Information System Technologies (United States)

To analyze the effects of co-administering various drugs with canine whole blood (WB), canine fresh frozen plasma (FFP), or canine freeze-dried plasma (FDP), and determine whether alterations to the blood constituents or drugs exist within the admixture. In vitro experimental study. Government blood and coagulation research laboratory. Seven units of commercially acquired canine FFP, 7units of canine FDP, and 8units of canine WB were co-administered with multiple drugs, including fentanyl, midazolam, ketamine, hydromorphone, tranexamic acid (TXA), ampicillin/sulbactam, enrofloxacin, ceftriaxone, and ertapenem, and delivered simultaneously into an IV line via infusion pumps using clinically relevant doses. The resultant solutions were analyzed for coagulation factor activities and fibrinogen concentration. Liquid chromatography-tandem mass spectroscopy was used to assess drug concentration, and impedance aggregometry and cell-free hemoglobin were used to evaluate platelet function in the WB samples. Platelet function decreased with each drug co-administered with WB in vitro. Cell-free hemoglobin increased when ketamine, fentanyl, and midazolam were co-administered with WB. Drug loss was seen when enrofloxacin was co-administered with FDP. Drug loss was also seen when hydromorphone was co-administered with FFP. Sulbactam and ertapenem resulted in drug loss when co-administered with FDP and FFP. Drug loss was seen when ceftriaxone, fentanyl, and midazolam were co-administered with each blood product. With each admixture, there were variable changes in coagulation factor activities. A statistically significant decrease in activity <50% was seen only in factors V and VIII when ceftriaxone and enrofloxacin, respectively, were co-administered with FDP. Platelet function will likely be adversely affected by the co-administration of any of the selected drugs. Co-administration of ketamine, fentanyl, and midazolam with WB resulted in significant hemolysis and is not recommended. It is reasonable to consider co-administering ampicillin, TXA, and ketamine with FDP and FFP.

This paper presents ongoing work in the context of a recently funded research project, MoniCrowd, aimed at advancing crowd monitoring in dynamic urban settings, particularly during temporary public events. The proposed system adopts an adaptive architecture based on passive Wi-Fi probe request detection, comprising portable sensors with multi-radio access connectivity and a rule-based fingerprinting method for anonymous and reliable device counting. Sensors autonomously select the best uplink from available connectivity options, enabling operation in suboptimal locations. A novel tool, the Information Elements Automatic Analyser (IEAA), enhances fingerprint robustness through fine-grained feature selection. A new dataset, collected under controlled Faraday cage conditions, supports this development. Preliminary field deployment results show strong correlation with manual counts, validating the approach under real-world conditions. The proposed fingerprinting method also achieved top accuracy in the international CONFRONT challenge. To optimise sensor performance in diverse environments, a UAV-assisted calibration tool is under development; its design and preliminary sensitivity mapping results are presented. Altogether, this work lays the foundation for scalable, autonomous crowd sensing solutions that can be rapidly deployed in dynamic urban contexts without requiring specialised local expertise.

Every single day, users are increasing as the world has explored technology where data is producing more and more big data and cloud technologies are the reason behind the scene because big data and cloud computing are the booming pieces of knowledge currently with AI helpful for users and for other professional and practical purposes. Nowadays integration of technologies also been an emerging topic it helps in the reduction of disadvantages of particular technology to provide better solutions. This paper talks about what, and why big data and cloud technology in daily lives and where big data in cloud technology is used. This paper examines recently proposed frameworks/models where big data and cloud technologies are utilized together and reviewed in a detailed way from the year 2020 to 2023. What Advantages can be there in utilizing both big data and cloud together is also discussed. This study explains the most used big data analytical tools and widely used cloud platforms and performed a simple case study on two datasets (world data and disease symptoms dataset) to draw some insights into which Rapid Miner and Power BI are used where RF and Gradient Boosted Trees got more accuracy than other classification algorithms with 0.7 accuracy while using disease symptoms dataset and some visualizations are drawn with use of world data.

The integration of blockchain technology into medical billing systems presents a transformative approach to addressing long-standing challenges in data security, transparency, and transaction integrity within healthcare finance. This quantitative study examines the implementation and performance of blockchain-enabled medical billing frameworks, focusing on their capacity to ensure immutability, traceability, and fraud prevention in electronic health payment transactions. By employing a mixed-method analytical model that integrates transaction latency metrics, cryptographic hash validation rates, and decentralized ledger consistency indices across 152 sampled healthcare billing networks, the study quantifies the impact of blockchain on billing accuracy and system resilience. Results indicate that blockchain-driven systems demonstrate a statistically significant 38% reduction in billing discrepancies and a 45% improvement in data reconciliation efficiency compared to traditional electronic billing infrastructures. Furthermore, smart contract automation within Ethereum-based frameworks reduced claim-processing times by up to 62%, mitigating human error and unauthorized modifications. The research also highlights how consensus algorithms, particularly Proof of Authority (PoA) and Practical Byzantine Fault Tolerance (PBFT), enhance the verification process for medical transactions without compromising processing speed. These findings underscore blockchain’s potential to redefine financial trust structures within healthcare ecosystems by ensuring end-to-end transactional integrity, auditability, and compliance with data privacy regulations such as HIPAA and GDPR. The study concludes that blockchain-enabled secure billing systems can significantly strengthen institutional accountability and patient trust while establishing a robust foundation for interoperable, tamper-proof financial data management across global healthcare networks.

The increasing digitization of hospital critical infrastructures has intensified the demand for secure yet highly usable interface systems that enable clinicians and administrative personnel to perform tasks efficiently without compromising data protection. This study presents a mixed-methods explanatory sequential investigation into the quantitative and behavioral dimensions of human-centered security interface design in healthcare environments. The research employed MATLAB-based statistical modeling and thematic analysis to evaluate how usability—defined through efficiency, satisfaction, and workload—mediates the relationship between system complexity and security compliance, while also examining workload as a moderating variable. Quantitative data were collected from 228 healthcare professionals across departments, and qualitative insights were drawn from 23 semi-structured interviews exploring perceptions of trust, cognitive fatigue, and workflow adaptation. The quantitative findings revealed that usability significantly predicts compliance (β = .61, p &lt; .001) and mediates the adverse effects of system complexity on security adherence. Workload demonstrated a negative moderating influence, reducing the effectiveness of usability under cognitive strain. The structural equation model accounted for 59% of the variance in security performance, confirming the robustness of the proposed framework. Thematic analysis supported these results by identifying four key contextual determinants—cognitive overload, trust calibration, workflow disruption, and adaptive workarounds—that explain the behavioral underpinnings of compliance and noncompliance. Together, these findings substantiate that cybersecurity in healthcare is a socio-technical phenomenon in which human cognition and interface design jointly determine system resilience. The study contributes to the theoretical and practical advancement of human-centered cybersecurity by empirically validating the usability–security nexus, demonstrating that intuitive, transparent, and cognitively efficient designs foster both operational reliability and institutional security culture. These insights offer a foundation for redesigning hospital security systems that are simultaneously secure, usable, and aligned with the cognitive demands of clinical practice.

Urban traffic safety research frequently models crash frequency using Poisson-family regression because it is interpretable, extensible, and policy relevant. This systematic review synthesizes how these models are specified, diagnosed, validated, and translated into practice for urban contexts. Following PRISMA protocols, we searched multidisciplinary and transportation databases from inception through 2022, applied dual-stage screening with explicit inclusion criteria, extracted standardized methodological and results fields, and appraised reporting quality; 110 studies met all criteria and were included. The modeling landscape shows a clear center of gravity around the Poisson family, with negative binomial as the most common primary specification at 40.0 percent, followed by canonical Poisson at 29.1 percent, zero-inflated or hurdle variants at 12.7 percent, Poisson-lognormal or multivariate forms at 7.3 percent, and mixed or spatial CAR/ICAR primaries at 5.5 percent each, while Poisson appears as a baseline in most studies. Practice quality is uneven. Offsets are specified in 83.6 percent of papers and are associated with stronger validation and more stable inference, yet only 35.5 percent report any out-of-sample validation and calibration plots appear infrequently. Across covariates, higher speeds, turning shares, and access density typically increase risk, whereas medians and coordinated signals are protective; pedestrian and cyclist volumes often exhibit safety-in-numbers curvature. Translation to policy commonly occurs through Safety Performance Functions paired with empirical-Bayes adjustment; among studies reporting re-ranking, median turnover in top-site lists is about 30 percent, underscoring the operational impact of correct EB use and calibration. Overall, the evidence supports a disciplined workflow: construct mechanism-matched offsets, diagnose dispersion, zeros, and dependence, escalate model complexity only when warranted, and pair likelihood-based selection with explicit predictive checks to ensure credible, auditable safety decisions in cities.

Control Systems operating in aircraft environments can be subject to a Single Event Upset (SEU). SEU can occur when the control system is exposed to ions or electromagnetic radiation which releases high-energy particles that could potentially damage the control system and lead to a catastrophic or hazardous failure in the aircraft. This frequent SEU error can cause many challenges to meet the reliability and availability requirements of the aircraft's system, which could pose a major challenge to meet the system's safety criteria and certification of the aircraft system. Mitigating the impact caused by the SEU is important to improve the reliability and availability of the safety-critical control system. In this paper, the software methods are discussed to detect and correct the SEU errors and provided an example method of uninterrupted execution of control software in an aircraft control system in the event of fault detection.

The field of Explainable Artificial Intelligence (XAI) tries to make learned models more understandable. One type of explanation for such models are counterfactual explanations. Counterfactual explanations explain the decision for a specific instance, the factual, by providing a similar instance which leads to a different decision, the counterfactual. In this work a new approaches around the idea of counterfactuals was developed. It generates a data structure over the feature space of a classification problem to accelerate the search for counterfactuals and augments them with global explanations. The approach maps the feature space by hierarchically dividing it into regions which belong to the same class. It is applicable in any case where predictions can be generated for input data, even without direct access to the model. The framework works well for lower-dimensional problems but becomes unpractical due to high computation times at around 12 to 15 dimensions.

This paper introduces a new subspace-based detection method for multichannel (high frequency and broadband) synthetic aperture sonar (SAS) imagery. An image-dependent dictionary learning method is applied to form the appropriate dictionary matrices for representing target and nontarget image snippets. The hypothesis testing is done by forming a test statistic that relies on the residual error power ratio in representing an unknown image snippet using the target and nontarget dictionary matrices. To avoid the computational bottleneck in most dictionary learning methods, a new recursive method is introduced which does not require any matrix inversion or singular value decomposition (SVD). The proposed detection method was then implemented and benchmarked against a matched subspace detection method for detecting mine-like objects. Results are then presented on two sonar imagery data sets collected in two geographically disparate locations.

Detection, localization and classification of battlefield acoustic transient events are of great importance especially for military operations in urban terrain (MOUT). Generally, there can be a wide variety of battlefield acoustic transient events such as different caliber gunshots, artillery fires, and mortar fires. The discrimination of different types of transient sources is plagued by highly non-stationary nature of these signals, which makes the extraction of representative features a challenging task. This is compounded by the variations in the environmental and operating conditions and existence of a wide range of possible interference. This paper presents new approaches for transient signal estimation and feature extraction from acoustic signatures collected by several distributed sensor nodes. A maximum likelihood (ML)-based method is developed to remove noise/interference and fading effects and restore the acoustic transient signals. The estimated transient signals are then represented using wavelets. The multi-resolution property of the wavelets allows for capturing fine details in the transient signals that can be utilized to successfully classify them. Wavelet sub-band higher order moments and energy-based features are used to characterize the transient signals. The discrimination ability of the subband features for transient signal classification has been demonstrated on several different caliber gunshots. Important findings and observations on these results are also presented.