Q-Strategy: A Bidding Strategy for Market-Based Allocation of Grid Services

The application of autonomous agents by the provisioning and usage of computational services is an attractive research field. Various methods and technologies in the area of artificial intelligence, statistics and economics are playing together to achieve i) autonomic service provisioning and usage of Grid services, to invent ii) competitive bidding strategies for widely used market mechanisms and to iii) incentivize consumers and providers to use such market-based systems. The contributions of the paper are threefold. First, we present a bidding agent framework for implementing artificial bidding agents, supporting consumers and providers in technical and economic preference elicitation as well as automated bid generation by the requesting and provisioning of Grid services. Secondly, we introduce a novel consumer-side bidding strategy, which enables a goal-oriented and strategic behavior by the generation and submission of consumer service requests and selection of provider offers. Thirdly, we evaluate and compare the Q-strategy, implemented within the presented framework, against the Truth-Telling bidding strategy in three mechanisms – a centralized CDA, a decentralized on-line machine scheduling and a FIFO-scheduling mechanisms

Demand Response Implementation in an Optimization Based SCADA Model Under Real-Time Pricing Schemes

Advancement of renewable energy resources, development of smart grids, and the effectiveness of demand response programs, can be considered as solutions to deal with the rising of energy consumption. However, there is no benefit if the consumers do not have enough automation infrastructure to use the facilities. Since the entire kinds of buildings have a massive portion in electricity usage, equipping them with optimization-based systems can be very effective. For this purpose, this paper proposes an optimization-based model implemented in a Supervisory Control and Data Acquisition, and Multi Agent System. This optimization model is based on power reduction of air conditioners and lighting systems of an office building with respect to the price-based demand response programs, such as real-time pricing. The proposed system utilizes several agents associated with the different distributed based controller devices in order to perform decision making locally and communicate with other agents to fulfill the overall system’s goal. In the case study of the paper, the proposed system is used in order to show the cost reduction in the energy bill of the building, while it respects the user preferences and comfort level.The present work was done and funded in the scope of the following projects: H2020 DREAM-GO Project (Marie Sklodowska-Curie grant agreement No 641794); Project GREEDI (ANI|P2020 17822); and UID/EEA/00760/2013 funded by FEDER Funds through COMPETE program and by National Funds through FCT.info:eu-repo/semantics/publishedVersio

Business and Information Technology Alignment Measurement -- a recent Literature Review

Since technology has been involved in the business context, Business and Information Technology Alignment (BITA) has been one of the main concerns of IT and Business executives and directors due to its importance to overall company performance, especially today in the age of digital transformation. Several models and frameworks have been developed for BITA implementation and for measuring their level of success, each one with a different approach to this desired state. The BITA measurement is one of the main decision-making tools in the strategic domain of companies. In general, the classical-internal alignment is the most measured domain and the external environment evolution alignment is the least measured. This literature review aims to characterize and analyze current research on BITA measurement with a comprehensive view of the works published over the last 15 years to identify potential gaps and future areas of research in the field.Comment: 12 pages, Preprint version, BIS 2018 International Workshops, Berlin, Germany, July 18 to 20, 2018, Revised Paper

A revised geographical range for Liolaemus elongates Koslowsky, 1896 (Squamata: Liolaemini) in Argentina: review of reported and new-data based distribution with new localities

Estimating the effective geographical ranges of species is central to species-oriented conservation and management. In this paper, we review the geographical distribution of Liolaemus elongatus Koslowsky, 1896 with three new records for northern Chubut and southern Río Negro provinces, Argentina. Based on detailed locality records pooled from multiple data sources, including new records obtained for this study, we revise the range of L. elongatus sensu stricto and provide geographical distribution maps comparing the previously recognized range to that proposed herein. Our results show that L. elongatus possesses a much more limited geographic distribution than previously thought, being restricted to areas south of 38°S latitude; the newly proposed range is merely half the species formerly recognized geographical distribution

New coil concept for endoluminal MR imaging: Initial results in staging of gastric carcinoma in correlation with Histopathology

Our aim was to conduct a prospective study to evaluate staging accuracy of a new coil concept for endoluminal magnetic resonance imaging (MRI) on ex vivo gastric carcinomas. Twenty-eight consecutive patients referred to surgery with a clinically proven primary gastric malignancy were included. Surgical specimens were examined with a foldable and self-expanding loop coil (8-cm diameter) at 1.5 Tesla immediately after total gastrectomy. T1- and T2-weighted and opposed-phase sequences (axial, frontal sections; 3- to 4-mm slice thickness) were acquired. Investigators blinded to any patient information analyzed signal intensity of normal gastric wall, gastric tumor, and lymph nodes. Findings were compared with histopathological staging. On surgical specimens, 2–5 gastric wall layers could be visualized. All gastric tumors (26 carcinomas, two lymphomas) were identified on endoluminal MR data (100%). Overall accuracy for T staging was 75% (18/24); sensitivity to detect serosal involvement was 80% and specificity 89%. N staging correlated in 58% (14/24) with histopathology (N+ versus N−). The endoluminal coil concept is feasible and applicable for an ex vivo setting. Endoluminal MR data provided sufficient detail for gastric wall layer differentiation, and therefore, identification of T stages in gastric carcinoma is possible. Further investigations in in vivo settings should explore the potential of our coil concept for endoluminal MR imaging

Research Directions in the Clinical Implementation of Pharmacogenomics: An Overview of US Programs and Projects

Response to a drug often differs widely among individual patients. This variability is frequently observed not only with respect to effective responses but also with adverse drug reactions. Matching patients to the drugs that are most likely to be effective and least likely to cause harm is the goal of effective therapeutics. Pharmacogenomics (PGx) holds the promise of precision medicine through elucidating the genetic determinants responsible for pharmacological outcomes and using them to guide drug selection and dosing. Here we survey the US landscape of research programs in PGx implementation, review current advances and clinical applications of PGx, summarize the obstacles that have hindered PGx implementation, and identify the critical knowledge gaps and possible studies needed to help to address them

Exploring Statistical and Population Aspects of Network Complexity

The characterization and the definition of the complexity of objects is an important but very difficult problem that attracted much interest in many different fields. In this paper we introduce a new measure, called network diversity score (NDS), which allows us to quantify structural properties of networks. We demonstrate numerically that our diversity score is capable of distinguishing ordered, random and complex networks from each other and, hence, allowing us to categorize networks with respect to their structural complexity. We study 16 additional network complexity measures and find that none of these measures has similar good categorization capabilities. In contrast to many other measures suggested so far aiming for a characterization of the structural complexity of networks, our score is different for a variety of reasons. First, our score is multiplicatively composed of four individual scores, each assessing different structural properties of a network. That means our composite score reflects the structural diversity of a network. Second, our score is defined for a population of networks instead of individual networks. We will show that this removes an unwanted ambiguity, inherently present in measures that are based on single networks. In order to apply our measure practically, we provide a statistical estimator for the diversity score, which is based on a finite number of samples

AgMIP-Wheat multi-model simulations on climate change impact and adaptation for global wheat, SDATA-20-01059

The climate change impact and adaptation simulations from the Agricultural Model Intercomparison and Improvement Project (AgMIP) for wheat provide a unique dataset of multi-model ensemble simulations for 60 representative global locations covering all global wheat mega environments. The multi-model ensemble reported here has been thoroughly benchmarked against a large number of experimental data, including different locations, growing season temperatures, atmospheric CO2 concentration, heat stress scenarios, and their interactions. In this paper, we describe the main characteristics of this global simulation dataset. Detailed cultivar, crop management, and soil datasets were compiled for all locations to drive 32 wheat growth models. The dataset consists of 30-year simulated data including 25 output variables for nine climate scenarios, including Baseline (1980-2010) with 360 or 550 ppm CO2, Baseline +2oC or +4oC with 360 or 550 ppm CO2, a mid-century climate change scenario (RCP8.5, 571 ppm CO2), and 1.5°C (423 ppm CO2) and 2.0oC (487 ppm CO2) warming above the pre-industrial period (HAPPI). This global simulation dataset can be used as a benchmark from a well-tested multi-model ensemble in future analyses of global wheat. Also, resource use efficiency (e.g., for radiation, water, and nitrogen use) and uncertainty analyses under different climate scenarios can be explored at different scales. The DOI for the dataset is 10.5281/zenodo.4027033 (AgMIP-Wheat, 2020), and all the data are available on the data repository of Zenodo (doi: 10.5281/zenodo.4027033).Two scientific publications have been published based on some of these data here

Quality Indicators for Colonoscopy Procedures: A Prospective Multicentre Method for Endoscopy Units

BACKGROUND AND AIMS: Healthcare professionals are required to conduct quality control of endoscopy procedures, and yet there is no standardised method for assessing quality. The topic of the present study was to validate the applicability of the procedure in daily practice, giving physicians the ability to define areas for continuous quality improvement. METHODS: In ten endoscopy units in France, 200 patients per centre undergoing colonoscopy were enrolled in the study. An evaluation was carried out based on a prospectively developed checklist of 10 quality-control indicators including five dependent upon and five independent of the colonoscopy procedure. RESULTS: Of the 2000 procedures, 30% were done at general hospitals, 20% at university hospitals, and 50% in private practices. The colonoscopies were carried out for a valid indication for 95.9% (range 92.5-100). Colon preparation was insufficient in 3.7% (range 1-10.5). Colonoscopies were successful in 95.3% (range 81-99). Adenoma detection rate was 0.31 (range 0.17-0.45) in successful colonoscopies. CONCLUSION: This tool for evaluating the quality of colonoscopy procedures in healthcare units is based on standard endoscopy and patient criteria. It is an easy and feasible procedure giving the ability to detect suboptimal practice and differences between endoscopy-units. It will enable individual units to assess the quality of their colonoscopy techniques

Global wheat production with 1.5 and 2.0°C above pre‐industrial warming

Efforts to limit global warming to below 2°C in relation to the pre‐industrial level are under way, in accordance with the 2015 Paris Agreement. However, most impact research on agriculture to date has focused on impacts of warming >2°C on mean crop yields, and many previous studies did not focus sufficiently on extreme events and yield interannual variability. Here, with the latest climate scenarios from the Half a degree Additional warming, Prognosis and Projected Impacts (HAPPI) project, we evaluated the impacts of the 2015 Paris Agreement range of global warming (1.5 and 2.0°C warming above the pre‐industrial period) on global wheat production and local yield variability. A multi‐crop and multi‐climate model ensemble over a global network of sites developed by the Agricultural Model Intercomparison and Improvement Project (AgMIP) for Wheat was used to represent major rainfed and irrigated wheat cropping systems. Results show that projected global wheat production will change by −2.3% to 7.0% under the 1.5°C scenario and −2.4% to 10.5% under the 2.0°C scenario, compared to a baseline of 1980–2010, when considering changes in local temperature, rainfall, and global atmospheric CO2 concentration, but no changes in management or wheat cultivars. The projected impact on wheat production varies spatially; a larger increase is projected for temperate high rainfall regions than for moderate hot low rainfall and irrigated regions. Grain yields in warmer regions are more likely to be reduced than in cooler regions. Despite mostly positive impacts on global average grain yields, the frequency of extremely low yields (bottom 5 percentile of baseline distribution) and yield inter‐annual variability will increase under both warming scenarios for some of the hot growing locations, including locations from the second largest global wheat producer—India, which supplies more than 14% of global wheat. The projected global impact of warming <2°C on wheat production is therefore not evenly distributed and will affect regional food security across the globe as well as food prices and trade