Access Framework Research Articles

Institutional transformation of the children’s after-sсhool sector in post-Soviet countries

ABSTRACT This study examines the institutional transformation of extracurricular education systems across the 15 former Soviet republics using Qualitative Comparative Analysis (QCA). Drawing on national statistics, policy documents, and expert interviews, the research identifies three distinct transformation scenarios: reproduction, modernisation, and degeneration. The analysis reveals varying degrees of change, from low-level transformation in countries maintaining significant state involvement to high-level transformation where the sector has undergone substantial restructuring. The study employs a composite index to quantify institutional change and explores key parameters such as coverage, accessibility, and legislative frameworks. While highlighting the persistence of Soviet legacy in some nations, the research also demonstrates the dynamic nature of these transformations, with some countries transitioning between scenarios. This comparative approach provides valuable insights into the evolving landscape of extracurricular education in post-Soviet space.

Automated Program Refinement: Guide and Verify Code Large Language Model with Refinement Calculus

Recently, the rise of code-centric Large Language Models (LLMs) has reshaped the software engineering world with low-barrier tools like Copilot that can easily generate code. However, there is no correctness guarantee for the code generated by LLMs, which suffer from the hallucination problem, and their output is fraught with risks. Besides, the end-to-end process from specification to code through LLMs is a non-transparent and uncontrolled black box. This opacity makes it difficult for users to understand and trust the generated code. Addressing these challenges is both necessary and critical. In contrast, program refinement transforms high-level specification statements into executable code while preserving correctness. Traditional tools for program refinement are primarily designed for formal methods experts and lack automation and extensibility. We apply program refinement to guide LLM and validate the LLM-generated code while transforming refinement into a more accessible and flexible framework. To initiate this vision, we propose Refine4LLM, an approach that aims to: (1) Formally refine the specifications, (2) Automatically prompt and guide the LLM using refinement calculus, (3) Interact with the LLM to generate the code, (4) Verify that the generated code satisfies the constraints, thus guaranteeing its correctness, (5) Learn and build more advanced refinement laws to extend the refinement calculus. We evaluated Refine4LLM against the state-of-the-art baselines on program refinement and LLMs benchmarks.The experiment results show that Refine4LLM can efficiently generate more robust code and reduce the time for refinement and verification.

Understanding reasons for delay in diagnosis of leprosy in Pakistan: A qualitative study.

Recent epidemiological data shows significant rates of grade 2 disability at point-of-diagnosis among new leprosy cases in Pakistan. This indicates a feature of extensive diagnostic delay; the disability burden appears unmoving and disproportionate to the falling leprosy incidence rates. Therefore, this study was required to understand reasons for delay in diagnosis and treatment of leprosy. A qualitative design of 7 semi-structured interviews was employed to reveal perceptions and understandings of various leprosy stakeholders in Pakistan, termed "leprosy experts". Subsequent inductive analysis was used to identify themes and subthemes concerned with delay in the diagnosis and treatment of leprosy. Leprosy experts identified three main areas, or domains, to which delay can be attributed: 1. Awareness and beliefs about leprosy, within the general population, 2. Knowledge and clinical experience of leprosy, among healthcare professionals, 3. Leprosy control program infrastructure, allocation of resources and institutional funding. These domains were each viewed as consequent to the larger theme of 'low-endemicity'. Strong correlations between diagnostic delay and socioeconomic status, gender, geography and health system challenges, were also mentioned, and which intersected the three major themes. Reasons for diagnostic delay are evident in all tiers of the healthcare hierarchy in Pakistan. Thus, an approach at multiple levels is justified, to improve the general awareness of leprosy, education of healthcare professionals, and organizational structuring. Additionally, cultural features relevant to different communities in Pakistan which might be different from other care access frameworks demonstrated a need for further study into the health beliefs of Pakistani patients in a wide range of communities.

A cell structure implementation of the multigrid method for the two-dimensional diffusion equation

To solve the two-dimensional diffusion equation using the finite difference method, we propose a simple MATLAB implementation of the multigrid method. The diffusion equation plays a fundamental role in modeling many significant physical phenomena and is ubiquitous in many governing equations. Some examples include the reaction–diffusion equations, the convection–diffusion equations, and others. These equations often lack analytical solutions or pose extreme challenges in finding them. Therefore, numerical techniques are indispensable for obtaining practical and accurate approximations for these equations. The multigrid method is known for its computational efficiency and effectiveness as an iterative technique for solving the discretized diffusion equation. Due to its popularity, the multigrid method has been implemented in several programming languages, such as Python, Java, C++, C, Fortran, and others. However, it is not easy for beginners to understand the implementation of the multigrid method due to its complex data structures and recursive routines. To resolve these difficulties, we develop a straightforward MATLAB implementation of the two-dimensional diffusion equation using a cell structure in MATLAB. This work provides an accessible and efficient framework for understanding and applying the multigrid method, thereby simplifying its implementation for researchers and practitioners.

A spatial accessibility framework for mapping the mismatch between wood supply and demand across Europe

A spatial accessibility framework for mapping the mismatch between wood supply and demand across Europe

IPNA clinical practice recommendations on care of pediatric patients with pre-existing kidney disease during seasonal outbreak of COVID-19.

The coronavirus disease 2019 (COVID-19) pandemic, instigated by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has profoundly impacted healthcare infrastructures around the globe. While children are usually asymptomatic or have mild symptoms, children with pre-existing kidney conditions require specialized attention. This pivotal report, championed by the International Pediatric Nephrology Association (IPNA), delivers precise and actionable recommendations tailored for pediatric patients with kidney ailments in this pandemic landscape. Central to our findings are rigorous infection control protocols. These are particularly stringent in high-risk zones, emphasizing telehealth's indispensable role, the significance of curtailing in-person consultations, and the imperative of following rigorous guidelines in regions with heightened COVID-19 prevalence. Additionally, the report delves into vaccination approaches for children with kidney issues, highlighting that the choice of vaccine is often governed by regional accessibility and policy frameworks, rather than a universal preference. A notable observation is the potential correlation between COVID-19 vaccines and specific kidney disorders. However, establishing a direct causal link remains elusive. In summary, our research accentuates the critical need for specialized pediatric kidney care during global health crises and reaffirms the continuous research imperative, especially regarding vaccination ramifications.

Social Risk Factors Associated With Microbial Keratitis.

The objective of this study was to identify social risk factors (SRFs) that affect microbial keratitis (MK) care using the Penchansky-Thomas (P-T) health care access framework. This combined retrospective and prospective cohort study recruited participants with newly diagnosed MK at an academic medical center. Participant demographic information and SRFs were collected using in-person interviews and chart review. SRFs were categorized into P-T framework domains. Primary analysis included proportion of participants reporting SRFs, distribution of reported SRFs, and demographic differences associated with SRFs using descriptive statistics, chi-square, and two-sample t tests. A subgroup analysis for participants who were lost to follow-up (LTFU) was performed. A total of 100 participants with MK were included in this study. Of the 100 participants, 60.0% reported at least 1 SRF affecting care, 42.0% reported ≥2 SRFs, and 12.0% reported ≥4 SRFs; 40.0% had no SRFs. More SRFs were reported for participants with lower income versus those with higher income ($25,000-$50,000 vs. $51,000-$100,000, P = 0.0363); there were no other demographic differences between groups. The most reported SRF was distance to appointment (45.0%). Accessibility was the most reported P-T domain (49.0%). Participants with LTFU, compared with those not LTFU, had more SRFs (100% vs. 52.4%, P = 0.0001) and reported a greater median number of SRFs (3.0 vs. 1.0, P < 0.0001). SRFs affected most patients with MK, most notably accessibility and affordability. Participants with lower income had more SRFs. SRFs are linked to patients being lost to follow-up care.

The expansive language access framework: an integrated approach to addressing oppression in language education

AbstractWhile language education programs were created with an equity stance in mind—with the goals of increasing access and facilitating academic success for ethnically and linguistically marginalized students—the larger educational context has compromised access for multiply marginalized students. Recognizing a need for more complex understandings of language access, scholars have turned to intersectionality; however, intersectional theory is often applied to individual encounters rather than systemic issues. In this conceptual paper, we showcase how this unitary and unidimensional approach manifests in policies, programs, and conceptualizations of languages and language practices that perpetuate the systematic marginalization of multilingual learners with disabilities (MLwDs). We illustrate how policy and programming have centered language while omitting critical perspectives of race and disability. By focusing on MLwDs, we demonstrate how MLs who are racialized and pathologized through language have been systematically ignored in ways that sustain systems of oppression. The expansive language access framework provides a structure for understanding what it would mean to attend to race, language, and dis/ability at the policy, program, and practice levels, and can thus serve as a tool for stakeholders (e.g., scholars, policymakers, educators) to critically examine, evaluate, and rethink their role in perpetuating and disrupting institutional oppressions that multiply marginalized students encounter. Ultimately, we hope the framework empowers stakeholders to identify opportunities for growth and expansion to increase access to more holistically responsive language programming.

Applying the access framework to address adult cochlear implant access.

Applying the access framework to address adult cochlear implant access.

Open access journals lack image accessibility guidelines

Abstract In recent decades, there has been a move to “open” science and research. One component of open access is ‘accessibility,’ often used to mean data and other products are free to use by others. However, accessibility also refers to considering and meeting the needs of people with disabilities. Our objective was to evaluate how open access journals incorporate disability accessibility as part of publishing. Using a random sample of 300 English language journals and image accessibility as a lens, we assessed author guidelines. Of 289 journals with guidelines, 38 (13%) included color choice, 6 (∼2%) included contrast ratios, and none included alternative text. We also assessed the open access statements for the same 300 journals to understand how they conceive of openness and accessibility. Of the 298 journals with open access statements, 228 (∼77%) included the words ‘access’ or ‘accessibility.’ Yet none included ‘disability’ or ‘disabled’ and only 2 journals (&amp;lt;1%) mentioned ‘inclusive’ or ‘inclusion.’ Our findings indicate that open access journals sampled are not considering disability accessibility in their submission guidelines or open access frameworks. Incorporating disability accessibility into open scholarship considerations is critical to bridge, and not exacerbate, information inequalities for people with disabilities. Peer Review https://www.webofscience.com/api/gateway/wos/peer-review/10.1162/qss_a_00338

Remarks on access to justice and the digital divide from the aspect of procedural law

The COVID-19 pandemic has proved that the use of electronic means can significantly facilitate effective, physical access to justice and simplify the initiation and conduct of court proceedings; however, the phenomenon of digital divide has entered into centre of attention as well. The current paper aims to give a summary on the concept of digital divide in the context of procedural rights, primarily the right to an efficient access to justice. It will be examined, how digital solutions can be integrated into the conceptual and legislative framework of access to justice so that they can contribute to the enhancement of efficiency, while preventing the marginalization of the most disadvantaged social groups and the “elitization” of justice systems. The analysis is based on synthesis of international standards, jurisprudence, recommendations, and secondary literature. This way the results can provide an impetus for developing new solutions in this area to ensure the efficient access to justice for all even in the context of unprecedented technological, social and economic challenges.

Novel use of deep neural networks on photographic identification of epaulette sharks (Hemiscyllium ocellatum) across life stages.

Photographic identification (photo ID) is an established method that is used to count animals and track individuals' movements. This method performs well with some species of elasmobranchs (i.e., sharks, skates, and rays) where individuals have distinctive skin patterns. However, the unique skin patterns used for ID must be stable through time to allow re-identification of individuals in future sampling events. More recently, artificial intelligence (AI) models have substantially decreased the labor-intensive process of matching photos in extensive photo ID libraries and increased the reliability of photo ID. Here, photo ID and AI are used for the first time to identify epaulette sharks (Hemiscyllium ocellatum) at different life stages for approximately 2 years. An AI model was developed to assess and compare the reliability of human-classified ID patterns in juvenile and neonate sharks. The model also tested the persistence of unique patterns in adult sharks. Results indicate that immature life stages are unreliable for pattern identification, using both human and AI approaches, due to the plasticity of these subadult growth forms. Mature sharks maintain their patterns through time and can be identified by AI models with approximately 86% accuracy. The approach outlined in this study has the potential of validating the stability of ID patterns through time; however, testing on wild populations and long-term datasets is needed. This study's novel deep neural network development strategy offers a streamlined and accessible framework for generating a reliable model from a small data set, without requiring high-performance computing. Since many photo ID studies commence with limited datasets and resources, this AI model presents practical solutions to such constraints. Overall, this approach has the potential to address challenges associated with long-term photo ID data sets and the application of AI for shark identification.

HTA242 Recent US and French Access Frameworks Could Pave the Way to the Reimbursement of Digital Health Technologies

HTA242 Recent US and French Access Frameworks Could Pave the Way to the Reimbursement of Digital Health Technologies

Geographic Determinants and Spatial Epidemiology of Prescription Drug Abuse: A Comprehensive Analysis of Distribution Patterns, Treatment Accessibility and Intervention Strategies

This study examines the complex spatial patterns and geographic variations in prescription drug abuse, analyzing their impact on treatment outcomes and public health interventions. Through comprehensive spatial epidemiological analysis, we investigate how geographic factors influence abuse patterns across urban, suburban, and rural landscapes. The research synthesizes data on healthcare accessibility, socioeconomic conditions, and regional policy frameworks to understand their collective impact on prescription drug misuse. Our findings reveal significant geographic disparities in treatment accessibility, with rural areas facing substantial barriers to addiction services compared to urban centers. Analysis of prescription drug monitoring programs demonstrates varying effectiveness across regions, highlighting the need for spatially-tailored interventions. The study identifies distinct regional profiles of drug misuse, influenced by factors including healthcare infrastructure, cultural norms, and local regulatory environments. Advanced spatial analysis techniques, including geospatial mapping and hotspot analysis, revealed significant correlations between geographic determinants and abuse patterns. Results indicate that proximity to healthcare facilities, pharmaceutical distribution networks, and demographic characteristics contribute to localized risk profiles. The integration of multiple data sources, including prescription records and treatment admissions, provided insights into space-time clustering of abuse patterns. This research emphasizes the importance of developing geographically-informed approaches to combat prescription drug abuse, suggesting that intervention strategies should be tailored to specific regional contexts while addressing broader systemic issues. The findings have significant implications for public health policy, healthcare resource allocation, and the development of targeted prevention strategies.

An up-to-date perspective of levelized cost of hydrogen for PV-based grid-connected power-to-hydrogen plants across all Italy

Green hydrogen holds potential for decarbonizing the energy sector, but high production costs are a major barrier. This study provides a comprehensive techno-economic-financial-environmental analysis of PV-based grid-connected hydrogen production plants, targeting hard-to-abate industries having constant hydrogen demand across all Italy. Using real hourly data, the Multi Energy System Simulator (MESS), an in-house developed rule-based tool, was employed and integrated with Genetic Algorithm for optimal plant sizing. The aim is to minimize the Levelized Cost of Hydrogen (LCOH) while complying with regulatory frameworks for green hydrogen incentives access. Key findings show that hydrogen storage is more advantageous than battery storage for supply-side flexibility, and the optimal PV-to-electrolyzer size ratio ranges from 1.8 in Southern Italy to 2.1 in Northern Italy, with hydrogen tank designed for daily storage. Considering photovoltaic, electrolyzer and battery aging models grid dependence increases by 60 % when comparing the first and worst year of operation and leads to a 7 % increase in LCOH. Transitioning from the strictest (hourly) to the least stringent (annual) temporal correlation increases certified green hydrogen by 22 %, while LCOH decreases by only 3 %, suggesting that the environmental benefits of stringent temporal requirements outweigh their moderate economic drawbacks. These findings underscore the need for additional national-level incentives to allow the deployment of this technology and achieving cost parity with grey hydrogen.

Markarian Multiwavelength Data Center (MMDC): A Tool for Retrieving and Modeling Multitemporal, Multiwavelength, and Multimessenger Data from Blazar Observations

The Markarian Multiwavelength Data Center (MMDC) is a web-based tool designed for accessing and retrieving multiwavelength and multimessenger data from blazar observations. MMDC facilitates the construction and interactive visualization of time-resolved multiband spectral energy distributions of blazars by integrating (i) archival data from over 80 catalogs and databases, (ii) optical data from all-sky survey facilities such as the All-Sky Automated Survey for Supernovae, Zwicky Transient Facility, and Panoramic Survey Telescope and Rapid Response System, and (iii) newly analyzed data sets in the optical/UV band from Swift-UVOT, in the X-ray band from Swift-X-Ray Telescope and NuSTAR observations, and the high-energy γ-ray band from Fermi-Large Area Telescope observations. MMDC distinguishes itself from other online platforms by the large quantity of available data. For instance, it includes data from all blazar observations by Swift and NuSTAR, as well as the results of detailed spectral analysis in the γ-ray band during different emission states, covering the period from 2008 to 2023. Another important distinguishing feature of MMDC is its ability to enable precise, self-consistent theoretical modeling of the observed data using machine learning algorithms trained on leptonic and lepto-hadronic models, which consider the injection of particles and all relevant cooling processes. MMDC is an innovative tool that significantly enhances blazar research by providing a comprehensive framework for data accessibility, analysis, and theoretical interpretation, thereby advancing our understanding of blazar emissions and the underlying astrophysical processes.

Adding imprecision to hypotheses: A Bayesian framework for testing practical significance in nonparametric settings

Instead of testing solely a precise hypothesis, it is often useful to enlarge it with alternatives deemed to differ negligibly from it. For instance, in a bioequivalence study one might test if the concentration of an ingredient is exactly the same in two drugs. In such a context, it might be more relevant to test the enlarged hypothesis that the difference in concentration between them is of no practical significance. While this concept is not alien to Bayesian statistics, applications remain mostly confined to parametric settings and strategies that effectively harness experts' intuitions are often scarce or nonexistent. To resolve both issues, we introduce the Pragmatic Region Oriented Test (PROTEST), an accessible nonparametric testing framework based on distortion models that can seamlessly integrate with Markov Chain Monte Carlo (MCMC) methods and is available as an R package. We develop expanded versions of model adherence, goodness-of-fit, quantile and two-sample tests. To demonstrate how PROTEST operates, we use examples, simulated studies that critically evaluate features of the test and an application on neuron spikes. Furthermore, we address the crucial issue of selecting the threshold—which controls how much a hypothesis is to be expanded—even when intuitions are limited or challenging to quantify.

Unsteady RANS simulations of under-expanded hydrogen jets for internal combustion engines

Hydrogen can be considered a suitable fuel for heavy-duty reciprocating internal combustion engines (ICEs) in order to limit carbon dioxide emissions. The low volumetric power density of hydrogen and the backfire problem suggest to employing the direct injection technology with relatively high nozzle pressure ratios (NPRs). This paper provides the analysis of under-expanded hydrogen jet dynamics using an open-source high-fidelity simulation tool based on the OpenFOAM framework. The unsteady Reynolds-averaged Navier–Stokes (URANS) equations are solved by an efficient pressure-based solver for compressible flow. URANS equations are attractive for fast engineering analysis of 3D engine cycle and optimization, where large eddy simulation (LES) is too computationally expensive. The accuracy of the simulations is enhanced by employing the weighted essentially non-oscillatory (WENO) approach for the spatial discretization, considering schemes from second-order to fourth-order accuracy. Those schemes are embedded in a pressure-implicit with splitting of operators (PISO) algorithm, obtaining a very robust and accurate numerical method for compressible multi-species flows, which can be shared in an open access framework. Hydrogen injection in air is simulated, with several values of the NPR typical of direct injection ICE in the low-medium range, 8.5≤NPR≤30. The main features of the developing jet are analyzed, such as barrel shock dimensions, cone angle and hydrogen–air mixing. The results are validated with respect to experimental and LES data available in the recent literature, demonstrating the efficiency and the accuracy of the employed URANS approach and evaluating its limits.

An Analysis of Australia's Legal Framework for Access to More Affordable but Unapproved Medicines and Biologics.

Approved medicines are not always sufficient to address the needs of patients so several legal pathways exist to enable access to unapproved medicines for treatment purposes. This article is the first to provide an in-depth analysis of this regulatory framework that governs access to unapproved medicines in Australia with a specific focus on affordability-motivated access. Legislation, regulations, and guidelines were critically analysed to identify the de jure basis for importation and supply of unapproved medicines in Australia. Most pathways for accessing unapproved medicines do not permit importation or supply for non-clinical justifications such as affordability. This is problematic as it fails to recognize that a medicine being unavailable is equivalent to a medicine being unaffordable for a patient. Better alignment can be achieved by permitting importation and supply of unapproved medicines if justified by good medical practice, which includes considerations of equity and access. It is also shown that the provisions of the Special Access Scheme Category A could be interpreted broadly to expand its use. As medicines become more expensive and cost-barriers to treatment are more prevalent, ignoring affordability as a valid criterion for importing medicines is a significant oversight of current regulation.

EQUAL AI: A Framework for Enhancing Equity, Quality, Understanding and Accessibility in Liberal Arts through AI for Multilingual Learners

The integration of artificial intelligence (AI) into liberal arts education offers a transformative opportunity to address the diverse needs of multilingual and multicultural learners. Consequently, this study introduces the EQUAL AI framework (Enhancing Equity, Quality, Understanding, and Accessibility in Liberal Arts through AI), a structured approach to utilizing AI to foster inclusion and innovation in liberal arts pedagogy. The framework identifies five key domains: linguistic support, cultural representation, creative expression, critical thinking, and collaborative learning. Additionally, the study underscores the necessity of systemic support, particularly through professional development programs that equip educators with technical proficiency, ethical awareness, and the ability to critically assess AI tools. By tackling challenges such as algorithmic bias, data privacy, and the digital divide, the study advocates for culturally responsive policies and inclusive practices. The EQUAL AI framework envisions liberal arts education as a space for equitable participation and cultural understanding, positioning AI as a tool to enhance rather than replace humanistic pedagogy, ensuring its relevance in a technology-driven, interconnected world.