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Our Latest eBooks
Statistical methods for comparing limits of detection in molecular diagnostics a practical guide
Abstract: The limit of detection (LoD) is a critical performance characteristic in molecular diagnostics, defining the lowest analyte concentration at which an assay achieves reliable detection.1,2 While numerous approaches exist .....[Read more]
Published On: Feb 27, 2026
Enhancing healthcare with AI - overcoming data preparation challenges
Abstract: Healthcare data today is vast but fragmented, inconsistent, and frequently incomplete, limiting the effectiveness of artificial intelligence (AI) models built for clinical decision-making. The central problem addresse.....[Read more]
Published On: Feb 20, 2026
Influence of surgical delay on complications in older adults with hip fracture
Abstract: .....[Read more]
Published On: Jul 17, 2025
Performance evaluation for the achievement of labor competences in the modular curriculum of professional technical high school
Abstract: .....[Read more]
Published On: Sep 05, 2023
Hepatitis B Virus HBV and Hepatitis C Virus HCV Nigerian American immigrants awareness knowledge and attitudes
Abstract: Over 300 million people live with the Hepatitis B virus (HBV), and over 170 million live with the Hepatitis C virus (HCV) worldwide. In sub-Saharan Africa, Nigeria has over 21 million people living with HBV and/or HCV. Y.....[Read more]
Published On: Jun 13, 2023
Large benthic symbiotic foraminifera biodiversity as means of propagules distribution
Abstract: The study indicates that both multivariate analyses and the FORAM index (FI) are dependable tools for evaluating water quality and reef health on coral reefs. The occurrence of foraminifera in reef areas is influenced by.....[Read more]
Published On: Mar 27, 2023
 models built for clinical decision-making. The central problem
addressed in this project is the persistent gap between the potential of AI
in healthcare and the poor quality, semantic inconsistency, and lack of
interoperability of the datasets on which such models depend. The overall
objective was to design an adaptive framework capable of refining,
standardizing, and harmonizing heterogeneous healthcare data to ensure
reliability, interpretability, and compliance for predictive and diagnostic
applications. The research evolved through five foundational studies
and one integrated system development. The first study examined the
inconsistencies in reporting alternative medicine treatments for diabetes
and showed how lack of structured representation distorts meta-analytical
outcomes. The second focused on diabetic readmission prediction using
machine learning, revealing that model accuracy collapses when data is
incomplete or biased. The third study on AI applications in orthopedics
identified the dependence of clinical models on data annotation quality.
The fourth and fifth studies explored medical imaging and multimodal
AI integration, demonstrating that transformer-driven harmonization and
feature alignment significantly improve interpretability and robustness
across diverse modalities. Building on these findings, the final stage
introduced the Multi-Stage AI Data Refinement Network (MADR-Net)
an adaptive pipeline that combines deep generative and sequential models
for missing data imputation, duplicate detection, outlier correction, data
augmentation, and semantic standardization using FHIR and SNOMED
mappings. Evaluations on large-scale healthcare admission datasets
confirmed substantial gains, with macro-precision, recall, and F1-scores
exceeding 0.96, alongside measurable reductions in class imbalance
and structural bias. The study concludes that the primary barrier to
dependable healthcare AI is data quality, not algorithmic sophistication.
By embedding intelligence throughout the preprocessing pipeline,
MADR-Net redefines data preparation as a foundational, auditable stage
in healthcare analytics transforming fragmented clinical information into
standardized,){kind=link}
, and over 170 million live with the Hepatitis C virus (HCV) worldwide. In sub-Saharan Africa, Nigeria has over 21 million people living with HBV and/or HCV. Yet, most Nigerian people living with hepatitis are unaware that they have the disease. U.S. Nigerian immigrants are at high risk for HBV and HCV because they originate from Nigeria, which has an increased number of HBV and HCV infections. This study examined the knowledge, perception, and attitude of Nigerian immigrants living in the US towards HBV and HCV infections to create awareness of the disease for Nigerians in general. A quantitative survey was conducted using survey monkey questionnaires. Fisher exact tests were performed to compare the perceptions of HBV and HCV infections, infection behaviors, the perceptions of Western and traditional medicine use among Nigerians, and educational awareness of the disease. IBM® SPSS® statistical software was used to analyze the data. A total of 132 participants responded to the survey. Statistically significant interactions were found in the perception of HBV and HCV infections, educational awareness of HBV and HCV infections, infection behaviors between Nigerian men and women, use of western and traditional medicine, and Nigerians' education level. Perceptions of HBV and HCV infections between age, marital status, and socioeconomic status of Nigerian immigrants were equally significant.){kind=link}
 are dependable tools for evaluating water quality and reef health on coral reefs. The occurrence of foraminifera in reef areas is influenced by factors such as hydrodynamics and reworking processes. The abundance of smaller and stress-tolerant foraminifera, and the absence of symbiont-bearing taxa, suggesting unfavorable conditions for foraminifera in both the Pirangi and Maracajaú reef areas. Cluster analysis reveals distinct patterns of grouping sites among stations, which could be related to differences in ecological indices.){kind=link}