Prevalence of Prediabetes and Associated Risk Factor Assessment Among Adults Attending Primary Healthcare Centers in Al Bahah, Saudi Arabia: A Cross-Sectional Study.

Background Prediabetes is an asymptomatic intermediate state of hyperglycemia with a major risk of developing type 2 diabetes (T2D). It is a progressive condition; it can take a few years for patients to become diabetic from a prediabetes state. Objectives This study aimed to detect the prevalence rate of prediabetes among the adult population and to assess the association of the risk factors to prediabetes in both the male and female genders. Methods A cross-sectional study was carried out in primary healthcare centers (PHC) in the city of Al Bahah, Saudi Arabia. A total of 378 study participants of both sexes from two central PHCs were eligible to participate in the study, which was conducted from January to February 2018. Data were collected through anthropometric measurements, laboratory investigations, and a predesigned questionnaire interview to identify demographic, lifestyle, and medical variables. Two blood tests were used to diagnose prediabetes: fasting plasma glucose (FBG) and hemoglobin A1c (HbA1C). Results The overall prevalence of prediabetes among all participants was 20% (around 21% in males and 19% in females of all ages). There was a statistically significant association between prediabetes and obesity (central obesity), hypertension, and a family history of diabetes mellitus (FHDM). Conclusion The prediabetic state is highly prevalent among adults attending PHC in Al Bahah city (20%). It is associated with obesity (especially central), hypertension, and FHDM. We highly recommend that public health professionals implement strategies for effective screening, diagnosis, and management of prediabetes.

Explainable Malware Detection System Using Transformers-Based Transfer Learning and Multi-Model Visual Representation.

Android has become the leading mobile ecosystem because of its accessibility and adaptability. It has also become the primary target of widespread malicious apps. This situation needs the immediate implementation of an effective malware detection system. In this study, an explainable malware detection system was proposed using transfer learning and malware visual features. For effective malware detection, our technique leverages both textual and visual features. First, a pre-trained model called the Bidirectional Encoder Representations from Transformers (BERT) model was designed to extract the trained textual features. Second, the malware-to-image conversion algorithm was proposed to transform the network byte streams into a visual representation. In addition, the FAST (Features from Accelerated Segment Test) extractor and BRIEF (Binary Robust Independent Elementary Features) descriptor were used to efficiently extract and mark important features. Third, the trained and texture features were combined and balanced using the Synthetic Minority Over-Sampling (SMOTE) method; then, the CNN network was used to mine the deep features. The balanced features were then input into the ensemble model for efficient malware classification and detection. The proposed method was analyzed extensively using two public datasets, CICMalDroid 2020 and CIC-InvesAndMal2019. To explain and validate the proposed methodology, an interpretable artificial intelligence (AI) experiment was conducted.

Stress-Controlled Creep-Fatigue of an Advanced Austenitic Stainless Steel at Elevated Temperatures.

Creep-fatigue interaction occurs in many structural components of high-temperature systems operating under cyclic and steady-state service conditions, such as in nuclear power plants, aerospace, naval, and other industrial applications. Thus, understanding micromechanisms governing high-temperature creep-fatigue behavior is essential for safety and design considerations. In this work, stress-controlled creep-fatigue tests of advanced austenitic stainless steel (Alloy 709) were performed at a 400 MPa stress range and 750 °C with tensile hold times of 0, 60, 600, 1800, and 3600 s, followed by microstructural examinations. The creep-fatigue lifetime of the Alloy 709 was found to decrease with increasing hold time until reaching a saturation level where the number of cycles to failure did not exhibit a significant decrease. Softening behavior was observed at the beginning of the test, possibly due to the recovery of entangled dislocations and de-twining. In addition, hysteresis loops showed ratcheting behavior, although the mean stress was zero during creep-fatigue cycling, which was attributed to activity of partial dislocations. Microstructural examination of the fracture surfaces showed that fatigue failure dominated at small hold times where the cracks initiated at the surface of the sample. Larger creep cracks were found for longer hold times with a lower probability of dimpled cavities, indicating the dominance of creep deformation. The results were compared with other commonly used stainless steels, and plausible reasons for the observed responses were described.

GEANT4 Simulation for Radioactive Particle Tracking (RPT) Technique.

In the past two decades, the radioactive particle tracking (RPT) measurement technique has been proven to visualize flow fields of most multiphase flow systems of industrial interest. The accuracy of RPT, and hence the data obtained, depend largely on the calibration process, which stands here as a basis for two subsequent processes: tracking and reconstruction. However, limitations in the RPT calibration process can be found in different experimental constrains and in assumptions made in the classical Monte Carlo approach used to simulate number of counts received by the detectors. Therefore, in this work, we applied a GEANT4-based Monte Carlo code to simulate the RPT calibration process for an investigated multiphase flow system (i.e., gas-liquid bubble column). The GEANT4 code was performed to simulate the number of counts received by 28 scintillation detectors for 931 known tracer positions while capturing all the types of photon interaction and overcoming solids' angle limitations in classical approaches. The results of the simulation were validated against experimental data obtained using an automated RPT calibration device. The results showed a good agreement between the simulated and experimental counts, where the maximum absolute average relative deviation detected was about 5%. The GEANT4 model typically predicted the number of counts received by all the detectors; however, it over-estimated the counts when the number of primary events applied in the model was not the optimal.

The Effect of Reinforcement Preheating Temperatures on Tribological Behavior of Advanced Quranic Metal-Matrix Composites (QMMC).

The growing applications of iron/copper bimetallic composites in various industries are increasing. The relationship between the properties of these materials and manufacturing parameters should be well understood. This paper represents an experimental study to evaluate the effect of reinforcement (steel rod) preheating temperature on the mechanical properties (bond strength, microhardness, and wear resistance) of copper matrix composites (QMMC). In preparing the QMMC samples, the melted copper was poured on a steel rod that had been preheated to various temperatures, namely, room temperature, 600 °C, 800 °C, and 1200 °C. Properties of the QMMC (interface microstructure, interfacial bonding strength, microhardness, and wear) were investigated. The experimental results revealed that the best bond between the copper matrix and steel rod formed only in the composites prepared by preheating the steel rods with temperatures lower than the recrystallization temperature of steel (723 °C). This is because the oxide layer and shrinkage voids (due to the difference in shrinkage between the two metals) at the interface hinder atom diffusion and bond formation at higher temperatures. The microhardness test showed that preheating steel rod to 600 °C gives the highest value among all the samples. Furthermore, the QMMC's wear behavior confirmed that the optimization of preheating temperature is 600 °C.

Management of Idiopathic Retroperitoneal Fibrosis, a Retrospective Study at Prince Hussein Urology and Organ transplantation center (PHUO), Jordan.

INTRODUCTION: to study the presentation, clinical course, laboratoryresults, imaging findings, medical and urological treatments ofidiopathic retroperitoneal fibrosis at our institution. METHODS: Between January 2006 and December 2017, medical recordsand operatives' notes of 116 patients with idiopathic retroperitonealfibrosis (IPRF) were reviewed retrospectively. Diagnosis was doneby clinical and radiological imaging that fulfilled a strict criterion.All patients were initiated on Prednisolone 60 mg for two months,and reduced until reaching 10 mg daily, with a total duration of24 months in the responding patients. Renal drainage was done incases of obstructed kidneys. To assess response, both laboratoryresults and imaging studies at initiation and after 4 months werereviewed and compared. RESULTS: Of 116 patients diagnosed with IRPF, eighty five (73.3%)were male and thirty one (26.7%) female, with mean ± SD age atpresentation was (50.5 ± 10.6) years. 79% of the patients complaintof abdominal and low back pain, 27% uremic symptoms, 10.3% hada new onset of hypertension, 3.4% presented with anejaculation,and 13.8% were totally asymptomatic. Uretric obstruction hadbeen resolved in 132 ureters after a mean of 152 days of treatment.Almost 30 % reduction in the fibrotic mass size was achieved in82 % of patients. CONCLUSION: Glucocorticoids is the the mainstay of treatment. Therenal function tests, of the vast majority of patients, normalizedwith treatment. Relapse may occur, so a follow-up over a longperiod of time is required. A high index of suspicion is neededfor diagnosis in asymptomatic patients.

Dynamic Fuzzy-Logic Based Path Planning for Mobility-Assisted Localization in Wireless Sensor Networks.

Mobile anchor path planning techniques have provided as an alternative option for node localization in wireless sensor networks (WSNs). In such context, path planning is a movement pattern where a mobile anchor node's movement is designed in order to achieve a maximum localization ratio possible with a minimum error rate. Typically, the mobility path planning is designed in advance, which is applicable when the mobile anchor has sufficient sources of energy and time. However, when the mobility movement is restricted or limited, a dynamic path planning design is needed. This paper proposes a novel distributed range-free movement mechanism for mobility-assisted localization in WSNs when the mobile anchor's movement is limited. The designed movement is formed in real-time pattern using a fuzzy-logic approach based on the information received from the network and the nodes' deployment. Our proposed model, Fuzzy-Logic based Path Planning for mobile anchor-assisted Localization in WSNs (FLPPL), offers superior results in several metrics including both localization accuracy and localization ratio in comparison to other similar works.