Electrocardiographic (ECG) criteria for determining left ventricular mass in young healthy men; data from the LARGE Heart study

Background: Doubts remain over the use of the ECG in identifying those with increased left ventricular (LV) mass. This is especially so in young individuals, despite their high prevalence of ECG criteria for LV hypertrophy. We performed a study using cardiovascular magnetic resonance (CMR), which provides an in vivo non-invasive gold standard method of measuring LV mass, allowing accurate assessment of electrocardiography as a tool for defining LV hypertrophy in the young.Methods and results: Standard 12-lead ECGs were obtained from 101 Caucasian male army recruits aged (mean +/- SEM) 19.7 +/- 0.2 years. LV mass was measured using CMR. LV mass indexed to body surface area demonstrated no significant correlation with the Cornell Amplitude criteria or Cornell Product for LV hypertrophy. Moderate correlations were seen with the Sokolow-Lyon Amplitude (0.28) and Sokolow-Lyon Product (0.284). Defining LV hypertrophy as a body surface area indexed left ventricular mass of 93 g/m(2), calculated sensitivities [and specificities] were as follows; 38.7% [74.3%] for the Sokolow-Lyon criteria, 43.4% [61.4%] for the Sokolow-Lyon Product, 19.4% [91.4%] for Cornell Amplitude, and 22.6% [85.7%] for Cornell Product. These values are substantially less than those reported for older age groups.Conclusion: ECG criteria for LV hypertrophy may have little value in determining LV mass or the presence of LV hypertrophy in young fit males

Impact factors for business system success

In most organizations, knowledge sharing is often lacking when it comes to business systems success. This paper investigates factors affecting business systems success in Saudi organisations. Data were collected from private organisations in Saudi Arabia and Partial Least Square approach has been applied to analyse the data. The results show that organisational culture influence knowledge sharing towards business systems success. In addition, both intrinsic motivation and perceived usefulness has positive influence on business system success. This indicates that business system success is built upon the concept of knowledge sharing and user motivation

Multiparametric MR imaging for detection of clinically significant prostate cancer: a validation cohort study with transperineal template prostate mapping as the reference standard.

PURPOSE: To evaluate the diagnostic performance of multiparametric (MP) magnetic resonance (MR) imaging for prostate cancer detection by using transperineal template prostate mapping (TTPM) biopsies as the reference standard and to determine the potential ability of MP MR imaging to identify clinically significant prostate cancer. MATERIALS AND METHODS: Institutional review board exemption was granted by the local research ethics committee for this retrospective study. Included were 64 men (mean age, 62 years [range, 40-76]; mean prostate-specific antigen, 8.2 ng/mL [8.2 μg/L] [range, 2.1-43 ng/mL]), 51 with biopsy-proved cancer and 13 suspected of having clinically significant cancer that was biopsy negative or without prior biopsy. MP MR imaging included T2-weighted, dynamic contrast-enhanced and diffusion-weighted imaging (1.5 T, pelvic phased-array coil). Three radiologists independently reviewed images and were blinded to results of biopsy. Two-by-two tables were derived by using sectors of analysis of four quadrants, two lobes, and one whole prostate. Primary target definition for clinically significant disease necessary to be present within a sector of analysis on TTPM for that sector to be deemed positive was set at Gleason score of 3+4 or more and/or cancer core length involvement of 4 mm or more. Sensitivity, negative predictive value, and negative likelihood ratio were calculated to determine ability of MP MR imaging to rule out cancer. Specificity, positive predictive value, positive likelihood ratio, accuracy (overall fraction correct), and area under receiver operating characteristic curves were also calculated. RESULTS: Twenty-eight percent (71 of 256) of sectors had clinically significant cancer by primary endpoint definition. For primary endpoint definition (≥ 4 mm and/or Gleason score ≥ 3+4), sensitivity, negative predictive value, and negative likelihood ratios were 58%-73%, 84%-89%, and 0.3-0.5, respectively. Specificity, positive predictive value, and positive likelihood ratios were 71%-84%, 49%-63%, and 2.-3.44, respectively. Area under the curve values were 0.73-0.84. CONCLUSION: Results of this study indicate that MP MR imaging has a high negative predictive value to rule out clinically significant prostate cancer and may potentially have clinical use in diagnostic pathways of men at risk

Wind Energy Potential at Badin and Pasni Costal Line of Pakistan

Unfortunately, Pakistan is facing an acute energy crisis since the past decade due to the increasing population growth and is heavily dependent on imports of fossil fuels. The shortage of the electricity is 14-18 hours in rural areas and 8-10 hours in urban areas. This situation has been significantly affecting the residential, industrial and commercial sectors in the country. At this time, it is immense challenges for the government to keep the power supply provision continue in the future for the country. In this situation, it has been the increased research to explore renewable energy resources in the country to fulfill the deficit scenario in the state. The renewable energy sector has not penetrated in the energy mix, currently in the upcoming markets. This paper highlights the steps taken by the country in the past and is taking steps at the present time to get rid of from the existing energy crisis when most urban areas are suffering from power outages for 12 hours on regular basis. Until 2009, no single grid interconnected wind established, but now the circumstances are changing significantly and wind farms are contributing to the national grid is the reality now. The initiation of the three wind farms interconnection network and many others in the pipeline are going to be operational soon. The federal policy on wind energy system has recently changed. Surprisingly, the continuing schemes of the wind farm are getting slow. This paper reviews developments in the wind energy sector in the country and lists some suggestions that can contribute to improving the penetration of wind energy in the national energy sector.Article History: Received Dec 16th 2016; Received in revised form May 15th 2017; Accepted June 18th 2017; Available onlineHow to Cite This Article: Kaloi,G.S., Wang, J., Baloch, M.H and Tahir, S. (2017) Wind Energy Potential at Badin and Pasni Costal Line Pakistan. Int. Journal of Renewable Energy Development, 6(2), 103-110.https://doi.org/10.14710/ijred.6.2.103-11

PennyLane: Automatic differentiation of hybrid quantum-classical computations

PennyLane is a Python 3 software framework for optimization and machine learning of quantum and hybrid quantum-classical computations. The library provides a unified architecture for near-term quantum computing devices, supporting both qubit and continuous-variable paradigms. PennyLane's core feature is the ability to compute gradients of variational quantum circuits in a way that is compatible with classical techniques such as backpropagation. PennyLane thus extends the automatic differentiation algorithms common in optimization and machine learning to include quantum and hybrid computations. A plugin system makes the framework compatible with any gate-based quantum simulator or hardware. We provide plugins for Strawberry Fields, Rigetti Forest, Qiskit, Cirq, and ProjectQ, allowing PennyLane optimizations to be run on publicly accessible quantum devices provided by Rigetti and IBM Q. On the classical front, PennyLane interfaces with accelerated machine learning libraries such as TensorFlow, PyTorch, and autograd. PennyLane can be used for the optimization of variational quantum eigensolvers, quantum approximate optimization, quantum machine learning models, and many other applications.Comment: Code available at https://github.com/XanaduAI/pennylane/ . Significant contributions to the code (new features, new plugins, etc.) will be recognized by the opportunity to be a co-author on this pape

Quantum Logic Gate Synthesis as a Markov Decision Process

Reinforcement learning has witnessed recent applications to a variety of tasks in quantum programming. The underlying assumption is that those tasks could be modeled as Markov Decision Processes (MDPs). Here, we investigate the feasibility of this assumption by exploring its consequences for two of the simplest tasks in quantum programming: state preparation and gate compilation. By forming discrete MDPs, focusing exclusively on the single-qubit case, we solve for the optimal policy exactly through policy iteration. We find optimal paths that correspond to the shortest possible sequence of gates to prepare a state, or compile a gate, up to some target accuracy. As an example, we find sequences of H and T gates with length as small as 11 producing ~99% fidelity for states of the form (HT)^{n} |0> with values as large as n=10^{10}. This work provides strong evidence that reinforcement learning can be used for optimal state preparation and gate compilation for larger qubit spaces.Comment: 10 pages, 2 figures, 2 tables. Comments and feedback welcom

Holographic zero sound at finite temperature in the Sakai-Sugimoto model

In this paper, we study the fate of the holographic zero sound mode at finite temperature and non-zero baryon density in the deconfined phase of the Sakai-Sugimoto model of holographic QCD. We establish the existence of such a mode for a wide range of temperatures and investigate the dispersion relation, quasi-normal modes, and spectral functions of the collective excitations in four different regimes, namely, the collisionless quantum, collisionless thermal, and two distinct hydrodynamic regimes. For sufficiently high temperatures, the zero sound completely disappears, and the low energy physics is dominated by an emergent diffusive mode. We compare our findings to Landau-Fermi liquid theory and to other holographic models.Comment: 1+24 pages, 19 figures, PDFTeX, v2: some comments and references added, v3: some clarifications relating to the different regimes added, matches version accepted for publication in JHEP, v4: corrected typo in eq. (3.18

Decision making on adoption of cloud computing in e-commerce using fuzzy TOPSIS

© 2017 IEEE. Cloud computing promises enhanced scalability, flexibility, and cost-efficiency. In practice, however, there are many uncertainties about the usage of cloud computing resources in the e-commerce context. As e-commerce is dependent on a reliable and secure online store, it is important for decision makers to adopt an optimal cloud computing mode (Such as SaaS, PaaS and IaaS). This study assesses the factors associated with cloud-based e-commerce based on TOE (technological, organizational, and environmental) framework using multi-criteria decision-making technique (Fuzzy TOPSIS). The results show that Fuzzy TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) approach proposes software-as-a-service (SaaS) as the best choice for e-commerce business

Predicting the effect of voids on mechanical properties of woven composites.

An accurate yet easy to use methodology for determining the effective mechanical properties of woven fabric reinforced composites is presented. The approach involves generating a representative unit cell geometry based on randomly selected 2D orthogonal slices from a 3D X-ray micro-tomographic scan. Thereafter, the finite element mesh is generated from this geometry. Analytical and statistical micromechanics equations are then used to calculate effective input material properties for the yarn and resin regions within the FE mesh. These analytical expressions account for the effect of resin volume fraction within the yarn (due to infiltration during curing) as well as the presence of voids within the composite. The unit cell model is then used to evaluate the effective properties of the composite.DelPHE 780 Project funded by UK Department of International Development (DFID), through British Council managed DelPHE scheme