American Scientific Research Journal for Engineering, Technology, and Sciences

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

2024-12-16T02:49:10+00:00

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.

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

2025-01-18T23:19:46+00:00

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.

Assessment of Background Radiations in Abuja, Federal Capital Territory, Nigeria

2025-02-07T23:29:29+00:00

Background radiation is everywhere and investigating the amount present in the background, surroundings and environment is essential to research and public health concerns. Humans experience it daily due to both natural and artificial incidents. These incidents include cosmic radiation, internal radiation, terrestrial radiation, nuclear events and medical operations. This research covered the investigation of two area councils, Gwagwalada and Kwali Area Councils in Abuja, Federal Capital Territory, Nigeria. Field exploration and data sampling dose rate were conducted at 11 locations which are the University of Abuja Mini Campus (8.9530N, 7.0730E), Gwagwalada Market (8.9417°N, 7.0775°E), Phase III Dumpsite (8.9637°N, 7.0646°E), Landfill @ Sharia Court (8.96175°N, 7.0816°E), University of Abuja Mini Campus- Boys Hostel (8.9453°N, 7.0703°E), Old Kutunku (8.9302°N, 7.0503°E), Kwali (8.8401°N, 7.0525°E), Yangoji (8.8208°N, 7.0341°E), Tunga Sarki (8.82079°N, 7.03408°E), Tongan Sanki Health Post (8.8249°N, 6.9484°E) and Tunga Galadima (8.8079°N, 6.9209°E). The average dose rate of the overall natural background radiations in Gwagwalada and Kwali Area Councils was 0.32±0.09µSv/hr, even though in places like Yangoji (0.42±0.11µSv/hr) and Tunga Sarki (0.45±0.13µSv/hr) which are in Kwali Area Council, have higher average dose rate than the average overall for the two area councils. This dose rate of 0.32±0.09 µSv/hr indicates a low level of radiation exposure. However, this level of radiation is slightly higher than typical background radiation, which is usually around 0.1 to 0.2µSv/hr, but it is still within a range considered safe for long-term exposure.

Reference Intervals for Haematological and Morphometric Parameters in Balkan Donkey in Serbia

2025-03-11T11:39:18+00:00

The Balkan donkey is an indigenous primitive breed that is bred in the hilly and mountainous regions of Serbia and Montenegro. It represents an important resource in terms of preserving the genotype of our country. Today, the majority of donkey population in Serbia is situated in Special Nature Reserve “Zasavica”, Nature park of “Stara Planina and Kovilj vilage, near Novi sad. They are mainly used for the production of donkey milk and as a tourist attraction on farms. There are no many literature data related to of the values of basic hematological and morphometric parameters in donkeys. This led to the development of a this study, especially on donkeys from Stara Planina, whose end result were the definition of basic physiological values of triassic parameters, hematological and biochemical values of blood parameters and precise morphometric measurements. Knowing the values of these parameters is of great importance for veterinarians for the correct interpretation of clinical findings and diagnosis of diseases in donkeys.

Novel Machine Learning Approach for Defect Detection in DFT Processes

2025-04-04T15:35:26+00:00

Recent advances in semiconductor technology have highlighted significant challenges in effectively testing modern integrated circuits (ICs). As device densities increase and defect mechanisms become more diverse, conventional Design for Testability (DFT) methodologies – while indispensable – must contend with exponential growth in test complexity. This paper reviews the essential DFT practices, including scan-based structures, boundary scan, and built-in self-test (BIST), and examines how these practices address a variety of logical fault models. It further explores machine learning (ML) techniques as valuable tools for enhancing defect detection and diagnosis. By leveraging classification algorithms such as support vector machines and neural networks, ML-driven approaches can reduce test pattern generation time, improve bridging-fault coverage, and streamline board- or wafer-level screening. Collectively, this paper underscores how strategic synergy between DFT and ML can raise fault coverage, improve diagnostic precision, and contain testing costs in the face of ongoing technology scaling.

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

2024-12-02T16:18:15+00:00

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

2025-01-02T10:33:31+00:00

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.

Use of Membrane Technologies to Increase Protein Content in Dairy Products

2025-01-31T12:37:10+00:00

The article considers modern approaches to increasing protein content in dairy products using membrane technologies. The introduction substantiates the relevance of the topic, including in the context of the growing demand for functional and high-protein products. A literature review is conducted, the scientific gap is identified and the objectives and hypotheses of the study are formulated. Further, the first section presents the basics of membrane filtration and membrane materials, and a comparative analysis of microfiltration (MF), ultrafiltration (UF), nanofiltration (NF) and reverse osmosis (RO) processes is made. Key factors affecting membrane performance (foliation, concentration polarization, etc.) and optimization methods (feedstock pretreatment, pressure and temperature control, CIP cleaning methods) are discussed. The second section describes in detail technological schemes with integration of different types of membranes in raw milk and whey processing, which allows to enrich finished products with protein and simultaneously reduce production losses. Special attention is paid to the increase of cheese yield due to the concentration of casein micelles, as well as to the production of valuable whey proteins (WPC, WPI) and reduction of lactose content. The obtained results and systematization of data indicate high efficiency of membrane technologies in dairy production, allowing to produce products with a given level of protein, improve quality and provide resource saving.

Cloud Carbon Footprint Tracker for Sustainable Practices

2025-03-02T21:48:18+00:00

In today’s digital era, businesses heavily rely on cloud-based solutions to support their operations, leading to substantial carbon emissions due to the extensive use of cloud resources. As organizations increasingly adopt cloud computing, the environmental impact of these infrastructures has become a growing concern. To address this issue, this paper presents a Carbon Footprint Tracker for Cloud Resources, a comprehensive solution that leverages cloud analytics and artificial intelligence (AI) to measure, analyze, and minimize carbon emissions. Our proposed system integrates seamlessly with Azure Cost Management APIs, Azure Monitor, and advanced Machine Learning models to provide organizations with real-time insights into their cloud consumption and its associated carbon footprint. By analyzing usage patterns and optimizing resource allocation, the system offers data-driven recommendations to enhance sustainability. Additionally, it generates detailed sustainability reports, enabling businesses to track their environmental impact and make informed decisions toward greener cloud strategies. Through this innovative approach, enterprises can effectively reduce their carbon footprint, improve operational efficiency, and align with global sustainability goals. By embracing eco-friendly cloud practices, organizations can contribute to a more sustainable future, ensuring responsible and energy-efficient cloud usage.

Outsourcing Logistics Functions to Third-party Specialized Companies: Advantages and Risks

2025-03-24T00:23:49+00:00

The article examines the outsourcing of logistics functions to third-party specialized organizations, focusing on its advantages and associated risks. Modern companies face the need to optimize logistics processes, making outsourcing a crucial tool for supply chain management. However, delegating these tasks to external entities not only reduces costs and provides access to advanced technologies but also introduces risks such as loss of control and potential data breaches. There is no unified stance in academic literature regarding the extent of outsourcing’s impact on business performance. Some researchers confirm its positive effects on financial indicators and operational flexibility, while others highlight potential challenges and the rise of hidden costs. This study aims to systematize the advantages and risks of logistics outsourcing and identify key factors that determine its successful implementation, forming the basis for strategic recommendations. The author's contribution lies in a comprehensive analysis of the topic, considering strategic, economic, and technological aspects. The findings can be applied in both academic research and practical management decision-making. The presented material is valuable for researchers in logistics and risk management, professionals involved in supply chain organization, and business leaders considering the outsourcing of logistics functions.

Development and Application of Modern Charging Solutions for Industrial Battery Systems

2024-12-13T12:13:46+00:00

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.

Simulation Prediction of Background Radiation Using Machine Learning

2025-01-19T21:19:14+00:00

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.

The Impact of Formative Assessment Feedback on Secondary Students’ Attitude towards Science and Academic Achievement in Grade Nine Biology

2025-02-22T01:17:18+00:00

The purpose of this research is to investigate the effect of formative feedback on secondary Biology students’ attitudes toward science and how formative feedback affect students academic scores in Biology at the grade 9 level. This research will explore how the academic performance of grade 9 Biology students can be improved using formative assessment feedback. The attitude of students toward science will be measured using an Assessment for learning tool (AFL). This research will also help the instructor to improve teaching practice and provides an opportunity for science students to maximize on their ability, hence improved their academic score in science.

This research used a quasi-experimental design, with intact classes using the embedded quantitative design methodology of an action investigation. The samples were selected using a convenience sampling technique. Data was collected using Two Instruments, Six Weeks and Science Attitude Scale (SAS), Two instruments, Six Weeks Test. Quantitative data will be collected using Science Attitude Scale (SAS). A positive relationship between academic success and formative feedback [r=.54, n=35, p > .001] has been found. The students in the treatment group performed significantly well on the Six Weeks Test, compared to the control group, t (61.83) = 3.58, P =.001. There was also, a statistically significant difference in the treatment group performance on the Science Attitude Scale (SAS), on the post-test compared to the pretest.

The use of formative feedback in the science classroom is a positive teaching method that can lead to higher students’ engagement which will result in an increase academic performance relative to the control group.

Automated Customer Support Systems in Service Companies: Analysis of AI Implementation

2025-03-14T08:42:43+00:00

This study analyzes the potential of integrating artificial intelligence (AI) into customer support systems within service companies. The research is based on a review of the technological foundations of AI solutions, including natural language processing (NLP, NLU), interactive systems (chatbots, virtual and voice assistants), business process automation, and analytical tools for predictive modeling. Through statistical analysis and case studies from various industries—such as the banking sector, retail, and the implementation of the Pega platform—the study identifies key performance changes, including reduced response times, an increase in first-contact resolution rates, improved customer satisfaction, and lower service costs. The study presents an integrated model for evaluating the effectiveness of AI implementation, offering recommendations for optimizing customer support automation. By addressing a scientific gap, this research combines technical, economic, and ethical aspects of AI applications in customer service. The findings will be of interest to service company executives, customer support specialists, and IT directors seeking to enhance customer interactions through AI-driven automation. Additionally, the study provides valuable insights for analysts and researchers in digital transformation, examining the impact of artificial intelligence on business processes and user experience.

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

2024-12-06T13:22:51+00:00

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.

Time, a three-directional Dimension I

2025-01-09T10:43:03+00:00

This paper revisits the concept of multidimensional time, extending discussions to incorporate orthogonal time dimensions ? and ? alongside the familiar linear t-axis, in resemblance to space and its intimate intertwinement with time. The presented framework explores how time's multidimensional nature might interact with spatial dimensions and quantum phenomena such as superposition and entanglement. Rather than redefining the arrow of time, this work underscores the need to broaden our perspective on time as multidirectional. By questioning certain conventional views about time’s nature and representation, this model advocates for a forward-moving perspective that aligns with quantum mechanics. Though experimental validation remains a challenge, the model lays a foundation for further theoretical exploration and discussion.

The Role of Algae in Bioenergy: Global Case Studies on Sustainable Biofuel Production and Future Prospects

2025-02-01T10:42:18+00:00

The growing demand for renewable energy sources has placed significant emphasis on bioenergy as a sustainable alternative to fossil fuels. Among the various bioenergy feedstocks, algae emerged as a promising candidate due to its high lipid content, rapid growth rates, and ability to thrive in diverse environments. This project aims to explore the role of algae in bioenergy production, with a particular focus on its potential as a biofuel feedstock. The research problem addresses the challenges of scaling algae-based biofuels for commercial use, including issues related to cultivation, harvesting, processing, and cost-effectiveness. To tackle these challenges, a combination of case studies, and a comprehensive review of global algae biofuel projects was conducted. Key methodologies included an analysis case study evaluations of algae biofuel production from regions with varying environmental conditions, including the United States, China, Brazil, and India. The findings highlight that while algae hold great potential for biofuel production, there remain significant barriers, including high production costs and technological limitations. However, case studies from these countries demonstrate promising advancements in algae-based biofuel research. In India, for example, local initiatives are leveraging the country’s vast coastline and agricultural byproducts to scale algae cultivation, with a focus on low-cost, high-efficiency production methods suited to its environmental and economic context. Additionally, integrated production systems and innovative biotechnological solutions have shown improved economic viability in these regions. This research is significant as it offers valuable insights into the feasibility of algae as a sustainable biofuel source, identifying key factors that could facilitate its commercialization and integration into global renewable energy strategies. The conclusions underscore the importance of continued investment in research, infrastructure, and policy support to fully realize algae's potential as a cornerstone of the future bioenergy landscape.

A Narrative Review on Multisensory Strategies for Enhancing Well-being of Pediatric Cancer Patients within Therapeutic Environments

2025-03-02T19:52:20+00:00

Pediatric cancer is a complex and challenging illness that not only affects physical health but also profoundly impacts the psychological and emotional well-being of young patients and their families. This research aims to understand the mental conditions arising from physical illnesses of pediatric cancer patients and address their holistic needs. This narrative literature review examines the potential of a multisensory approach to enhance the well-being of pediatric cancer patients. By synthesizing existing research and evidence, this study explores various sensory elements such as visual, auditory, and tactile stimulation, and their role in improving patient outcomes. Furthermore, the review investigates the effectiveness of multisensory interventions to enhance the quality of living in hospitals, promote mental wellness, and reduce stress among pediatric cancer patients. Ultimately, the findings of this narrative literature review seek to inform healthcare professionals, policymakers, and researchers about the potential benefits of incorporating multisensory therapeutic approaches into pediatric cancer care.

Methodological Aspects of Implementing Artificial Intelligence in the Processes of Monitoring and Maintenance of Network Systems

2025-04-04T15:34:43+00:00

This paper presents a comprehensive analysis of the methodological aspects of implementing artificial intelligence in network monitoring and maintenance processes. As modern networks evolve in scale and complexity, traditional monitoring techniques often fall short in ensuring optimal performance and reliability. The study reviews state-of-the-art AI approaches—including supervised, unsupervised, and deep learning methods—for anomaly detection, predictive maintenance, and automated fault response. It draws upon recent scholarly research and authoritative industry reports to evaluate the effectiveness of these methodologies. Key challenges such as data quality, model performance, and seamless integration into existing operational workflows are critically examined. The paper further discusses best practices and emerging trends, including intent-based networking, generative AI applications, and the use of digital twins for simulation and prediction. Through practical case studies and comparative analyses, the research demonstrates how AI-driven systems can significantly reduce downtime, lower operational costs, and transform traditional network operations into proactive, self-healing systems. The findings provide actionable recommendations for organizations aiming to enhance their network operations through AI, paving the way for future advancements in autonomous network management.

Detection of Rare Events: The Need to Know the Customer

2024-11-06T16:02:58+00:00

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

2024-12-19T10:50:04+00:00

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.

Investigating the Relationship among High School Students’ Mathematics Anxiety, Attitude towards Mathematics and Mathematics Achievement

2025-01-31T03:10:34+00:00

This research design is quantitative, cross-sectional survey design that investigated the relationships among 700 fourth-form students’ (aged 14 - 16 years) mathematics anxiety, attitude towards mathematics and their mathematics achievement. This study was conducted at a co-educational secondary school located in Providenciales, Turks and Caicos Islands. The data related to students’ mathematics anxiety, attitude towards mathematics and mathematics achievement were collected with the use of modified shortened math anxiety scale (mAMAS), the Short Form attitude towards mathematics scale (ATMI-Short Form) and a teacher-made End-of-Term mathematics examination, respectively. This data was analyzed using Pearson correlation and Spearman rank order correlation. The results showed that there was a negative correlation and a non-significant between 4th-form students’ mathematics anxiety and their academic achievement, a medium significant positive correlation between 4th-form students’ attitude towards mathematics and their mathematics academic achievement, and a great significant negative correlation between 4th-form students’ attitude toward mathematics and their mathematics anxiety. However, the correlation was greater between students’ mathematics anxiety and their attitude towards mathematics.

Verification of Design Solutions for Port Hydraulic Structures with the Use of Physical and Mathematical (Numerical) Simulation to Ensure Safety of Marine Terminals and Seaports

2025-02-27T13:01:43+00:00

The article considers the possibility of verification of design solutions for port hydraulic structures with the help of physical and mathematical (numerical) types of simulation to ensure the safety of marine terminals and seaports, including under the impact of various natural phenomena. The features of these types of simulation are discussed, a brief description of the range of problems to be solved is given.

AI-driven and Non-AI Methods for Electronic Health Records Duplication Remediation for Healthcare Organizations

2025-03-24T00:21:25+00:00

Duplicate Electronic Health Records (EHRs) represent a critical challenge for healthcare organizations, leading to incomplete patient data, potential medical errors, increased operational costs, and compromised quality of care. Traditional methods such as deterministic and probabilistic matching, combined with Enterprise Master Patient Index (EMPI) systems and robust data governance, have long been the cornerstone in tackling duplicates. However, these approaches face limitations when handling large-scale, heterogeneous patient data. In response, AI-driven techniques—particularly Machine Learning (ML)—have emerged as powerful alternatives, enhancing record linkage accuracy, automation, and adaptive capabilities. This article provides an in-depth review of current non-AI (deterministic and probabilistic) and AI-based deduplication strategies, including advanced ML algorithms, biometric patient identification, and real-time re-checking services. We analyze case studies from leading healthcare systems, demonstrating a reduction of duplicate rates from over 20% to under 2%. Additionally, the paper explores key management and organizational factors for successful adoption of deduplication solutions, emphasizing the need for adequate training, policy development, and continuous monitoring. Concluding with practical recommendations and future directions, this research serves as a comprehensive resource for healthcare IT and data management executives aiming to ensure high-quality patient records, strengthen compliance, and support value-based care initiatives.