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BACKGROUND: The appendix is a small organ with no particular known function. Primary appendiceal neoplasms (ANs) are rare. While the prevalence is increasing worldwide over the past two decades, no apparent increase in the prevalence of ANs has been reported in the Arabian Gulf States. Recently, a significant decline in the age at diagnosis of some types of ANs has been reported worldwide, with a female predominance. OBJECTIVES: Evaluate the prevalence and clinicopathological characteristics of ANs within our institution in Saudi Arabia and compare them to limited existing studies from different regions as well as the Arabian Gulf States. DESIGN: Retrospective cohort SETTING: Tertiary care center in Riyadh PATIENTS AND METHODS: All patients who underwent appendectomy and had the appendix submitted for histopathological evaluation between May 2015 and June 2020 were included to allow for a follow-up of 5 years or more at the time of data collection. MAIN OUTCOME MEASURES: Demographics, clinical presentations, surgical interventions, histopathological findings, complications, and recurrence rates. SAMPLE SIZE: 25 AN patients RESULTS: Of 1110 patients, 25 had ANs (13 female and 12 male participants) with a mean (standard deviation) age of 54.6 (14.1) years. Only 40% presented with acute appendicitis, 64% had comorbidities, and less than 50% underwent laparoscopic appendectomy. Histopathologically, 72% were low-grade appendiceal mucinous neoplasms (LAMNs). Complications were minimal grades (Clavien-Dindo classification), with 80% experiencing none. The mean hospital stay was 9.96 days. Local recurrence occurred in 8% of cases, and distant metastasis was documented in one adenocarcinoma case. However, the 5-year overall and disease-free survival rates were 88% and 80%, respectively. CONCLUSIONS: The incidence of ANs is increasing in Saudi Arabia with the higher prevalence of LAMNs. The pathological examination of the resected appendix played a pivotal role in the diagnosis of ANs. LIMITATIONS: Data collected retrospectively, a single institution, and a small population.

Summary: Background: Primary sclerosing cholangitis (PSC) is one of the leading indications for liver transplantation in Europe, and a major risk factor for cancer in inflammatory bowel disease (IBD). However, it is not known how the epidemiology of PSC will change as that of IBD evolves. The aim of this study is to provide nationwide statistics on the past and current prevalence of PSC and IBD across England, and forecast how this is likely to change over time. Methods: We accessed and analysed a nationwide population-based administrative healthcare registry, which houses prospectively accrued data since April 1st 2001. In so doing, the past and current prevalence of PSC-IBD and IBD alone was determined among 18–60-year-olds in England, alongside average annual percentage change rates (AAPC), between the 1st of January 2015 and 2020. Past and current prevalence data, alongside trends in incidence and event-free survival rates, were then used to forecast future prevalence between 2021 and 2027. Findings: In 2015, the prevalence of PSC with prior IBD diagnosis was 5.0 per 100,000 population, rising to 5.7 when including those with IBD diagnosed after PSC. In 2020, prevalence increased to 7.6 (8.6 accounting for IBD developing after PSC), yielding an AAPC of 8.8. In 2027, PSC-IBD prevalence is forecast to be 11.7 (95% prediction interval [PI]: 10.8–12.7), and 13.3 when accounting for IBD developing after PSC (AAPC: 6.4; 95% PI: 5.3–7.5). Comparatively, the prevalence of IBD alone rose among 18–60-year-olds from 384.3 in 2015 to 538.7 in 2020 (AAPC 7.0), and forecast to increase to 742.5 by 2027 (95% PI: 736.4–748.0; AAPC: 4.7, 95% PI: 4.6–4.8). Interpretation: The rate of growth in PSC-IBD is predicted to exceed IBD-alone. Further research is needed to understand changes in disease epidemiology, including aetiological drivers of developing (invariably progressive) liver disease in IBD, and the implications of rising case burden on health care resources. Funding: This study was supported by an unrestricted grant provided by Gilead Sciences.

Abstract Images are an important source of information for spacecraft navigation. Based on an image and a known attitude, triangulation techniques (intersection or resection) are often used for positioning and navigation. In the resection problem, an observer estimate its unknown location by using angle measurements to points at known locations (i.e., landmarks), the localization performance depending on the accuracy of the angle measurements. As a contribution to resection for spacecraft navigation, we considers the dynamic image estimation problem based on radio interferometry, i.e., image of radio source power, where the measurements are sample covariance matrices (SCMs). Considering the case where several measurements are available as well as a known dynamic linear model of image evolution, a.k.a a linear state model, the minimum mean-squared error image estimator (MMSE) is given by the Kalman filter (KF) or one of its variants. However standard Kalman-like filters are not a priori suitable for the problem at hand since the measurements (i.e., SCMs) cannot be formulated analytically as a function of state parameters to be estimated (i.e., radio source power). In fact, this lack of analytical formulation can be circumvented by a statistical linear fitting allowing the SCMs to be expressed in terms of the state. This linear fitting introduces an additive residual noise, equivalent to a measurement noise, whose covariance matrix depends on the current state, a non-standard case for a measurement model. The covariance matrix of the residual noise is derived whatever the distributions of the radio sources and of the additive noise at the samples level, unveiling the contribution of their multivariate kurtosis. The proposed method is evaluated on simulated data representative of a dynamic radio interferometry framework. The results show that the proposed method is capable of effectively tracking moving radio sources in complex scenes with theoretical guaranties when the signal multivariate kurtosis is known.

Bearings are critical components in rotating machinery, and their failure can lead to costly repairs and downtime. To prevent such failures, it is important to detect and diagnose bearing faults early. In recent years, deep-learning techniques have shown promise for detecting and diagnosing bearing faults automatically. While these algorithms can all achieve diagnostic accuracy of over 90%, their generalizability and robustness in complex, extreme variable loading conditions have not been thoroughly validated. In this paper, a feature extraction method based on Synchro-squeezing Wavelet Transform (SSWT), Random projection (RP), and deep learning (DL) is presented. To fulfill the data requirements of neural networks, data augmentation is initially utilized to augment the size of the original data. Subsequently, the SSWT technique is employed to convert the signals from the Time domain to the Time-Frequency domain, resulting in the conversion of the 1-D signal to a 2-D feature image. To decrease the complexity of deep learning computation, data preprocessing involves utilizing Random projection to reduce feature dimensionality. The final step involves constructing a Convolutional Neural Network (CNN) model that can identify fault features from the obtained Time-Frequency images and perform accurate fault classification. By utilizing the CWRU and IMS datasets to evaluate the method, the study demonstrates that the suggested approach outperforms advanced techniques in terms of both diagnostic accuracy and robustness.

Background: Numerous infections can be caused by Pseudomonas aeruginosa, particularly in hospitalized patients. The aim of the study was to estimate the antibacterial and biofilm inhibition effects of Silver nanoparticles (AgNPs) on P.aeruginosa isolates. Methodology: A total of 283 samples were taken from hospitalized patients at Menoufia University Hospitals. P. aeruginosa isolates were identified by standard microbiological methods and VITEK 2 system. Antibiogram and biofilm formation were assessed by disk diffusion and modified congo red agar methods respectively. PslA and lasR genes were detected by conventional PCR. The antibacterial and anti-biofilm effects of AgNPs were also tested. Results: P. aeruginosa isolates represented 16.7% of isolates, 92% of them were resistant to piperacillin and ciprofloxacin. Resistance to aztreonam, piperacillin/tazobactam, cefepime, ceftazidime, and gentamicin were 74%, 72%, 72%, 62%, and 52% respectively. P. aeruginosa isolates were sensitive to meropenem (68%) and imipenem (60%). Approximately 54% and 44% of the isolates were ESβL and biofilm producers respectively. PslA and lasR genes were found among 52% and 48% of P. aeruginosa isolates respectively. The MIC and MBC of AgNPs ranged from (6.25 to 50 μg/mL), inhibiting 40.9% of biofilms at 6.25 μg/mL and further inhibiting 40.9% of biofilms at 12.5 μg/mL. Conclusion: P. aeruginosa are serious nosocomial pathogens due to high antimicrobial resistance that is mainly due to ESβLs production and biofilm formation. AgNPs exert potent antibacterial and anti-biofilm effects at concentrations of 6.25–50 μg/ml.

Over the past 20 years, there has been a surge of diabetes cases in Iraq. Blood tests administered in the absence of professional medical judgment have allowed for the early detection of diabetes, which will fasten disease detection and lower medical costs. This work focuses on the use of a Long-Short Term Memory (LSTM) neural network for diabetes classification in Iraq. Some medical tests and body features were used as classification features. The most relevant features were selected using the Binary Dragon Fly Algorithm (BDA) Binary version of the selection method because the features either selected or not. To reduce the number of features that are used in prediction, features without effects will be eliminated. This effects the classification accuracy, which is very important in both the computation time of the method and the cost of medical test that the individual will take during annual check ups.This work found out that among 11 features, only five features are most relevant to the disease. These features provide a classification accuracy up to 98% among three classes: diabetic, non diabetic and pre-diabetic.

Ensuring the reliability of data obtained from Wireless Sensor Networks (WSNs) deployed in farmland to monitor soil parameters is crucial for optimizing smart irrigation. These distributed sensor nodes encounter various challenges and are vulnerable to sensor faults that can significantly degrade the network's service quality. In this article, we present an innovative approach for detecting sensor faults within WSNs for smart irrigation by combining an autoregressive model with a Kalman filter. Integrating the Kalman filter and the autoregressive model combines their strengths in a synergistic manner. The algorithm is developed with consideration for the resource constraints of the sensor nodes and addresses the challenge of lacking ground truth information for the monitored area. The primary advantage of this proposed technique lies in its simplicity of implementation, requiring minimal computational complexity while enhancing the application's reliability. Through experimentation and validation, we demonstrate the effectiveness of this combined approach in detecting sensor fault detection in real-world WSNs scenarios.

Wind field characteristics in a complex topography are significantly influenced by the nature of the surrounding terrains. This study employs onsite measurements to investigate the wind field characteristics at a towering peak protruding out of a steep mountainside, where butterfly−lookalike landscape platform will be constructed; the impact of the surrounding topography on the wind flow is highlighted. The results showed that the blocking effect of the mountains in the mountainous side of the valley caused a significant drop in the mean wind speed from that direction. The stationary test (reverse arrangement test) indicated that the wind speed had a strong nonstationary characteristic, necessitating the employment of a steady and nonstationary wind speed model to assess the wind turbulence characteristics. The three directions’ wind turbulence integral scales were critically influenced by the occurrence of the wind speedup effect, unexpectedly resulting in the vertical turbulence integral scale being the greatest of the three. Furthermore, the measured wind turbulence properties under both wind speed models showed certain variations from the recommended specifications. Consequently, the impact of the local terrain and the speedup effect on the wind characteristics must be thoroughly evaluated to ensure the structural stability of structures installed at a similar topography.

The cracking of ultra-long and large concrete structures with strong constraints is a key issue under the action of shrinkage and hydration heat. The length of section pouring during construction is one of the main parameters to control the cracking of concrete. In this paper, the shrinkage test of concrete specimens under the condition of coculture is carried out under the background of the landing gear slide test platform of large aircraft. The measured early shrinkage curve of the expanded concrete is obtained, and the finite element model is established. The effects of the casting thickness, mould temperature, and limited expansion rate on the stress and cracking of super-long and large concrete are studied. The results show that factors such as the casting thickness, mould temperature, and limited expansion rate have significant effects on the limited length of the section after pouring. When the casting thickness is increased by 200%, the limit of the section length is reduced by 42%. When the mould temperature increases by 66.7%, the section length limit decreases by 28.2%, while the value increases by 24.2%, with an increasing expansion rate of 75%. The relationship between the three parameters and the piecewise pouring length is approximately linear. The exact calculation of the section length limit of strongly constrained ultra-long mass concrete under different pouring thicknesses, mould temperatures, and limited expansion rates is derived, and a simplified calculation formula is also proposed through data regression analysis. The errors between them are less than 1.7%, which provides a basis for calculating the section length of strongly constrained ultra-long mass concrete construction.

The goal of this study To assess if high frequency ultrasound correlates with modified Rodnan skin score and inflammatory markers and if it can differentiate between Systemic sclerosis and healthy individuals and if HFU can detect subclinical skin thickening. We recruited 30 consecutive patients with SSc and 40 controls in this case control study. Skin thickness was measured by 20-22 MHz ultrasonic probe at 3 different skin sites bilaterally (at third proximal interphalyngeal, third metacarpophalyngeal and wrist joints bilaterally). Total skin thickness (TST) by HFU and skin thickness using mRSS were recorded and compared to HFU.The final results of our work show that total skin thickness in patients with SSc was higher than in healthy controls (P < 0.001), and correlated positively with total mRSS and correlated negatively with CRP. Patients with higher TST had higher mRSS . The area under the receiver operator characteristic (ROC) curve yielded sensitivity of 90% and specificity of 85% at the predicted probability of 1.4 mm as the optimal cut off point to access of mean skin thickness.