Foreign body embolization is a rare but serious iatrogenic complication that might necessitate transcatheter or even surgical retrieval. A broken double-lumen catheter was snared using a goose neck snare kit. The procedure was successful, and the patient experienced no further complications.

This study employs evolutionary optimization and Artificial Intelligence algorithms to determine an individual’s age using a single-faced image as the basis for the identification process. Additionally, we used the WIKI dataset, widely considered the most comprehensive collection of facial images to date, including descriptions of age and gender attributes. However, estimating age from facial images is a recent topic of study, even though much research has been undertaken on establishing chronological age from facial photographs. Retrained artificial neural networks are used for classification after applying reprocessing and optimization techniques to achieve this goal. It is possible that the difficulty of determining age could be reduce

A new and hybrid deep learning-based approach for diagnosing faults in electric vehicle (EV) drive motors is proposed in this article. This article presents a new and hybrid deep learning-based method of diagnosing faults in the drive motors of electric vehicles (EV). In contrast to standard CNNLSTM approaches that depend on SoftMax classification, the introduced framework combines a Random Forest (RF) classifier to enhance the generalization, interpretability, and robustness of fault prediction. Furthermore meant for use on edge computing equipment with IoT integration, the design allows for real-time monitoring in resource-limited settings. The introduced algorithm utilizes a Random Forest (RF) classifier for accurate fault classification

Breast cancer is highlighted in recent research as one of the most prevalent types of cancer. Timely identification is essential for enhancing patient results and decreasing fatality rates. Utilizing computer-assisted detection and diagnosis early on may greatly improve the chances of recovery by accurately predicting outcomes and developing suitable treatment plans. Grading breast cancer properly, especially evaluating nuclear atypia, is difficult owing to faults and inconsistencies in slide preparation and the intricate nature of tissue patterns. This work explores the capability of deep learning to extract characteristics from histopathology photos of breast cancer. The research introduces a new method called SMOTE-based Convolut

The deficiency of potable water resources and energy supply is emerging as a significant and concerning obstacle to sustainable development. Solar and waste heat-powered humidification dehumidification (HDH) desalination systems become essential due to the severe impacts of global warming and water shortages. This problem highlights the need to apply boosted water desalination solutions. Desalination is a capital-intensive process that demands considerable energy, predominantly sourced from fossil fuels worldwide, posing a significant carbon footprint risk. HDH is a very efficient desalination method suitable for remote areas with moderate freshwater requirements for domestic and agricultural usage. Several operational and maintenance conce

The removal of COD from wastewater generated by petroleum refinery has been investigated by adopting electrocoagulation (EC) combined with adsorption using activated carbon (AC) derived from avocado seeds. The process variables influencing COD removal were studied: current density (2–10 mA/cm2), pH (4–9), and AC dosage (0.2–1 g/L). Response surface methodology (RSM) based on Box–Behnken design (BBD) was used to construct a mathematical model of the EC/AC process. Results showed that current density has the major effect on the COD removal with a percent of contribution 32.78% followed by pH while AC dosage has not a remarkable effect due to the good characteristics of AC derived from avocado seeds. Increasing current density gives be