Patients’ satisfaction with sedoanalgesia versus subarachnoid analgesia in endourology

AbstractObjectiveIn this study the effectiveness and safety of sedoanalgesia technique compared to spinal anesthesia in endourology procedure as well as patients’ satisfaction was assessed.Patients and methodsA prospective randomized study was performed in 80 adult patients, ASA I, II, and III who underwent various endoscopic procedures randomly allocated into two groups 40 patients each: Sedoanalgesia group, received local anesthesia (2% lignocaine gel), i.v. midazolam incremental doses 0.015mg/kg on demand, and i.v. fentanyl 2μg/kg, and 0.5μg/kg on demand interaoperative, and Spinal anesthesia group received 2.5ml heavy bupivacaine 0.5% to achieve around T10 level. We recorded vital parameters, and the number of cases with hemodynamic, respiratory complications, nausea and vomiting, and conversion to general anesthesia (failure). Postoperatively the intensity of pain (VAS 0-100mm), time to first analgesic request (VAS ⩾30), patient satisfaction (complete, partial or not satisfied) and time to readiness for discharge were assessed.ResultsThere was no significant difference in intra, postoperative hemodynamic changes and complications between groups but hypotension was more frequent in Spinal group. Immediate postoperative, there was no significant difference in pain score between groups, but 1 and 2h postoperatively there were higher pain scores in Sedoanalgesia group. Time to first analgesic request and readiness for discharge were significantly less in Sedoanalgesia group, but the difference was not significant as regard satisfaction scores.ConclusionSedoanalgesia is an effective, safe and simple alternative to Spinal anesthesia for endourology, with good patients’ satisfaction and less time to discharge

Hyper-parameter tuning for long short-term memory (LSTM) algorithm to forecast a disease spreading

Глубокое обучение, искусственный интеллект и машинное обучение - это способы поддержки технологий в принятии решений в режиме реального времени для контроля распространения пандемии, что может помочь организациям здравоохранения. Цель данного исследования – изучить настройка гиперпараметров для долговременной краткосрочной памяти для прогнозирования случаев заражения Ковид-19 в Российской Федерации путем выборки наилучшей функции потерь, функции активации, количество эпох, количество нейронов в ячейке и оптимизатор для минимизации ошибки в дополнение к хорошей подгонке модели, где производительность модели хороша как на обучающем, так и на валидационных наборах. Основанная на машинном обучении долговременная кратковременная памяти (LSTM), преимущество которой заключается в анализе взаимосвязи между временными рядами данных благодаря своей функции памяти мы предлагаем метод прогнозирования для ежедневных случаев заражения Covid-19 инфекции на основе двунаправленной LSTM. При этом мы используем около 10 различнDeep learning, artificial intelligence, and machine learning are ways for technologies to support decision-making in real-time to control the spread of the pandemic, which can help healthcare organizations. This study aims to investigate hyper-parameter tuning for Long Short-Term Memory to forecast Covid-19 infection cases in the Russian Federation by pick the best loss function, activation function, number of epochs, number of neurons in a cell, and optimizer to minimize the error in addition to a good fit for the model where the performance of the model is good on both the training and validation sets. Based on machine learning long short-termmemory (LSTM), which has the advantage of analyzing relationships among time series data through its memory function, we propose a forecasting method for daily Covid-19 infection cases based on bidirectional LSTM. In the meanwhile, we use about 10 different forecasting models to forecast the daily Covid-19 infection cases one by one. Moreover, the results of these mo

Automatic classification infectious disease X-ray images based on deep learning algorithms

Последние технологические достижения позволяют использовать глубокое обучения практически во всех сферах жизни. Поскольку методы глубокого обучения точны, они могут быть использовать в медицине для классификации и выявления различных заболеваний. SARSCoV2 можно диагностировать с помощью ПЦР и медицинской визуализации. Для диагностики SARSCoV2 используется рентгеновский снимок грудной клетки.Recent technological advancements allow deep learning to be employed in practically every aspect of life.Because deep learning techniques are so precise, they can be utilized in medicine to classify and detect various diseases. The coronavirus (SARSCoV2) epidemic has recently affected global health systems. SARSCoV2 may be diagnosed via PCR and medical imaging. A chest X-ray is used to diagnose SARSCoV2. This paper proposes a deep learning technique to distinguish SARSCoV2 positive and normal cases. X-rays are the traditional method for diagnosing SARSCoV2, and deep learning models have proven their superior ability to classify medical images, which will be the tool in the future for the classification of any other epidemics that may appear in the future. In this study, not only are the deep learning models finetuned, but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. we developed a system based on deep learning algorithms to cla

Risk of Staphylococcus aureus Isolated from Poultry Meat of Chicken with Arthritis in Poultry Farms

Staphylococcus aureus is a major pathogen that affects both people and animals. Staphylococcus aureus causes food poisoning in addition to invasive diseases as arthritis and septicemia. This study was done on 70 chicken samples obtained from 7 different farms of chickens with symptoms of arthritis in Kafr El-sheikh government, Egypt. In this study out of 70 samples of chickens from different farms, 37 (52.8%) samples were recognized as coagulase-positive staphylococci (CoPS) and 33 (47.1%) were recognized as coagulase-negative staphylococci (CoNS). By using the microtitre plate method, seven out of 37 (18.9%) CoPS were positive for biofilm production with variable degrees. The pattern of antibacterial sensitivity of 7 Staphylococcus aureus isolates against 12 commercially available antibiotic discs showed 100 % resistance to oxytetracycline then Amoxicillin (71.43%), Erythromycin (57.14%), Norfloxacin (14.29%), Tetracycline (42.86), Sulphamethoxazole (42.86%), Gentamicin (42.86%), Ampicillin (42.86%), kanamycin (28.57), cephatotin (28.57), doxycycline (0%) and the least was observed with chloramphenicol (0%). seven of positive S. aureus isolates were introduced in order to identify the staphylococcal enterotoxin genes, SEA, SEB, SEC, SED, and SEE and integron by PCR test Which 4 out of 7 isolates (57.1 %) were positive for SEB and SED only while were other isolate were negative for all SE gene. Class 1 integron cassettes were detected in 6 isolates from 7 (85.7%) of tested isolates. In conclusion, this is the first study to report the detection and identification of enterotoxin and class 1 integron in S. aureus isolated from poultry meat of chicken that suffered from arthritis.

Assessment of Sustainable Green Lightweight Concrete Incorporated in New Construction Technologies

Recent studies have led to the development of approaches for recycling plastic waste and using it as an alternative for natural aggregates in concrete. The studies mainly focused on the material properties and sustainability aspects of such implementation, with little focus on the financial implications and the technical feasibility. The purpose of this research is to investigate the different lifecycle costs associated with the use of green recycled plastic lightweight aggregates (GLACs) in concrete construction in different structural systems. For that purpose, the authors evaluated a concrete structure with several variable design systems and conducted structural design once using conventional concrete and once using concrete with recycled plastic aggregates, resulting in a total of 36 distinct scenarios. The lifetime cost analysis was performed on such scenarios. Finally, a sensitivity analysis was carried out to determine how structural characteristics and critical element costs influence cost-effectiveness. According to the findings, this approach can save up to 6% in life-cycle expenses. The findings of this research will contribute to the upcoming paradigm shift of using recycled plastic in concrete, which will reduce the environmental impacts of both the concrete and plastic industries while also assisting developers in lowering their life cycle costs

Studying the Relationship between Stock Prices of Publicly Traded US Construction Companies and Gross Domestic Product: Preliminary Step toward Construction-Economy Nexus

Many scholars from multiple professional and academic disciplines have investigated the various links between the construction industry and economic output. Nevertheless, there remains a noticeable dearth of studies that address the potential impact of the players within the construction industry on various economic indicators. The goal of this research is to study how the economic performance of the US-measured in GDP-is impacted by the performance of the construction industry and its key players and how the performance of the construction industry could help in forecasting future US GDP. This goal is achieved by studying the relationship between GDP, total construction spending (TTLCONS), the Standard and Poor\u27s 500 (S&P500) index (GSPC), and the stocks of major publicly traded construction companies. The authors applied an interdependent research methodology that included (1) data collection, (2) statistical testing on the data using correlation analysis and Granger causality testing, and (3) vector autoregression (VAR) for both fitting and prediction purposes. A positive correlation was found between GDP, the S&P500, TTLCONS, and the stocks of major publicly traded construction-related companies. Also, the Granger causality test showed that some major construction company stocks are useful in forecasting GDP. The developed VAR model was used to forecast GDP for 2 years with acceptable accuracy. In this connection, the model was validated by successfully forecasting in a retrospective manner the effect of the 2008 financial crisis. This shows that the stock prices of select publicly traded construction and equipment companies can be used to predict GDP. In fact, a similar model could have been used to predict the 2008 economic collapse and develop ex ante mitigation strategies. The findings of this study could open opportunities for abandoning the notion of studying the construction industry solely using the health of residential construction. As such, this research should help in moving toward the development of a construction-economy nexus

Updating the Landweber Iteration Method for Solving Inverse Problems

The Landweber iteration method is one of the most popular methods for the solution of linear discrete ill-posed problems. The diversity of physical problems and the diversity of operators that result from them leads us to think about updating the main methods and algorithms to achieve the best results. We considered in this work the linear operator equation and the use of a new version of the Landweber iterative method as an iterative solver. The main goal of updating the Landweber iteration method is to make the iteration process fast and more accurate. We used a polar decomposition to achieve a symmetric positive definite operator instead of an identity operator in the classical Landweber method. We carried out the convergence and other necessary analyses to prove the usability of the new iteration method. The residual method was used as an analysis method to rate the convergence of the iteration. The modified iterative method was compared to the classical Landweber method. A numerical experiment illustrates the effectiveness of this method by applying it to solve the inverse boundary value problem of the heat equation (IBVP)

Updating the Landweber Iteration Method for Solving Inverse Problems

The Landweber iteration method is one of the most popular methods for the solution of linear discrete ill-posed problems. The diversity of physical problems and the diversity of operators that result from them leads us to think about updating the main methods and algorithms to achieve the best results. We considered in this work the linear operator equation and the use of a new version of the Landweber iterative method as an iterative solver. The main goal of updating the Landweber iteration method is to make the iteration process fast and more accurate. We used a polar decomposition to achieve a symmetric positive definite operator instead of an identity operator in the classical Landweber method. We carried out the convergence and other necessary analyses to prove the usability of the new iteration method. The residual method was used as an analysis method to rate the convergence of the iteration. The modified iterative method was compared to the classical Landweber method. A numerical experiment illustrates the effectiveness of this method by applying it to solve the inverse boundary value problem of the heat equation (IBVP)

Contractual Guidelines for Contractors Working under Projects Funded by Southeastern US DOTs

Transportation projects in the infrastructure sector contribute to approximately 42% of the total expenditures on public construction projects in the US. The main source of funding of these projects is the taxpayer�s hard-earned money. The ever-growing problem by transportation projects is that the available funds are less than those required to have a stable and well maintained transportation network. Unnecessary costs in these projects are mainly caused by Conflicts, Claims and Disputes (C2D). According to recent reports, C2D in construction is greatly attributed to poor contract administration. The goal of this paper is to provide better understanding and utilization of contracts that are managed by the Departments of Transportations (DOTs) of 6 southeastern states: Tennessee, South Carolina, North Carolina, Georgia, Alabama and Florida. To this end, the authors: (1) analyzed the standard contract agreements published by these 6 states; (2) highlighted commonalities and differences in key subject areas including bidding, contract award, selection criteria, payment and control work. Usually, local contractors in southeastern areas work in their home states as well as in the neighbouring states. The outcomes of this work is that contractors conducting business in the southeast will benefit greatly from the presented contractual guidelines. This research method could be implemented in other states to cover different regions in the US. Ultimately, this will help in minimizing cost due to conflicts and disputes in projects; thus, making better use of the US taxpayer\u27s money