Coupled plasmon - phonon excitations in extrinsic monolayer graphene

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

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

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

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

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

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

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

Critical delay factors in housing construction projects in Saudi Arabia: assessment and solutions

Given the Kingdom of Saudi Arabia’s (KSA) current housing shortage, the government recently made substantial investments in public-sector housing projects across the country. To maximise the efficiency of these investments, the problem of construction projects delay must be overcome. Therefore, this research project identified delay factors in public-sector housing construction projects in the KSA, and proposed solutions for mitigating or avoiding the construction delays. The main aim of the study is to assess housing projects in the KSA, in terms of delays in the construction phase, and to develop a comprehensive framework based on the analysis of the delay factors (such as client-related, consultantrelated, etc.) to mitigate or prevent their occurrence. The study highlights the importance of delays in construction and provides a complete review of issues of importance in terms of concepts, principles and challenges. In addition, the literature was reviewed through an effective survey to find out the causes of delays in order to build general knowledge of delayed construction projects. A mixed-method research design was adopted involving a literature review, surveys, interviews and case studies. The primary data focused on the delay factors contributing to housing projects delay in the KSA’s construction industry, as well as viable solutions. Data analysis involved descriptive statistics and thematic analysis, and the participants were clients, consultants and contractors in the KSA’s construction industry. Consequently, more than 80 delay factors were identified, then classified into 10 groups and ranked in terms of their severity. A conceptual framework has been developed to advise on the processes that stakeholders should embrace in housing construction projects. Whereas, the solutions identified were used to generate an evidence-based framework, which practitioners can use to mitigate or avoid delays in housing projects. Key aspects of the solution may include the use of building information modelling (BIM), review and assess the government's role in housing projects. Furthermore, all stakeholders should take measures to prevent and reduce delay in housing projects, by promoting FIDIC contracts, establishing an independent planning board, integrating training and awareness initiatives, and eventually implementing the proposed conceptual framework

Deeply-trapped molecules in self-nanostructured gas-phase material

Since the advent of atom laser-cooling, trapping or cooling natural molecules has been a long standing and challenging goal. Here, we demonstrate a method for laser-trapping molecules that is radically novel in its configuration, in its underlined physical dynamics and in its outcomes. It is based on self-optically spatially-nanostructured high pressure molecular hydrogen confined in hollow-core photonic-crystal-fibre. An accelerating molecular-lattice is formed by a periodic potential associated with Raman saturation except for a 1-dimentional array of nanometer wide and strongly-localizing sections. In these sections, molecules with a speed of as large as 1800 m/s are trapped, and stimulated Raman scattering in the Lamb-Dicke regime occurs to generate high power forward and backward-Stokes continuous-wave laser with sideband-resolved sub-Doppler emission spectrum. The spectrum exhibits a central line with a sub-recoil linewidth of as low as 14 kHz, more than 5 orders-of-magnitude narrower than in conventional Raman scattering, and sidebands comprising Mollow triplet, molecular motional-sidebands and four-wave-mixing.Comment: 28 pages 1-12 for main manuscript 13-28 for Methodes and appendices 4 figures for Main manuscript 12 figures for the Methods par