, several common stylized facts of financial series were verified, such as the non-stationarity of financial time series, the existence of volatility clusters, among others. It was possible to find three (03) models with good adjustment to model the volatility of exchange rate returns, two (02) for metical/dollar namely: AR(1)-GARCH(1,1) and AR(1)- EGARCH(1,1) and ; one (01) for metical/rand designated AR(1)-ARCH(1). Based on the selection criteria, the results obtained show that for metical/dollar exchange returns the model with the best performance in terms of forecasting is AR(1)-EGARCH(1,1) and for metical/rand exchange returns the AR(1)-ARCH(1) model stands out, being this the only candidate model found for the series. The volatility forecasts made for the two series based on the two (02) best models point to slightly low values for 2021, meaning that there will not be major fluctuations in the short term.

Using AI and Machine Learning in QA Testing

Nikita Klimov — 2025-02-11

The article will consider the possibilities of using artificial intelligence (AI) and machine learning (ML) technologies in the field of software quality control due to the fact that they are able to change the usual approaches to testing due to their abilities. Methods of using AI and ML to increase the effectiveness of quality assurance (QA) will be considered: automation of tests, detection of defects, prediction of anomalies. The methodology is based on the analysis of scientific papers, which will describe achievements in the application of these technologies during QA, including adaptive algorithms that automatically generate tests, clustering methods that systematize errors, and big data analysis that allows predicting defects. As part of the work, examples of organizations that demonstrate comparing user interface testing using a manual method and automated regression tests will also be considered. The data obtained show a decrease in the time spent on testing, a decrease in the probability of missing errors, and an improvement in the quality of processes. The information in the work will be useful to quality specialists, developers, and AI researchers working on optimizing testing. In conclusion, the article notes the success of applying such technological solutions in achieving QA goals.

Development and Application of Modern Charging Solutions for Industrial Battery Systems

Kostiantyn Kalus — 2023-01-25

The article will consider the features of the development of modern charging solutions for industrial battery systems. This is necessary for the growing use of electric vehicles, renewable energy sources, and storage systems. The aim is to analyze advanced charging technologies to improve industrial batteries' efficiency, safety, and durability. The methodology includes consideration of modern charging algorithms, intelligent battery management systems (BMS), and integration with telematics systems for remote monitoring of battery status. The results demonstrate that the use of adaptive charging algorithms, and BMS systems allows you to extend the life of batteries, increase operational safety, and reduce equipment downtime. The conclusion highlights the importance of integrating intelligent charging solutions into industrial processes to improve energy efficiency and reduce carbon emissions.