An anal fissure which does not heal with conservative measures as sits baths and laxatives is a chronic anal fissure. Physiologically, it is the high resting tone of the internal anal sphincter that chiefly interferes with the healing process of these fissures. Until now, the gold standard treatment modality is surgery, either digital anal dilatation or lateral sphincterotomy. However, concerns have been raised about the incidence of faecal incontinence after surgery. Therefore, pharmacological means to treat chronic anal fissures have been explored. A Medline and pub med database search from 1986-2012 was conducted to perform a literature search for articles relating to the non-surgical treatment of chronic anal fissure. Pharmacological

An anal fissure which does not heal with conservative measures as sits baths and laxatives is a chronic anal fissure. Physiologically, it is the high resting tone of the internal anal sphincter that chiefly interferes with the healing process of these fissures. Until now, the gold standard treatment modality is surgery, either digital anal dilatation or lateral sphincterotomy. However, concerns have been raised about the incidence of faecal incontinence after surgery. Therefore, pharmacological means to treat chronic anal fissures have been explored.A Medline and pub med database search from 1986-2012 was conducted to perform a literature search for articles relating to the non-surgical treatment of chronic anal fissure.Pharmacological s

A hand gesture recognition system provides a robust and innovative solution to nonverbal communication through human–computer interaction. Deep learning models have excellent potential for usage in recognition applications. To overcome related issues, most previous studies have proposed new model architectures or have fine-tuned pre-trained models. Furthermore, these studies relied on one standard dataset for both training and testing. Thus, the accuracy of these studies is reasonable. Unlike these works, the current study investigates two deep learning models with intermediate layers to recognize static hand gesture images. Both models were tested on different datasets, adjusted to suit the dataset, and then trained under different m

Corona virus sickness has become a big public health issue in 2019. Because of its contact-transparent characteristics, it is rapidly spreading. The use of a face mask is among the most efficient methods for preventing the transmission of the Covid-19 virus. Wearing the face mask alone can cut the chance of catching the virus by over 70\%. Consequently, World Health Organization (WHO) advised wearing masks in crowded places as precautionary measures. Because of the incorrect use of facial masks, illnesses have spread rapidly in some locations. To solve this challenge, we needed a reliable mask monitoring system. Numerous government entities are attempting to make wearing a face mask mandatory; this process can be facilitated by using face m

Support Vector Machines (SVMs) are supervised learning models used to examine data sets in order to classify or predict dependent variables. SVM is typically used for classification by determining the best hyperplane between two classes. However, working with huge datasets can lead to a number of problems, including time-consuming and inefficient solutions. This research updates the SVM by employing a stochastic gradient descent method. The new approach, the extended stochastic gradient descent SVM (ESGD-SVM), was tested on two simulation datasets. The proposed method was compared with other classification approaches such as logistic regression, naive model, K Nearest Neighbors and Random Forest. The results show that the ESGD-SVM has a

This study was conducted to evaluate the efficiency of some chemicals and biological agents to induce systemic resistance (ISR) against to wheat common bunt disease caused by the two species of fungus Tilletia tritici (Bjerk.) Wint (T. caries (Dac.) Tul.) and T. laevis Kuhn (T. foetida (Wall.) Liro. Trails in the efforts to find an alternative, safe and environmentally friendly means to control the disease. Results of this study which carried out during two consecutive seasons for the years 2012 - 2013 and 2013 - 2014 at two different environmental locations. Seed treatment by (SA 100 and 200 mg/L, 500 ?–aminobutyric acid (BABA) and 1000 mg/L, Effective Microorganisms (EM1) 40 and 150 ml/kg seeds) have led to high significant redu

A mathematical model has been formulated to predict the influence of high outdoor air temperature on the performance of small scale air - conditioning system using R22 and alternative refrigerants R290, R407C, R410A. All refrigerants were investigated in the cooling mode operation. The mathematical model results have been validated with experimental data extracted from split type air conditioner of 2 TR capacity. This entailed the construction of an experimental test rig which consists of four main parts. They are, the refrigeration system, psychrometric test facility, measuring instrumentation, and auxiliary systems. The conditioned air was maintained at 25 0C dry bulb and 19 0C wet bulb for all tests. The outdoor ambient air temperatur

Background/Objectives: The purpose of this study was to classify Alzheimer’s disease (AD) patients from Normal Control (NC) patients using Magnetic Resonance Imaging (MRI). Methods/Statistical analysis: The performance evolution is carried out for 346 MR images from Alzheimer's Neuroimaging Initiative (ADNI) dataset. The classifier Deep Belief Network (DBN) is used for the function of classification. The network is trained using a sample training set, and the weights produced are then used to check the system's recognition capability. Findings: As a result, this paper presented a novel method of automated classification system for AD determination. The suggested method offers good performance of the experiments carried out show that the

A novel robust finite time disturbance observer (RFTDO) based on an independent output-finite time composite control (FTCC) scheme is proposed for an air conditioning-system temperature and humidity regulation. The variable air volume (VAV) of the system is represented by two first-order mathematical models for the temperature and humidity dynamics. In the temperature loop dynamics, a RFTDO temperature (RFTDO-T) and an FTCC temperature (FTCC-T) are designed to estimate and reject the lumped disturbances of the temperature subsystem. In the humidity loop, a robust output of the FTCC humidity (FTCC-H) and RFTDO humidity (RFTDO-H) are also designed to estimate and reject the lumped disturbances of the humidity subsystem. Based on Lyapunov theo

Thermal performance of closed wet cooling tower has been investigated experimentally and theoretically
in this work. The theoretical model based on heat and mass transfer equations and heat and mass transfer balance equations which are established for steady state case. A new small indirect cooling tower was used for conducting experiments. The cooling capacity of cooling tower is 1 kW for an inlet water temperature of 38oC, a water mass velocity 2.3 kg/m2.s and an air wet bulb temperature of 26oC. This study investigates the relationship between saturation efficiency, cooling capacity and coefficient of performance of closed wet cooling tower versus different operating parameters such wet-bulb temperature, variable air-spray water fl