A scientific consensus is emerging on the benefits of dc distribution in medium voltage power systems of ships and cities. At least 50% space savings and increased power transfer capacity are estimated with enhanced voltage dc operation of electric cables. The goal of this research is to contribute to developing the empirical knowledge on the insulation performance in order to validate the feasibility of such anticipated gains of dc versus ac, and to determine the comparative impact of different operational conditions from a component engineering point of view. The partial discharge (PD) activity in cables is measured under ac and dc conditions as an indicator of insulation performance. Specifically, PDs in defects at the semicon-insulation interface are studied in terms of inception voltage, repetition rate and discharge magnitude. Empirical understanding is drawn for operating voltage and frequency dependence of the discharge behavior in such voids in the range of 10 to 20\,kV and 0 to 0.1\,Hz, respectively. The change in PD activity with void evolution is explored.

The specifics of a shock wave propagation down a positive column of a dc discharge in molecular chemically inert gases has been investigated. It was shown that axial gradients caused by the imbalance in the charged particle momentum transfer to the gas molecules can be a reason for the shock velocity dependence on the electric field direction. In the pure nitrogen gas, the calculated shock velocity difference up to 13.5% is in good agreement with 12% value obtained in the experiment. A returning gas flow organizing in the discharge as a possible mechanism for an extended shock structure, and a number of kinetical factors capable of affecting the shock motion are discussed.

Puerto Rico is a neotropical island located in the Caribbean. Like other neotropical regions, Puerto Rico streams discharges can vary with the seasons, atmospheric events, and human activities. The Lapa River is part of the Puerto Rico Heritage Rivers Program. This program identifies and manages the protection of hydrographic systems that maintain their natural conditions. The necessity of increasing the knowledge of the natural processes of the Lapa River and providing valuable information for the development of a future management plan for the ecosystem, the following objectives were established in this research: (1) to describe the temporal variations in water discharges in the Lapa River and (2) to describe the impact of atmospheric events on the water discharges at the Lapa River. This study concluded that the highest annual discharge averages recorded from 1989 to 2021 in the Lapa River occurred in 2011 and 2017. 2011, the annual mean discharge was 31.20 (ft3/s); in 2017, the average was 40.44 (ft3/s). In terms of atmospheric events, this study concludes that, during the evaluated period, the highest discharge event occurred after the impact of hurricanes Irma and Maria, with an average discharge of 164.40 ft3/s in October and 182.60 ft3/s in September. This event set a precedent in the river Lapa’s discharges, impacting the river basin’s flora, fauna, and morphology from its headwaters downstream.

Physical reason for the small scale tortuosity observed in sparks and lightning channels is unknown at present. In this paper it is suggested that the small scale tortuosity of the discharge channels is caused by the natural tendency for subsequent leader streamer bursts to avoid each other but at the same time to align as much as possible along the direction of the background electric field. This process will give rise to a discharge channel that re-orients in space during each streamer burst creating the small scale tortuosity.

Watching the international COVID-19 epidemics unfold during February and early March 2020, we began to highlight how outbreaks in care homes were inevitable, given the vulnerable patients in close proximity, and pressures on social care to help relieve pressure on the NHS. We suggested public health measures would significantly impact on care homes. Specifically we felt all homes would have multiple individuals isolated within days of introduction of guidance requiring isolation of new cough (or fever), and that a clear definition of an outbreak would be needed that differentiated COVID19 from influenza. We share the experiences of a GP practice looking after 900 nursing or dual registration care homes in the London Borough of Ealing in the early stages of the COVID19 Pandemic 2020. We believe that the altered presentation of cases of COVID19 in care homes contributed to the size of outbreaks, and that keeping COVID19 out of homes is the only way to manage this disease, with early isolation and complete segregation of positive and negative cases. We have seen over 300 suspected cases resulting in four fold (n=175) average monthly death rates, three fold usual issue of anticipatory medications, and approximately 32% mortality rate (up to 43% in over 90’s). Discharge pathways from hospital and admissions to care homes must be clear and robust. COVID19 naïve patients should not be admitted to units with outbreaks and COVID19 positive patients should not be admitted to anything other than a designated “hot” home until risk of contagion is passed. Some patients are testing positive at over 30 days since initial mild symptoms.

An analysis of the physical and chemical phenomena accompanying the electrical discharges was carried out, principal factors inactivating microorganisms is studied. Characteristic factors of an electric discharge in cavitation mode providing a synergistic effect when disinfecting water with a discharge and chemical reagents are identified in order of importance. As a result of the integral action of cavitation, the inactivation of bacteria is intensified both due to the emergence of chem-ically active particles and due to local shock waves, acoustic currents and ultraviolet radiation in the area of cavitating bubbles. The similarity of cavitation processes in water systems implemented by underwater electric discharges and with the ultrasound has been experimentally proved. The main advantage of electric discharge cavitation in comparison with ultrasonic one is the wide band of ultrasonic frequencies of radiation inherent in electric discharge, high intensity and power of cav-itation processes, and, accordingly, the possibility of a significant increase in the volume of the disinfected liquid. The study enable better understanding and prediction of the effects that occur during a high-voltage underwater electrical discharge and have a bactericidal effect.

Abstract: Marine eutrophication is a pervasive and growing threat to global sustainability. Thereby nutrient discharges to the marine environment should be reduced to a minimum. When fertilizers are loaded to vessels in ports, a significant amount of nutrients are released into the sea, but so far these actions have received little attention. Here, we employed the Boosted Regression Trees modeling (BRT) to define relationships between fertilizer loading, loading area, rain intensity and nutrient discharge to the marine environment and then used the established relationships to predict daily nutrient discharge due to fertilizer loading. The studied subject was a port in the Gulf of Finland, where significant amount of both nitrogen and phosphorus are loaded to vessels. BRT models accounted for a significant proportion of the variability of nutrient discharge. As expected, the nutrient discharge increased with the amounts of fertilizers loaded and the intensity of rain. On the other hand, with increasing loading area the amounts of total nitrogen discharge increased, but phosphorus discharge decreased. The latter result may be due to different char-acteristics of the loading areas of different terminals. The model predicted that at the studied port the total nitrogen and phosphorus discharge into the marine environment due to fertilizer loading was 272,906 and 196 kg per year, respectively. Im-portantly, the developed model can be used to predict nutrient loads for different future scenarios to propose the best mitigation methods for nutrient discharges to the sea.

This paper proposes a novel phase-resolved partial discharge (PRPD) sensor embedded in MV-class bushing for high-accuracy insulation analysis. The design, fabrication, and evaluation of a PRPD sensor embedded in MV-class bushing aimed to detect partial discharge (PD) pulses that are phase-synchronized with the applied primary HV signals. A prototype PRPD sensor was composed of a flexible printed circuit board (PCB) with dual-sensing electrodes, utilizing a capacitive voltage divider (CVD) principle for voltage measurement and D-dot principle for PD detection, and a signal transducer with passive elements. A PD simulator was prepared to emulate typical PD defects, i.e., a metal protrusion. The voltage measurement precision of the prototype PRPD sensor was satisfied with the accuracy class of 0.2 specified in IEC 61869-11, as the maximum corrected voltage error ratios and corrected phase errors in 80%, 100%, and 120% of the rated voltage (13.2 kV) were less than 0.2% and 10 minutes, respectively. In addition, the prototype PRPD sensor had good linearity and high sensitivity for PD detection compared with a conventional electrical detection method. According to performance evaluation tests, the prototype PRPD sensor embedded in the MV-class bushing can measure PRPD patterns phase-synchronized with the primary voltage without any additional synchronization equipment or system. Therefore, the prototype PRPD sensor holds potential as a substitute for conventional commercial PD sensors. Consequently, this advancement could lead to the enhancement of power system monitoring and maintenance, contributing to the digitalization and minimization of power apparatus.

Propagation effects on the Narrow Bipolar Pulses (NBPs) or the radiation fields generated by compact cloud discharges as they propagate over finitely conducting ground are presented. The results are obtained using a sample of NBPs recorded with high time resolution from close thunderstorms in Sri Lanka. The results show that the peak amplitude and the temporal features such as the Full Width at Half Maximum (FWHM), zero crossing time and the time derivative of NBPs can be significantly distorted by propagation effects. For this reason the study of peak amplitudes and temporal features of NBPs and the remote sensing of current parameters of compact cloud discharges should be conducted using NBPs recorded under conditions where the propagation effects are minimal.

In the whole world, including Mexico, the rising prevalence of diabetes poses significant challenges to healthcare systems, with a notable impact on hospital admissions even being an ambulatory care sensitive condition, that means, hospital admissions are avoidable. Traditional healthcare methodologies have been instrumental in managing diabetes and preventing complications, yet they often face limitations such as merging, cleansing, and outlier analysis of health data to identify and address diseases effectively. This paper aims to address this gap by conducting a comprehensive comparison of the methodologies used in healthcare and data science for this purpose. This work uses hospital diabetes discharge records from 2010 to 2023, a total of 36,665,793 records, which belong to medical units of the Ministry of Health of Mexico. In this work, we seek to provide the arguments why as a data scientist it is mandatory to learn the field of knowledge of the problem and its implications if this is not done, and therefore to disclose insights that can help in policy decisions and reduce the burden of avoidable hospitalizations. The approach is mainly based on the standardization or adjust rates by sex and age groups. This study provides the foundations for a new way of data scientist must deal with health data.

Delayed discharge for non-clinical reasons is defined as a period of stay that continues after a patient has been deemed medically fit to leave the hospital but is unable to do so for non-medical reasons. This circumstance overburdens the healthcare system and constitutes a major problem for healthcare systems and the patients themselves in this situation.The aim of this study was to evaluate the delay in effective discharge for non-medical reasons in patients admitted to acute care hospitals in Spain. A scoping review was conducted in order to revise and obtain an up-to-date review. The initial search strategy identified a total of 123 references, which were successively screened to a final selection of 13 studies. To conclude, delayed discharge from hospital for non-clinical reasons is a multifactorial problem. It may be due to factors internal or external to the hospital, as well as personal factors. The main causes of the delay are similar among the studies found, as are the clinical characteristics of the patients, most of whom are elderly, frail, and more dependent due to declining functional capacities. Further studies addressing the socio-familial characteristics of the patients and the perspective of the patient and families would be necessary.

Water quality retrieval for small urban waterbodies by remote sensing get used to be difficult due to coarse spatial resolution of the remote sensing imagery. The recently launched Sentinel-2 produces imagery with a spatial resolution of 10 m. It provides an opportunity to solve the problem of retrieving water quality for small waterbodies. Additionally, many water management issues also require fine resolution of imagery, e.g. illegal discharge to an urban waterbody. Since illegal discharges are an important issue for urban water management, chemical oxygen demand (COD), total phosphorous (TP), and total nitrogen (TN) were chosen as the target parameters for water quality retrieval in this study. COD, TP and TN, however, are non-optically active parameters. There were limited studies in the past to retrieve these parameters in comparison with optically active parameters, e.g. Chlorophyll-A etc. This study compared three machine learning models, namely Random Forest (RF), Support Vector Regression (SVR), and Neural Networks (NN), to investigate the opportunity to retrieve the above non-optically active parameters. Results showed that R² of TP, TN, and COD by NN, RF and SVR were 0.94, 0.88, and 0.86, respectively. The performances of water quality retrieval for these non-optically active parameters were significantly improved by the optimized machine learning models. These models hence solved the problem to use remote sensing data to retrieve these non-optically active water quality parameters and provided a new monitoring strategy for small waterbodies. Water quality mapping obtained by Sentinel-2 imagery provided a full spatial coverage of the water quality characterization for the entire water surface. Compared with water samples collecting and testing, it greatly reduced labor cost, reagents cost, and waste treatment cost. It also may help identify illegal discharges to urban waterbodies. The method developed in this research provides a new practical and efficient water quality monitoring strategy in managing water with consideration of environmental sustainability.

This work presents a methodology for the generation and classification of phase resolved partial discharges (PRPD) patterns based on the use of a printed UHF monopole antenna and signal con-ditioning circuit to reduce hardware requirements. For this purpose, envelope detection technique was applied. In addition, test objects such as a hydrogenerator bar, dielectric disks with internal cavities in an oil cell, potential transformer, and tip-tip electrodes immersed in oil were used to generate partial discharge (PD) signals. To detect and classify partial discharges, the standard IEC 60270 (2000) method was used as reference. After acquisition of conditioned UHF signals, digital signal filtering threshold technique was used, and peaks of partial discharges envelopes pulses were extracted. Feature selection techniques were used to classify the discharges and choose the best features to train machine learning algorithms, such as multilayer perceptron, support vector ma-chine and decision tree. Accuracies greater than 84% were met, revealing the classification potential of the methodology proposed in this work.

Electric streamer discharges (streamers) in air are a very important stage of lightning, taking place before formation of the leader discharge, and with which an electric discharge starts from conducting objects which enhance the background elecric field, such as airplanes. Despite years of research, it is still not well understood what mechanism determines the values of streamer parameters, such as its radius and propagation velocity. The Streamer Parameter Model (SPM) is aimed to understand this mechanism, as well as to provide a way to efficiently calculate streamer parameters. Previously, we demonstrated that SPM results compared well with a limited set of experimental data. In this Brief Report, we compare SPM predictions to the published hydrodynamic simulation (HDS) results.

The most common source of transformer failure is in the insulation, and the most prevalent warning signal for insulation weakness is partial discharge (PD). Locating positons of these partial discharges would help repair the transformer to prevent failures. This work investigates algorithms that could be deployed to locate the position of a PD event using data from ultra-high frequency (UHF) sensors inside the transformer. These algorithms typically proceed in two steps: first determine the signal arrival time and then locate the position based on time differences. This paper reviews available methods for each task and then propose new algorithms: a convolutional iterative filter with thresholding (CIFT) to determine the signal arrival time and a reference table of travel times to resolve the source location. The effectiveness of these algorithms are tested with a set of laboratory-triggered PD events and two sets of simulated PD events inside transfers in production use. Tests show the new approach provides more accurate locations than the best-known data analysis algorithms, and the difference is particularly large, 3.7X, when the signal sources are far from sensors.