QCD at High Temperature : Results from Lattice Simulations with an Imaginary mu

We summarize our results on the phase diagram of QCD with emphasis on the high temperature regime. For $T \ge 1.5 T_c$ the results are compatible with a free field behavior, while for $T \simeq 1.1 T_c$ this is not the case, clearly exposing the strongly interacting nature of QCD in this regionComment: 7 pages, 2 figures; To appear in the proceedings of QCD@Work 2005,International Workshop on Quantum Chromodynamics, Conversano, Bari, Italy, 16-20 Jun 200

Providing Transaction Class-Based QoS in In-Memory Data Grids via Machine Learning

Elastic architectures and the ”pay-as-you-go” resource pricing model offered by many cloud infrastructure providers may seem the right choice for companies dealing with data centric applications characterized by high variable workload. In such a context, in-memory transactional data grids have demonstrated to be particularly suited for exploiting advantages provided by elastic computing platforms, mainly thanks to their ability to be dynamically (re-)sized and tuned. Anyway, when specific QoS requirements have to be met, this kind of architectures have revealed to be complex to be managed by humans. Particularly, their management is a very complex task without the stand of mechanisms supporting run-time automatic sizing/tuning of the data platform and the underlying (virtual) hardware resources provided by the cloud. In this paper, we present a neural network-based architecture where the system is constantly and automatically re-configured, particularly in terms of computing resources

Machine Learning For In-Region Location Verification In Wireless Networks

In-region location verification (IRLV) aims at verifying whether a user is inside a region of interest (ROI). In wireless networks, IRLV can exploit the features of the channel between the user and a set of trusted access points. In practice, the channel feature statistics is not available and we resort to machine learning (ML) solutions for IRLV. We first show that solutions based on either neural networks (NNs) or support vector machines (SVMs) and typical loss functions are Neyman-Pearson (N-P)-optimal at learning convergence for sufficiently complex learning machines and large training datasets . Indeed, for finite training, ML solutions are more accurate than the N-P test based on estimated channel statistics. Then, as estimating channel features outside the ROI may be difficult, we consider one-class classifiers, namely auto-encoders NNs and one-class SVMs, which however are not equivalent to the generalized likelihood ratio test (GLRT), typically replacing the N-P test in the one-class problem. Numerical results support the results in realistic wireless networks, with channel models including path-loss, shadowing, and fading

Exergetic and Economic Analysis of Energy Recovery from the Exhaust Air of Organic Waste Aerobic Bioconversion by Organic Rankine Cycle

Abstract The amount of heat rejected by the exhaust air generated by the aerobic treatment of organic waste (OW) was investigated with the aim of evaluating the amount of electrical energy recoverable by a micro organic Rankine cycle (micro-ORC). Both an energetic and exergetic analysis were performed along with an evaluation of the investment costs. The investigation of the heat content and composition of the exhaust air was experimentally performed on a full scale facility processing 32,000 tonnes/year of OW. Results shows that the average exhaust air rate is of about 4,000 Nm 3 /h with a temperature of 341 K and a relative humidity of 100%. By cooling thi gaseous stream up to 316 K the net power output of the micro-ORC ranges from about 2 kW to about 20 kW. Contemporary the net electrical efficiency decreases from 5% to about 2% whereas the exergetic efficiency ranges in parallel with the net power output from 11% to 1%. Specific investment ranges from about 2,800 €/kW to about 3,900 €/kW and the cost of the electrical energy results of about 0.1 €/kWh to about 0,13 €/kWh

Analysis of the Energetic Potential Generable from an Hybrid Bioreactor Landfill for Waste Organic Fraction

Abstract The analysis of the amount of landfill gas generable from a full scale hybrid bioreactor landfill was investigated. The waste disposed in the landfill biocells was constituted by the waste organic fraction (WOF) arising from the mechanical pre-treatment of the residual municipal solid waste. The average humidity of the WOF was of about 40% by weight on wet basis whereas the volatile solids (VS) where about 50% by weight on total solids. The average total organic carbon concentration was of about 20% by weight of TS. The landfill gas generation potential of WOF was investigate by a standardized anaerobic test shows an average value of about 180 NL/kgVS. The construction of two full scale hybrid bioreactor landfill biocells was followed and the evolution of the composition of the landfill gas generated investigated. Results shows that in about 12 weeks the gas generated shows a methane concentration that rise rapidly from about 20% by vol. up to 55-60% by vol. In about 4 month of collection for energy recovery the amount of landfill gas generated results of about 36 Nm 3 /tonne significantly higher than the one detected for other traditional landfills

replacing energy crops with bio waste for an existing anaerobic digestion plant energetic and carbon footprint in a lca perspective

Abstract The energetic and greenhouse gases emissions performances related to energy crops substitution with bio-waste for an existing anaerobic digestion (AD) facility were investigated in a life cycle assessment (LCA) perspective. For this reason two different scenarios were compared In the base scenario 17,667Mg/year of energy crops were processed in an existing AD facility generating about 7,700 MWh/year whereas 23,000 Mg/year of bio-waste were processed separately in an existing composting facility for organic fertilizer production. In this case the cumulative energy demand (CED) resulted of 11,000 MWh. In the modified scenario the whole energy crops were substituted by the bio-waste in the AD facility leading to the generation of about 5,000 MWh/year of energy with a correspondent CED of 8,600 MWh. The life cycle analysis detected an higher impact for the base scenario. On the other hand the amount of kgCO 2eq generated per each kWh recovered resulted practically the same for both cases

Brain imaging in Kufs disease type B. case reports

The clinical traits of Kufs disease (KD) type B (CLN13), an adult-onset neuronal ceroid lipofuscinosis (NCL), are well established according to the neurological features of the cases reported with mutations in CTSF. The neuroradiological characteristics of this uncommon disease have not yet been outlined

Location-Verification and Network Planning via Machine Learning Approaches

In-region location verification (IRLV) in wireless networks is the problem of deciding if user equipment (UE) is transmitting from inside or outside a specific physical region (e.g., a safe room). The decision process exploits the features of the channel between the UE and a set of network access points (APs). We propose a solution based on machine learning (ML) implemented by a neural network (NN) trained with the channel features (in particular, noisy attenuation values) collected by the APs for various positions both inside and outside the specific region. The output is a decision on the UE position (inside or outside the region). By seeing IRLV as an hypothesis testing problem, we address the optimal positioning of the APs for minimizing either the area under the curve (AUC) of the receiver operating characteristic (ROC) or the cross entropy (CE) between the NN output and ground truth (available during the training). In order to solve the minimization problem we propose a twostage particle swarm optimization (PSO) algorithm. We show that for a long training and a NN with enough neurons the proposed solution achieves the performance of the Neyman-Pearson (N-P) lemma.Comment: Accepted for Workshop on Machine Learning for Communications, June 07 2019, Avignon, Franc