Under a one health perspective and the worldwide antimicrobial resistance concern, we investigated extraintestinal pathogenic Escherichia coli (ExPEC), uropathogenic E. coli (UPEC), and multidrug resistant (MDR) E. coli from 197 isolates recovered from healthy dogs in Spain between 2013 and 2017. A total of 91 (46.2%) isolates were molecularly classified as ExPEC and/or UPEC, including 50 clones, among which (i) four clones were dominant (B2-CH14-180-ST127, B2-CH52-14-ST141, B2-CH103-9-ST372 and F-CH4-58-ST648) and (ii) 15 had been identified among isolates causing extraintestinal infections in Spanish and French humans in 2015 and 2016. A total of 28 (14.2%) isolates were classified as MDR, associated with B1, D, and E phylogroups, and included 24 clones, of which eight had also been identified among the human clinical isolates. We selected 23 ST372 strains, 21 from healthy dogs, and two from human clinical isolates for whole genome sequencing and built an SNP-tree with these 23 genomes and 174 genomes (128 from canine strains and 46 from human strains) obtained from public databases. These 197 genomes were segregated into six clusters. Cluster 1 comprised 74.6% of the strain genomes, mostly composed of canine strain genomes (p < 0.00001). Clusters 4 and 6 also included canine strain genomes, while clusters 2, 3, and 5 were significantly associated with human strain genomes. Finding several common clones and clone-related serotypes in dogs and humans suggests a potentially bidirectional clone transfer that argues for the one health perspective.

With photovoltaic (PV) systems proliferating in the last few years due to the high prices of fossil fuels and pollution issues, among others, it is extremely important to monitor the efficiency of these plants and optimize the energy production process. This will also result in improvements related to the maintenance and security of the installation. In order to do so, the main parameters in the plant must be continuously monitored so that the appropriate actions can be carried out. This monitoring should not only be carried out at a global level, but also at panel-level, so that a better understanding of what is actually happening in the PV plant can be obtained. This paper presents a system based on a wireless sensor network (WSN) that includes all the components required for such monitoring as well as a power supply obtaining the energy required by the sensors from the photovoltaic panels. The system proposed succeeds in identifying all the nodes in the network and provides real-time monitoring while tracking efficiency, features, failures and weaknesses from a single cell up to the whole infrastructure. Thus, the decision-making process is simplified, which contributes to reducing failures, wastes and, consequently, costs.

Electrostatic precipitators (ESPs) are devices used in industry to eliminate polluting particles in gases. In order to supply them, an interface must be included between the three-phase main line and the required high DC voltage of tens of kilovolts. This paper describes an 80-kW power supply for such an application. Its structure is based on the series parallel resonant converter with a capacitor as output filter (PRC-LCC), which can adequately cope with the parasitic elements of the step-up transformer involved. The physical implementation of the prototype includes the use of silicon carbide-SiC-semiconductors, which provide better switching capabilities than their traditional silicon-Si-counterparts. As a result, a new control strategy results as a better alternative in which the resonant current is maintained in phase with the first harmonic of the inverter voltage. Although this operation mode imposes hard switching in one of the inverter legs, it minimizes the reactive energy that circulates through the resonant tank, the resonant current amplitude itself and the switching losses. Overall efficiency of the converter benefits from this. These ideas are supported mathematically using the steady state and dynamic models of the topology. They are confirmed with experimental measurements that include waveforms, Bode plots and thermal behavior. The experimental setup delivers 80 kW with an estimated efficiency of 98%.

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Medical X-ray appliances use high-voltage power supplies that must be able to work with very different energy requirements. Two techniques can be distinguished in X-ray medical imaging: fluoroscopy and radioscopy. The former involves low power radiation with a long exposure time, while radioscopy requires large power during short radiographic exposure times. Since the converter has to be designed by taking into account the maximum power specification, it will exhibit a poor efficiency when operating at low power levels. Such a problem can be solved by using a new multilevel LCC topology. This topology is based on a classical series-parallel resonant topology, but includes an additional low-voltage auxiliary transformer whose function depends on the X-ray technique considered. When radioscopy operation is selected, the transformer will allow the power to be shared between two full-bridges. If fluoroscopy mode is activated, the auxiliary full bridge is disconnected and the magnetizing inductance of the auxiliary transformer is used to increase the resonant inductor in order to reduce the resonant currents, thus improving the efficiency of the converter.

Due to their exceptional performance in coping with large variations in output voltage and current, parallel resonant converters (PRC) are commonly used in high-voltage applications. The incorporation of step-up transformer parasitic components as part of a power topology, on the right duty and a suitable switching frequency, determines the high efficiency and wide variety of applications with PRC. Switching losses are reduced in the same topology by tracking and running on the optimum mode for each power and voltage by a set frequency and duty. The PRC’s static model behaviors, under optimum operating circumstances, are illustrated. The equivalent polynomial model is used to quickly compute the switching frequency and duty cycle required to achieve the converter’s desired output voltage and power. The polynomial model is simple and easy to implement in any form of a digital signal controller (DSC). Normalized parameters are used to widen the operational range and generalize the model. This also offers the essential protection against current and voltage spikes. The work in progress depicts the specific procedures involved in developing a polynomial model. The normalized equations provide a graphical description of the static model, from which the graphical representation of the polynomial are derived. Hence, polynomial equations are obtained. This paper describes the PRC static model, how to convert it to a polynomial model, how to validate it with MATLAB-Simulink, how to program F28335 using Simulink, and how to use it in practice.

Mechanical contention (MC) is a restrictive, vital but controversial measure, prescribed in the majority of EU countries to handle patients with psycho-motor agitation that do not respond to other types of intervention, with an imminent risk of physical violence and aggression involved. This last resort approach implies risks for the somatic health of the contained individual that go from trauma injuries to, in some extreme cases, sudden death. Despite these risks, somatic supervision and the monitoring of patients under MC is limited, being periodically and manually carried out by nursing personnel with portable equipment. In this context, ensuring continuous monitoring using fully automated equipment is an uncovered yet urgent need. There are several devices already in the market capable of monitoring vital signs, but they are not specifically designed for these type of patients and they can be expensive and/or difficult to integrate with other systems from a software perspective. The work described in this paper gives answers to these necessities with the introduction of a low-cost system, targeted at psychiatric patients, for the acquisition and wireless transmission in real-time of physiological parameters, making use of micro-controllers for collecting and processing sensor data, and WiFi technology to upload the information to the server where a patient’s profile with all the relevant vital parameters resides. In addition to data collection and processing, an application aimed at use by nursing staff has also been developed to raise alerts in case any critical condition is detected.