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Background and Aims: Multiple myeloma (MM) is a plasma cell dyscrasia that is common among patients with autoimmune diseases. However, the association between ulcerative colitis (UC) and multiple myeloma (MM) is yet to be established. We aimed to evaluate the prevalence of MM among patients with UC in the United States. Methods: This cross-sectional cohort analysis used the National Inpatient Sample from 2015–2018 to assess the overall MM prevalence among patients with and without UC, and within specific demographic subgroups. Prevalences were compared using a logistic regression model controlling for sex and age. Results: The crude prevalence of MM among patients with UC (n = 1750) compared with patients without UC (n = 366,265) was 0.44% vs. 0.37%, respectively. Patients with UC had increased overall odds of having MM (odds ratio (OR), 1.26). Males with UC had higher prevalence of MM (53.7% vs. 46.3%, respectively) than females. Patients with UC and MM were more likely to be African American than White (15.6% vs. 9.2%, respectively). Patients with UC age >64 had a higher prevalence of MM than those aged below 65 (70.9% vs. 29.1%, respectively). Patients with UC who were obese (BMI > 30) had a higher prevalence of MM than those who were non-obese (12.6% vs. 8.3%). Conclusions: Overall, UC appears to be associated with MM. This association can be particularly observed in specific demographic groups, such as obese, African American males, or patients >64 years of age. Thus, a high degree of clinical suspicion for MM is warranted, even with minimal symptomatology, in patients with UC, in particular among elder, obese, and African American males.

Bug bounty schemes make use of outside ethical hackers to find and fix a variety of security flaws, guaranteeing quicker and more affordable problem solving. Better confidence in and image of the company in the cybersecurity space, faster solving issues, and increased community collaboration are some of its results. Computer vision relies on face detection, which has several uses. This article uses convolutional neural networks (CNNs) and an error reward algorithm in the facial recognition simulation library to enhance face detection. Trainers trained CNNs to detect faces from other visual components and extract human facial traits, making them powerful facial identification tools. These networks classify and extract face characteristics automatically, obtaining approaching 100% identification rates. CNNs have greater identification rates and easier face-image extraction than earlier methods. Network architecture determines its performance, transcending machine learning methodologies. This article suggests a bug reward scheme to discover and resolve bugs in the face recognition library. The program has helped Google find flaws in its intelligent systems, including model manipulation and adversarial assaults. These activities enhance AI safety and security studies, highlight possible concerns, and promote AI safety. CNN-based facial recognition models enhance accuracy and offer advantages over previous approaches. The CNN-based method and Bug Bounty software improved the facial recognition library.

Cryptographic tools like Hill's encryption algorithm protect digital data. In this work, we present a novel Hill cipher security method that utilizes Gaussian integers from number theory. Using these intriguing mathematical entities to disguise plaintext values dramatically boosts assault resistance and duration. This research includes a three-pass protocol for encryption and decryption without key exchange, ensuring a safe, efficient, and dependable solution. Pandas is used for efficient data processing, and Numpy for computational tasks, notably matrices. Hill cipher-based encryption and decryption can be utilized in real life. It also demonstrates how to save Pandas DataFrame data to Excel. This strategy assures progress in cryptography. Uniquely designed for banking, it emphasizes its applicability and possible influence on present financial systems.

Due to a lack of legal enforcement, numerous vehicle accidents occur every year throughout the world. An effective vehicle tracking system is developed, tested, and classified in order to identify the speed of any vehicle in any street at any time and from any location (highways, crowded or secondary roads). The suggested system makes use of IoT technology, which is a relatively new development that has already made significant contributions to scientific research as well as offers many wonderful conveniences in daily life. The in-vehicle device utilizes GPS and GSM/GPRS, two of the most commonly used tracking technologies. The device is integrated into a moving object (vehicle) that is automatically tracked and observed in real-time. Through the OBD interface, the Raspberry Pi microcontroller periodically reads the vehicle speed. The Raspberry Pi microcontroller is equipped with GPS and GSM/GPRS modules to track the location of the vehicle. and, when the speed limit is exceeded, sends the coordinates and speed to the central station over the internet. The data pertaining to each vehicle is kept in a database at the central station. Based on the data gathered from the in-vehicle gadget, the driver who exceeded the speed limit will immediately receive a fine through the central station. The proposed system will help in reducing the likelihood of driver fraudulence and has demonstrated its efficiency and accuracy to reduce excessive speed and traffic accidents, leading to the daily saving of many lives. The system has been successfully built and effectively tested at a lower cost than traditional systems that rely on the use of cameras on each road.