Partial discharge localization in power transformer tanks using machine learning methods.

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SVM) to more complex approaches like convolutional neural networks (CNN). Multiple case studies are considered, each with different attributes, including sensor position, frequency content of the PD signal, and size of the transformer tank.

Strategies for robust renovation of residential buildings in Switzerland.

Building renovation is urgently required to reduce the environmental impact associated with the building stock. Typically, building renovation is performed by envelope insulation and/or changing the fossil-based heating system. The goal of this paper is to provide strategies for robust renovation considering uncertainties on the future evolution of climate, energy grid, and user behaviors, amongst others by applying life cycle assessment and life cycle cost analysis.

Worldwide Survey on Digital Assistive Technology (DAT) Provision.

Occupational therapists have long been involved in assistive technology (AT) provision worldwide. AT is recognized by the World Health Organization (WHO) to enhance functioning, independence, and autonomy and ultimately promote well-being for people living with disabilities. With the digitalisation of societies, the everyday lives and occupations of individuals are changing, becoming more reliant on digital solutions.

Regression-based Deep-Learning predicts molecular biomarkers from pathology slides.

Deep Learning (DL) can predict biomarkers from cancer histopathology. Several clinically approved applications use this technology. Most approaches, however, predict categorical labels, whereas biomarkers are often continuous measurements.

Strontium-90 pollution can be bioremediated with the green microalga .

Strontium-90 (Sr) is an artificial radioisotope produced by nuclear fission, with a relatively long half-life of 29 years. This radionuclide is released into the environment in the event of a nuclear incident, posing a serious risk to human and ecosystem health. There is a need to develop new efficient methods for the remediation of Sr, as current techniques for its removal have significant technical limitations and involve high energy and economic costs.

METhodological RadiomICs Score (METRICS): a quality scoring tool for radiomics research endorsed by EuSoMII.

Purpose: To propose a new quality scoring tool, METhodological RadiomICs Score (METRICS), to assess and improve research quality of radiomics studies.

Methods: We conducted an online modified Delphi study with a group of international experts. It was performed in three consecutive stages: Stage#1, item preparation; Stage#2, panel discussion among EuSoMII Auditing Group members to identify the items to be voted; and Stage#3, four rounds of the modified Delphi exercise by panelists to determine the items eligible for the METRICS and their weights.

On the robustness of democratic electoral processes to computational propaganda.

There is growing evidence of systematic attempts to influence democratic elections by controlled and digitally organized dissemination of fake news. This raises the question of the intrinsic robustness of democratic electoral processes against external influences. Particularly interesting is to identify the social characteristics of a voter population that renders it more resilient against opinion manipulation.

Detection of urological cancers by the signature of organic volatile compounds in urine, from dogs to electronic noses.

Purpose Of Review: Urine volatile organic compound (VOC) testing for early detection of urological cancers is a minimally invasive and promising method. The objective of this review was to present the results of recently published work on this subject.

Recent Findings: Organic volatile compounds are produced through oxidative stress and peroxidation of cell membranes, and they are eliminated through feces, urine, and sweat.

CheckList for EvaluAtion of Radiomics research (CLEAR): a step-by-step reporting guideline for authors and reviewers endorsed by ESR and EuSoMII.

Even though radiomics can hold great potential for supporting clinical decision-making, its current use is mostly limited to academic research, without applications in routine clinical practice. The workflow of radiomics is complex due to several methodological steps and nuances, which often leads to inadequate reporting and evaluation, and poor reproducibility. Available reporting guidelines and checklists for artificial intelligence and predictive modeling include relevant good practices, but they are not tailored to radiomic research.

A global taxonomy of interpretable AI: unifying the terminology for the technical and social sciences.

Since its emergence in the 1960s, Artificial Intelligence (AI) has grown to conquer many technology products and their fields of application. Machine learning, as a major part of the current AI solutions, can learn from the data and through experience to reach high performance on various tasks. This growing success of AI algorithms has led to a need for interpretability to understand opaque models such as deep neural networks.

Dataset of service life data for 100 building elements and technical systems including their descriptive statistics and fitting to lognormal distribution.

This article presents the descriptive statistics of service life data of building elements, gathered through an international, European and Swiss literature review of LCA, LCC and other sources called "Real-Estate Management sources" that include building owners, banks, insurances, associations of tenants and owners, etc. Furthermore, the properties of the fitted lognormal distribution are given. The data are structured, using a hybrid decomposition (functional decomposition, according to the eBKP-H - SN506511 and material decomposition, as well).

An experimental validation of partial discharge localization using electromagnetic time reversal.

The localization of partial discharge (PD) sources is of importance for the monitoring and maintenance of power transformers. Time difference of arrival (TDoA) based methods are widely adopted in the literature for the localization of PDs. Recently, time reversal (TR) was suggested as an efficient means to locate PD sources.

Periodic coupling inhibits second-order consensus on networks.

Consensus algorithms on networks have received increasing attention in recent years for various applications, ranging from distributed decision making to multivehicle coordination. In particular, second-order consensus models take into account the Newtonian dynamics of interacting physical agents. For this model class, we uncover a mechanism inhibiting the formation of collective consensus states via rather small time-periodic coupling modulations.

Eigenstate thermalization and ensemble equivalence in few-body fermionic systems.

We investigate eigenstate thermalization from the point of view of vanishing particle and heat currents between a few-body fermionic Hamiltonian prepared in one of its eigenstates and an external, weakly coupled Fermi-Dirac gas. The latter acts as a thermometric probe, with its temperature and chemical potential set so that there is neither particle nor heat current between the two subsystems. We argue that the probe temperature can be attributed to the few-fermion eigenstate in the sense that (i) it varies smoothly with energy from eigenstate to eigenstate, (ii) it is equal to the temperature obtained from a thermodynamic relation in a wide energy range, (iii) it is independent of details of the coupling between the two systems in a finite parameter range, (iv) it satisfies the transitivity condition underlying the zeroth law of thermodynamics, and (v) it is consistent with Carnot's theorem.

Concept attribution: Explaining CNN decisions to physicians.

Deep learning explainability is often reached by gradient-based approaches that attribute the network output to perturbations of the input pixels. However, the relevance of input pixels may be difficult to relate to relevant image features in some applications, e.g.

Partial Discharge Localization Using Time Reversal: Application to Power Transformers.

In this work, we present a novel technique to locate partial discharge (PD) sources based on the concept of time reversal. The localization of the PD sources is of interest for numerous applications, including the monitoring of power transformers, Gas Insulated Substations, electric motors, super capacitors, or any other device or system that can suffer from PDs. To the best of the authors' knowledge, this is the first time that the concept of time reversal is applied to localize PD sources.

Network desynchronization by non-Gaussian fluctuations.

Many networks must maintain synchrony despite the fact that they operate in noisy environments. Important examples are stochastic inertial oscillators, which are known to exhibit fluctuations with broad tails in many applications, including electric power networks with renewable energy sources. Such non-Gaussian fluctuations can result in rare network desynchronization.

The key player problem in complex oscillator networks and electric power grids: Resistance centralities identify local vulnerabilities.

Identifying key players in coupled individual systems is a fundamental problem in network theory. We investigate synchronizable network-coupled dynamical systems such as high-voltage electric power grids and coupled oscillators on complex networks. We define key players as nodes that, once perturbed, generate the largest excursion away from synchrony.

Single-Sensor Source Localization Using Electromagnetic Time Reversal and Deep Transfer Learning: Application to Lightning.

Electromagnetic Time Reversal (EMTR) has been used to locate different types of electromagnetic sources. We propose a novel technique based on the combination of EMTR and Machine Learning (ML) for source localization. We show for the first time that ML techniques can be used in conjunction with EMTR to reduce the required number of sensors to only one for the localization of electromagnetic sources in the presence of scatterers.

Global robustness versus local vulnerabilities in complex synchronous networks.

In complex network-coupled dynamical systems, two questions of central importance are how to identify the most vulnerable components and how to devise a network making the overall system more robust to external perturbations. To address these two questions, we investigate the response of complex networks of coupled oscillators to local perturbations. We quantify the magnitude of the resulting excursion away from the unperturbed synchronous state through quadratic performance measures in the angle or frequency deviations.

Noise-induced desynchronization and stochastic escape from equilibrium in complex networks.

Complex physical systems are unavoidably subjected to external environments not accounted for in the set of differential equations that models them. The resulting perturbations are standardly represented by noise terms. If these terms are large enough, they can push the system from an initial stable equilibrium point, over a nearby saddle point, outside of the basin of attraction of the stable point.

Inertia location and slow network modes determine disturbance propagation in large-scale power grids.

Conventional generators in power grids are steadily substituted with new renewable sources of electric power. The latter are connected to the grid via inverters and as such have little, if any rotational inertia. The resulting reduction of total inertia raises important issues of power grid stability, especially over short-time scales.

Robustness of Synchrony in Complex Networks and Generalized Kirchhoff Indices.

In network theory, a question of prime importance is how to assess network vulnerability in a fast and reliable manner. With this issue in mind, we investigate the response to external perturbations of coupled dynamical systems on complex networks. We find that for specific, nonaveraged perturbations, the response of synchronous states depends on the eigenvalues of the stability matrix of the unperturbed dynamics, as well as on its eigenmodes via their overlap with the perturbation vector.

Development and Evaluation of a Case-Based Retrieval Service.

Identifying similar patients might greatly facilitate the treatment of a given patient, enabling to observe the response and outcome to a particular treatment. Case-based retrieval services dealing with natural language processing are of major importance to deal with the significant amount of unstructured clinical data. In this paper, we present the development and evaluation of a case-based retrieval (CBR) service tested on a collection of Italian pediatric cardiology cases.