Search

Medication nonadherence significantly reduces the effectiveness of therapies, yet it remains prevalent among patients. Nonadherence has been linked to adverse outcomes, including increased risks of mortality and hospitalization. Although various methods exist to help patients track medication schedules, such as the Intelligent Drug Administration System (IDAS) and Smart Blister, these tools often face challenges that hinder their commercial viability. Building on the principles of dosage measurement and information communication in IoT, we introduce the Smart Pill Case a smart health adherence tool that leverages RFID-based data recording and NFC-based data extraction. This system incorporates a load cell for precise dosage measurement and features an Android app to monitor medication intake, offer suggestions, and issue warnings. To enhance the effectiveness and personalization of the Smart Pill Case, we propose integrating federated learning into the system. Federated learning allows the Smart Pill Case to learn from medication adherence patterns across multiple users without compromising individual privacy. By training machine learning models on decentralized data collected from various Smart Pill Cases, the system can continuously improve its recommendations and warnings, adapting to the diverse needs and behaviors of users. This approach not only enhances the tools ability to support medication adherence but also ensures that sensitive user data remains secure and private.

We discuss a system of stochastic differential equations with a stiff linear term and additive noise driven by fractional Brownian motions (fBms) with Hurst parameter H>1/2, which arise e. g., from spatial approximations of stochastic partial differential equations. For their numerical approximation, we present an exponential Euler scheme and show that it converges in the strong sense with an exact rate close to the Hurst parameter H. Further, based on (E. Buckwar, M.G. Riedler, and P.E. Kloeden 2011), we conclude the existence of a unique stationary solution of the exponential Euler scheme that is pathwise asymptotically stable.

As artificial intelligence (AI) transforms human resource management (HRM), understanding the research landscape becomes crucial for both academics and practitioners. While existing studies examine isolated aspects of AI in HRM, a comprehensive analysis of collaboration patterns and emerging themes remains lacking. This research employs social network analysis (SNA) to examine the co-authorship network within AI applications in HRM research, providing insights into collaboration dynamics and identifying key research directions. Through analysis of centrality measures and application of the TOPSIS method, the study identifies influential authors, institutions, and emerging research themes. Analysis of 102,296 authors and 287,799 collaborations reveals distinct communities focusing on specific aspects of AI-HRM across regions. The findings identify four primary research themes: AI for System Identification and Control, focusing on workforce planning and adaptive management; HR Analytics and Performance Management, emphasizing data-driven decision making; Machine Learning for Classification and Prediction, addressing talent acquisition and retention; and AI-Driven HR Decision-Making, exploring strategic planning and unbiased evaluation systems. The country co-authorship network analysis uncovers three main communities: Global HR Applications, HRM in the Middle East and Asia, and Global Integration of AI in HRM, reflecting shared regional challenges. Institutional collaboration patterns indicate five distinct communities, from established Asian AI research centers to emerging research hubs in developing economies.

Developing a structured method for analyzing various aspects of a system requires a novel methodology. This study is aimed at developing such as methodology through combining two major matrix methods, namely, Design Structure Matrix (DSM) and Interface Structure Matrix (ISM). Through this paper, a business process modeling method is applied to turn a real work project to a process model. Then that process model is written in two various matrix forms of DSM and ISM. These two matrices are analyzed by two types of algorithm for extracting activity levels and sub-processes. In the end, a Mixed Matrix Model (MMM) is built upon these activity levels and sub-processes, which can be used as a framework for the engineering of real-world systems., Comment: 22 pages, 11 figures

Pressure alters the physical, chemical and electronic properties of matter. The development of the diamond anvil cell (DAC) enables tabletop experiments to investigate a diverse landscape of high-pressure phenomena ranging from the properties of planetary interiors to transitions between quantum mechanical phases. In this work, we introduce and utilize a novel nanoscale sensing platform, which integrates nitrogen-vacancy (NV) color centers directly into the culet (tip) of diamond anvils. We demonstrate the versatility of this platform by performing diffraction-limited imaging (~600 nm) of both stress fields and magnetism, up to pressures ~30 GPa and for temperatures ranging from 25-340 K. For the former, we quantify all six (normal and shear) stress components with accuracy $<0.01$ GPa, offering unique new capabilities for characterizing the strength and effective viscosity of solids and fluids under pressure. For the latter, we demonstrate vector magnetic field imaging with dipole accuracy $<10^{-11}$ emu, enabling us to measure the pressure-driven $\alpha\leftrightarrow\epsilon$ phase transition in iron as well as the complex pressure-temperature phase diagram of gadolinium. In addition to DC vector magnetometry, we highlight a complementary NV-sensing modality using T1 noise spectroscopy; crucially, this demonstrates our ability to characterize phase transitions even in the absence of static magnetic signatures. By integrating an atomic-scale sensor directly into DACs, our platform enables the in situ imaging of elastic, electric and magnetic phenomena at high pressures., Comment: 18 + 50 pages, 4 + 19 figures

In this paper, we investigate the optimal strong convergence rate of numerical approximations for the Cox--Ingersoll--Ross model driven by fractional Brownian motion with Hurst parameter $H\in(1/2,1)$. To deal with the difficulties caused by the unbounded diffusion coefficient, we study an auxiliary equation based on Lamperti transformation. By means of Malliavin calculus, we prove that the backward Euler scheme applied to this auxiliary equation ensures the positivity of the numerical solution, and is of strong order one. Furthermore, a numerical approximation for the original model is obtained and converges with the same order., Comment: 12 figures

Financing high-tech projects always entails a great deal of risk. The lack of a systematic method to pinpoint the risk of such projects has been recognized as one of the most salient barriers for evaluating them. So, in order to develop a mechanism for evaluating high-tech projects, an Artificial Neural Network (ANN) has been developed through this study. The structure of this paper encompasses four parts. The first part deals with introducing paper's whole body. The second part gives a literature review. The collection process of risk related variables and the process of developing a Risk Assessment Index system (RAIS) through Principal Component Analysis (PCA) are those issues that are discussed in the third part. The fourth part particularly deals with pharmaceutical industry. Finally, the fifth part has focused on developing an ANN for pattern recognition of failure or success of high-tech projects. Analysis of model's results and a final conclusion are also presented in this part., Comment: 23 Pages, 6 Figures, 6 Tables, 33 Equations. arXiv admin note: text overlap with arXiv:1805.10307

Volatile driving, characterized by fluctuations in speed and accelerations and aggressive lane changing/merging, is known to contribute to transportation crashes. To fully understand driving volatility with the intention of reducing it, the objective of this study is to identify its key correlates, while focusing on highly volatile trips. First, a measure of driving volatility based on vehicle speed is applied to trip data collected in the California Household Travel Survey during 2012-2013. Specifically, the trips containing driving cycles (N=62839 trips) were analyzed to obtain driving volatility. Second, correlations of volatility with the trip, vehicle, and person level variables were quantified using Ordinary Least Squares and quantile regression models. The results of the 90th percentile regression (which distinguishes the 10% highly volatile trips from the rest) show that trips taken by pickup trucks, hatchbacks, convertibles, and minivans are less volatile when compared to the trips taken by sedans. Moreover, longer trips have less driving volatility. In addition, younger drivers are more volatile drivers than old ones. Overall, the results of this study are reasonable and beneficial in identifying correlates of driving volatility, especially in terms of understanding factors that differentiate highly volatile trips from other trips. Reductions in driving volatility have positive implications for transportation safety. From a methodological standpoint, this study is an example of how to extract useful (volatility) information from raw vehicle speed data and use it to calm down drivers and ultimately improve transportation safety., Comment: Presented at the 97th Annual Meeting Transportation Research Board, January 2018. Washington D.C

With the emergence of high-frequency connected and automated vehicle data, analysts have become able to extract useful information from them. To this end, the concept of "driving volatility" is defined and explored as deviation from the norm. Several measures of dispersion and variation can be computed in different ways using vehicles' instantaneous speed, acceleration, and jerk observed at intersections. This study explores different measures of volatility, representing newly available surrogate measures of safety, by combining data from the Michigan Safety Pilot Deployment of connected vehicles with crash and inventory data at several intersections. The intersection data was error-checked and verified for accuracy. Then, for each intersection, 37 different measures of volatility were calculated. These volatilities were then used to explain crash frequencies at intersection by estimating fixed and random parameter Poisson regression models. Results show that an increase in three measures of driving volatility are positively associated with higher intersection crash frequency, controlling for exposure variables and geometric features. More intersection crashes were associated with higher percentages of vehicle data points (speed & acceleration) lying beyond threshold-bands. These bands were created using mean plus two standard deviations. Furthermore, a higher magnitude of time-varying stochastic volatility of vehicle speeds when they pass through the intersection is associated with higher crash frequencies. These measures can be used to locate intersections with high driving volatilities, i.e., hot-spots where crashes are waiting to happen. Therefore, a deeper analysis of these intersections can be undertaken and proactive safety countermeasures considered at high volatility locations to enhance safety., Comment: Forthcoming, submitted for publication in Transportation Research Record: Journal of the Transportation Research Board

Traditionally, evaluation of intersection safety has been largely reactive, based on historical crash frequency data. However, the emerging data from Connected and Automated Vehicles (CAVs) can complement historical data and help in proactively identify intersections which have high levels of variability in instantaneous driving behaviors prior to the occurrence of crashes. Based on data from Safety Pilot Model Deployment in Ann Arbor, Michigan, this study developed a unique database that integrates intersection crash and inventory data with more than 65 million real-world Basic Safety Messages logged by 3,000 connected vehicles, providing a more complete picture of operations and safety performance of intersections. As a proactive safety measure and a leading indicator of safety, this study introduces location-based volatility (LBV), which quantifies variability in instantaneous driving decisions at intersections. LBV represents the driving performance of connected vehicle drivers traveling through a specific intersection. As such, by using coefficient of variation as a standardized measure of relative dispersion, LBVs are calculated for 116 intersections in Ann Arbor. To quantify relationships between intersection-specific volatilities and crash frequencies, rigorous fixed- and random-parameter Poisson regression models are estimated. While controlling for exposure related factors, the results provide evidence of statistically significant (5% level) positive association between intersection-specific volatility and crash frequencies for signalized intersections. The implications of the findings for proactive intersection safety management are discussed in the paper.

The aim of this paper is the derivation of an a-posteriori error estimate for a numerical method based on an exponential scheme in time and spectral Galerkin methods in space. We obtain analytically a rigorous bound on the mean square error conditioned to the calculated data, which is numerically computable and uses the given numerical approximation. Thus one can check a-posteriori the error for a given numerical computation without relying on an asymptotic result. All estimates are only based on the numerical data and the structure of the equation, but they do not use any a-priori information of the solution, which makes the approach applicable to equations where global existence of solutions is not known. For simplicity of presentation, we develop the method here in a relatively simple situation of a stable one-dimensional Allen-Cahn equation with additive forcing.