Energy rating of a water pumping station using multivariate analysis

Among water management policies, the preservation and the saving of energy demand in water supply and treatment systems play key roles. When focusing on energy, the customary metric to determine the performance of water supply systems is linked to the definition of component-based energy indicators. This approach is unfit to account for interactions occurring among system elements or between the system and its environment. On the other hand, the development of information technology has led to the availability of increasing large amount of data, typically gathered from distributed sensor networks in so-called smart grids. In this context, data intensive methodologies address the possibility of using complex network modeling approaches, and advocate the issues related to the interpretation and analysis of large amount of data produced by smart sensor networks. In this perspective, the present work aims to use data intensive techniques in the energy analysis of a water management network. The purpose is to provide new metrics for the energy rating of the system and to be able to provide insights into the dynamics of its operations. The study applies neural network as a tool to predict energy demand, when using flowrate and vibration data as predictor variables

Industrial energy management systems in Italy: state of the art and perspective

Despite the economic crisis, the impact of industry sector Share on the total primary energy demand in Italy is still significant. The certification of companies according to the standard ISO 50001:2011 ("Energy management systems Requirements and guidelines for use"), can represent a key element in the achievement of objectives set in the 20-20-20 Climate-Energy Package. This paper illustrates the state of implementation of ISO 50001 certifications in Italy, reporting on the results of a questionnaire carried out as a part of a master's thesis project at Sapienza, University of Rome in collaboration with FIRE (Italian Federation for the Rational Use of Energy) that included the major certification bodies, certified companies and consultants. The purpose is to outline the current situation, identify the perspectives and highlight the pros and cons related to the implementation of an Energy Management System (EnMS). The big picture shows that Italy, one of the leading countries in energy efficiency policies, suffer from a significant delay in the implementation of the EnMS in industry with respect to Germany. The results of the survey also show that the definition of energy performance indicators, as hell as the individuations of an energy baseline and a. monitoring plan constitute the requirements most critical to comply with for companies than for consultants. It also appears that more than 35% of companies already ISO 50001 certified have received benefits in terms of cumulative energy saving above 5%, and that the main reason why they have implemented an EnMS is related to the potential impact on increasing the competitiveness of the core business

On the use of artificial Intelligence for condition monitoring in horizontal-axis wind turbines

Wind power is one of the fastest-growing renewable energy sectors and is considered instrumental in the ongoing decarbonization process. However, wind turbines (WTs) present high operation and maintenance costs caused by inefficiencies and failures, leading to ever-increasing attention to effective Condition Monitoring (CM) strategies. Nowadays, modern WTs are integrated with sensor networks as part of the Supervisory Control and Data Acquisition (SCADA) system for supervision purposes. CM of wind farms through predictive models based on routinely collected SCADA data is envisaged as a viable mean of improving producibility by spotting operational inefficiencies. In this paper, we introduce an unsupervised anomaly detection framework for wind turbine using SCADA data. It involves the use of a multivariate feature selection algorithm based on a novel Combined Power Predictive Score (CPPS), where the information content of combinations of variables is considered for the prediction of one or more key parameters. The framework has been tested on SCADA data collected from an off-shore wind farm, and the results showed that it successfully detects anomalies and anticipates major bearing failures by outperforming a recent deep neural approach

The Interplay of Lipids, Lipoproteins, and Immunity in Atherosclerosis

Purpose of Review: Atherosclerosis is an inflammatory disorder of the arterial wall, in which several players contribute to the onset and progression of the disease. Besides the well-established role of lipids, specifically cholesterol, and immune cell activation, new insights on the molecular mechanisms underlying the atherogenic process have emerged. Recent Findings: Meta-inflammation, a condition of low-grade immune response caused by metabolic dysregulation, immunological memory of innate immune cells (referred to as “trained immunity”), cholesterol homeostasis in dendritic cells, and immunometabolism, i.e., the interplay between immunological and metabolic processes, have all emerged as new actors during atherogenesis. These observations reinforced the interest in directly targeting inflammation to reduce cardiovascular disease. Summary: The novel acquisitions in pathophysiology of atherosclerosis reinforce the tight link between lipids, inflammation, and immune response, and support the benefit of targeting LDL-C as well as inflammation to decrease the CVD burden. How this will translate into the clinic will depend on the balance between costs (monoclonal antibodies either to PCSK9 or to IL-1ß), side effects (increased incidence of death due to infections for anti-IL-1ß antibody), and the benefits for patients at high CVD risk

On the Expressivity and Applicability of Model Representation Formalisms

A number of first-order calculi employ an explicit model representation formalism for automated reasoning and for detecting satisfiability. Many of these formalisms can represent infinite Herbrand models. The first-order fragment of monadic, shallow, linear, Horn (MSLH) clauses, is such a formalism used in the approximation refinement calculus. Our first result is a finite model property for MSLH clause sets. Therefore, MSLH clause sets cannot represent models of clause sets with inherently infinite models. Through a translation to tree automata, we further show that this limitation also applies to the linear fragments of implicit generalizations, which is the formalism used in the model-evolution calculus, to atoms with disequality constraints, the formalisms used in the non-redundant clause learning calculus (NRCL), and to atoms with membership constraints, a formalism used for example in decision procedures for algebraic data types. Although these formalisms cannot represent models of clause sets with inherently infinite models, through an additional approximation step they can. This is our second main result. For clause sets including the definition of an equivalence relation with the help of an additional, novel approximation, called reflexive relation splitting, the approximation refinement calculus can automatically show satisfiability through the MSLH clause set formalism.Comment: 15 page

Internal combustion engine sensor network analysis using graph modeling

In recent years there has been a rapid development in technologies for smart monitoring applied to many different areas (e.g. building automation, photovoltaic systems, etc.). An intelligent monitoring system employs multiple sensors distributed within a network to extract useful information for decision-making. The management and the analysis of the raw data derived from the sensor network includes a number of specific challenges still unresolved, related to the different communication standards, the heterogeneous structure and the huge volume of data. In this paper we propose to apply a method based on complex network theory, to evaluate the performance of an Internal Combustion Engine. Data are gathered from the OBD sensor subset and from the emission analyzer. The method provides for the graph modeling of the sensor network, where the nodes are represented by the sensors and the edge are evaluated with non-linear statistical correlation functions applied to the time series pairs. The resulting functional graph is then analyzed with the topological metrics of the network, to define characteristic proprieties representing useful indicator for the maintenance and diagnosis

Is aortic wall degeneration related to bicuspid aortic valve anatomy in patients with valvular disease?

ObjectivePatients with bicuspid aortic valve are at increased risk for aortic complications.MethodsA total of 115 consecutive patients with bicuspid aortic valve disease underwent surgery of the ascending aorta. We classified the cusp configuration by 3 types: fusion of left coronary and right coronary cusps (type A), fusion of right coronary and noncoronary cusps (type B), and fusion of left coronary and noncoronary cusps (type C). Histopathologic changes in the ascending aortic wall were graded (aortic wall score).ResultsWe observed type A fusion in 85 patients (73.9%), type B fusion in 28 patients (24.3%), and type C fusion in 2 patients (1.8%). Patients with type A fusion were younger at operation than patients with type B fusion (51.3 ± 15.5 years vs 58.7 ± 7.6 years, respectively; P = .034). The mean ascending aorta diameter was 48.9 ± 5.0 mm and 48.7 ± 5.7 mm in type A and type B fusion groups, respectively (P = .34). The mean aortic root diameter was significantly larger in type A fusion (4.9 ± 6.7 mm vs 32.7 ± 2.8 mm; P < .0001). The aortic wall score was significantly higher in type A fusion than in type B fusion (P = .02). The prevalence of aortic wall histopathologic changes was significantly higher in type A fusion. Moreover, there were no statistically significant differences between type A and type B fusion in terms of prevalence of bicuspid aortic valve stenosis, regurgitation, or mixed disease.ConclusionIn diseased bicuspid aortic valves, there was a statistically significant association between type A valve anatomy and a more severe degree of wall degeneration in the ascending aorta and dilatation of the aortic root at younger age compared with type B valve anatomy

Splitting Proofs for Interpolation

We study interpolant extraction from local first-order refutations. We present a new theoretical perspective on interpolation based on clearly separating the condition on logical strength of the formula from the requirement on the com- mon signature. This allows us to highlight the space of all interpolants that can be extracted from a refutation as a space of simple choices on how to split the refuta- tion into two parts. We use this new insight to develop an algorithm for extracting interpolants which are linear in the size of the input refutation and can be further optimized using metrics such as number of non-logical symbols or quantifiers. We implemented the new algorithm in first-order theorem prover VAMPIRE and evaluated it on a large number of examples coming from the first-order proving community. Our experiments give practical evidence that our work improves the state-of-the-art in first-order interpolation.Comment: 26th Conference on Automated Deduction, 201

Thermo-mechanical behavior of surface acoustic waves in ordered arrays of nanodisks studied by near infrared pump-probe diffraction experiments

The ultrafast thermal and mechanical dynamics of a two-dimensional lattice of metallic nano-disks has been studied by near infrared pump-probe diffraction measurements, over a temporal range spanning from 100 fs to several nanoseconds. The experiments demonstrate that, in these systems, a two-dimensional surface acoustic wave (2DSAW), with a wavevector given by the reciprocal periodicity of the array, can be excited by ~120 fs Ti:sapphire laser pulses. In order to clarify the interaction between the nanodisks and the substrate, numerical calculations of the elastic eigenmodes and simulations of the thermodynamics of the system are developed through finite-element analysis. At this light, we unambiguously show that the observed 2DSAW velocity shift originates from the mechanical interaction between the 2DSAWs and the nano-disks, while the correlated 2DSAW damping is due to the energy radiation into the substrate.Comment: 13 pages, 10 figure