Search Results (901)

This paper presents the multi-triangular ring and self-routing protocol (MTRSRP), which is a new decentralized strategy designed to boost throughput and network efficiency in multiring scatternets. MTRSRP comprises two primary phases: leader election and scatternet formation, which collaborate to establish an effective multi-triangular ring topology. In the leader election phase, nodes exchange broadcast messages to gather neighbor information and elect coordinators through a competitive process. The scatternet formation phase determines the optimal number of rings based on the coordinator’s collected node information and predefined rules. The master nodes then send unicast connection requests to establish piconets within the scatternet, following a predefined role table. Intra- and inter-bridge nodes were activated to interconnect the piconets, creating a cohesive multi-triangular ring scatternet. Additionally, MTRSRP incorporates a self-routing addressing scheme within the triangular ring architecture, optimizing packet transmission paths and reducing overhead by utilizing master/slave relationships established during scatternet formation. Simulation results indicate that MTRSRP with dual-bridge connectivity outperforms the cluster-based on-demand routing protocol and Bluetooth low-energy mesh schemes in key network transmission performance metrics such as the transmission rate, packet delay, and delivery ratio. In summary, MTRSRP significantly enhances throughput, optimizes routing paths, and improves network efficiency in multi-ring scatternets through its multi-triangular ring topology and self-routing capabilities. Full article

This article describes the design, methods, and baseline characteristics of the social needs assessment (SNA) of participants enrolled in an ongoing randomized clinical trial implementing a comprehensive approach to improving diabetes self-management and providing an intensive Diabetes Self-Management Education and Support (iDSMES) Program at St. Vincent’s House Clinic, a primary care practice serving resource-challenged diverse populations in Galveston, Texas. Standardized SNA was conducted to collect information on financial needs, psychosocial well-being, and other chronic health conditions. Based on their identified needs, participants were referred to non-medical existing community resources. A series of in-depth interviews were conducted with a subset of participants. A team member independently categorized these SNA narratives and aggregated them into two overarching groups: medical and social needs. Fifty-nine participants (with a mean age of 53 years and equal representation of men and women) completed an SNA. Most (71%) did not have health insurance. Among 12 potential social needs surveyed, the most frequently requested resources were occupational therapy (78%), utility assistance (73%), and food pantry services (71%). SNA provided data with the potential to address barriers that may hinder participation, retention, and outcomes in diabetes self-management. SNA findings may serve as tertiary prevention to mitigate diabetes-related complications and disparities. Full article

Background: Urine microbial analysis is a frequently requested test that is often associated with contamination during specimen collection or storage, which leads to false-positive diagnoses and delayed reporting. In the era of digitalization, machine learning (ML) can serve as a valuable tool to support clinical decision-making. Methods: This study investigates the application of a simple artificial neural network (ANN) to pre-identify negative and contaminated (false-positive) specimens. An ML model was developed using 8181 urine samples, including cytology, dipstick tests, and culture results. The dataset was randomly split 2:1 for training and testing a multilayer perceptron (MLP). Input variables with a normalized importance below 0.2 were excluded. Results: The final model used only microbial and either urine color or urobilinogen pigment analysis as inputs; other physical, chemical, and cellular parameters were omitted. The frequency of positive and negative specimens for bacteria was 6.9% and 89.6%, respectively. Contaminated specimens represented 3.5% of cases and were predominantly misclassified as negative by the MLP. Thus, the negative predictive value (NPV) was 96.5% and the positive predictive value (PPV) was 87.2%, leading to 0.82% of the cultures being unnecessary microbial cultures (UMC). Conclusions: These results suggest that the MLP is reliable for screening out negative specimens but less effective at identifying positive ones. In conclusion, ANN models can effectively support the screening of negative urine samples, detect clinically significant bacteriuria, and potentially reduce unnecessary cultures. Incorporating morphological information data could further improve the accuracy of our model and minimize false negatives. Full article

This research addresses the long-standing dependence on printed maps for navigation and highlights the limitations of existing digital services like Google Street View and Google Street View Player in providing comprehensive solutions for route analysis and understanding. The absence of a systematic approach to route analysis, issues related to insufficient street view images, and the lack of proper image mapping for desired roads remain unaddressed by current applications, which are predominantly client-based. In response, we propose an innovative automatic system designed to generate videos depicting road routes between two geographic locations. The system calculates and presents the route conventionally, emphasizing the path on a two-dimensional representation, and in a multimedia format. A prototype is developed based on a cloud-based client–server architecture, featuring three core modules: frames acquisition, frames analysis and elaboration, and the persistence of metadata information and computed videos. The tests, encompassing both real-world and synthetic scenarios, have produced promising results, showcasing the efficiency of our system. By providing users with a real and immersive understanding of requested routes, our approach fills a crucial gap in existing navigation solutions. This research contributes to the advancement of route planning technologies, offering a comprehensive and user-friendly system that leverages cloud computing and multimedia visualization for an enhanced navigation experience. Full article

Background: Tobacco use is disproportionately high in rural areas, contributing to elevated cancer mortality, yet it often goes untreated due to limited access to care, high poverty and uninsured rates, and co-occurring substance use disorders (SUDs). This study explored the utility of using rapid qualitative analysis (RQA) to guide the adaptation of a tobacco-free workplace program (TFWP) in Texas healthcare centers serving adults with SUDs in medically underserved areas. Methods: From September–December 2023 and May–July 2024, we conducted 11 pre-implementation, virtual semi-structured group interviews focused on adapting the TFWP to local contexts (N = 69); 7 with providers (n = 34) and managers (n = 12) and 4 with patients (n = 23) in 6 healthcare centers. Two qualified analysts independently summarized transcripts, using RQA templates of key domains drawn from interview guides to summarize and organize data in matrices, enabling systematic comparison. Results: The main themes identified were minimal organizational tobacco cessation support and practices, and attitudinal barriers, as follows: (1) the need for program materials tailored to local populations; (2) limited tobacco cessation practices and partial policies—staff requested guidance on enhancing tobacco screenings and cessation delivery, and integrating new interventions; (3) contradictory views on treating tobacco use that can inhibit implementation (e.g., wanting to quit yet anxious that quitting would cause SUD relapse); and (4) inadequate environmental supports—staff requested treating tobacco-use training, patients group cessation counseling; both requested nicotine replacement therapy. Conclusions: RQA identified key areas requiring capacity development through participants’ willingness to adopt the following adaptations: program content (e.g., trainings and tailored educational materials), delivery methods/systems (e.g., adopting additional tobacco care interventions) and implementation strategies (e.g., integrating tobacco cessation practices into routine care) critical to optimizing TFWP fit and implementation. The study findings can inform timely formative evaluation processes to design and tailor similar intervention efforts by addressing site-specific needs and implementation barriers to enhance program uptake. Full article

Background: The emergency department (ED) often represents the entry point to care for patients that require urgent medical attention or have no alternative for medical treatment. This has implications on scope of practice and how quality of care is measured. A diverse array of methodologies has been developed to evaluate the quality of clinical care and broadly includes quality improvement (QI), quality assurance (QA), observational research (OR) and clinical quality registries (CQRs). Considering the overlap between QI, QA, OR and CQRs, we conceptualized a modular framework for TECOR to effectively and efficiently streamline clinical quality evaluations. Streamlining is both appropriate and justified as it reduces redundancy, enhances clarity and optimizes resource utilization, thereby allowing clinicians to focus on delivering high-quality patient care without being overwhelmed by excessive data and procedural complexities. The objective of this study is to describe the process for designing a modular framework for ED CQRs using TECOR as a case study. Methods: We performed a scoping audit of all quality projects performed in our ED over a 1-year period (1 January 2021 to 31 December 2021) as well as data mapping and categorical formulation of key themes from the TECOR dataset with clinical data sources. Both these processes then informed the design of TECOR. Results: For the audit of quality projects, we identified 29 projects. The quality evaluation methodologies for these projects included 12 QI projects, 5 CQRs and 12 OR projects. Data mapping identified that clinical information was fragmented across 11 distinct data sources. Through thematic analysis during data mapping, we identified three extraction techniques: self-extractable, manual entry and on request. Conclusions: The modular framework for TECOR aims to enable an efficient streamlined approach that caters to all aspects of clinical quality evaluation to enable higher throughput of clinician-led quality evaluations and improvements. TECOR is also an essential component in the development of a learning health system to drive evidence-based practice and the subject of future research. Full article

Background/Objectives: The aim of this study was to assess the structure of comorbidities, the reasons for seeking medical care, and the main causes of fatal outcomes in patients with atrial fibrillation (AF) and chronic obstructive pulmonary disease (COPD). Methods: A retrospective analysis of 40,772 electronic medical records in the database of the medical information system with the analysis of medical care requests and causes of fatal outcomes over a 4-year period (from 1 February 2021 to 1 February 2025) was performed. The study participants were divided into three groups. The first group included 1247 participants with AF and COPD (AF + COPD group). The second group included 25,474 patients with AF and without COPD (AF group), and the third group included 14051 patients with COPD and without AF (COPD group). Results: Patients with AF + COPD compared to patients with AF alone and COPD alone were more likely to have anemia (5.21% vs. 3.64% and 2.8%, respectively), pulmonary embolism (2.0% vs. 0.52% and 0.46% respectively), type 2 diabetes mellitus (28.2% vs. 22.7% and 14.32%), obesity (24.86% vs. 22.2% and 17.72%), chronic ischemic heart disease (89.25% vs. 78.69% and 49.31%), and chronic heart failure (16.76% vs. 9.47% and 3.22%). In addition, patients with AF + COPD demonstrated the highest mortality among all groups. Conclusions: Patients who have both AF and COPD have more comorbidities, seek medical care more frequently, and have worse survival compared with patients with only AF or only COPD. Full article

Background: Pneumococcal vaccination rates in the United States (US) remain suboptimal, especially for adults aged 19 to 64 with high-risk medical conditions. Community-pharmacy-based immunization services increase vaccine access, particularly in rural areas. This study describes the provision of pneumococcal immunization services, assesses the processes used to identify and confirm patient eligibility, and determines barriers to immunization services in rural community pharmacies. Methods: A cross-sectional survey was emailed to members of the Rural Research Alliance of Community Pharmacies, located in the southeastern US. The survey assessed which pneumococcal vaccines were offered, age groups, prescription requirements, and how patient eligibility was determined. In addition, participants were asked to rate a series of patient-related and organizational barriers to pneumococcal vaccination. Results: Ninety-four pharmacies completed the survey, with most (96.8%) offering pneumococcal vaccines, most commonly PCV20 (95.6%). Most pharmacies vaccinated patients upon request (98.9%) or when patients presented with a prescription (82.4%), but few proactively contacted patients to schedule the vaccination (17.6%). Pharmacists most often administered pneumococcal vaccines to patients aged 65 and older and used patient age and immunization information systems to identify eligible patients. The most common patient-related barrier was the patient’s belief that they do not need the vaccine. The most common organizational barriers were inadequate reimbursements for vaccine administration and vaccine products. Conclusions: Pneumococcal vaccinations are commonly offered in rural community pharmacies, which play an important role in immunization access. With recent guideline changes to the age-based recommendation, there is an opportunity to optimize strategies to increase vaccine uptake. Full article

Ride-pooling (RP) services, in which passengers with similar destinations share a ride, offer considerable potential for enhancing urban mobility by bridging gaps in public transportation (PT) networks and providing a convenient alternative to private car use. For the effective design and operation of such services, a detailed understanding of user preferences and usage patterns is essential. This study investigates differences in RP preferences and usage between day and night (with nighttime defined as 10:00 p.m. to 5:00 a.m.), drawing on both a stated choice experiment (SCE) and revealed preference data collected in Mannheim, Germany. The focus lies on the local RP service fips, which is integrated into the PT system. The SCE, conducted in 2024 with 566 participants, was analyzed using a nested logit model. The analysis of the SCE reveals that nighttime preferences for RP are characterized by reduced sensitivity to travel time and cost, creating an opportunity for RP operators to optimize stop network designs during nighttime hours by increasing pooling rates. In addition, it indicates a greater likelihood of private car usage at night, especially among women, likely due to safety concerns and limited PT availability. The analysis of revealed preference data provides a complementary perspective. It shows that the RP nighttime service primarily attracts younger users, while many respondents report not being active on weekend nights. However, the combination of low public awareness and limited service availability, evidenced by rejected booking requests, suggests that existing demand is not being fully captured. This implies that low usage is not merely the result of low demand, but also of structural barriers on both the supply and information side. Overcoming these barriers through targeted information campaigns and expansion of nighttime service capacity could substantially enhance sustainable urban travel options during nighttime. Full article

Generative artificial intelligence is reshaping how people interact with digital technologies, emphasizing the need to develop effective skills for engaging with it. In this context, prompt engineering has emerged as a critical skill for optimizing AI-generated outputs. However, research on how higher education students interact with these technologies remains limited, particularly in non-English-speaking contexts. This exploratory study examines how 102 higher education students in Chile formulated prompts in Spanish and how their techniques influenced the responses generated by ChatGPT (free version 3.5). A quantitative analysis was conducted to assess the relationship between prompt techniques and response quality. Two emergent prompt engineering strategies were identified: the Guide Contextualization Strategy and the Specific Purpose Strategy. The Guide Contextualization Strategy focused on providing explicit contextual information to guide ChatGPT’s responses, aligning with few-shot prompting, while the Specific Purpose Strategy emphasized defining the request’s purpose, aligning with structured objective formulation strategies. The regression analysis indicated that the Guide Contextualization Strategy had a greater impact on response quality, reinforcing the importance of contextual information in effective interactions with large language models. As an exploratory study, these findings provide preliminary evidence on prompt engineering strategies in Spanish, a relatively unexplored area in artificial intelligence education research. Based on these results, a methodological framework is proposed, encompassing four key dimensions: grammatical skills; prompt strategies; response from the large language model; and evaluation of response quality. This framework lays the groundwork for future artificial intelligence digital literacy interventions, fostering critical and effective engagement with generative artificial intelligence while also highlighting the need for further research to validate and expand these initial insights. Full article

Background: Immunization information systems (IISs) in the United States forecast vaccine due dates, which can inform when providers recommend vaccines to patients. IIS forecasting for HPV vaccination at 9 years, the minimum age of licensure, and when vaccination is likely most effective is not documented or well-understood. Methods: We documented characteristics of HPV vaccination forecasts in jurisdictional IISs through Internet searches and requests to immunization program managers. Next, we conducted focus groups with stakeholders from seven jurisdictions to elucidate their processes for determining and implementing HPV vaccination forecasts. Results: Forecast data were available from 49 out of 64 CDC-funded jurisdictions, of which 14 (29%) recommended HPV vaccination at age 9 and 35 (71%) recommended HPV vaccination starting at ages 11 through to 15. Jurisdictions that recommended HPV vaccination at age 9 cited the positions of the American Cancer Society and American Academy of Pediatrics and reported little or no provider opposition to this recommendation. Jurisdictions reported variable flexibility in programming their forecasts. Those that changed their HPV vaccination forecast from 11 to 9 years did so easily while some experienced limitations. Other jurisdictions adhered strictly to the CDC’s routine recommendation at age 11–12 years and would only update the forecast in tandem with updated CDC guidance. The impact of IISs and electronic health record interoperability on how providers view and utilize IIS forecasting is unclear. Conclusions: Jurisdictions can share best practices for forecasting at 9 and future studies can evaluate the effects of forecasting age on the vaccination rates, providing evidence for nationwide vaccination recommendations. Full article

This paper proposes a technological framework designed to mitigate the inherent risks associated with the deployment of artificial intelligence (AI) in decision-making and task execution within the management processes. The Agreement Validation Interface (AVI) functions as a modular Application Programming Interface (API) Gateway positioned between user applications and LLMs. This gateway architecture is designed to be LLM-agnostic, meaning it can operate with various underlying LLMs without requiring specific modifications for each model. This universality is achieved by standardizing the interface for requests and responses and applying a consistent set of validation and enhancement processes irrespective of the chosen LLM provider, thus offering a consistent governance layer across a diverse LLM ecosystem. AVI facilitates the orchestration of multiple AI subcomponents for input–output validation, response evaluation, and contextual reasoning, thereby enabling real-time, bidirectional filtering of user interactions. A proof-of-concept (PoC) implementation of AVI was developed and rigorously evaluated using industry-standard benchmarks. The system was tested for its effectiveness in mitigating adversarial prompts, reducing toxic outputs, detecting personally identifiable information (PII), and enhancing factual consistency. The results demonstrated that AVI reduced successful fast injection attacks by 82%, decreased toxic content generation by 75%, and achieved high PII detection performance (F1-score ≈ 0.95). Furthermore, the contextual reasoning module significantly improved the neutrality and factual validity of model outputs. Although the integration of AVI introduced a moderate increase in latency, the overall framework effectively enhanced the reliability, safety, and interpretability of LLM-driven applications. AVI provides a scalable and adaptable architectural template for the responsible deployment of generative AI in high-stakes domains such as finance, healthcare, and education, promoting safer and more ethical use of AI technologies. Full article

A series of incidents involving damage to vehicle speed sensor cables occurred in an urban area in Japan. At the request of the police, DNA analysis was conducted to identify the animal species responsible. Swab samples collected from the damaged sections of the cables were subjected to PCR testing using mtDNA fragments. Sequencing analysis with universal primers (SCPH02500, SCPL02981) detected DNA from the Japanese marten (Martes melampus). A comprehensive examination that included morphological analysis of the cable damage and consideration of the ecological characteristics of the Japanese martens suggested that the damage was likely caused by this species. DNA analysis using mtDNA markers is a valuable tool for species identification in wildlife forensic veterinary investigations and serves as important scientific evidence in criminal cases involving animals. The findings from this case may contribute to future investigations in forensic veterinary science and ecological research and may also inform measures to prevent human–wildlife conflicts involving animals. Full article

The increasing prevalence of malicious Microsoft Office documents poses a significant threat to cybersecurity. Conventional methods of detecting these malicious documents often rely on prior knowledge of the document or the exploitation method employed, thus enabling the use of signature-based or rule-based approaches. Given the accelerated pace of change in the threat landscape, these methods are unable to adapt effectively to the evolving environment. Existing machine learning approaches are capable of identifying sophisticated features that enable the prediction of a file’s nature, achieving sufficient results on existing samples. However, they are seldom adequately prepared for the detection of new, advanced malware techniques. This paper proposes a novel approach to detecting malicious Microsoft Office documents by leveraging the power of large language models (LLMs). The method involves extracting textual content from Office documents and utilising advanced natural language processing techniques provided by LLMs to analyse the documents for potentially malicious indicators. As a supplementary tool to contemporary antivirus software, it is currently able to assist in the analysis of malicious Microsoft Office documents by identifying and summarising potentially malicious indicators with a foundation in evidence, which may prove to be more effective with advancing technology and soon to surpass tailored machine learning algorithms, even without the utilisation of signatures and detection rules. As such, it is not limited to Office Open XML documents, but can be applied to any maliciously exploitable file format. The extensive knowledge base and rapid analytical abilities of a large language model enable not only the assessment of extracted evidence but also the contextualisation and referencing of information to support the final decision. We demonstrate that Claude 3.5 Sonnet by Anthropic, provided with a substantial quantity of raw data, equivalent to several hundred pages, can identify individual malicious indicators within an average of five to nine seconds and generate a comprehensive static analysis report, with an average cost of USD 0.19 per request and an F1-score of 0.929. Full article

Background/Objectives: The clinical implementation of pharmacogenetics (PGx) remains limited, even for well-established drug–gene interactions. In addition to insufficient infrastructure and PGx education among healthcare professionals, there is currently no consensus regarding which genetic variants should be tested, the most appropriate testing approach (e.g., single-gene vs. multi-gene panels), or how to translate genotypes into actionable therapeutic recommendations. Methods: We describe the implementation of PGx in real daily clinical routine at a single institution to guide other centers. We analyze the drug–gene interactions and genetic variants included in our program based on allelic, genotypic, and phenotypic frequencies, resulting therapeutic recommendations. Linkage disequilibrium and haplotype analyses are also performed. Results and Conclusions: PGx testing was primarily requested by the oncology department. Not all variants included in typical panels had clinical utility in our setting. We do not recommend testing CYP2C19*17 prior to clopidogrel prescription, as it does not translate into a dosing recommendation. TPMT*3B may be considered just to confirm TPMT*3A due to its linkage with TPMT*3C. Similarly, we do not recommend the routine testing of CYP2C9*2 prior to siponimod prescription, as it does not inform therapeutic decisions according to the current drug label. Full article