Access Model Research Articles

Pedestrian Accessibility Analysis of Sidewalk-Specific Networks: Insights from Three Latin American Central Squares

Limited research from the Global South has examined pedestrian accessibility to key destinations, particularly while considering efficient and practical sidewalk-specific conditions. This study employs a case-based approach, scrutinising walking access to three central squares, Mexico City’s Zocalo, Lima’s Plaza San Martin, and Buenos Aires’ Plaza de la República, within a 10-min walking radius. Geographic Information Systems (GIS) and Google Street View (GSV) were leveraged to conduct a virtual street audit, assessing six microscale features influencing the walking experience (kerb ramps, pavement continuity, sidewalk width, well-maintained sidewalks, active uses, and green spaces). These data facilitated the construction of a genuine pedestrian network and allowed the assessment of three-tiered pedestrian accessibility models that comprised easy access, comfortable routes, and vibrant walks. The findings reveal significant spatial inequities in pedestrian access. About 10% of buildings near Mexico City’s and Lima’s central squares lack pedestrian accessibility due to inadequate and interrupted sidewalk and crosswalk infrastructure, disproportionately impacting those with mobility limitations. Conversely, Buenos Aires exhibits better pedestrian facilities, with accessible, comfortable, and lively routes reaching approximately 20% of buildings surrounding the Plaza de la República. These results underscore the significant disparities in street infrastructure and networks within these historic neighbourhoods, emphasising the imperative for more inclusive, pedestrian-friendly urban planning in Latin American cities.

Exploring the Effect of Road Network Structure on Inter-Regional Accessibility in a Diverse Road Network

Abstract. Accessibility analyses quantify the level of access to certain areas or opportunities, such as employment and healthcare facilities. Since public data is often aggregated at the level of regions, such as administrative units, it is useful to quantify accessibility between regions. Many factors influence inter-regional accessibility, most notably the accessibility metric used, and the way in which regions are chosen. This paper investigates the effects of road network structure on accessibility, using a previously developed inter-regional accessibility model that bases its accessibility metric on travel distance via the road network. This paper considers an area within the City of Tshwane municipality in South Africa. We investigate the effects of road structure in two ways. Firstly, regions are chosen based on the road network structure, which is done by extending a previously developed road network clustering algorithm for this novel use. Different spatial scales of regionalisation are considered, and the accessibility between these regions is compared to the accessibility between administrative units within the study area. Secondly, the effect of road network homogeneity on accessibility is investigated, where homogeneity corresponds to a uniform concentration of roads across a region. The results show that although road network homogeneity does not significantly correlate with accessibility, the way in which regions are chosen and their spatial scale has a strong effect on the results of the accessibility model. Our novel method of obtaining regions thus provides fresh insights into road-based accessibility within the City of Tshwane.

Open Access Publication at a Crossroads—Implications for Researchers and Beyond

This Viewpoint describes the wide range of open access (OA) models, examines controversies as OA publication attempts to achieve equitable access, and recommends ways to address gaps.

“If I Go, I’ll Probably End Up Dropping Out too”: College Enrollment Choices in a Free College Context

ABSTRACT In this qualitative study, we explored why students in a free community college environment in Tennessee chose not to enroll in college or dropped out of college shortly after enrolling. We conducted 27 in-depth interviews with individuals who were eligible for the Tennessee Promise. Perna’s conceptual model for college access and choice guided our analysis. We analyzed data using a three-tier approach, which included open/emergent coding, followed by a priori/theoretical analysis. We identified 15 emergent themes common among interview participants, which we then categorized into the four theoretical themes based on Perna’s (2006) model. The Tennessee Promise increases access to and enrollment in postsecondary education for many students. Our study highlights potential gaps policymakers and practitioners can address to continue improving the Tennessee Promise and other student supports. We provide our recommendations for practice, policy, and future research.

The utility of ChatGPT in gender-affirming mastectomy education

IntroductionThe integration of AI such as ChatGPT in medicine has been showing promise in enhancing patient education. Gender-affirming mastectomy (GAM) is a surgical procedure designed to help individuals transition to their self-identified gender, playing a crucial role in mitigating psychological distress for many transmasculine and non-binary (TNB) patients. With increased demand and attention towards GAM, plastic and reconstructive surgeons may rely on AI-driven chatbots as an accessible, accurate, and patient-driven model for relevant details on this procedure. Specific Aim(s)This study aimed to assess the quality and readability of information provided by ChatGPT in response to frequently asked questions (FAQs) related to GAM. MethodsInspired by online forums and physician websites, 10 frequently asked questions (FAQs) about pre- and postoperative topics were submitted to ChatGPT and assessed using validated readability score measures and expert interpretation. ResultsThe average readability score was 16.0 ± 1, indicating a college or graduate reading level. Mean accuracy, comprehensiveness, and danger scores were 8.8 ± 0.5, 7.8 ± 0.7, and 2.2 ± 0.4, respectively. Although physicians appreciated ChatGPT's tone, patient autonomy, and advice to seek professional medical and mental help, they also cited instance of generic information, misinformation, support of debated techniques, and pathologization of gender dysphoria. ConclusionEven with its promise in providing accurate and comprehensive information on GAM, ChatGPT’s current limitations suggest caution as a supplementary tool to physician consultation.

Caries Detection and Classification in Photographs Using an Artificial Intelligence-Based Model-An External Validation Study.

This ex vivo diagnostic study aimed to externally validate a freely accessible AI-based model for caries detection, classification, localisation and segmentation using an independent image dataset. It was hypothesised that there would be no difference in diagnostic performance compared to previously published internal validation data. For the independent dataset, 718 dental images representing different stages of carious (n = 535) and noncarious teeth (n = 183) were retrieved from the internet. All photographs were evaluated by the dental team (reference standard) and the AI-based model (test method). Diagnostic performance was statistically determined using cross-tabulations to calculate accuracy (ACC), sensitivity (SE), specificity (SP) and area under the curve (AUC). An overall ACC of 92.0% was achieved for caries detection, with an ACC of 85.5-95.6%, SE of 42.9-93.3%, SP of 82.1-99.4% and AUC of 0.702-0.909 for the classification of caries. Furthermore, 97.0% of the cases were accurately localised. Fully and partially correct segmentation was achieved in 52.9% and 44.1% of the cases, respectively. The validated AI-based model showed promising diagnostic performance in detecting and classifying caries using an independent image dataset. Future studies are needed to investigate the validity, reliability and practicability of AI-based models using dental photographs from different image sources and/or patient groups.

Galleria mellonella as an Antimicrobial Screening Model.

To combat the rising global issue of antibiotic resistance, the accelerated development of novel antibiotics is essential. Current preclinical antimicrobial development yields a significant number of leads that prove unsuitable either prior to or during clinical trials. To increase the efficiency of preclinical development, relevant, standardized, accessible, and cost-effective models must be developed. Galleria mellonella (greater wax moth) larvae are widely used as an infection model to assess microbial virulence, conduct drug toxicity testing, and serve as a preliminary means of evaluating the in vivo efficacy of novel antimicrobial compounds. These infection models have greater biological relevance than many in vitro screens of comparable throughput and decrease reliance on mammalian models when used as a pre-screen for antimicrobial testing. This protocol describes a standardized methodology for the optimization of G. mellonella infection models, which can be applied to bacterial species and antimicrobial therapeutics of choice. Using the WHO priority pathogen Pseudomonas aeruginosa as an exemplar, we outline steps that can be undertaken to develop a reproducible model of infection and therapeutic testing. This includes recommendations on experimental setup, sample preparation, and infection and treatment protocols. Integration of this model within preclinical antimicrobial development pipelines would decrease reliance on mammalian models, reduce the number of ineffective compounds reaching clinical trials, and ultimately increase the efficiency of preclinical antimicrobial development.

Comprehensive model for simultaneous monitoring of primary tumor to metastatic cancer utilizing Prkdc and Il2rg double knockout mice

Liver cancer is the second leading cause of cancer-related deaths worldwide, motivating major scientific efforts to understand and treat this cancer type. Over 30% of patients with liver cancer progress to metastasis, which reduces the survival rate. Extensive studies on primary tumors have been conducted to improve the prognosis. However, there is a lack of appropriate and accessible models for studying the progression and metastasis of liver cancer. Moreover, conventional metastasis models do not reproduce metastasis as it occurs in patients. To address this lack of an appropriate model for the monitoring of cancer progression and evaluation of anticancer drugs, we established a spontaneous metastatic xenograft model using NSG mice subcutaneously transplanted with SK-Hep-1 cells. Compared to the conventional experimental metastasis model (intravenous transplantation), in which only lung metastasis was observed, the established spontaneous metastatic xenograft model was superior, as it showed both a primary tumor and metastatic patterns similar to those observed in human patients. Additionally, this model was successfully used to assess the antimetastatic efficacy of sorafenib. In conclusion, our results demonstrate that the established spontaneous metastatic xenograft model better reflects liver cancer metastasis and can be utilized to assess the efficacy of anticancer drugs for liver cancer.

Design and Development Model of a Web Accessibility Ecosystem

The article examines issues of web accessibility ecosystems for people with special needs. Methods, models, accessibility standards, and technologies related to the structure, design, and functionality of the web accessibility ecosystem are studied. The stages of developing an accessibility ecosystem are explored. The accessibility of the design, functionalities, structure, and content of a particular ecosystem are presented. Several themes for the design of the system with an emphasis on its accessibility for blind users are explored and analyzed. UX/UI design and the ontological model of accessibility, used in the implementation of the model of the ecosystem and its elements, are studied. A web accessibility ecosystem model has been developed, compliant with the Web Content Accessibility Guidelines and based on semantic technologies. Other qualities of this model are easy access to information resources on the topic of accessibility, convenience for users with different needs, and the possibility of expansion and enrichment in the future.

Vaping to quit smoking: Qualitative study of people receiving opioid agonist treatment.

Most patients receiving opioid agonist treatment (OAT) smoke tobacco. Approved cessation interventions are less effective in this group than the wider population. We investigated how people on OAT experience nicotine vaping to quit smoking. Patients on OAT randomised to the vaping arm of a smoking cessation trial were invited to participate in structured interviews incorporating broad pre-determined themes. A qualitative descriptive approach employing template analysis was used. Four authors coded transcripts, discussed discrepancies, modified the template using both inductive and deductive approaches. Authors made explicit their starting orientations and independent authors sought disconfirmatory data in a subsequent round of analysis. Four women and eight men (median age 44 years) participated, including four who identified as Aboriginal. Participants reported vaping as cheaper, more acceptable and less stigmatising than smoking but expressed concerns about ongoing accessibility due to the Australian prescription access model. Some found it technically challenging at first, but not more so than standard nicotine replacement therapies. Participants gave accounts of craving and withdrawal experiences, including supplementary use of nicotine patches, and compulsions to vape frequently and intensely, potentially indicating need for higher nicotine dosage. Participants generally reported that vaping helped them quit smoking, though some worried about swapping nicotine addictions. Others were glad to be using a lower-risk alternative. This group reported varied experiences of nicotine vaping but were mainly optimistic that it could help them and others quit smoking. This was despite initial nicotine cravings and concerns about remaining addicted long-term.

The impact of COVID-19 on the debate on open science: a qualitative analysis of published materials from the period of the pandemic

This study is an analysis of the international debate on open science that took place during the pandemic. It addresses the question, how did the COVID-19 pandemic impact the debate on open science? The study takes the form of a qualitative analysis of a large corpus of key articles, editorials, blogs and thought pieces about the impact of COVID on open science, published during the pandemic in English, German, Portuguese, and Spanish. The findings show that many authors believed that it was clear that the experience of the pandemic had illustrated or strengthened the case for open science, with language such as a “stress test”, “catalyst”, “revolution” or “tipping point” frequently used. It was commonly believed that open science had played a positive role in the response to the pandemic, creating a clear ‘line of sight’ between open science and societal benefits. Whilst the arguments about open science deployed in the debate were not substantially new, the focuses of debate changed in some key respects. There was much less attention given to business models for open access and critical perspectives on open science, but open data sharing, preprinting, information quality and misinformation became most prominent in debates. There were also moves to reframe open science conceptually, particularly in connecting science with society and addressing broader questions of equity.

An open window into endothelial dysfunction: the HUVEC model

Type 2 diabetes mellitus is a prevalent chronic disease globally, with complications such as retinopathy, nephropathy, neuropathy, and cardiovascular disease being major causes of morbidity and mortality. Endothelial dysfunction is a central factor in these complications. Human umbilical vein endothelial cells (HUVEC) serve as a valuable in vitro model within translational research, enabling detailed investigation of endothelial dysfunction and evaluation of therapeutic interventions. HUVEC-based studies bridge basic science and clinical applications, offering a reproducible and accessible model that closely mimics human vascular biology, making them instrumental in developing targeted therapies for cardiovascular complications in diabetes.

Variation in forest root image annotation by experts, novices, and AI

BackgroundThe manual study of root dynamics using images requires huge investments of time and resources and is prone to previously poorly quantified annotator bias. Artificial intelligence (AI) image-processing tools have been successful in overcoming limitations of manual annotation in homogeneous soils, but their efficiency and accuracy is yet to be widely tested on less homogenous, non-agricultural soil profiles, e.g., that of forests, from which data on root dynamics are key to understanding the carbon cycle. Here, we quantify variance in root length measured by human annotators with varying experience levels. We evaluate the application of a convolutional neural network (CNN) model, trained on a software accessible to researchers without a machine learning background, on a heterogeneous minirhizotron image dataset taken in a multispecies, mature, deciduous temperate forest.ResultsLess experienced annotators consistently identified more root length than experienced annotators. Root length annotation also varied between experienced annotators. The CNN root length results were neither precise nor accurate, taking ~ 10% of the time but significantly overestimating root length compared to expert manual annotation (p = 0.01). The CNN net root length change results were closer to manual (p = 0.08) but there remained substantial variation.ConclusionsManual root length annotation is contingent on the individual annotator. The only accessible CNN model cannot yet produce root data of sufficient accuracy and precision for ecological applications when applied to a complex, heterogeneous forest image dataset. A continuing evaluation and development of accessible CNNs for natural ecosystems is required.

Healthcare system navigation difficulties among informal caregivers of older adults: a logistic regression analysis of social capital, caregiving support and utilization factors

BackgroundInformal caregivers of older adults play a vital role in improving the degree to which older adults access community and healthcare services in a seamless and timely manner. They are fulfilling important navigation and support roles for their older care recipients. However, there is still little knowledge of the most significant facilitators and barriers to effective and efficient system navigation among caregivers. This paper aims to fill these knowledge gaps through investigation of the key factors (i.e., social capital/cohesion, caregiving supports, and utilization factors) affecting navigation difficulties faced by informal caregivers of older adults.MethodsThe Behavioural-Ecological Framework of Healthcare Access and Navigation (BEAN) model is used to frame the study. Using the General Social Survey on Caregiving and Care Receiving 2018, we analyzed 2,733 informal caregivers whose primary care recipients were aged 65 or older. Hierarchical logistic regression was conducted to identify the relationship between system navigation difficulties among informal caregivers and four sequentially ordered blocks of predictors: (1) sociodemographic (2), social capital/cohesion (3), caregiving supports, and (4) healthcare demand.ResultsThe fully adjusted model showed that the probability of reporting navigation difficulties was lower for caregivers with social capital/cohesion compared to those without social capital/cohesion. In comparison, the probability of reporting navigation difficulties was higher among caregivers with caregiving support and among caregivers whose care receivers use a higher amount of health service use. Several sociodemographic covariates were also identified.ConclusionOur findings support certain aspects of the BEAN model. This study extends our understanding of potential facilitators and barriers that informal caregivers of older adults face while navigating complex community and health systems. There is a need to implement coordinated schemes and health policies especially for older adults with mental/neurological issues to address the challenges of their caregivers given the specific vulnerability identified in this study. The need for further research using different approaches to examine the disproportionate impact of COVID-19 on caregivers’ system navigation experience is crucial.

Access to an ELM-suppressed X-point radiator regime in TCV snowflake minus configurations

TCV’s operating regime with an X-point radiator (XPR) has been broadened by changing the magnetic geometry. XPRs have properties that could make them an attractive power exhaust solution for fusion reactors. These include the conversion of a high fraction of exhaust power into radiation. TCV had previously accessed the XPR regime only with difficulties, as predicted for plasmas where radiative losses are dominated by carbon impurities, that are ubiquitous in TCV. Guided by this theoretical model of the XPR, recent experiments employed TCV’s configurational versatility to demonstrate that XPR access can be facilitated by introducing a second X-point in the vicinity of the separatrix. This configuration, which has a snowflake-minus topology, features a particularly long magnetic connection length from the region just above the X-point to the outer midplane together with a wide geometrical interface with the private flux region that reaches high neutral pressure. Transitioning to this configuration in a high-power H-mode leads to a shift in the radiating region across the separatrix from the divertor to a volume above the X-point, i.e. within the last closed flux surface (LCFS). This displacement of the radiating region is co-incident with the disappearance of edge localised modes (ELMs), while retaining H-mode confinement, a behaviour only, to date, observed in devices with metallic walls. In contrast to observations in these other devices, on TCV, the primary strike points in these configurations remain attached. Detailed measurements of the plasma kinetic parameters inside and outside of the separatrix now challenge the models for access and stability of the XPR and ELMs alike.

Developing a Bookless Library for Enhanced Literacy in Islamic Boarding Schools

Abstract: The 4.0 industrial revolution compels educational institutions, including Islamic boarding schools, to adopt advanced digital library services for enhanced literacy. General Background: Many libraries, such as that of Al Fattah Islamic Boarding School in Sidoarjo, still depend on physical books and lack accreditation, resulting in outdated collections. Specific Background: There is a significant gap in structured digital library frameworks that provide extensive access to both general and Islamic literature. Aims: This study aims to develop a school development plan for a Bookless Library that enhances digital literacy. Results: Employing a Research and Development (R&D) approach, the study outlines a five-year strategic plan for implementing a digital library, utilizing QR codes for quick access to resources. Novelty: It proposes a global access model for the Bookless Library, allowing users beyond the server's immediate area to engage with digital resources. Implications: The expected outcome is a dynamic learning environment that promotes literacy and positions Al Fattah Islamic Boarding School competitively in a technology-driven educational landscape. Highlights: Technological Adaptation: The implementation of digital libraries addresses the evolving needs of students in the 4.0 industrial revolution. Resource Accessibility: QR codes facilitate instant access to a vast array of digital resources, enhancing learning opportunities. Global Reach: The proposed Bookless Library model allows users beyond local boundaries to engage with diverse literature. Keywords: Digital Library, Bookless Library, Islamic Boarding School, Literacy Enhancement, Research and Development

Utility of cerebrovascular imaging biomarkers to detect cerebral amyloidosis.

The relationship between cerebrovascular disease (CVD) and amyloid beta (Aβ) in Alzheimer's disease (AD) is understudied. We hypothesized that magnetic resonance imaging (MRI)-based CVD biomarkers-including cerebral microbleeds (CMBs), lacunar infarction, and white matter hyperintensities (WMHs)-would correlate with Aβ positivity on positron emission tomography (Aβ-PET). We cross-sectionally analyzed data from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=1352). Logistic regression was used to calculate odds ratios (ORs), with Aβ-PET positivity as the standard-of-truth. Following adjustment, WMHs (OR=1.25) and superficial CMBs (OR=1.45) remained positively associated with Aβ-PET positivity (p<0.001). Deep CMBs and lacunes exhibited a varied relationship with Aβ-PET in cognitive subgroups. The combined diagnostic model, which included CVD biomarkers and other accessible measures, significantly predicted Aβ-PET (pseudo-R2=0.41). The study highlights the translational value of CVD biomarkers in diagnosing AD, and underscores the need for more research on their inclusion in diagnostic criteria. gov: ADNI-2 (NCT01231971), ADNI-3 (NCT02854033). Cerebrovascular biomarkers linked to amyloid beta (Aβ) in Alzheimer's disease (AD). White matter hyperintensities and cerebral microbleeds reliably predict Aβ-PET positivity. Relationships with Aβ-PET vary by cognitive stage. Novel accessible model predicts Aβ-PET status. Study supports multimodal diagnostic approaches.

The Accessibility Levels of the Websites of Federal Higher Education Institutions in Northeast Brazil

The general aim of this study is to make a diagnosis on the accessibility of information made available on the websites of the Federal Higher Education Institutions (IFES) in the Northeast. It is based on the hypothesis that the websites of the IFES in the Northeast Region of Brazil do not have high levels of adequacy, results that do not allow any user, regardless of being a person with disabilities or other limitations, to access the knowledge made available there, either with the help of assistive technologies or autonomously. The methodological procedures will be bibliographical and documentary research with analysis of primary sources on institutional websites and administrative acts made available, or not, on these sources. All the open data portals will be evaluated and simulated to determine their suitability for eMAG. This will be done using the public software ASES, a system for comparing and validating the standards of construction and behaviour of the source code of electronic sites, pointing out the levels of usability, navigability, alternative text, and content markers regarding the eMAG parameters, which are mandatory for public institutional sites in Brazil in terms of digital accessibility. As a result of the research, it was possible to identify that the levels of accessibility on the websites of these institutions were not in line with the parameters of the Digital Government Accessibility Model (eMag) of Brazil. This reveals divergences in the promotion of access to information; while some IFES make information available autonomously, without facing noise, interference, or impediments to the use of information, others do not so much, which has a negative impact on the guarantee of rights such as the exercise of full citizenship.

The Scientific Reference Model—A Methodological Approach in the Hypothetical 3D Reconstruction of Art and Architecture

Scientific practice relies on the rigorous documentation of procedures, methods, and outcomes, governed by principles like method verification, objectivity, and source disclosure. In the computer-based hypothetical 3D reconstruction of destroyed or never realised art and architecture, adhering to these principles faces challenges due to evolving software, methods, and data types, leading to a lack of standardised documentation and publication practices for 3D models. Consequently, the traceability, accessibility, and sustainability of research outcomes are compromised. Decades after the advent of computer-aided 3D visualisation in cultural heritage, there is a critical need to define applicable methodology and comprehensive documentation standards. Web-based platforms necessitate technical infrastructures and clear scientific methodologies to ensure understandable model creation and sustainable accessibility to 3D research data. The Scientific Reference Model proposes an accessible academic framework for this kind of 3D reconstruction, aiming to facilitate broad adoption. Developed and tested in research projects and educational contexts, this model aims to establish clear, accessible 3D models on the web, serving as foundational references for future research and knowledge dissemination.

"Like and Subscribe": A Compendium of Infectious Diseases Audio Podcasts.

In little more than 15 years, the podcast has gone from being virtually unknown to becoming a multibillion-dollar industry. There are an estimated half-billion podcast listeners worldwide. Podcasts are available for various interests, from entertainment to news, politics, sports, hobbies, and education. Podcasting in medicine has similar origins, beginning with journals and academic/educational institutions wishing to share information on new clinical trials or publications, as well as by those who wanted to promote the free and open access model of online education. The COVID-19 outbreak led to a renaissance in the availability of infectious diseases-oriented audio podcasts, due to the need for regular information about SARS-CoV-2 and the pandemic-related preference for virtual learning. However, many in the infectious diseases community have not yet been introduced to available infectious diseases podcast resources. In this article, we highlight 38 of the most popular infectious diseases-oriented audio podcasts.