Miracle-dz/UniFAR-FTUs huggingface dataset:
REVIEW OF PHARMACEUTICAL CARE SERVICES PROVIDED BY THE PHARMACIST S

Pharmaceutical Care is a patient - centered, outcome - oriented pharmacy practice that requires the qualified pharmacist to work in concert with the patient and the patient's other healthcare providers to promote health, to prevent disease, and to assess, monitor, initiate, and modify medication use to assure that drug therapy regimens are safe and effective. The goal of Pharmaceutical Care is to optimize the patient's health - related quality o f life and achieve positive clinical outcomes. Pharmaceutical care entails the work of addressing a patient's medication related needs. The involvement of the pharmacist in India in this regard has been somewhat restricted, contrary to trends in developed countries. Nowadays medicine options have multiplied manifold thus raising the complexity of therapies. Pharmacists have a unique role to play in evaluating these options and also medication errors which are most common in occurrence . The present article e xplain ed about the functions of pharmacist in pharmaceutical care and barriers towards pharmaceutical care.
CMAR_A_221976 9067..9072

*These authors contributed equally to this work Background: Platelets play a crucial role in breast cancer (BC) progression and metastases. Mean platelet volume (MPV) is an indicator of platelet activation. The aim of the present study was to assess whether there is a difference in MPV between patients with metastatic BC with liver metastases and those with BC without liver metastases. Methods: Between January 2014 and December 2017, 211 metastatic BC patients with synchronous liver metastases and 215 BC patients without metastases were retrospectively analyzed. Patients’ clinicopathological characteristics data were collected. Results: MPV levels were reduced in patients with liver metastases compared with those in patients without liver metastases. There were significant differences in MPV levels according to liver metastases status both in premenopausal and in postmenopausal non-TNBC or non-HER2+ patients. Moreover, in postmenopausal HER2+ or TNBC patients, MPV levels were lower in patients with livermetastases comparedwith those in patients without livermetastases. In the group with non-liver metastasis, platelet distribution width was significantly associated with tumor N stage. In addition, the prevalence of BC liver metastases decreased as MPV quartiles increased. After adjusting for other risk factors, the odds ratios for livermetastases according toMPVquartiles were 1.000, 0.267 (0.134–0.530), 0.072 (0.034–0.152), and 0.137 (0.066–0.281), respectively. Conclusion: MPV is reduced in BC patients with liver metastases compared with that in BC patients without metastases. Moreover, MPV is independently associated with the presence of liver metastases.
Disrupting Unwanted Habits in Online Gambling Through Information Technology

Abstract The instant access to gambling anytime, anywhere, has made online gambling highly habitual for some people. As a result, some online gamblers choose to volitionally enable the website-provided disruptive information technology (IT) features to control their gambling routines. The objective of this study is to examine the role of these features in regulating online gambling behavior. It is worthwhile to note that in this research, we do not make the assumption that habitual or regular online gambling is a bad thing; nor does the development of our conceptual framework — which focuses on the effect of disruptive IT features and moderating roles of individual regularity and game type in modifying gambling routines — depend on such an assumption. Drawing on theories of habitual automaticity and habit disruption, the conceptual framework theorizes the efficacy and mechanism of disruptive features while taking into account heterogeneity in individual regularity and game type. We tested the model using data collected over 10 years from 3,526 users of a gambling website. First, we found that individuals’ repetitive gambling patterns weakened as the duration of exposure to disruptive features increased. Second, the behavior of more regular gamblers was more resistant to the disruptive features, because more regular gamblers exhibited a stronger habitual pattern. Third, disruptive features were less effective on sports games compared with casino games, because sports gamblers tended to exhibit stronger gambling routines. Overall, the present study contributes to the information systems (IS) literature by clarifying how simple IT features may disrupt unwanted and difficult-to-break online gambling habits as judged by the gamblers. Our findings are likeliest to apply to broader areas of online services in which the application in question is integrated into everyday life and the system can offer a disruptive mechanism.
Simultaneous Estimation of Defocus and Motion Blurs From Single Image Using Equivalent Gaussian Representation

The occurrence of motion blur along with defocus blur is a common phenomena in natural images. Usually, these blurs are spatially varying in nature for any general image and estimation of one type of blur is affected by presence of other. In this paper, we propose a novel method to estimate the concurrent defocus and motion blurs in a single image. Unlike the recent methods, which perform well only on simulated conditions or in presence of single type of blur, proposed method works well for real images as well as for compressed images. In this paper, we consider only commonly associated motion and defocus blurs for analysis. Decoupling of motion and defocus blur provides a fundamental tool that can be used for various analysis and applications.
A modular process to improve the georeferencing of the Siegfried map

The Topographic Atlas of Switzerland, short “Siegfried map”, is a historical map series covering the whole of Switzerland for the time period between 1870 and 1926. Georeferencing each single map sheet in a high quality is critical to minimize error propagation when subsequent tasks are to be performed, such as feature extraction or sheet comparison. In this paper, we present a modular process consisting of six steps that facilitates the georeferencing of the scanned Siegfried map sheets by using tailored algorithms: The first step consists of pre-processing the scanned map sheet using common image processing approaches, removing non-relevant colour regions and enhancing the contrast and the sharpness of the map sheet. The second step performs image segmentation by detecting the corners of the map frame using a pattern matching approach to determine the actual map content. The third step involves the extraction of the coordinate grid based on the Hough transform and subsequently the computation of the grid intersection points. In the fourth step, the coordinate information is extracted and translated into machine-readable form. In the fifth step, the user can examine these automatically generated results and may manually correct them. Finally, in the sixth step, the map sheet is automatically georeferenced using quadrilateral-based bi-linear interpolation. This approach is used for georeferencing each coordinate grid cell separately and aligning the results rather than georeferencing the whole map sheet at once. This way, distortions resulting from aging and scanning can be reduced while preserving the locations of the control points. The modularization of the process allows to replace single components, when better algorithms will be developed. The aim is to use this process to gradually georeference all Siegfried map sheets and to make them accessible in the geodata4edu.ch geoportal.
Learning Very Large Configuration Spaces: What Matters for Linux Kernel Sizes

Linux kernels are used in a wide variety of appliances, many of them having strong requirements on the kernel size due to constraints such as limited memory or instant boot. With more than ten thousands of configuration options to choose from, obtaining a suitable trade off between kernel size and functionality is an extremely hard problem. Developers, contributors, and users actually spend significant effort to document, understand, and eventually tune (combinations of) options for meeting a kernel size. In this paper, we investigate how machine learning can help explain what matters for predicting a given Linux kernel size. Unveiling what matters in such very large configuration space is challenging for two reasons: (1) whatever the time we spend on it, we can only build and measure a tiny fraction of possible kernel configurations; (2) the prediction model should be both accurate and interpretable. We compare different machine learning algorithms and demonstrate the benefits of specific feature encoding and selection methods to learn an accurate model that is fast to compute and simple to interpret. Our results are validated over 95,854 kernel configurations and show that we can achieve low prediction errors over a reduced set of options. We also show that we can extract interpretable information for refining documentation and experts' knowledge of Linux, or even assigning more sensible default values to options.
Impact of Dietary Habits on Prevalence of Obesity in Children Aged 5-17 Years

BACKGROUND: Obesity is the result of an imbalance between energy intake and energy expenditure. Increasing urbanisation in traditionally rural societies is associated with changed pattern of diet, physical activity and body composition. Controversial information exists about the strongest energy balance aspects influencing body fatness. OBJECTIVE: To study the impact of eating habits on the prevalence of obesity in school going children aged 5-17 years. DESIGN: Cross sectional descriptive study. METHODS: The present study was conducted in 10000 students aged 5-17 years of randomly selected 20 schools of Amritsar. All children were asked about dietary habits on a pre planned questionnaire and BMI was calculated to measure obesity. The information regarding the eating habits, intake of fast foods and daily caloric intake was collected. RESULTS: No significant difference was found between energy intake per day of obese and non obese children. The percentage of children taking fast foods and regularly eating in between meals (snacking) was significantly higher in the obese children, when compared to non obese children. CONCLUSION: Intake of fast food, sugar sweetened beverages and snacking on ready prepared food has direct correlation with increasing body fatness. Association of a sedentary lifestyle and genetic predisposition needs to be evaluated in further studies.
Real-Time Nowcasting Nominal GDP Under Structural Break Barnett, William A. and Chauvet, Marcelle and Leiva-Leon, Danilo

This paper provides early assessments of current U.S. Nominal GDP growth, which has been considered as a potential new monetary policy target. The nowcasts are computed using the exact amount of information that policy makers have available at the time predictions are made. However, real time information arrives at di¤erent frequencies and asynchronously, which poses the challenge of mixed frequencies, missing data, and ragged edges. This paper proposes a multivariate state space model that not only takes into account asynchronous information inow it also allows for potential parameter instability. We use small scale con rmatory factor analysis in which the candidate variables are selected based on their ability to forecast GDP nominal. The model is fully estimated in one step using a nonlinear Kalman lter, which is applied to obtain simultaneously both optimal inferences on the dynamic factor and parameters. Di¤erently from principal component analysis, the proposed factor model captures the comovement rather than the variance underlying the variables. We compare the predictive ability of the model with other univariate and multivariate speci cations. The results indicate that the proposed model containing information on real economic activity, ination, interest rates, and Divisia monetary aggregates produces the most accurate real time nowcasts of nominal GDP growth. Keywords: Mixed Frequency, Ragged Edges, Real-Time, Nowcasting, Missing Data, Nonlinear, Structural Breaks, Dynamic Factor, Monetary Policy. JEL Classi cation: C32, E27, E31, E32 We would like to thank Maximo Camacho, Gabriel Perez-Quiros, Michael Bauer, Hashem Pesaran, Michael McCracken, and participants of the 9th Annual CIRANO Workshop and the 2013 CAFE/USC Workshop for helpful comments and suggestions. The views expressed in this paper are those of the authors and do not represent the views of the Bank of Canada. Department of Economics, University of Kansas, Lawrence, KS, 66045-7585. Email: barnett@ku.edu. Center for Financial Stability, NY City, 1120 Avenue of the Americas, 4th Floor, New York, NY 1003. Email: wbarnett@the-cfs.org Department of Economics, University of California Riverside, CA 92521. Email: chauvet@ucr.edu International Economic Analysis Department, Bank of Canada, 234 Laurier Avenue West, Ottawa, Ontario, Canada, K1A 0G9. E-mail: leiva@bankofcanada.ca
THE USER SATISFACTION LEVEL OF ELEARNING FOR BUSINESS AND MANAGEMENT SUBJECTS BASED ON TECHNOLOGY ACCEPTANCE MODEL

Elearning is the implementation of information technology in learning. Elearning was used in courses in Introduction to Business and Management, Business Process Management and Ebusiness. This study aims to use the Technology Acceptance model (TAM) to measure the level of user satisfaction. TAM method is used to determine the relationship between content, accuracy, format, ease of use, timelines, organizational support, user attitudes towards the information system (user attitude towards information). system) and perceived attitude of top management on the level of satisfaction of using e-learning on learning business courses and management at the Faculty of Computer Science, Duta Bangsa University Surakarta. This research is a descriptive study using the modified End User Computing Satisfaction (EUCS) approach method. The results of the evaluation study show that the 5 variables (content), the level of accuracy of the system, format, easy of use, and timeliness significantly influence user satisfaction. While organizational support variables have a significant effect on user satifaction but variable usser attitude toward information system and perceived attitude of top management has an effect but not significant to the support organization. Key words : elearning, user satisfaction, technology acceptance model, end user computing satisfaction.
Low-Delay Scheduling for Internet of Vehicles: Load-Balanced Multipath Communication With FEC

The proliferation of devices with multiple wireless interfaces and (automobile) manufacturers’ interest in implementing reliable connectivity for vehicles create an ideal scenario for multipath transmission control protocol (MPTCP) over vehicular networks. Next generation vehicular networks will be highly dynamic and exploit MPTCP over various wireless technologies including WiFi and 5th generation (5G) cellular networks to form the Internet of vehicles (IoV). However, the probability of packet loss can be high and/or packets arrive at the destination out-of-order due to time-varying heterogeneous wireless paths. Further, the IoV traffic is delay sensitive, which urges the need to investigate MPTCP algorithms for reliable communication over heterogeneous lossy networks, while satisfying delay constraints. We approach these challenges by jointly using load balancing and forward error correction (FEC) for performing coupled congestion control inside MPTCP. As a result, the proposed MPTCP-IoV is TCP-friendly by design, and provably stable and convergent to a unique equilibrium point. In addition, a comprehensive mathematical analysis (developed to approximate the reordering delay) is carried out and shown that the delay penalty for MPTCP-IoV over such networks becomes insignificant when considering the potential gains obtained by the convergence of multiple networks. Experiments are carried out and the results demonstrate these characteristics.
Resource Allocation for Wireless-Powered Full-Duplex Relaying Systems With Nonlinear Energy Harvesting Efficiency

In wireless power transfer (WPT)-assisted relaying systems, spectral efficiency (SE) of source-relay link plays a dominant role in system SE performance due to the limited transmission power at the WPT-aided relay. In this paper, we propose a novel protocol for a downlink orthogonal frequency division multiple access (OFDMA) system with a WPT-aided relay operating in full-duplex (FD) decode-and-forward (DF) mode, where the time slot durations of the source-relay and relay-users hops are designed to be dynamic, to enhance the utilization of degrees of freedom and hence the system SE. In particular, a multiple-input and signal-output (MISO) source-relay channel is considered to satisfy the stringent sensitivity of the energy harvesting (EH) circuit at the relay, while a single-input and single-output (SISO) relay-user channel is considered to alleviate the power consumption at the relay node. Taking into account the non-linearity of EH efficiency, a near-optimal iteration-based dynamic WPT-aided FD relaying (A-FR) algorithm is developed by jointly optimizing the time slot durations, subcarriers, and transmission power at the source and the relay. Furthermore, self-interference generated at the relay is utilized as a vital energy source rather than being canceled, which increases substantially the total energy harvested at the FD relay. We also reveal some implicit characteristics of the considered WPT-aided FD relaying system through intensive discussions. Simulation results confirm that the proposed A-FR achieves a significant enhancement in terms of SE with different relay's locations and the number of users, compared to the conventional symmetric WPT-aided FD relaying (S-FR) and the time-switching-based WPT-aided FD relaying (TS-FR) benchmarks.
Towards Leveraging End-of-Life Tools as an Asset: Value Co-Creation based on Deep Learning in the Machining Industry

Sustainability is the key concept in the management of products that reached their end-of-life. We propose that end-of-life products have—besides their value as recyclable assets—additional value for producer and consumer. We argue this is especially true for the machining industry, where we illustrate an automatic characterization of worn cutting tools to foster value co-creation between tool manufacturer and tool user (customer) in the future. In the work at hand, we present a deep-learning-based computer vision system for the automatic classification of worn tools regarding flank wear and chipping. The resulting Matthews Correlation Coefficient of 0.878 and 0.644 confirms the feasibility of our system based on the VGG-16 network and Gradient Boosting. Based on these first results we derive a research agenda which addresses the need for a more holistic tool characterization by semantic segmentation and assesses the perceived business impact and usability by different user groups.
Analysis of Wing-Engine Nacelle Aerodynamic Interference

Abstract The article presents an analysis of the wing-engine nacelle flow interference phenomenon on the example of a light twin-engine commuter aircraft. The problems of propulsion system integration with the wing in airplanes are now frequently the subject of advanced optimization research performed by aircraft manufacturers. The shape of the engine nacelle and its connection with the wing determines the quality of the flow around the wing in that area. This is important for high-lift devices placed at the wing trailing edge behind engine nacelle used during the take-off and landing process. Additionally the flow is effected by the disturbances generated by working propellers, the presence of air inlets and an exhaust system of the engine. The article presents a process of numerical optimization of an engine nacelle rear part shape. The main goal of the process was to eliminate the flow disturbances caused by the engine nacelle-wing interference phenomenon. During analysis, the Adjoint Solver method was used to designate nacelle body areas where modification should have the most important impact on the flow quality. The results obtained from adjoint solver were used in the process of finding the optimum shape of the rear part of the nacelle using a parametric geometry generator powered by Ansys Design Modeler and PARADES software. Comparative computational analysis for selected geometries of the engine nacelle was performed using commercial Ansys Fluent solver. Ansys Fluent is an advanced computational solver based on the finite volume method for solving the Navier-Stokes flow equations. Several dozen of geometric shapes were analysed in the optimization process of the nacelle rear part. The final result was the shape of the engine nacelle with correct flow without separation and vortex structures. The article presents results of calculations and visualization of the flow pattern for analysed cases.
Optimization methods for structured machine learning problems

Solving large-scale optimization problems lies at the core of modern machine learning applications. Unfortunately, obtaining a sufficiently accurate solution quickly is a difficult task. However, the problems we consider in many machine learning applications exhibit a particular structure. In this thesis we study optimization methods and improve their convergence behavior by taking advantage of such structures. In particular, this thesis constitutes of two parts: In the first part of the thesis, we consider the Temporal Difference learning (TD) problem in off-line Reinforcement Learning (RL). In off-line RL, it is typically the case that the number of samples is small compared to the number of features. Therefore, recent advances have focused on efficient algorithms to incorporate feature selection via `1-regularization which effectively avoids over-fitting. Unfortunately, the TD optimization problem reduces to a fixed-point problem where convexity of the objective function cannot be assumed. Further, it remains unclear whether existing algorithms have the ability to offer good approximations for the task of policy evaluation and improvement (either they are non-convergent or do not solve the fixed-point problem). In this part of the thesis, we attempt to solve the `1regularized fixed-point problem with the help of Alternating Direction Method of Multipliers (ADMM) and we argue that the proposed method is well suited to the structure of the aforementioned fixed-point problem. In the second part of the thesis, we study multilevel methods for large-scale optimization and extend their theoretical analysis to self-concordant functions. In parAbstract 6 ticular, we address the following issues that arise in the analysis of second-order optimization methods based either on sampling, randomization or sketching: (a) the analysis of the iterates is not scale-invariant and (b) lack of global fast convergence rates without restrictive assumptions. We argue that, with the analysis undertaken in this part of the thesis, the analysis of randomized second-order methods can be considered on-par with the analysis of the classical Newton method. Further, we demonstrate how our proposed method can exploit typical spectral structures of the Hessian that arise in machine learning applications to further improve the convergence rates.6 ticular, we address the following issues that arise in the analysis of second-order optimization methods based either on sampling, randomization or sketching: (a) the analysis of the iterates is not scale-invariant and (b) lack of global fast convergence rates without restrictive assumptions. We argue that, with the analysis undertaken in this part of the thesis, the analysis of randomized second-order methods can be considered on-par with the analysis of the classical Newton method. Further, we demonstrate how our proposed method can exploit typical spectral structures of the Hessian that arise in machine learning applications to further improve the convergence rates.