Today, access control systems are used in almost every institution and building. This is because they are an effective solution that provides a high level of security. There are many commercially available systems that provide security-related access features for buildings, including biometric options. Most use a centralized architecture, where each building can be remotely controlled via an Internet connection. This paper presents a completely different system from those on the market, a decentralized system with clone-detection and data-integrity verification mechanisms that allows access to buildings. The overall architecture includes hardware encoding of the access system’s location, and access is granted based on information written to the RFID card by the card-issuing center. This allows the system to be easily reconfigured at the hardware level prior to installation in the access area. The proposed system uses a confidential RFID card data integrity algorithm that uses the card data and immutable UID to determine a checksum in order to validate the RFID card data. As a result, any unwanted modification of at least one bit invalidates the card and blocks access to the building. The system was implemented, validated, and extensively tested over a one-year period with no reported operational issues.

Psychological distress is highly prevalent among patients in intensive care units (ICUs), yet current management remains predominantly pharmacological and often insufficient to address emotional and cognitive wellbeing. Virtual reality (VR) is emerging as a potential non-pharmacological adjunct for stress and anxiety reduction in critical care. To explore ICU professionals' perspectives on the design, feasibility and clinical integration of a gamified, art-based VR prototype intended to support psychological wellbeing and reduce stress and anxiety in ICU patients. Qualitative study using a semi-structured focus group and thematic analysis. Seven ICU participants (three doctors, three nurses, one allied health clinician) trialled an early-stage VR prototype designed for ICU patients that included narrative-driven content, artistic environments and planned accessibility features such as eye-tracking and multiple modes of engagement. ICU professionals' perspectives on VR's therapeutic potential, recommended content features, perceived facilitators and barriers to implementation and strategies for clinical integration. Seven themes emerged. Participants viewed VR as a calming, purposeful form of diversional therapy offering emotional escape, cognitive engagement and restoration of patient agency. Flexible content modes and opportunities for personalisation were valued, as were accessibility features such as eye-tracking. For implementation, participants emphasised clear infection-control workflows, reliable device-management processes and basic staff training. A phased introduction, beginning with carefully selected patient groups, was recommended before broader clinical adoption. ICU professionals consider VR a promising adjunct to support emotional wellbeing in ICU, provided it is purpose-built, flexible, personalised and integrated smoothly into clinical routines. Their insights offer actionable guidance for refining and piloting this VR intervention within ICU settings.

A small subset of isocitrate dehydrogenase-wild-type (IDH-wt) glioblastomas (GBMs) initially present as nonenhancing, T2 FLAIR hyperintense cortical/superficial lesions on MRI, potentially leading to misdiagnosis on the initial imaging and hence delayed treatment. This study aimed to characterize the clinical and MRI features of nonenhancing IDH-wt GBMs to help radiologists in differentiating them from nonmalignant mimic diagnoses (eg, encephalitis). Additionally, the histologic, genomic, and survival profiles of nonenhancing GBMs were compared with those of enhancing GBMs. Clinical and MRI features from 32 patients, each with nonenhancing and enhancing GBMs, and 16 patients with nonmalignant mimic differential diagnoses from a single institution and publicly available data set were retrospectively analyzed. Imaging features were reviewed using the Visually Accessible Rembrandt Images features and the split ADC sign. χ2 tests and a binary logistic regression model were used to compare nonenhancing IDH-wt GBMs with nonmalignant mimics. Histopathologic and genomic analyses were performed on institutional cases. Overall survival between nonenhancing and enhancing GBMs was compared using Kaplan-Meier analysis. No significant difference in age, clinical presentation, or duration of symptoms was found between nonenhancing GBMs and nonmalignant mimics. Imaging features favoring nonenhancing GBMs included a greater proportion of non-contrast-enhancing tumor (OR, 7.4), larger anterior-posterior tumor dimension (OR, 8.4), restricted diffusion (OR, 3.6), and eloquent brain involvement (OR, 3.0) while features favoring mimics included greater edema (OR, 0.07), infiltrative T1 FLAIR ratio (OR, 0.68), hemorrhage (OR, 0.76), satellite lesions (OR, 0.84), and the split ADC sign (OR, 0.89). The logistic regression model achieved a mean area under the receiver operator characteristic curve of 0.89 (SD, 0.20) (accuracy 0.84, sensitivity 0.91, specificity 0.70, and precision 0.88). Twelve of 18 nonenhancing GBMs lacked histologic evidence of necrosis or microvascular proliferation ("molecular GBMs"). Genomic profiles were similar between nonenhancing and enhancing GBMs. Median overall survival was nonsignificantly longer in nonenhancing GBMs compared with enhancing GBMs (39 versus 21 months, P = .078). Nonenhancing GBMs demonstrate distinct MRI features that must be recognized for early diagnosis and differentiation from nonmalignant mimics. Nonenhancing GBMs demonstrated longer overall survival compared with enhancing GBMs, though they were not statistically significant.

The English Government introduced a target for 80% of Care Quality Commission registered adult social care providers to be using electronic care planning solutions by March 2024 (extended to March 2025) and made available funding to support the transition from paper to digital social care records. The study aimed to generate timely evidence to support care providers to implement digital social care records and maximise the benefit from their introduction. A co-created rapid evaluation, involving two data collection phases and feedback to study sites. We interviewed 30 senior leaders, 30 care staff, and 23 people who draw on care services and their relatives from 30 care providers (19 care homes, 11 home care agencies) in 4 sites across England and senior leaders of 3 digital social care record suppliers to understand experiences of adoption and implementation, and financial and economic implications. Policy attention and availability of funding have driven adoption of digital social care records, saving time and delivering other benefits, although experiences varied and there was evidence of suboptimal choice of digital social care record system, alongside buyer regret and abandonment. Providers were concerned about ongoing affordability in the context of continued austerity. Implementation is time- and resource-intensive with providers experiencing similar and predictable challenges. Planning, leading, managing and resourcing implementation, including investing in training and involving all users or people affected by digital social care records, were important for mitigating and overcoming challenges. A responsive supplier able to resolve technical problems and reasonable requests for flexibility was also important. Care providers in a franchise or group were at an advantage as they could draw on additional support and the experiences of others. While some features were not used or a matter of preference, a reliable offline working feature was critical for functionality due to patchy internet access. The ability to easily upload images and conduct simple analysis improved functionality; as did the client/relative portal, although this was rarely used and clients/relatives had limited knowledge of digital social care records and their rights. Systems with greater interoperability maximised the benefit from digital social care records. Unfamiliarity with technology was a barrier to using digital social care records, but training and gradual implementation allowed time for adaptation and increased acceptance. People with poor eyesight, dexterity or English had difficulty using digital social care records. We did not find evidence of providers capturing data to assess return on investment from digital social care record introduction. Assessing care providers' capacity to estimate their return on investment was difficult as interviewees often lacked knowledge of the financial aspects of the business. Where implementation is successful digital social care records, over time, deliver benefits to care providers. However, implementation was too often suboptimal due to poor choice of digital social care record supplier, inadequate planning, management and resourcing of change, an unresponsive supplier and limited accessibility features. Ongoing affordability and continuation with digital social care records are a concern for the future, especially for small providers. Research investigating the abandonment process and impact of digital social care record adoption on the structure and stability of the care market would be valuable. Phase one ethical approval: the Health Research Authority (23/HRA/4966, IRAS Project ID: 3347698) and phase two from the NHS Research Ethics Committee (24/LO/0204, IRAS Project ID: 335300). This award was funded by the National Institute for Health and Care Research (NIHR) Health and Social Care Delivery Research programme (NIHR award ref: NIHR163639) as part of the Social Care Rapid Evaluation Team (NIHR153673) and is published in full in Health and Social Care Delivery Research; Vol. 14, No. 16. See the NIHR Funding and Awards website for further award information.

Machine Learning Prediction of Hospital Stay in Pediatric Typhoid Intestinal Perforation: Pilot Study.

Abstract Artificial Intelligence (AI) is playing an important role in improving modern education by enabling learning experiences that are tailored to individual student needs. Unlike traditional teaching methods that treat all learners the same, AI-based educational systems focus on personalization by adapting content, pace, and teaching strategies according to each student’s abilities and progress. These systems use technologies such as machine learning and data analysis to monitor student performance and identify strengths and weaknesses. Based on this information, they provide customized study materials, interactive lessons, and instant feedback. This adaptive approach helps learners understand concepts more effectively and stay engaged throughout the learning process. AI tools like virtual tutors, automated evaluation systems, and intelligent recommendations also support teachers by reducing repetitive tasks and allowing them to focus more on guidance and mentoring. In addition, AI-based learning platforms promote inclusive education by supporting different learning styles and offering accessibility features for diverse learners. Keywords: Adaptive Learning, Personalized Learning, Data Analytics, Intelligent Tutoring Systems, Automated Assessment, Educational Technology, Student Performance, E-learning Systems, Inclusive Education

As the population continues to age, and gaming continues to grow as a hobby for older people, heterogeneity among older adult gamers is increasing. We argue that traditional game-based accessibility features, like simplified input schemes, redundant information channels, and increased legibility of digital user interfaces, are limited in the face of this heterogeneity. This is because such features affect all older adult players and therefore are designed generically. We introduce artificial intelligence-although it has its own limitations and ethical concerns-as a method of creating player-based accessibility features, given the adaptive nature of the technology. These features may help to address unique assemblage of accessibility needs that may accumulate through age. We argue that existing AI technologies can build upon extant accessibility design techniques to improve digital games accessibility for heterogenous older adults. We adopt insights from gerontology, human-computer interaction, and disability studies into the digital game design discourse for older adults, and we contribute insight that guides the integration of player-based accessibility features to supplement game-based counterparts. The accessibility of digital games for heterogenous older adults is paramount, as the medium offers short-term social, emotional, psychological, cognitive, and physical benefits that support the long-term goal of aging well.

This research develops a framework and wireframes for an AI-driven personalization and recommendation system designed to enhance Library Management Systems (LMS). AI adapts library services dynamically to individual user characteristics and behaviors, such as reading preferences and interaction patterns, using predictive algorithms and behavioral analysis to deliver tailored recommendations. The study is primarily grounded in a User-Centered Design Thinking approach to ensure the system is intuitive, responsive, and meets diverse user needs. The proposed framework emphasizes seamless data integration and adaptive interface design. Prototypes created using Figma reflect intuitive, inclusive, and accessible features aligned with user needs. The prototype was evaluated in a controlled environment with high-frequency LMS users using the System Usability Scale (SUS) to assess usability and user satisfaction, achieving a score indicating excellent usability. Although the AI processing engine remains conceptual, this research provides a structured foundation for the future implementation of AI-driven recommendation systems in LMS, supporting enhanced user engagement and improved Selective Dissemination of Information through personalized and inclusive library experiences.

Peritoneal dialysis (PD) is a common treatment for end-stage renal disease, yet cardiovascular disease (CVD) remains a major cause of morbidity. Inadequate fluid management can elevate CVD risk, and bioelectrical impedance spectroscopy (BIS) is increasingly used to assess fluid status. This study aimed to develop an artificial intelligence model integrating BIS measurements and medical history to predict major adverse cardiovascular events (MACEs) within six months in clinically stable PD patients. Data were stratified by MACE occurrence and subject, with 80% assigned to training and 20% to testing. Class imbalance was addressed using the synthetic minority over-sampling technique. Four algorithms – logistic regression, random forest, XGBoost, and deep neural networks – were trained using fivefold cross-validation and grid search for optimal hyperparameters. Model performance was evaluated with area under the ROC curve (AUC), calibration plots, and decision curve analysis (DCA). Feature ablation experiments compared models using all 15 features, only 11 BIS-recorded features, and only 4 medical history features. The random forest model achieved the highest performance (AUC = 0.88), with CVD history as the most influential predictor. Isotonic regression calibration improved probability alignment (brier score = 0.0451). DCA suggested potential clinical benefit. While the model relied on four medical history features, its initial sensitivity was modest (0.68). Integrating BIS features significantly enhanced diagnostic sensitivity (0.84). The random forest model, based on 15 clinically accessible features, accurately predicts the risk of MACE within six months in clinically stable PD patients, demonstrating strong discriminatory ability with a performance reaching 88%.

This study explores students’ perspectives on the integration of TikTok storytelling in Project-Based Learning (PjBL) classrooms. The purpose of the research is to investigate how the use of TikTok as a digital storytelling platform influences students’ engagement, learning experiences, and perceived learning outcomes. A qualitative descriptive approach was employed, involving students who participated in project-based activities that required them to create and share storytelling videos using TikTok. Data were collected through questionnaires, classroom observations, and interviews, and were analyzed thematically. The findings indicate that the integration of TikTok storytelling in PBL promotes positive student engagement, enhances creativity, and increases motivation in the learning process. Students perceived improvements in their communication skills, particularly in speaking and storytelling abilities, as well as greater confidence in expressing ideas. Additionally, the platform’s accessibility and multimedia features supported collaborative learning and learning autonomy. However, some challenges were identified, including technical limitations and the need for clear instructional guidance. Overall, the study concludes that TikTok storytelling can serve as an effective and innovative learning medium when integrated thoughtfully into Project-Based Learning classrooms, contributing to meaningful and student-centered learning experiences.

This study aims to examine the level of accessibility of primary and supporting facilities at Trans Jatim bus stops along Corridor V, particularly in terms of compliance with standards for persons with disabilities. The study employs a field observation method by assessing several accessibility indicators, including ramps, stairs, guiding blocks, and designated wheelchair spaces. The results indicate that the overall level of accessibility remains low, with the average score falling below 50.00. Although some facilities have met the required standards, achieving scores above 50.00, the majority of bus stops do not provide accessibility features in a complete and consistent manner. Many essential facilities for persons with disabilities are either unavailable or not functioning optimally, indicating that the existing infrastructure has not fully complied with the standards stipulated in Ministry of Public Works Regulation No. 30/PRT/M/2006. These limitations have a significant impact on the mobility, independence, and safety of persons with disabilities in accessing public transportation. Furthermore, inadequate facilities may increase safety risks and reduce the overall quality of service. Therefore, improvements in infrastructure provision are necessary, supported by regular monitoring, maintenance, and stricter enforcement of regulatory standards.

Objective: This study aims to examine the physical, sensory, and social dimensions of design, revealing how universal design in demonstration gardens provides accessible and multi-sensory experiences for diverse user groups.Material and Methods: The sub-spaces and user profiles of Kapias Gardens were observed, and the functions, universal design approaches, and effects on well-being were analyzed using a monographic method. Spaces were evaluated according to universal design principles, with user experiences supported through qualitative observation and spatial assessment.Results: Inclusive landscape design ensures equal opportunities for diverse users through physical accessibility, orientation, circulation, and sensory features. Spaces are effective when they provide comfort and functionality; spaces that encourage social interaction enhance user satisfaction and social integration.Conclusion: This study shows that inclusive landscape design enhances spatial quality and user experience. Approaches supporting accessibility, sensory diversity, and social interaction facilitate usage and positively affect psychological and social well-being. Thus, landscapes integrated with universal design serve as exemplary models for inclusivity and spatial quality, guiding contemporary space production.

The increasing prevalence of paralysis and mobility-related disorders has created a strong demand for continuous healthcare monitoring systems, with neurological disorders affecting millions globally and nearly 60% of patients requiring constant assistance, while IoT-based healthcare solutions are projected to grow at over 20% annually. Traditional patient care relies heavily on manual observation and periodic monitoring, which may fail to detect sudden health deterioration, falls, or emergency conditions in time. Furthermore, conventional systems lack real-time alerts, remote accessibility, and interactive assistance features, reducing their effectiveness in supporting paralysis patients. To address these challenges, the proposed IoT-based health assistance system for paralysis patients utilizes the ESP32 microcontroller to develop an intelligent and wearable healthcare solution. The system integrates DHT11 sensors for temperature and humidity monitoring, SpO2 sensors for blood oxygen level measurement, vibration sensors for fall detection, and flex sensors to capture patient movements or specific gestures. When abnormal conditions or predefined triggers are detected, the system activates a voice module to alert nearby caregivers and simultaneously sends real-time notifications through an IoT platform for remote monitoring. This smart system enhances patient safety, enables rapid emergency response, improves caregiver support, and provides a scalable and efficient solution for modern assistive healthcare applications.

Triple-negative breast cancer (TNBC) is an aggressive malignancy lacking effective targeted therapies, underscoring the need for robust and interpretable biomarkers for personalized treatment. Here, we propose a graph attention network (GAT)-based multimodal framework that integrates scRNA-seq, scATAC-seq, and radiomics to capture cross-modal regulatory interactions underlying TNBC heterogeneity. Transcriptional, chromatin accessibility, and imaging features are aligned via canonical correlation analysis, with intercellular communication-derived gene relationships and transcription factor-binding-guided edges incorporated into a multimodal graph. Multi-head attention enables adaptive weighting of omics-specific interactions, while an ensemble multilayer perceptron with variational dropout stratifies patient prognosis. The model demonstrates strong predictive performance in an external TCGA-TNBC cohort (log-rank p<0.01), outperforming single-omics and alternative graph-based approaches (AUC-ROC = 0.839, 95% CI: 0.81-0.87). Pathway analysis validates canonical TNBC drivers, including PI3K, ERBB2, PTK6, and EGFR signaling, while revealing previously underappreciated regulatory programs involving complement-coagulation cascades, ECM-integrin-focal adhesion signaling, leukocyte transendothelial migration, sphingolipid-mediated metabolic-immune coupling, and nanoparticle-receptor interactions. Collectively, this framework provides an interpretable strategy for multimodal biomarker discovery in TNBC, uncovering both established and novel therapeutic vulnerabilities and offering a scalable approach toward precision oncology.

Research demonstrates a higher prevalence of intimate partner abuse perpetrated against disabled women, than for their majority-population non-disabled peers. Compounding this disadvantage, are disability-specific barriers to help-seeking including the lack of accessible and inclusive domestic violence services. This article entails a presentation of research findings that is twofold. Findings of a survey of domestic violence services are presented, alongside findings of a mapping exercise of accessibility and inclusion features of domestic violence services (n = 45). Recommendations are made toward improving the accessibility of domestic violence services, as well as better resourcing services so that improvements can become a reality.

Identifying isocitrate dehydrogenase (IDH) mutation and α-thalassemia/mental retardation syndrome X-linked (ATRX) mutation status is helpful for diagnosis and specific classification of diffuse gliomas, while currently, the detection of IDH and ATRX status mainly relies on invasive methods. In this study, we aimed to predict IDH and ATRX mutation status of diffuse gliomas utilizing clinically available MRI Visually Accessible Rembrandt Images (VASARI) features. Five hundred ninety-two patients (352 IDH wild-type and 240 IDH-mutant patients) with pathologically proved diffuse gliomas from our institution were randomly divided into training set (n=414) and validation set (n=178) for IDH mutation prediction according to a ratio of 7 to 3. Patients with IDH mutant were further stratified into ATRX mutant (n=109) and ATRX wild-type (n=131) subgroups, with the cohort then divided into training set (n=168) and validation set (n=72) for ATRX mutation prediction. Two radiologists independently analyzed the patients' MR images based on the VASARI feature set. Multivariable logistic regression analysis was employed to develop the prediction models. Receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA) were utilized to validate the models and nomograms were developed to visualize the models. For IDH prediction, 6 VASARI features combined with age and relative ADC values contributed to the model, with the area under the curve (AUC) of 0.96 (0.94-0.98) in training set and 0.92 (0.88-0.97) in validation set. For ATRX prediction, 3 VASARI features combined with age and minimum ADC values contributed to the model, with the AUC of 0.76 (0.68-0.83) in training set and 0.71 (0.58-0.83) in validation set. The DCA and calibration plots further confirmed the clinical utility of the 2 nomograms for IDH and ATRX prediction. The integration of MRI VASARI features and clinical data demonstrates strong predictive capability for IDH mutation status and moderate predictive capability for ATRX status in diffuse gliomas.

Chronic myeloid leukemia (CML) often presents with hematologic findings that overlap with reactive leukocytosis and other myeloproliferative neoplasms (MPNs), creating diagnostic uncertainty that may delay targeted therapy or prompt unnecessary molecular testing. Harlequin cells-abnormal eosinophils containing basophilic granules-are well described in acute myeloid leukemia (AML) with CBFB::MYH11 fusion, but their diagnostic relevance in CML has not been systematically assessed. We retrospectively reviewed 177 peripheral blood smears: 53 CML; 30 non-CML MPN and related disorders; 59 AML (including three with CBFB::MYH11 fusion); 11 eosinophilia; and 24 reactive cytosis cases. Harlequin cells were stringently defined as abnormal eosinophils containing both typical eosinophilic granules and large, distinctly basophilic (not purplish-orange) cytoplasmic granules to exclude reactive mimics. Harlequin cells were identified in 72% (38 out of 53) of CML cases, a frequency significantly higher than in non-CML MPN (10%, P < 0.01), AML without CBFB::MYH11 fusion (3.6%, P < 0.01), eosinophilia (0%), and reactive cytosis (0%) groups. They were also observed in 67% (2/3) of AML with CBFB::MYH11 fusion and in 20% (3/15) of primary myelofibrosis, but were absent in polycythemia vera, essential thrombocythemia, and chronic myelomonocytic leukemia. Strictly defined Harlequin cells were not found in any reactive condition. In the appropriate clinical context, strictly defined Harlequin cells on routine peripheral blood smears may serve as a sensitive and highly specific morphologic clue for CML. Recognition of this readily accessible feature may facilitate prompt BCR::ABL1 confirmatory testing, reduce diagnostic ambiguity, and help avoid unnecessary ancillary studies.

Exploring a user-centered approach for movement-based features in interaction design

The aim of this study is to investigate the exclusion of people with disabilities from the digital economy. This will help to provide a conceptual framework that incorporates the different digital platforms, accessibility features, and training that will best allow populations with special needs to gain the digital empowerment they need. The proposed framework integrates the Social Model of Disability, Universal Design for Learning (UDL), and Digital Accessibility and Inclusion models. The major challenges explored lead to actionable solutions, positioning the framework as a strategic guide for educators, platform creators, and policymakers. Ultimately, the practical and theoretical contributions of this paper outline how inclusive training translates into greater participation in the digital economy, fostering digital equity, financial independence, and broader socio-economic benefits for people with disabilities. The framework provides conceptual and practical insights for enhancing digital inclusion and economic participation among people with disabilities

Down syndrome (DS) is a common chromosomal condition associated with intellectual disability &amp; multiple medical complications. In order to conduce the study, null hypotheses were formulated to precede the study. A descriptive cross-sectional study with the inferential analysis was conducted to assess the demographic characteristics, related health conditions as well as healthcare accessibility amid children with Down syndrome attending special education center in District Mardan. The total of 100 students diagnosed with DS were included using purposive sampling. The standardized 15-item questionnaire was used to collect data, which was then analysed using descriptive statistics and the chi-square test. Most participants were between 13 and 18 years old, and 70% were men. There was a statistically significant link (p &lt; 0.05) between respiratory disorders, thyroid abnormalities, and sensory impairments. Still, congenital heart defects &amp; neurological illnesses were not significant. There were also significant differences in healthcare access features, with high medical prices (60%) being the biggest problem. The findings of current study suggest that children with down syndrome in this context experience many health difficulties and inequitable access to medical care. Similarly, results reveal that these patterns are not random and have a statistically significant meaning in specific context. The improved care for those affected could come from earlier diagnosis, more awareness and better coordination of healthcare at district level.