Analysis Software Research Articles

Pheno-Ranker: a toolkit for comparison of phenotypic data stored in GA4GH standards and beyond

BackgroundPhenotypic data comparison is essential for disease association studies, patient stratification, and genotype–phenotype correlation analysis. To support these efforts, the Global Alliance for Genomics and Health (GA4GH) established Phenopackets v2 and Beacon v2 standards for storing, sharing, and discovering genomic and phenotypic data. These standards provide a consistent framework for organizing biological data, simplifying their transformation into computer-friendly formats. However, matching participants using GA4GH-based formats remains challenging, as current methods are not fully compatible, limiting their effectiveness.ResultsHere, we introduce Pheno-Ranker, an open-source software toolkit for individual-level comparison of phenotypic data. As input, it accepts JSON/YAML data exchange formats from Beacon v2 and Phenopackets v2 data models, as well as any data structure encoded in JSON, YAML, or CSV formats. Internally, the hierarchical data structure is flattened to one dimension and then transformed through one-hot encoding. This allows for efficient pairwise (all-to-all) comparisons within cohorts or for matching of a patient’s profile in cohorts. Users have the flexibility to refine their comparisons by including or excluding terms, applying weights to variables, and obtaining statistical significance through Z-scores and p-values. The output consists of text files, which can be further analyzed using unsupervised learning techniques, such as clustering or multidimensional scaling (MDS), and with graph analytics. Pheno-Ranker’s performance has been validated with simulated and synthetic data, showing its accuracy, robustness, and efficiency across various health data scenarios. A real data use case from the PRECISESADS study highlights its practical utility in clinical research.ConclusionsPheno-Ranker is a user-friendly, lightweight software for semantic similarity analysis of phenotypic data in Beacon v2 and Phenopackets v2 formats, extendable to other data types. It enables the comparison of a wide range of variables beyond HPO or OMIM terms while preserving full context. The software is designed as a command-line tool with additional utilities for CSV import, data simulation, summary statistics plotting, and QR code generation. For interactive analysis, it also includes a web-based user interface built with R Shiny. Links to the online documentation, including a Google Colab tutorial, and the tool’s source code are available on the project home page: https://github.com/CNAG-Biomedical-Informatics/pheno-ranker.

Prevalence and associated factors of post-stroke depression among patients on follow-up at medical referral clinics of Bahir Dar city public specialized hospitals

BackgroundPost-stroke depression is among the most common neuropsychiatric complications in stroke survivors. Post-stroke depression is commonly under-recognized and under-treated. This study aimed to assess the prevalence and associated factors of post-stroke depression among patients on follow-up at medical referral clinics of Bahir Dar City public specialized hospitals.MethodAn institutional-based prospective cross-sectional study was conducted from September to December 2023 on 394 patients. Public Health Questionnaire-9 was used to diagnose depression. Data was checked for cleanness manually and then exported to SPSS version 25 software for analysis. A systematic randomized sampling method was used to identify study participants. Bivariate and multivariate logistic regression analysis was performed to identify independent predictors of post-stroke depression. A p-value less than 0.05 is considered as significant with a 95% confidence interval.ResultThe overall prevalence of post-stroke depression was found to be 42.9%. Employment status (AOR = 0.282, 95% CI: 0.112–0.709 C, P-value = 0.007), depression before stroke (AOR = 3.237, 95% CI: 1.098–9.544, P-value = 0.033), ischemic heart disease (AOR = 4.090, 95% CI: 1.745–9.583, P-value = 0.001) and physical disability (AOR = 4.051 95% CI: 2.239–7.332, P-value = 0.000) were found to be statistically significant independent predictors of post-stroke depression (PSD).ConclusionThe prevalence of post-stroke depression was found to be high relative to other similar studies. Patients with known depression before stroke, ischemic heart disease, and significant physical disability were at a higher risk of developing PSD. Employed patients had less risk of developing PSD. Sufficient and comprehensive evaluation of stroke patients for depression is recommended.

Antimicrobial resistance profile of Escherichia coli in drinking water from one health perspective in low and middle income countries

IntroductionAntimicrobial resistance is a major global public health concern, especially in low-resource settings. In low- and middle-income countries, the existing evidence about antimicrobial resistance in drinking water is inconsistence and not comprehensive. Therefore, this study aimed to estimate the pooled prevalence of antimicrobial resistance profiles of Escherichia coli from drinking water in low- and middle-income countries.MethodsThis study was conducted using comprehensive literature searches using various databases such as PubMed, Scientific Direct, HINARI, and Google Scholar. Data extraction was performed using Microsoft Excel and exported to STATA 14/SE software for analysis. We used the Joanna Briggs Institute’s quality appraisal tool to ensure the quality of the included studies. A random effects model was employed to estimate the pooled prevalence. Publication bias was evaluated using funnel plots and Egger’s regression test. Subgroup and sensitivity analysis were also conducted in this study.ResultsThe study found that the pooled prevalence of Escherichia coli isolates in drinking water was 37.94% (95% CI: 26.73–49.13). The prevalence of multidrug resistance was 43.65% (95% CI: 31.15–56.15). Regarding specific antimicrobials, the pooled resistance levels of Escherichia coli were 54.65% (95% CI: 41.35–67.96) against contrimoxazole, followed by 48.64% (95% CI: −3.6–101) against amoxicillin and 48% (95% CI: −18.1–114.2) against cefuroxime.ConclusionThe findings indicated a significant prevalence of antimicrobial resistance of Escherichia coli isolated from drinking water and its multidrug resistance. To address this issue, it recommends focusing on improving basic hygiene and sanitation practices and enhancing water and wastewater treatment systems.Systematic review registrationIdentifier CRD42024533592.

Mapping the spatial patterns of ethnic segregation and its implications to urban policy in Nairobi city

In modern times, cities around the world have grappled with the challenges of racial and ethnic segregation. In Nairobi city, with its diverse ethnic makeup, there is widening inequalities and emerging patterns of ethnic segregation, where the five main ethnic groups - Kamba, Luo, Kikuyu, Luhyia, and Kisii - experience varying levels of spatial concentration. This study analysed the spatial patterns of ethnic segregation in Nairobi, using geocoded questionnaire data from the 2019 Kenya population and housing census data. We used the Index of Dissimilarity in STATA software and Geo-segregation Analyzer and Anselin’s Local Moran I method in GIS to map ethnic segregation patterns. Our findings uncovered a striking socio-spatial divide based on ethnicity. Anselin Local Moran’s I indicators further pinpointed areas with the highest levels of segregation and spatial clustering of specific ethnic groups. These findings offer crucial insights for urban planners and policymakers. By pinpointing areas experiencing the most severe spatial segregation, our research could inform spatially targeted interventions and resource allocation. This could inform policies that foster inclusivity, reduce spatial inequalities, and build a more equitable and socially cohesive city.

Investigating the Impact of Host Factors on Vaccinia Virus Infection Through Single-Cell Analysis via Flow Cytometry.

To investigate the impact of a host factor on viral replication, conventional techniques involve analyzing viral replication within a homogeneous cell population engineered to uniformly express the host factor. In this chapter, we present a method to explore the intricate dynamics between host factors and vaccinia virus (VACV) infection using flow cytometry, which enables the rapid, multi-parametric analysis of individual cells in solution. Cultured cells are transfected to express a host factor fused with a fluorescent protein, followed by infection with a VACV encoding a different fluorescent reporter. Subsequently, cells are washed, fixed, and filtered for flow cytometry analysis. The data are then analyzed with flow cytometry analysis software to evaluate the influence of the host factor on various infection parameters, including the stage of viral infection and viral replication level. This method allows for the precise investigation of proviral or antiviral factor expression in relation to viral replication within a heterogeneous population of cells.

Breed differentiation in northern Ethiopian cattle: The application of univariate and multivariate analyses of phenotypic traits.

The aim of this study was to characterize and differentiate the Adwa, Arado, Medenes, and Begait cattle breeds in northern Ethiopia via univariate and multivariate analyses of phenotypic traits. Sixteen qualitative characteristics and nine morphometric traits were recorded for a total of 946 (604 females and 342 males) purposively selected adult cattle. The frequency, general linear model (GLM), and canonical discriminant (CANDISC) analysis procedures of the Statistical Analysis Software (SAS 9.0) were used to analyze the data. Qualitatively, Medenes and Begait, cattle breeds from lowland agroecology and livestock-based production systems, possess a convex facial profile, relatively larger dewlap, naval flap, and perpetual sheath, as well as more uniform and lighter body colors than the Adwa and Arado cattle breeds, which are from the midland agroecology and mixed crop-livestock production systems. Morphometrically, clear differences were observed among all of the studied cattle breeds, where the Begait, Medenes, Arado, and Adwa cattle breeds were ordered in descending order according to their overall body size. Moreover, multivariate analysis revealed significant differences among the breeds where each breed was placed under a separate group. However, such phenotypic distinctions among the four cattle breeds do not necessarily indicate genetic dissimilarities. Therefore, further inclusive genetic characterization studies involving the use of representative samples from the Adwa and Medenes cattle breeds are recommended to quantify the degree of genetic relationships among these breeds. Moreover, owing to the unknown breed-level population size, urgent conservation programs as well as genetic improvement strategies are needed to ensure sustainable utilization.

Dynamic response analysis of ancient pagodas under rockfall impact

In order to protect the precious cultural heritage of the pagoda in the mountainous area from the damage caused by falling rocks, we provide targeted and effective research support and protection strategy basis. ABAQUS finite element analysis software is used to accurately simulate and analyze the response of the pagoda under the impact load of falling rocks. Firstly, the geometric modeling of the tower structure is carried out in a meticulous manner to ensure that the model accurately reflects the real shape and structural characteristics of the tower, and the material properties are assigned to it rigorously and accurately, so as to construct a finite element model that is highly adapted to the actual situation. Subsequently, the impacts of falling rocks on the pagoda under different working conditions, such as different velocities, radii, impact angles and impact heights, are comprehensively and systematically simulated to assess the structural safety and stability of the pagoda from all angles. This paper clearly reveals that the velocity, radius, impact angle and impact height of falling stones are the core key position in the structural safety assessment of the pagoda, and they are the decisive factors affecting the structural safety of the pagoda. These findings not only deepen the scientific knowledge of the response mechanism of the pagoda under the impact of falling rocks, but also provide a solid theoretical foundation and a clear direction for the subsequent development of accurate and efficient protection measures against falling rocks.

Learning pathways of necessity and opportunity-driven women entrepreneurs: a qualitative study

Purpose This paper aims to provide insights into the learning approaches of necessity (NEC)-driven and opportunity (OPP) driven women entrepreneurs (WEs) in the Indian context. It investigates the various pathways small business owners use to acquire knowledge and develop competencies. Additionally, it examines the potential differences between the two groups to better understand their learning processes. This understanding, particularly from the perspective of an emerging nation, can inform policymakers, WE organizations, NGOs and families in creating a supportive environment that fosters positive and effective learning experiences for WEs. Design/methodology/approach Qualitative research study using semi-structured, in-depth interviews with 16 micro, small and medium enterprises entrepreneurs in the state of Karnataka, India. Thematic analysis was used to draw out the themes from the rich textual data collected using the computer assisted qualitative data analysis software (CAQDAS) tool, ATLAS.ti 23. Findings Thematic analysis identified four major themes, namely, routinized learning, learning through critical experiences, self-driven mechanisms and learning from others. The findings reveal that NEC and OPP women entrepreneurs do not greatly differ in their pathways to learning except for some minor dissimilarities. Originality/value This study provides a unique viewpoint on the learning mechanisms of women entrepreneurs in small businesses in the context of an emerging economy. It additionally deliberates on the similarities and differences among NEC and OPP motivated WEs, where studies are rare.

A roadmap for a Metaverse-based digital governance: A case of the Gambia.

This paper explores the integration of Metaverse technologies into the digital governance framework of The Gambia, in order to identify the sectors where these innovations can be implemented most effectively. Driven by the pursuit of cutting-edge solutions to improve governance, improve public service delivery, and promote citizen engagement, the study uses qualitative analysis methodologies, including interviews with Information Technology (IT) professionals and an examination of government reports. Through the application of NVivo software for data analysis, essential themes and areas suitable for Metaverse applications were uncovered. These include the potential of the Metaverse to fundamentally transform sectors such as education, public service, infrastructure development, urban planning, and cultural heritage tourism. Despite recognizing the capacity of the Metaverse for transformative impact, this research identifies significant obstacles, including technical infrastructure readiness, ethical issues, and the need for policy reform. The findings highlight the need to address these challenges through substantial improvements in digital infrastructure, literacy, ethical guidelines, and policy frameworks. The study calls for a multistakeholder collaboration model, which incorporates government, the private sector, academia, and international bodies, to ensure the successful integration of the Metaverse into the digital governance of The Gambia. Offering comprehensive insights and strategic recommendations, this research underscores the collective effort required of all stakeholders to harness the full potential of the Metaverse, thus facilitating an inclusive and efficient digital governance ecosystem in the Gambia.

The Effect of the Arcs Model of Motivation on Students' Learning Achievement: A Meta-Analysis

Objective: The objective of this study is to investigate the effect of ARCS motivation model on students' learning achievement, with the aim of is to test the validity of the ARCS motivational modelling approach.It aligns with UN Sustainable Development Goal (SDG) 4(Quality Education), promoting effective and inclusive learning outcomes. Theoretical Framework: In this topic, the main concepts and theories that underpin the research are presented. Activity theory stand out, providing a solid basis for understanding the context of the investigation. Method: The methodology adopted for this research comprisesMeta-analytical design, for the included literature, strictly followed the operational guidelines of PRISMA. Data collection was carried out through four databases, Web of Science, Proquest, Scopus and Eric, coded and classified through activity theory, and the tool used CMA 3.7 software for data analysis to ensure the accuracy and comprehensiveness of the results. Results and Discussion: The results obtained revealed ARCS model of motivation has a large effect size on student learning achievement (ES=1.256). In addition, sample size, learning domain, ARCS motivational classroom model, intervention duration, teaching method, sample region, interaction type, and teaching tool significantly moderated the effect sizes. Research Implications: This study systematically evaluates the impact of the ARCS motivation model on academic performance through meta-analysis, providing empirical support for motivation theory in educational psychology. Practically, this study offers effective strategies for educators to enhance students' motivation and academic performance, and serves as a reference for optimizing the integration of course design and teaching models. Originality/Value: This study contributes to the literature by adopting an activity theory coding design to systematically analyse the effect of the ARCS motivational model on student achievement. Its significance and value lies in revealing the effectiveness of the ARCS motivational model in improving students' academic achievement, providing empirical support for instructional design and practice, and providing guidance for motivation-driven curriculum design in particular. This study has important applications in promoting academic progress, improving the quality of education and achieving sustainability in quality education.

Global Healthy and Sustainable City Indicators: Collaborative development of an open science toolkit for calculating and reporting on urban indicators internationally

Measuring and monitoring progress towards achieving healthy, equitable and sustainable cities is a priority for planners, policymakers and researchers in diverse contexts globally. Yet data collection, analysis, visualisation and reporting on policy and spatial indicators involve specialised knowledge, skills, and collaboration across disciplines. Integrated open-source tools for calculating and communicating urban indicators for diverse urban contexts are needed, which provide the multiple streams of evidence required to influence policy agendas and enable local changes towards healthier and more sustainable cities. This paper reports on the development of open-source software for planning, analysis and generation of data, maps and reports on policy and spatial indicators of urban design and transport features for healthy and sustainable cities. We engaged a collaborative network of researchers and practitioners from diverse geographic contexts through an online survey and workshops, to understand and progressively meet their requirements for policy and spatial indicators. We outline our framework for action research-informed open-source software development and discuss benefits and challenges of this approach. The resulting Global Healthy and Sustainable City Indicators software is designed to meet the needs of researchers, planners, policy makers and community advocates in diverse settings for planning, calculating and disseminating policy and spatial urban indicators.

Phenomenological Studies: Experiences of Family Members Taking Care of Post-Stroke Patients

Most of the global stroke burden occurs in low- and lower-middle-income countries. The most common obstacles families face when caring for stroke patients appear to cause various tensions between patients and their families. This study examines family members' experiences of caring for patients after a stroke. This study uses a qualitative method with a descriptive phenomenological design. The research participants were 11 participants using purposive sampling techniques. Data were analyzed using NVivo 12 qualitative data analysis software, and interview results were analyzed using thematic analysis. This study identified five phenomena related to the experience of caring for patients after stroke: fulfillment of patient's daily activities, patient treatment, participant psychological response, participant support, and barriers to patient care. Most participants had a less pleasant experience caring for patients after a stroke. This is reflected in sad reactions, angry reactions, the need for help and support from the government, health workers, and other family members, and barriers to patient care. It is important to ensure good coordination between health services and patients and their families, as patients and participants do not have access to health services due to distance and physical disability.

The Influence of the Visionary Leadership of Madrasah Heads, Organizational Culture and Motivation for Achievement on the Performance

Background:Changes in the education sector present significant challenges and problems for communities, influencing various sectors, including the social domain. Educational institutions, such as madrasahs, are crucial in addressing these challenges. Purpose:This study aims to analyze the impact of the visionary leadership of madrasah heads, organizational culture, and achievement motivation on teacher performance. Methods:A quantitative approach with path analysis was used in this research. Data collection was conducted through a questionnaire, followed by normality, validity, and reliability tests. The data were processed using SPSS software for path analysis. Results:The study found that visionary leadership of madrasah heads, organizational culture, and motivation for achievement significantly influenced teacher performance. Conclusions:The findings suggest that strengthening visionary leadership, enhancing organizational culture, and fostering achievement motivation can improve teacher performance in madrasahs. However, the study is limited to madrasah aliyah (high school) level, and future research is recommended at higher educational levels.

Numerical Simulation Study on Reverse Source Tracing for Heating Pipeline Network Leaks Based on Adjoint Equations

In order to identify the leak source in complex heating pipeline networks, a timely and effective simulation of the leakage process was conducted. The open-source computational fluid dynamics software OpenFOAM 5.0 was combined with the PISO algorithm to simulate the pressure during the leakage in water supply networks, transforming the reverse source tracing problem into the solution of an adjoint equation. The validation of the transient adjoint equation for single-phase flow was completed through simulation, and the pressure wave change graph at the moment of the network leakage was solved, which was consistent with the experimental results. Using the open-source finite element analysis software OpenFOAM 5.0, the positioning accuracy of pipeline leak points can be controlled within the range from 92% to 96%. Based on the pressure wave change graph, the position of the leak source in the complex network was determined using the reverse source tracing method combined with the second correlation theory. The results show that the calculation speed of the PISO algorithm combined with the adjoint equation is significantly better than that of the individual SIMPLE and PISO algorithms, thereby proving the superiority of the adjoint method.

Tissue Cadherin 17 (CDH17): An Important Prognostic Determinant of Colorectal Cancer Using Digital Image Analysis

ABSTRACTBackgroundColorectal cancer (CRC) is a commonly diagnosed malignancy with significant mortality rates worldwide. The identification of robust prognostic biomarkers for prediction of survival outcomes and recurrence can aid disease management and improve patients' quality of living.AimThis study aimed to investigate the prognostic value of cadherin 17 (CDH17) tissue expression in CRC patients by utilizing a standardized automated immunohistochemistry (IHC) platform integrated with a digitalized scoring system.Methods and ResultsIHC was conducted to assess CDH17 expression on tumor tissues obtained from 150 retrospective CRC cases. A computer‐assisted imaging analysis was performed to quantify CDH17 tissue expression using an IHC scoring algorithm known as the Membrane (M) Score. The relationship between CDH17 M Score and clinicopathological factors such as TNM staging, distant metastasis, and recurrence status was analyzed. The prognostic value of CDH17 M Score was determined by Kaplan–Meier curves and Cox regression analysis for overall survival (OS) and recurrence‐free survival (RFS). Comparison of M Score and pathologist visual scoring was made to assess the validity of software‐derived IHC scoring. CDH17 expression, as measured by M Score, was found to be significantly increased in tumor tissues compared to adjacent normal tissues, and its expression is associated with advanced staging and distant metastasis (normal vs. tumor, p = 0.0011; Stages IV vs. I–III, p < 0.0162; with vs. without metastasis, p = 0.0026; Mann–Whitney U test). Prognostic analysis revealed that high CDH17 M Score was associated with poor OS and RFS (OS, p = 0.0118; RFS, p = 0.0021; log‐rank test). Furthermore, multivariate Cox regression analysis identified that CDH17 M Score was an independent prognostic predictor for OS (HR = 2.296, 95% CI = 1.154–4.968, p = 0.0240) and RFS (HR = 2.489, 95% CI = 1.062–6.494, p = 0.0447).ConclusionIncreased CDH17 M Score was associated with advanced tumor staging and poor survival outcomes using an automated IHC system integrated with a digital image analysis software, highlighting CDH17 could serve as an independent prognostic marker for CRC patients.

MolAR: Memory-Safe Library for Analysis of MD Simulations Written in Rust.

Transition to the memory safe natively compiled programming languages is a major software development trend in recent years, which eliminates memory-related security exploits, enables a fearless concurrency and parallelization, and drastically improves ergonomics and speed of software development. Modern memory-safe programing languages, such as Rust, are currently not used for developing molecular modeling and simulation software despite such obvious benefits as faster development cycle, better performance and smaller amount of bugs. This work introduces MolAR-the first memory-safe library for analysis of MD simulations written in Rust. MolAR is intended to explore the advantages and challenges of implementing molecular analysis software in the memory-safe natively compiled language and to develop specific memory-safe abstractions for this kind of software. MolAR demonstrates an excellent performance in benchmarks outperforming popular molecular analysis libraries and tools, which makes it attractive for implementing computationally intensive analysis tasks. MolAR is freely available under Artistic License 2.0 at https://github.com/yesint/molar.

Research on Stiffness Analysis and Technology of the Heavy Spidle Top

Background: The spindle top is an important component used to withstand the shaft workpiece on machine tools so that the spindle can meet high efficiency and high precision requirements. However, the selection principles under various load conditions are not stipulated in use. In addition, material selection, manufacturing, heat treatment technology, etc., are of practical significance for the production of high hardness, high wear resistance, and high precision spindle tops. Objectives: The spindle top type, material selection principles, heat treatment, cold working, and other manufacturing processes are given. Provide a reference for highperformance and top-notch design and manufacturing. Methods: The model of the spindle top will be created in UG software, then using ANSYS finite element analysis software to analyze stiffness of spindle top whose height-to-diameter ratios are 1:4 and 1:7 types in a variety of different load cases. The design and manufacturing process of the spindle top is analyzed and expounded from the selection and performance comparison of metal materials, heat treatment of different materials, cold manufacturing technology, and other aspects. Results: The deformation laws of different types of spindle tops are obtained. According to the deformation regular, find the selection principle of height to diameter ratio of spindle top. The defects that are easy to occur in the technology are obtained and the preventive and solution measures are put forward. Conclusion: According to the deformation regular, find the selection principle of height to diameter ratio of spindle top. The material selection, heat treatment technology, and other technical research on the spindle top provide the necessary basis for the production of the spindle top.

Health workers’ knowledge and practices toward vaccine cold chain management and its associated factors in a resource-limited setting of Sheger, Oromia, Ethiopia: a multicenter cross-sectional study

BackgroundCold chain management is a continuous and cohesive process of preserving vaccines to ensure their availability and potency. However, the quality control of vaccines has been neglected in resource-limited areas such as Ethiopia. Adequate levels of health workers’ knowledge and practices in vaccine cold chain management are important to maintain the potency of vaccines and achieve the intended vaccine end goal.ObjectiveTo assess the level of knowledge and practices of health workers toward vaccine cold chain management and associated factors in resource-limited settings in Ethiopia.MethodsAn institution-based cross-sectional survey was conducted from May to June 2023 in eight sub-cities of Sheger City, Oromia Region, Ethiopia. 255 health workers were selected via simple random sampling from 17 health centers and 17 health posts. Data were collected through a self-administered questionnaire and entered in to EPI DATA version 3.1 and exported to SPSS version 26.0 software for analysis. Binary and multiple logistic regression analyses were carried out to identify factors associated with the knowledge and practices of health workers toward vaccine cold chain management. A variable with a p value < 0.25 was considered a candidate for further regression analysis, whereas a variable with a 95% CI and a p value < 0.05 was considered statistically significant.ResultA total of 255 respondents participated, for a 100% response rate. The overall magnitude of good knowledge and practice of vaccine cold chain management was 58% (95% CI: 52.2%--64.3%) and 52.2% (95% CI: 46.3-58.4%), respectively. Receiving pre-service training (AOR = 8.2; 95% CI: (2.93–22.99), availability of SOP/guidelines (AOR = 6.55; 95% CI: (2.0–21.36) and receiving supportive supervision (AOR = 4.2; 95% CI: (1.67–10.59)) were significantly associated with good knowledge of cold chain management. The availability of SOP/guidelines (AOR = 2.13; 95% CI: (1.22–3.7)) and the knowledge of health workers (AOR = 2.32; 95% CI: (1.11–4.8) were significantly associated with good cold chain management practices.ConclusionThere are gaps in knowledge and practice concerning cold chain management. There is an urgent need to improve the knowledge and practice of health workers in cold chain management in the newly organized but resource-limited administrative structure (Sheger) in Ethiopia.

Generative Adversarial Networks (GANs) 2D imaging optimization in image–J for estimation of endodontic filling material measurement: A review

Background: Root canal treatment (RCT) remains the most common clinical intervention in dentistry, treating a wide range of pulp-related conditions and structural tooth injuries. RCT begins by partially removing the crown to access and remove the infected pulp through a meticulous process of cleaning, reshaping, and irrigating the canal. This prepared canal is filled with gutta-percha as the endodontic filling material (EFM), and adhesive cement is used to seal the canal before final restoration with a crown. The success of RCT, however, hinges on accurately measuring and applying the EFM volume within the canal. Current measurement practices using 2D radiographic imaging fall short in accuracy due to operator limitations and the inherent lack of depth and clarity in 2D images, which contribute to high variability in EFM application. CBCT has emerged as a viable alternative, offering comprehensive 3D imaging for a more accurate EFM volume assessment, yet its high radiation levels, cost, and accessibility issues pose challenges. Thus, with increasing demand for safer and cost-effective alternatives, AI-driven enhancements in 2D imaging are being explored to achieve CBCT-level accuracy without its drawbacks, marking a significant step forward in RCT reliability and success rates. Purpose: To explore the potential of GANs and Image–J integration on 2D images for estimation of EFM measurement. Method: A literature review of studies published within the past five years from sources like ResearchGate, ScienceDirect, PubMed, and Google Scholar. Result: This study explores the integration of artificial intelligence, specifically convolutional neural networks (CNN) and generative adversarial networks (GANs), to enhance the accuracy of endodontic filling material (EFM) measurements in root canal treatments (RCT). CNN's structure, comprising multilayered networks with pooling layers, has shown potential in medical imaging by segmenting anatomical details for EFM measurement. However, challenges remain due to CNN's reliance on extensive datasets and its susceptibility to misinterpretation when faced with complex anatomical variations. GANs, combining a generator and discriminator, address these limitations by enabling unsupervised learning, allowing the generation of photorealistic data samples for more precise EFM measurement. This bidirectional learning system refines measurement accuracy through continuous feedback between the generator and discriminator, achieving superior outcomes across parameters like radiopacity, dimensional consistency, classification performance, and sealing ability. Furthermore, the AI model is enhanced by integrating Image-J, a Java-based image analysis software with precise 2D measurement capabilities. Despite its effectiveness in analyzing length and volume, Image-J has limitations due to its dependence on the operator’s interpretation, especially with low-quality 2D images. The combined use of GANs with Image-J allows for automated, detailed border detection of the pulp anatomy, improving measurement accuracy even with complex root canal forms. The results suggest that this integrated AI approach can advance clinical practices by providing precise, rapid, and consistent EFM measurements, potentially overcoming the limitations of traditional 2D radiographic methods and addressing CBCT’s drawbacks in radiation exposure and accessibility. Conclusion: The integration of generative adversarial networks (GANs) with Image-J offers a promising advancement in the measurement of endodontic filling materials (EFM) for root canal treatments based on 2D imaging. GANs enhance Image-J’s measurement precision and accuracy by utilizing bidirectional deep learning, which enables continuous learning and refinement without requiring extensive external inputs. This approach not only improves EFM estimation but also presents a viable alternative to cone-beam computed tomography (CBCT), reducing potential health risks and economic burdens associated with CBCT. The combined GANs and Image-J system thus supports a more accessible, efficient, and precise methodology for EFM assessment in clinical dentistry.

Evaluation of Load Response in Elastomeric Bearings Through Link Element and Finite Element Modelling

Abstract: The Finite element modeling (FEM) and link element analysis of elastomeric bearings were conducted in this study to evaluate load behaviour and ensure compliance with design standards. A detailed FEM was developed, where material properties and boundary conditions were incorporated to simulate load transfer mechanisms, which were enhanced through link element analysis. Validation of results was performed against design codes and analysis software data, confirming the model's accuracy. A reliable method for optimizing elastomeric bearing design, improving structural safety, and meeting essential design checks was presented. Ten models in total were analyzed with variations, firstly the models were selected as per the design checks criteria. Then selected parameter’s output values are compared with each passed model case and then to finalize the research conducted, the data validation table has created with providing recommendations to show the suitability, aiming to improve design practices and address challenges in modern bridge engineering