768 research outputs found

    An Investigation into Patient Satisfaction from the Healthcare System in Saudi Arabia: Survey and Analysis of the Major Determinants in the Qassim Region

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    The main aim of this empirical study is to investigate and assess the degree of patient satisfaction in hospitals in the Al-Qassim region of Saudi Arabia and the primary causes that may contribute to satisfaction or dissatisfaction with the healthcare system. Primary data were acquired from a sample consisting of 292 patients in the Kingdom of Saudi Arabia's Al-Qassim region. The level of patient satisfaction with the healthcare system in Al-Qassim region was investigated using a self-administered questionnaire. The main determinants of patient satisfaction in the healthcare system included patient demographics, expectations, perceptions, and experiences. In Al-Qassim region patient experiences and expectations were the most important elements in influencing their satisfaction level. Further, demographics factors have favorable and substantial effects on people's perceptions and behavior regarding their satisfaction level with healthcare services in the Al-Qassim region. This research will aid healthcare administrators, policymakers, paramedical staff, and physicians in identifying the reasons for patient dissatisfaction and considering viable options to improve patient satisfaction levels in the health care system

    Coupled plasmon - phonon excitations in extrinsic monolayer graphene

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    The existence of an acoustic plasmon in extrinsic (doped or gated) monolayer graphene was found recently in an {\it ab initio} calculation with the frozen lattice [M. Pisarra {\it et al.}, arXiv:1306.6273, 2013]. By the {\em fully dynamic} density-functional perturbation theory approach, we demonstrate a strong coupling of the acoustic plasmonic mode to lattice vibrations. Thereby, the acoustic plasmon in graphene does not exist as an isolated excitation, but it is rather bound into a combined plasmon-phonon mode. We show that the coupling provides a mechanism for the {\em bidirectional} energy exchange between the electronic and the ionic subsystems with fundamentally, as well as practically, important implications for the lattice cooling and heating by electrons in graphene.Comment: 5 pages, 4 figure

    Deterministic creation of stationary entangled states by dissipation

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    We propose a practical physical system for creation of a stationary entanglement by dissipation without employing the environment engineering techniques. The system proposed is composed of two perfectly distinguishable atoms, through their significantly different transition frequencies, with only one atom addressed by an external laser field. We show that the arrangement would easily be realized in practice by trapping the atoms at the distance equal to the quarter-wavelength of a standing-wave laser field and locating one of the atoms at a node and the other at the successive antinode of the wave. The undesirable dipole-dipole interaction between the atoms, that could be large at this small distance, is adjusted to zero by a specific initial preparation of the atoms or by a specific polarization of the atomic dipole moments. Following this arrangement, we show that the dissipative relaxation can create a stationary entanglement on demand by tuning the Rabi frequency of the laser field to the difference between the atomic transition frequencies. The laser field dresses the atom and we identify that the entangled state occurs when the frequency of one of the Rabi sidebands of the driven atom tunes to frequency of the undriven atom. It is also found that this system behaves as a cascade open system where the fluorescence from the dressed atom drives the other atom with no feedback.Comment: Published versio

    ANN Models to Correlate Structural and Functional Conditions in AC Pavements at Network Level

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    Artificial Neural Network (ANN) model was developed to estimate the correlation between structural capacity and functional conditions in Asphalt Cement (AC) pavements at the network level. To achieve this objective, the relevant data were obtained and integrated from the Iowa Pavement Management Program (IPMP) including construction parameters, traffic loading and subgrade stiffness, and Iowa Environmental Mesonet (IEM) for climate data. The ANN model proves its ability to learn and generalize from the input data. Overall, rutting data were found to be appropriate indicator of the structural capacity. Since the deflection tests are expensive and require experience and knowledge to deal with such data, this approach might be feasible for small transportation agencies (cities and counties) that do not have these capabilities

    The role of lifestyle habits in the prevalence of overweight and obesity among students

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    The key objective of the present study is to explore the prevalence of being overweight and/or being obese using the body mass index (BMI). We investigated the relationship between lifestyle habits (sleeping patterns, dietary habits, physical activities, and screen times) and obesity. We used a cross-sectional study involving male students of medical and non-medical at the College of Medicine and College of Management and Economics at Saudi Arabia’s Qassim University. To gather data, a tailor-made, self-administered questionnaire was the tools of choice. The first part of the form collected a data pertaining to the height and weight of respondents. This measured BMI. Participants then categorized as underweight (BMI < 18.5), normal weight (BMI = 18.5–24.9), overweight (BMI = 25–29.9), and obese (BMI >30.0). The second part of the study involved questions about the participants’ lifestyle habits. To assess the significance of the questions, aChi-squared test was applied. We found that prevalence of being overweight and obese among medical students was (24.4%) and (19%) respectively. for non-medical students the prevalence of being overweight and obesity was (25.6%) and (16.5%) respectively. regarding dietary habits more than half of the students (54.2%) who had three meals or more have a positive relation with obesity. A positive relation was noticed between lack of physical activity and high BMI. Positive relation was found between high BMI and screen time. Regarding sleep hours more than half of the students spend 6-8 hours in sleeping per day

    Database Development for Pavement Performance Modeling

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    Pavement Management System (PMS) is defined as set of tools or methods that can support decision makers in finding the optimum strategies for providing, evaluating, and maintaining pavement condition in acceptable level. The Iowa Pavement Management Program (IPMP) provides information about Iowa highways such as distress data and maintenance activates. One of the most factors that affect pavement performance is weather factors (temperature, freeze-thaw cycles, and rainfall). The historical climate data was obtained from Iowa Environmental Mesonet (IEM) for counties in the state of Iowa. The pavement condition and climate data can be integrated for pavement performance modeling. The Geographic Information System (GIS) is identified as an effective tool for data integration. The primary goal of this paper is to utilize the GIS tools to integrate pavement conditions and climate data for improving Iowa PMS

    Understanding the Characteristics of IT Capability in Delivering a Customer-Focused Strategy: The case of Saudi Bank

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    The rationale is to address the perceived gap in the existing literature by exploring the relationship between information technology capabilities (ITC) and customer-focused strategies (CFS). It is essential to explore how technologies enable organizations to implement strategies through a more interactive approach to their customers. Thus, the research objective seeks to improve the understanding of the relationship between ITC and the organizational goal of achieving a customer-focused strategy. Identifying the characteristics of ITC makes the organization focus on developing these characteristics, which may help to achieve an appropriate level of customer-focused strategies. The analysis of the data collected indicates five major characteristics of ITC.: 1) The fulfilment of business requirements on time, 2) the automation of business processes, 3) supporting business continuity, 4) the integration of multiple business systems and applications, and 5) the availability of timely and correct information. The conclusion summarizes these characteristics in the context of how they might affect a customer-focused strategy

    Synthetic Sensor Data for Human Activity Recognition

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    Human activity recognition (HAR) based on wearable sensors has emerged as an active topic of research in machine learning and human behavior analysis because of its applications in several fields, including health, security and surveillance, and remote monitoring. Machine learning algorithms are frequently applied in HAR systems to learn from labeled sensor data. The effectiveness of these algorithms generally relies on having access to lots of accurately labeled training data. But labeled data for HAR is hard to come by and is often heavily imbalanced in favor of one or other dominant classes, which in turn leads to poor recognition performance. In this study we introduce a generative adversarial network (GAN)-based approach for HAR that we use to automatically synthesize balanced and realistic sensor data. GANs are robust generative networks, typically used to create synthetic images that cannot be distinguished from real images. Here we explore and construct a model for generating several types of human activity sensor data using a Wasserstein GAN (WGAN). We assess the synthetic data using two commonly-used classifier models, Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM). We evaluate the quality and diversity of the synthetic data by training on synthetic data and testing on real sensor data, and vice versa. We then use synthetic sensor data to oversample the imbalanced training set. We demonstrate the efficacy of the proposed method on two publicly available human activity datasets, the Sussex-Huawei Locomotion (SHL) and Smoking Activity Dataset (SAD). We achieve improvements of using WGAN augmented training data over the imbalanced case, for both SHL (0.85 to 0.95 F1-score), and for SAD (0.70 to 0.77 F1-score) when using a CNN activity classifier
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