Identification of the resonant-grounded system parameters by evaluating fault measurement records

Copyright © 2004 IEEEThe operation of a resonant-grounded network during an earth-fault condition depends on the three basic parameters: damping, detuning, and unbalance factor. These parameters are influenced by the environmental conditions (e.g. humidity, temperature, and pollution), and the network topology. Accurate values of these parameters during an earth-fault condition are required to examine the operation of the compensation system. The fault records could be used for that purpose. The recorded neutral-to-ground voltage signals have been parameterized (using damping and detuning as parameters) according to the mathematical model of the transient process. Iteratively reweighted least squares algorithm has been used to fit the model. This algorithm is the major improvement over the classical least squares approach. It is able to filter out noise more efficiently. As a direct result, very accurate parameter identification has been achieved. This paper concludes with the practical examples.Rastko Zivanovic´, Peter Schegner, Olaf Seifert, and Georg Pil

Literature review on the potential of urban waste for the fertilization of urban agriculture: A closer look at the metropolitan area of Barcelona

Urban agriculture (UA) activities are increasing in popularity and importance due to greater food demands and reductions in agricultural land, also advocating for greater local food supply and security as well as the social and community cohesion perspective. This activity also has the potential to enhance the circularity of urban flows, repurposing nutrients from waste sources, increasing their self-sufficiency, reducing nutrient loss into the environment, and avoiding environmental cost of nutrient extraction and synthetization.The present work is aimed at defining recovery technologies outlined in the literature to obtain relevant nutrients such as N and P from waste sources in urban areas. Through literature research tools, the waste sources were defined, differentiating two main groups: (1) food, organic, biowaste and (2) wastewater. Up to 7 recovery strategies were identified for food, organic, and biowaste sources, while 11 strategies were defined for wastewater, mainly focusing on the recovery of N and P, which are applicable in UA in different forms.The potential of the recovered nutrients to cover existing and prospective UA sites was further assessed for the metropolitan area of Barcelona. Nutrient recovery from current composting and anaerobic digestion of urban sourced organic matter obtained each year in the area as well as the composting of wastewater sludge, struvite precipitation and ion exchange in wastewater effluent generated yearly in existing WWTPs were assessed. The results show that the requirements for the current and prospective UA in the area can be met 2.7 to 380.2 times for P and 1.7 to 117.5 times for N depending on the recovery strategy. While the present results are promising, current perceptions, legislation and the implementation and production costs compared to existing markets do not facilitate the application of nutrient recovery strategies, although a change is expected in the near future

Adenosine-stress cardiac magnetic resonance imaging in suspected coronary artery disease: a net cost analysis and reimbursement implications

The health and economic implications of new imaging technologies are increasingly relevant policy issues. Cardiac magnetic resonance imaging (CMR) is currently not or not sufficiently reimbursed in a number of countries including Germany, presumably because of a limited evidence base. It is unknown, however, whether it can be effectively used to facilitate medical decision-making and reduce costs by serving as a gatekeeper to invasive coronary angiography. We investigated whether the application of CMR in patients suspected of having coronary artery disease (CAD) reduces costs by averting referrals to cardiac catheterization. We used propensity score methods to match 218 patients from a CMR registry to a previously studied cohort in which CMR was demonstrated to reliably identify patients who were low-risk for major cardiac events. Covariates over which patients were matched included comorbidity profiles, demographics, CAD-related symptoms, and CAD risk as measured by Morise scores. We determined the proportion of patients for whom cardiac catheterization was deferred based upon CMR findings. We then calculated the economic effects of practice pattern changes using data on cardiac catheterization and CMR costs. CMR reduced the utilization of cardiac catheterization by 62.4%. Based on estimated catheterization costs of € 619, the utilization of CMR as a gatekeeper reduced per-patient costs by a mean of € 90. Savings were realized until CMR costs exceeded € 386. Cost savings were greatest for patients at low-risk for CAD, as measured by baseline Morise scores, but were present for all Morise subgroups with the exception of patients at the highest risk of CAD. CMR significantly reduces the utilization of cardiac catheterization in patients suspected of having CAD. Per-patient savings range from € 323 in patients at lowest risk of CAD to € 58 in patients at high-risk but not in the highest risk stratum. Because a negative CMR evaluation has high negative predictive value, its application as a gatekeeper to cardiac catheterization should be further explored as a treatment option

Fully Automated Optimization of Robot‐Based MOF Thin Film Growth via Machine Learning Approaches

Metal–organic frameworks (MOFs), have emerged as ideal class of materials for the identification of structure–property relationships and for the targeted design of multifunctional materials for diverse applications. While the powder form is most common, for the integration of MOFs into devices, typically thin films of surface anchored MOFs (SURMOFs), are required. Although the quality of SURMOFs emerging from layer-by-layer approaches is impressive, previous works revealed that the optimum growth conditions are very different between different types of MOFs and different substrates. Furthermore, the choice of appropriate synthesis conditions (e.g., solvents, modulators, concentrations, immersion times) is crucial for the growth process and needs to be adjusted for different substrates. Machine learning (ML) approaches show great promise for multi-parameter optimization problems such as the above discussed growth conditions for SURMOF on a particular substrate. Here, this work presents an ML-based approach allowing to quickly identify optimized growth conditions for HKUST-I SURMOFs with high crystallinity and uniform orientation. This process can subsequently be used to optimize growth on other types of substrates. In addition, an analysis of the results allows to gain further insights into the factors governing the growth of MOF thin films