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Short-term fluctuations in photovoltaic power plants, known as ramps and caused by clouds, challenge grid stability and efficient energy use. These issues are traditionally managed with battery energy storage systems, which, while effective, are expensive. We propose an alternative solution: the use of short-term irradiance forecasts, or nowcasts. Using a photovoltaic power plant in Germany and its simulated model, we demonstrated that nowcasts could cut ramp rate violations by 81%. This led to a reduction in required battery capacity by 71% and the required maximum battery power provided by 48%, at the cost of a 13% curtailment loss, i.e. loss through reduction of power. Our data set of 18 chosen days from 2020 with high variability conditions was scaled up to a year for the economic analysis. From an economic standpoint, nowcasts could lower the Levelized Cost of Electricity by 5.5% from 4.74 to 4.48 EUR cents, and even by 35% to 3.09 EUR cents with ideal forecasting, showing its potential. While nowcasts cannot completely replace batteries, they substantially reduce the need for such storage solutions. This results in cost savings and adherence to grid stability requirements, making nowcasts a complement or partial alternative to battery systems for mitigating power fluctuations in photovoltaic power plants.

The maintenance of photovoltaic (PV) power plants is of central importance for their yield. To reach higher efficiencies in PV parks, it is helpful to detect soiling such as dust deposition and to apply this information to optimize cleaning strategies. Furthermore, a periodic inspection of the PV modules with infrared (IR) imagery is of advantage to detect and potentially remove faulty PV modules. Soiling can be erroneously interpreted as PV module defects and hence spatially resolved soiling measurements can improve the results of IR-based PV inspection. So far, soiling measurements are mostly performed only locally in PV fields, thus not supporting the above-mentioned IR inspections. This study presents a method for measuring the soiling of PV modules at cell resolution using RGB images taken by aerial drones under sunny conditions. The increase in brightness observed for soiled cells under evaluation, compared to clean cells, is used to calculate the transmission loss of the soiling layer. Photos of a clean PV module and a soiled module for which the soiling loss is measured electrically are used to determine the relation between the brightness increase and the soiling loss. To achieve this, the irradiance at the time of the image acquisitions and the viewing geometry are considered. The measurement method has been validated with electrical measurements of the soiling loss yielding root mean square deviations in the 1% absolute range. The method has the potential to be applied to entire PV parks in the future. [ABSTRACT FROM AUTHOR]

Intermittent solar irradiance due to passing clouds poses challenges for integrating solar energy into existing infrastructure. By making use of intrahour nowcasts (very short‐term forecasts), changing conditions of solar irradiance can be anticipated. All‐sky imagers, capturing sky conditions at high spatial and temporal resolution, can be the basis of such nowcasting systems. Herein, a deep learning (DL) model for solar irradiance nowcasts based on the transformer architecture is presented. The model is trained end‐to‐end using sequences of sky images and irradiance measurements as input to generate point‐forecasts up to 20 min ahead. Further, the effect of integrating this model into a hybrid system, consisting of a physics‐based model and smart persistence, is examined. A comparison between the DL and two hybrid models (with and without the DL model) is conducted on a benchmark dataset. Forecast accuracy for deterministic point‐forecasts is analyzed under different conditions using standard error metrics like root‐mean‐square error and forecast skill. Furthermore, spatial and temporal aggregation effects are investigated. In addition, probabilistic nowcasts for each model are computed via a quantile approach. Overall, the DL model outperforms both hybrid models under the majority of conditions and aggregation effects. [ABSTRACT FROM AUTHOR]

Background: Bacterial infections are among the most serious infections worldwide. They can cause miscarriage, premature birth, stillbirth, and ectopic pregnancy in pregnant women. The aim of this study was to investigate the relationship between bacterial infections and pregnancy outcomes through a systematic review and meta-analysis. Materials and Methods: PubMed, Scopus, Web of Science, and Embase databases were searched from January 2000 to December 2018 using appropriate keywords to identify related articles. The final related studies were selected and evaluated using the Newcastle-Ottawa Scale (NOS). Results: Results of this meta-analysis based on combining case-control studies showed that the presence of bacterial infections could lead increase in the odds of all pregnancy outcomes like premature infant birth (odd ratio [OR]: 1.50; 95% Confidence Interval [CI], 1.39-1.61), preterm delivery (OR: 1.54; 95% CI, 1.39-1.70), abortion (OR: 1.16; 95% CI, 1.04-1.29), stillbirth (OR, 1.29; 95% CI, 1.12-1.49), and ectopic pregnancy (OR: 1.12; 95% CI, 1.05-1.19). The results showed that the Risk Ratio (RR) of preterm delivery in pregnant women with vaginal infections was 1.57 (95% CI, 1.46-1.67), whereas the RR of abortion was 2.02 (95% CI, 1.72-2.38). Conclusions: Based on the results of this meta-analysis, the presence of bacterial infections in pregnant women can lead increase in the risk of pregnancy outcomes especially, preterm delivery, abortion, stillbirth, and ectopic pregnancy. Therefore, it is necessary for obstetricians and gynecologists to pay attention to the diagnosis of these infections in women before pregnancy and during pregnancy in order to prevent the consequences of these infections. [ABSTRACT FROM AUTHOR]

Due to the need to know the availability of solar resources for the solar renewable technologies in advance, this paper presents a new methodology based on computer vision and the object detection technique that uses convolutional neural networks (EfficientDet-D2 model) to detect clouds in image series. This methodology also calculates the speed and direction of cloud motion, which allows the prediction of transients in the available solar radiation due to clouds. The convolutional neural network model retraining and validation process finished successfully, which gave accurate cloud detection results in the test. Also, during the test, the estimation of the remaining time for a transient due to a cloud was accurate, mainly due to the precise cloud detection and the accuracy of the remaining time algorithm. [ABSTRACT FROM AUTHOR]

Background & aim: Nursing care is an essential part of health care services and should be fully provided. Missed nursing care is a major threat to patient safety and health care quality. The purpose of this study was to determine the amount and reasons for missed nursing care in infection control and prevention from the point of view of care providers working in the medical, educational, and therapeutic center of Besat Sanandaj during 2023. Methods: This cross-sectional study was conducteed in Besat Hospital, one of the teaching hospitals of Kurdistan University of Medical Sciences. The research samples in this cross-sectional study included 389 nurses and midwives working in Besat Hospital who were included in the study by census. To collect data, three tools were used: a demographic information registration form, a questionnaire on the missed nursing care in infection prevention and control, and a questionnaire on factors related to missed care. Results: The findings of the present study showed that the average score of missed nursing care in infection prevention and control was 75.83 ± 23.28 and below the average. The most important missed nursing care from the point of view of the nurses and midwives of Besat Hospital was the use of personal protective equipment by the service personnel of the department (4.88 %), the report of the patient's infection status when transferred to another department or paraclinical unit such as radiology (3.86 %), and teaching patients about bathing before surgery (3.86 %). The most important factor of forgotten nursing care was unexpected increase in workload and number of patients in the ward (79.69 %) along with insufficient number of nursing and midwifery personnel in the ward (32.75 percent). A statistically significant relationship was observed between forgotten nursing care in infection prevention and control with the related factors (P = 0.0001), (R = - 0.1940). Conclusion: The missed nursing care in the prevention and control of infection from the point of view of nurses and midwives of Besat Hospital was average. The most important factor was the unexpected increase in the workload the number of patients and the lack of personnel. The people participating in the research emphasized the role of systemic, organizational, and management factors in preventing effective infection control. Therefore, it seems necessary to reduce the factors that cause missed care with proper planning and management by reducing nurses' workload and providing budget and manpower. [ABSTRACT FROM AUTHOR]

Background & aim: The outbreak of the COVID-19 crisis has unprecedented pressure to the health systems around the world. Missed nursing care as a care that is needed by the patient but is omitted affects the quality and safety of care. The aim of this study was to investigate the rate and causes of missed nursing care of COVID-19 hospitalized patients in Intensive Care Units (ICUs) of Saqez and Diwandara hospitals. Methods: This cross-sectional descriptive study was conducted in 2021 on 110 nurses working in the special care units for COVID-19. Demographic Characteristics Questionnaire and the Forgotten Care Questionnaires were used to collect information. Data were analyzed using STATA version 12 software and Mann-Whitney, Kruskal-Wallis and Spearman's correlation coefficient tests. Results: The mean score of missed nursing care in ICUs for Covid-19 was 55.18 (20.24). The most common missed nursing care was controlling the patient's O2 saturation (18.75%) and recording the necessary information of patients (17.86%). The highest percentage of the factors related to missed nursing care was assigned to the lack of nursing staff (86.61%). The most common causes of neglected nursing care were lack of nursing staff (86.61%), lack of assistants or secretaries (74.77%), and the large volume of activities related to the admission and discharge of patients (67.66%). Conclusion: A high percentage of nursing care in the special care units of Covid-19 is lost under the influence of factors such as the shortage of nursing staff and forced overtime working hours beyond the tolerance of nurses. Nursing managers can create appropriate interventions to support the professional role of nurses during the pandemic by considering related factors. [ABSTRACT FROM AUTHOR]