High Mass Standard Model Higgs searches at the Tevatron

We present the results of searches for the Standard Model Higgs boson decaying predominantly to WW pairs, at a center-of-mass energy of sqrt(s)=1.96 TeV, using up to 8.2 fb^{-1} of data collected with the CDF and D0 detectors at the Fermilab Tevatron collider. The analysis techniques and the various channels considered are discussed. These searches result in exclusions across the Higgs mass range of 156.5<mH<173.7 GeV for CDF and 161<mH<170 GeV for D0.Comment: Presented at the 2011 Hadron Collider Physics symposium (HCP-2011), Paris, France, November 14-18 2011, 4 pages, 8 figure

Superpixel-based conditional random fields (SuperCRF) : incorporating global and local context for enhanced deep learning in melanoma histopathology

Computational pathology-based cell classification algorithms are revolutionizing the study of the tumor microenvironment and can provide novel predictive/prognosis biomarkers crucial for the delivery of precision oncology. Current algorithms used on hematoxylin and eosin slides are based on individual cell nuclei morphology with limited local context features. Here, we propose a novel multi-resolution hierarchical framework (SuperCRF) inspired by the way pathologists perceive regional tissue architecture to improve cell classification and demonstrate its clinical applications. We develop SuperCRF by training a state-of-art deep learning spatially constrained- convolution neural network (SC-CNN) to detect and classify cells from 105 high-resolution (20×) H&E-stained slides of The Cancer Genome Atlas melanoma dataset and subsequently, a conditional random field (CRF) by combining cellular neighborhood with tumor regional classification from lower resolution images (5, 1.25×) given by a superpixel-based machine learning framework. SuperCRF led to an 11.85% overall improvement in the accuracy of the state-of-art deep learning SC-CNN cell classifier. Consistent with a stroma-mediated immune suppressive microenvironment, SuperCRF demonstrated that (i) a high ratio of lymphocytes to all lymphocytes within the stromal compartment (p = 0.026) and (ii) a high ratio of stromal cells to all cells (p < 0.0001 compared to p = 0.039 for SC-CNN only) are associated with poor survival in patients with melanoma. SuperCRF improves cell classification by introducing global and local context-based information and can be implemented in combination with any single-cell classifier. SuperCRF provides valuable tools to study the tumor microenvironment and identify predictors of survival and response to therapy

Investigating the factors that affect the time of maximum rejection rate of e-waste using survival analysis

This study aims at investigating the factors which influence positively or negatively electronic waste (e-waste) rejection rates. E-waste quantities have been calculated based on historical sales data worldwide and lifespan distribution. The methodology, which is adopted in this paper in order to estimate the effect that economic, cultural, and demographic factors have upon the time at which maximum e-waste rejection is achieved, is a Weibull parametric accelerated failure time model. Considering the event at which the maximum rejection of e-waste takes place as the dependent variable, it is assumed that it is a function of economic (GDP, GINI index, Internet users, exports/imports and prices), demographic (dependency ratio), and cultural covariates (literacy, masculinity, uncertainty avoidance). The variables are fed to the model after transformation into two major constructs derived from Factor Analysis: the first construct is Wealth (exports, imports, and GDP) and the second is Economic Disparity (size of households, literacy, Internet users, and GINI). The results demonstrate that the time of maximum e-waste rejection rate is prolonged by economic disparity and cultural variables (uncertainty avoidance), while wealth causes a shorter time of rejection rate. The proposed methodology is of great value, as its application could provide useful information in order to develop policies for optimal management of e-waste quantities

Estimation of computer waste quantities using forecasting techniques

Technology changes rapidly over years providing continuously more options for computer alternatives and making life easier for economic, intra-relation or any other transactions. However, the introduction of new technology “pushes” old Information and Communication Technology (ICT) products to non-use. E-waste is defined as the quantities of ICT products which are not in use and is bivariate function of the sold quantities, and the probability that specific computers quantity will be regarded as obsolete. In this paper, an e-waste generation model is presented, which is applied to the following regions: Western and Eastern Europe, Asia/Pacific, Japan/Australia/New Zealand, North and South America. Furthermore, cumulative computer sales were retrieved for selected countries of the regions so as to compute obsolete computer quantities. In order to provide robust results for the forecasted quantities, a selection of forecasting models, namely (i) Bass, (ii) Gompertz, (iii) Logistic, (iv) Trend model, (v) Level model, (vi) AutoRegressive Moving Average (ARMA), and (vii) Exponential Smoothing were applied, depicting for each country that model which would provide better results in terms of minimum error indices (Mean Absolute Error and Mean Square Error) for the in-sample estimation. As new technology does not diffuse in all the regions of the world with the same speed due to different socio-economic factors, the lifespan distribution, which provides the probability of a certain quantity of computers to be considered as obsolete, is not adequately modeled in the literature. The time horizon for the forecasted quantities is 2014-2030, while the results show a very sharp increase in the USA and United Kingdom, due to the fact of decreasing computer lifespan and increasing sales

Measuring Incineration Plants' Performance using Combined Data Envelopment Analysis, Goal Programming and Mixed Integer Linear Programming

Incineration plants produce heat and power from waste, reduce waste disposal to landfills, and discharge harmful emissions and bottom ash. The objective of the incineration plant is to maximize desirable outputs (heat and power) and minimize undesirable outputs (emissions and bottom ash). Therefore, studying the overall impact of incineration plants in a region so as to maximize the benefits and minimize the environmental impact is significant. Majority of prior works focus on plant specific decision making issues including performance analysis. This study proposes a hybrid Data Envelopment Analysis (DEA), Goal Programming (GP) and Mixed Integer Linear Programming (MILP) model to assess the performance of incineration plants, in a specific region, to enhance overall power production, consumption of waste and reduction of emissions. This model not only helps the plant operators to evaluate the effectiveness of incineration but also facilitates the policy makers to plan for overall waste management of the region through decision-making on adding and closing plants on the basis of their efficiency. Majority of prior studies on incineration plants emphasize on how to improve their performance on heat and power production and neglect the waste management aspects. Additionally, optimizing benefits and minimizing negative outputs through fixing targets in order to make decision on shutting down the suboptimal plants has not been modeled in prior research. This research combines both the aspects and addresses the overall performance enhancement of incineration plants within a region from both policy makers and plant operators’ perspectives. The proposed combined DEA, GP and MILP model enables to optimize incineration plants performance within a region by deriving efficiency of each plant and identifying plants to close down on the basis of their performance. The proposed model has been applied to a group of 22 incineration plants in the UK using secondary data in order to demonstrate the effectiveness of the model.

A financial approach to renewable energy production in Greece using goal programming

Investing in renewable energy production is a high interest venture considering global energy needs and the environmental impact of fossil fuel consumption. Motivated by the goals set by the European Union towards 2020, this study aims at designing a renewable energy map (installing solar power plants) in Greece. Three aspects are considered, namely, social, financial, and power production aspects. A goal programming model is developed under target and structural constraints, and all possible weight combinations are examined. The solutions derived from each iteration are subjected to a financial meta-analysis, considering different tax and return scenarios aligned to the Greek taxation and banking system. The analysis considers Greece and each region separately, taking net present value (NPV) as an objective measure to assess the solutions. From the results, it is concluded that the internal rate of return is approximately 22.5%−25%22.5%−25% for the overall network. In addition, higher NPV values are obtained when the financial and power production aspects are given greater emphasis. The proposed model provides multi-dimensional information for decision makers; investors can determine the optimal budgeting mix, and policy makers can determine the weight on each aspect that guarantees the success of the venture

Analyzing M-Service Quality Dimensions Using Multivariate Statistical Techniques

This paper continues previous work of the authors concerning the identification and statistical analysis of the quality dimensions in mobile services (m-services). In this work, the structure of mservice quality into dimensions and criteria, which these dimensions are further analyzed into, is examined and grounded through an empirical analysis. The use of multivariate statistical techniques is decomposed into two stages: in the first stage, Factor Analysis in order to explore the relationship between the examined items (quality criteria) and the constructs (dimensions) proposed through the study of the relevant literature. In the second stage, Cluster Analysis and Principal Component Analysis are employed in order to explore intra-construct relationships. The contribution of this paper lies on the fact that a mix of multivariate statistical techniques is all integrated in a single framework, so that information about the structure of m-service quality criteria and constructs is obtained. The findings of the study confirm the theoretical background and provide valuable managerial insights

Optimal design of the renewable energy map of Greece using weighted goal-programming and data envelopment analysis

Renewable energy forms have been widely used in the past decades highlighting a "green" shift in energy production. An actual reason behind this turn to renewable energy production is EU directives which set the Union's targets for energy production from renewable sources, greenhouse gas emissions and increase in energy efficiency. All member countries are obligated to apply harmonized legislation and practices and restructure their energy production networks in order to meet EU targets. Towards the fulfillment of 20-20-20 EU targets, in Greece a specific strategy which promotes the construction of large scale Renewable Energy Source plants is promoted. In this paper, we present an optimal design of the Greek renewable energy production network applying a 0-1 Weighted Goal Programming model, considering social, environmental and economic criteria. In the absence of a panel of experts Data Envelopment Analysis (DEA) approach is used in order to filter the best out of the possible network structures, seeking for the maximum technical efficiency. Super-Efficiency DEA model is also used in order to reduce the solutions and find the best out of all the possible. The results showed that in order to achieve maximum efficiency, the social and environmental criteria must be weighted more than the economic ones