Real-time Prediction of Cascading Failures in Power Systems

Blackouts in power systems cause major financial and societal losses, which necessitate devising better prediction techniques that are specifically tailored to detecting and preventing them. Since blackouts begin as a cascading failure (CF), an early detection of these CFs gives the operators ample time to stop the cascade from propagating into a large-scale blackout. In this thesis, a real-time load-based prediction model for CFs using phasor measurement units (PMUs) is proposed. The proposed model provides load-based predictions; therefore, it has the advantages of being applicable as a controller input and providing the operators with better information about the affected regions. In addition, it can aid in visualizing the effects of the CF on the grid. To extend the functionality and robustness of the proposed model, prediction intervals are incorporated based on the convergence width criterion (CWC) to allow the model to account for the uncertainties of the network, which was not available in previous works. Although this model addresses many issues in previous works, it has limitations in both scalability and capturing of transient behaviours. Hence, a second model based on recurrent neural network (RNN) long short-term memory (LSTM) ensemble is proposed. The RNN-LSTM is added to better capture the dynamics of the power system while also giving faster responses. To accommodate for the scalability of the model, a novel selection criterion for inputs is introduced to minimize the inputs while maintaining a high information entropy. The criteria include distance between buses as per graph theory, centrality of the buses with respect to fault location, and the information entropy of the bus. These criteria are merged using higher statistical moments to reflect the importance of each bus and generate indices that describe the grid with a smaller set of inputs. The results indicate that this model has the potential to provide more meaningful and accurate results than what is available in the previous literature and can be used as part of the integrated remedial action scheme (RAS) system either as a warning tool or a controller input as the accuracy of detecting affected regions reached 99.9% with a maximum delay of 400 ms. Finally, a validation loop extension is introduced to allow the model to self-update in real-time using importance sampling and case-based reasoning to extend the practicality of the model by allowing it to learn from historical data as time progresses

Assessment of Thermal Comfort in Operating Rooms Using PMV-PPD Model

Operating rooms (ORs) are the most critical and expensive sector of healthcare facilities. The air conditioning system is designed to provide a well-controlled indoor air quality (IAQ). This design guarantees a perfect infection control and a good thermal comfort of patient and operating staff.This paper aims to analyze and evaluate indoor thermal comfort at different cases to assign the proper inlet air temperature to the OR. The predicted mean vote (PMV) and the predicted percentage dissatisfied (PPD) models in accordance with ISO 7730 were used for this study.Field measurements were first carried out in an OR at Kafr El-Sheikh educational hospital to get the thermal environment parameters. These parameters are required to determine the thermal comfort indices namely (PMV & PPD). Four different cases of supplied air temperature 17.5, 18.5, 19.5 and 20.5oC were studied and compared through 105 measuring points distributed in the operating room. The PMV and PPD indices were computed at each case for three groups of medical staff: surgeons (metabolic rate equal to 120 W/m2), nurses and surgeon\u27s assistants (100 W/m2), anesthetists (70 W/m2).The results revealed that inlet air temperature has a minor effect on the air velocities and airflow patterns inside the OR at the same air change rate. For the current ventilation system, it is difficult to create a very comfortable work conditions for all operating staff at the same time due to their different thermal requirements. It was concluded that a supplied air temperature of 18.5oC provides almost comfortable conditions for all surgical staff

PANC Study (Pancreatitis: A National Cohort Study): national cohort study examining the first 30 days from presentation of acute pancreatitis in the UK

Abstract Background Acute pancreatitis is a common, yet complex, emergency surgical presentation. Multiple guidelines exist and management can vary significantly. The aim of this first UK, multicentre, prospective cohort study was to assess the variation in management of acute pancreatitis to guide resource planning and optimize treatment. Methods All patients aged greater than or equal to 18 years presenting with acute pancreatitis, as per the Atlanta criteria, from March to April 2021 were eligible for inclusion and followed up for 30 days. Anonymized data were uploaded to a secure electronic database in line with local governance approvals. Results A total of 113 hospitals contributed data on 2580 patients, with an equal sex distribution and a mean age of 57 years. The aetiology was gallstones in 50.6 per cent, with idiopathic the next most common (22.4 per cent). In addition to the 7.6 per cent with a diagnosis of chronic pancreatitis, 20.1 per cent of patients had a previous episode of acute pancreatitis. One in 20 patients were classed as having severe pancreatitis, as per the Atlanta criteria. The overall mortality rate was 2.3 per cent at 30 days, but rose to one in three in the severe group. Predictors of death included male sex, increased age, and frailty; previous acute pancreatitis and gallstones as aetiologies were protective. Smoking status and body mass index did not affect death. Conclusion Most patients presenting with acute pancreatitis have a mild, self-limiting disease. Rates of patients with idiopathic pancreatitis are high. Recurrent attacks of pancreatitis are common, but are likely to have reduced risk of death on subsequent admissions. </jats:sec

Impact of Foliar Application of Chitosan Dissolved in Different Organic Acids on Isozymes, Protein Patterns and Physio-Biochemical Characteristics of Tomato Grown under Salinity Stress

In this study, the anti-stress capabilities of the foliar application of chitosan, dissolved in four different organic acids (acetic acid, ascorbic acid, citric acid and malic acid) have been investigated on tomato (Solanum lycopersicum L.) plants under salinity stress (100 mM NaCl). Morphological traits, photosynthetic pigments, osmolytes, secondary metabolites, oxidative stress, minerals, antioxidant enzymes activity, isozymes and protein patterns were tested for potential tolerance of tomato plants growing under salinity stress. Salinity stress was caused a reduction in growth parameters, photosynthetic pigments, soluble sugars, soluble proteins and potassium (K+) content. However, the contents of proline, ascorbic acid, total phenol, malondialdehyde (MDA), hydrogen peroxide (H2O2), sodium (Na+) and antioxidant enzyme activity were increased in tomato plants grown under saline conditions. Chitosan treatments in any of the non-stressed plants showed improvements in morphological traits, photosynthetic pigments, osmolytes, total phenol and antioxidant enzymes activity. Besides, the harmful impacts of salinity on tomato plants have also been reduced by lowering MDA, H2O2 and Na+ levels. Chitosan treatments in either non-stressed or stressed plants showed different responses in number and density of peroxidase (POD), polyphenol oxidase (PPO) and superoxide dismutase (SOD) isozymes. NaCl stress led to the diminishing of protein bands with different molecular weights, while they were produced again in response to chitosan foliar application. These responses were varied according to the type of solvent acid. It could be suggested that foliar application of chitosan, especially that dissolved in ascorbic or citric acid, could be commercially used for the stimulation of tomato plants grown under salinity stress

Paulownia Leaves as A New Feed Resource: Chemical Composition and Effects on Growth, Carcasses, Digestibility, Blood Biochemistry, and Intestinal Bacterial Populations of Growing Rabbits

This experiment was conducted to study the effects of paulownia leaf meal (PLM) as a nontraditional feed on the growth, carcasses, digestibility, blood chemistry, and intestinal microbiota of growing rabbits. Sixty rabbits (5-weeks old) were randomly allotted to three dietary treatments containing three amounts of PLM (0%, 15%, and 30%). The results showed that PLM has a higher content of ether extract, organic matter, methionine, tyrosine, histidine, manganese, and zinc than alfalfa hay. Body weight gain decreased when 30% PLM was provided. The best feed conversion ratio was recorded in the rabbits fed 15% PLM. A notable increase in high-density lipoprotein levels with a significant decrease in low-density lipoprotein was noted in the rabbits fed the PLM diets. Total fungi and Enterobacteriaceae and total bacterial count in the feed were significantly reduced because of PLM. In the cecum, coliforms, Enterobacteriaceae species, and total bacterial count declined in the rabbits fed the PLM diets. Conclusively, up to 15% PLM can be used in rabbit diets without any deleterious effects on the performance, nutrient digestibility, and blood constituents. In addition, dietary inclusion of PLM has the potential to reduce cecal pathogenic bacteria in rabbits

Isolation and Identification of Streptomyces spp. from Desert and Savanna Soils in Sudan

The purpose of this study was to investigate streptomycete populations in desert and savanna ecozones in Sudan and to identify species based on 16S rRNA gene sequences. A total of 49 different Streptomyces phenotypes (22 from sites representing the desert and semi-desert ecozone; 27 representing the savanna ecozone) have been included in the study. The isolates were characterized phenotypically and confirmed using 16S rRNA gene sequence analysis. The two ecozones showed both similarities and uniqueness in the types of isolates. The shared species were in cluster 1 (Streptomyces (S.) werraensis), cluster 2 (Streptomyces sp.), cluster 3 (S. griseomycini-like), and cluster 7 (S. rochei). The desert ecozone revealed unique species in cluster 9 (Streptomyces sp.) and cluster 10 (S. griseomycini). Whereas, the savanna ecozone revealed unique species in cluster 4 (Streptomyces sp.), cluster 5 (S. albogriseolus/ S. griseoincarnatus), cluster 6 (S. djakartensis), and cluster 8 (Streptomyces sp.). Streptomycetes are widely distributed in both desert and the savanna ecozones and many of these require full descriptions. Extending knowledge on Streptomyces communities and their dynamics in different ecological zones and their potential antibiotic production is needed