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Rectal prolapse procidentia is an intussusception of the whole rectal wall through the anal canal, resulting in a portion of the rectum staying periodically or occasionally permanently distal to the anus. Full-thickness prolapse and partial-thickness prolapse are the two kinds of rectal prolapse. Rectal prolapse procidentia is an intussusception of the whole rectal wall through the anal canal, resulting in a portion of the rectum staying periodically or occasionally permanently distal to the anus. It is more frequent in older females. Rectal prolapse was first recorded on papyrus circa 1500 BC. Hippocrates described rectal prolapse therapy as hanging patient’s upside down from a tree, putting sodium hydroxide to the mucosa, and fixing for three days. Today, is mostly treated surgically. Perineal surgical repairs are typically well tolerated; however, they are linked with a greater incidence of recurrence. Abdominal repairs, however, have the lowest recurrence rates. The goal of therapy is to remove the prolapse, cure any related incontinence or constipation issues, and avoid de novo bowel dysfunction. When compared to laparotomy, laparoscopic rectopexy offers fewer side effects, a shorter hospital stays, faster healing, and quicker return to work. This review aims to assess recent updates on different surgical approaches for management of rectal prolapse.

In oil and gas industry, drilling shale formations requires careful design of drilling fluids to control shale-fluid interactions and mitigate wellbore instability. As the aqueous phase of the fluid invades shale formations and contacts clay particles, that may cause shale swelling and disintegration. As a result, solids loading increase in the borehole and that might lead to tight holes and, eventually, stuck pipes. Also, the rate of penetration might be reduced due to low hole cleaning efficiency and bit balling. Therefore, differet types of inhibitive drilling fluids have been developed to counter act the interactions between the aquwous phase and clay particles.In order to develop an inhibitive water-based mud capable of mitigating the adverse effects of shale-mud interactions, it is important to characterize the shale sample in terms of mineralogical composition. Also, the shale-mud interactions should be studied through a series of experimental dispersion.This paper presents the results of characterization and testing one shale sample and its interactions with an inhibitive drilling fluid and a comparison to non-inhibitive drilling fluid.

In the current state of the art of process industries/manufacturing technologies, computer-instrumented and computer-controlled autonomous techniques are necessary for damage diagnosis and prognosis in operating machinery. From this perspective, the paper addresses the issue of fatigue damage that is one of the most commonly encountered sources of degradation in polycrystalline-alloy structures of machinery components. It is possible to conduct in-situ detection & classification of damage as well as an assessment of the remaining service life through ultrasonic measurements of material degradation and their computer-based analysis. In this paper, tools of machine learning (e.g., convolutional neural networks (CNNs)) are applied to synergistic combinations of ultrasonic measurements and images from a confocal microscope (Alicona) to detect and evaluate the risk of fatigue damage. The database of the confocal microscope has been used to calibrate the ultrasonic database and to provide the ground truth for fatigue damage assessment. The results show that both the ultrasonic data and confocal microscope images are capable of classifying the fatigue damage into their respective classes with considerably high accuracy. However, the ultrasonic CNN model yields better accuracy than the confocal microscope CNN model by almost 9%.

Obesity is a global epidemic that threatens to deplete health-care resources by increasing the prevalence of diabetes, heart disease, hypertension, and cancer. Obesity is the leading preventable cause of death worldwide, affecting both adults and children. Obesity has two effects: an increase in the bulk of adipose tissue and an increase in pathogenetic product release by larger fat cells. This view of obesity as a disease allows for a straightforward distinction between the negative effects of obesity caused by fat bulk and those caused by fat cell metabolic effects. Obesity stigma causes social difficulties, sleep apnea is caused in part by increased parapharyngeal fat deposits, and osteoarthritis is caused by the wear and tear on joints caused by carrying an enlarged quantity of fat fall under the first category. A wide range of policy issues could have an impact on food ecosystems. Fiscal food policies, obligatory nutrition panels on the formulation and reformulation of manufactured foods, food and nutrition labelling implementation, and limiting marketing and advertising prohibitions on unhealthy foods are among these topics. In this review, we overview current evidences on health consequences of obesity.

A number of advancements in the therapy of perforated duodenal ulcers have been made in the recent two decades, suggesting that the disease's morbidity and mortality may be reduced. Recently, there has been a return to a more conservative first approach, with reports of either delayed resection or two-stage surgery, in which a non-radical resection is performed first, followed by lymphadenectomy at a later date. Furthermore, because gastric lymphoma can be cured without resection, many upper GI surgeons recommend performing a biopsy and repair at the index operation and then considering how best to continue if adenocarcinoma is discovered later.

In this study, the effects of the machining type on the notch of a specimen, made of 7075-T6 aluminum alloy, have been investigated. The CNC machining and waterjet cutting are used to machine specimens for in fatigue testing. The surface texture characterization, resulting from machining, is examined by using an optical metrology device (Alicona). The tested surface texture is characterized by the average height, Ra, over a small selected range of data. The effect of the stress concentration was studied by designing the surface texture using a computer-aided design (CAD) tool. It is observed that the waterjet cutting produces very rough surfaces, hence increasing the fatigue stress concentration. In addition, this study presents an automated prediction for the machining type using the tools of artificial intelligence (AI), where a neural network (NN) is applied to predict the machining type, which is based on the extracted surface textures, derived from the measurement data. The classification methodology, which uses a NN model, has been built based on the concept of pattern recognition. Results of this study show that the NN model is capable of successfully predicting the type of surface roughness with (up to) 90% accuracy.

The global objective of this study was to investigate the best features of the surface topography for fatigue damage detection and classification. The presence of the stress concentration in valleys of the surface topography causes a grain slip and a crack initiation at the surface of the machined structure and finally leads to fatigue failures. Therefore, the surface topography has a major influence on the fatigue strength of the machined structure. An optical confocal measurement system (Alicona) was applied to measure the surface topography parameters. These parameters are the arithmetical mean height S a , the root-mean-square height S q , the maximum peak height S p , the maximum valley depth S v , the maximum height S z , and ten-points height S z 1 0 . In this paper, feature selection using the Pearson correlation method was adopted to select the best surface textures that provide best the neural network (NN) model performance.The proposed NN models have been trained using the scaled conjugate-gradient back-propagation method. Results showed that the best surface topography parameters were S a , S v , S 1 0 Z , S z , where the NN model can detect and classify the damage with an accuracy of up to ∼94 .4%.

Purpose of the study: This research intended to develop a valid and reliable survey instrument to measure Situational Leadership Model II (SLM II) through Exploratory Factor Analysis (EFA), including the four quadrants, evidently describe the Situational Leadership aspects style which is; directing, coaching, supporting, and delegating. Methodology: A survey with an interval scale between 1 and 10. Using the extraction method of Principal Component with Varimax Rotation, the researcher performed the EFA procedure on construct elements using Amos 24.0. Bartletts’ Test of Sphericity and the sampling adequacy by (KMO) was performed also. Cronbach’s Alpha was applied to test the reliability of the retained items. Main Findings: The Bartletts’ Test of Sphericity is highly significant (sig. 000). Furthermore, the sampling adequacy by (KMO=0.889) is excellent. The four components have Cronbach’s alpha values with more than 0.7. Furthermore, Cronbach’s Alpha value for all 24 items also exceeded the threshold value of 0.7. The development scale and validation confirmed that the instrument is consistent and stable across samples. Applications of this study: This study was applied to the leading telecommunications companies in Jordan that are exclusively providing mobile phones and internet service. It Includes Orange, Zain, and Umniah. The study targeted all permanent middle managers of (JTS) who hold supervisory positions, heads of departments, directors of divisions, or directors who are supervising three or more subordinates. Novelty/Originality of this study: It adds a remarkable contribution to the measurement of the SLM II, mainly in the JTS context. The EFA outcomes formed a configuration that extracts four components of SLM II, which can be measured by 24 items established in this research, which reveals that the elements are applicable in this study.

Total knee arthroplasty is an effective way to manage osteoarthritis patients surgically. However, patients may encounter post-surgical complications, such as quadriceps rupture in rare instances, in addition to surgical complications. In our clinical practice, we encountered a 67-year-old Saudi male patient with a rare bilateral quadriceps rupture two weeks post-total knee arthroplasty. The cause of the bilateral rupture was a history of falls in both knees. The patient was reported to our clinic with clinical features like pain in the knee joint, immobility, and bilateral swelling in the knees. The X-ray did not show any periprosthetic fracture, but an ultrasound of the anterior thigh revealed a complete cut of the quadriceps tendon on both sides. The repair of the bilateral quadriceps tendon was done by direct repair using the Kessler technique and was reinforced with fiber tape. Following knee immobilization for six weeks, the patient began intensive physical therapy management to decrease pain, enhance muscle strength, and increase range of motion. After rehabilitation, the patient regained a complete range of motion in the knee and improved functionality, and he could walk independently without crutches.

Objective: To evaluate non-psychiatric physicians’ knowledge, attitude and practices regarding management of cases with addiction. Subjects and Methods: Following a cross-sectional study, 126 non-psychiatric physicians in Aseer Region were interviewed. A study questionnaire was used for data collection. It included socio-demographic data; general attitudes and interest toward addictions, addiction psychiatry; knowledge about addictions, addictive disorders, including treatment, and practice about addictions, addictive disorders, and treatment. The survey was anonymously self-administered and participant physicians were asked to provide their written informed consent to participate in this study. Data were collected through direct interviews with physicians. Results: Age of participants ranged from 26 to 66 years (Mean±SD: 37.1±9.1 years). Most participants (81%) were males. Only 9.5% attended a training course or a conference on management of addiction, while 12.7% attended training or a course on palliative care. Generally, participants had very low knowledge regarding management of addiction, especially maintenance daily doses of Buprenorphine and Methadone and their overdose (4.8%, 12.7% and 7.9%, respectively). Regarding positive attitude statements, the least agreed upon were “illicit drugs addicts are easy patients”; and “being at ease in working with persons with heroin addiction” (11.1%, and 14.3%, respectively), while the highest agreed upon negative statements were “deficient resources to treat drug-dependent patients”; and “need to have more training on addiction management” (82.5% and 69.8%, respectively). Acute or chronic pain crises were the most common addiction-related conditions managed by participant physicians (39.7% and 31.7%, respectively). Cases of substance-induced psychosis were managed by 12.7% of participant physicians. The most commonly prescribed drugs for patients with addiction-related conditions were morphine, fentanyl, tramadol and pregabalin (44.4%, 19% 19%, and 17.5% respectively). Conclusions: Non-psychiatric physicians in Aseer Region have insufficient knowledge, negative attitude and poor practice about psychoactive substance use disorders. Continuing medical education and training is necessary to promote physicians’ knowledge and practices related to prevention and treatment strategies for addiction diseases. Key Words: Addiction, psychoactive substance use disorders, knowledge, attitude, practice, non-psychiatric physicians

Internal hernias are quite rare, accounting for fewer than 1% of all abdominal hernias. Moreover, the most frequent cause of internal herniation is paraduodenal hernia (PDH). Diagnosing paraduodenal hernias can be difficult due to the wide range of symptoms that can occur. It is a case report of paraduodenal hernia that was diagnosed and managed in a tertiary center. We describe the case of a 55-year-old male patient who had been experiencing nonspecific abdominal discomfort for the last 5 years and had several comorbidities and positive serology. An abdominal computed tomography (CT) revealed that he had a left PDH, which was effectively treated with laparoscopic surgical repair. The study's strength is that it was correctly identified by CT and subsequently laparoscopically corrected. A gastrografin swallow study was also performed postoperatively to ensure there was no leak. The study's flaw is that the patient was misdiagnosed for five years in other medical facilities. Because paraduodenal hernia is an uncommon diagnosis, it's important to keep it in mind as one of the differential diagnoses for persistent discomfort. It also needs accurate imaging in order to identify and successfully manage it. To demonstrate the relevance of this uncommon condition, future research is needed to report on any misdiagnosis or treatment. To conclude, nowadays the best option for paraduodenal hernia repair is laparoscopic surgery. However, because it can present with a wide variety of symptoms and diagnosis might be challenging, it demands a high index of suspicion. The gold standard for identifying paraduodenal hernia is still a CT scan of the abdomen. Patients who are stable and have no signs of intestinal obstruction might decide to have their hernia repaired to avoid future problems. To demonstrate the relevance of this uncommon condition, future research is needed to report on any misdiagnosis or treatment.

This paper develops a data-driven autonomous method for detection of fatigue damage and classification of the associated damage risk in mechanical structures, based on ultrasonic signal energy. The underlying concept is built upon attenuation of the signal and stability of the attenuation process. The attenuation provides pertinent information for damage quantification, whereas the stability represents resistance towards the fatigue damage growth. The proposed neural network (NN) model has been trained using the scaled conjugate-gradient back-propagation method. The NN model is capable of damage detection and damage classification into five classes of increasing risk. The Daubechies wavelet transform has been used to reduce the noisy pattern of the ultrasonic signal energy by using the associated approximation coefficients. The results show that the proposed method of approximation signal energy can detect and classify the damage with an accuracy of up to ∼ 9 8 . 5 % .

Forecasting and detection of fatigue cracks play a key role in damage mitigation of mechanical structures (e.g., those made of polycrystalline alloys) to enhance their service life, and ultrasonic testing (UT) has emerged as a powerful tool for detection of fatigue cracks at early stages of damage evolution. Along this line, the work reported in this paper aims to improve the performance of fatigue crack forecasting and detection based on a synergistic combination of discrete wavelet transform (DWT) and Hilbert transform (HT) of UT data, collected from a computer-instrumented and computer-controlled fatigue-testing apparatus. Performance of the proposed method is evaluated by comparison with the images generated from a digital microscope, which are treated as the ground truth in this paper. The results of comparison reveal that forthcoming fatigue cracks can be detected ahead of their appearance on the surface of test specimens. The proposed method apparently outperforms both HT and conventional DWT, when they are applied individually, because the synergistic combination of DWT and HT provides a better characterization of UT signal attenuation for detection of fatigue crack damage.

Background: Menopause is the estimated end of reproductive life. Having a positive attitude towards it has been shown to outcome in a positive experience, whereas a negative attitude is connected with negative experiences and symptoms. Conventionally, women regularly abstain from sex after menopause. Purpose: The study aimed to determine the level of awareness and perceptions about the menopause and sex in perimenopausal women attending a general outpatient clinic. Methods: Women over 40 years were enlisted from the Family Medicine Department of King Abdulaziz Hospital, excluding those who were menopausal. Data analyses were done with chi-square test (P

This paper proposes an autonomous method for detection and classification of fatigue crack damage and risk assessment in polycrystalline alloys. In this paper, the analytical and computational tools are developed based on convolutional neural networks (CNNs), where the execution time is much less than that for visual inspection, and the detection and classification process is expected to be significantly less error-prone. The underlying concept has been experimentally validated on a computer-instrumented and computer-controlled MTS fatigue testing apparatus, which is equipped with optical microscopes for generation of image data sets. The proposed CNN classifier is trained by using a combination of the original images and augmented images. The results of experimentation demonstrate that the proposed CNN classifier is able to identify the images into their respective classes with an accuracy greater than 90%.