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The significance of connectedness and compactness plays a vital role in studying the properties of topological spaces. Numerous authors explored several characterizations of topological spaces using these notions. Researchers had attempted to generalize the concepts of connectedness and compactness in various generalized topological spaces like ideal topological spaces, soft topological spaces, fuzzy topological spaces etc. In the year 2013, Babitha and John studied the concept of connectedness in soft multi topological spaces. Since then the study of these concepts has created a gap in the literature. The uniosity about the generalizations of connectedness and compactness in soft multi topological spaces explored this article.

The compounds in pharmaceutical and personal care products (PPCPs) that may be potential environmental pollutants include metformin and triclosan. The suitable method to estimate the presence of metformin (MET) and triclosan (TRI) in wastewater treatment facilities is developed and validated in this research study. This study aimed to quantify the MET and TRI in sewage through a liquid chromatograph – mass spectrometer (LC-MS 8040) by a Quick, Easy, Cheap, Effective, Rugged and Safe (QuEChERS) technique of sample preparation. In LC-MS 8040, the Milli-Q water and sewage were examined at three different concentrations (0.01, 0.02 and 0.03 mg L−1) of MET and TRI. The recoveries of MET in both the matrixes ranged between 88.10 to 116.10% whereas the recoveries were ranged from 86.70 to 116.30% for TRI (n = 7, RSD <10%). The respective limit of detection (LOD) and the limit of quantification (LOQ) for MET and TRI were 0.005 and 0.01 mg L−1. With the matrix effect of < 10% in sewage, the approach was shown to be very linear (R2 = 0.9969 for MET and R2 = 0.9939 for TRI) and reliable. The precision was also exemplary with the RSD value of < 10% (n = 6). This method accurately assessed MET (BDL to 0.0657 mg L−1) and TRI (BDL to 0.1833 mg L−1) concentrations in sewage samples of Ukkadam sewage treatment plant (USTP) and Tamil Nadu Agricultural University treatment plant (TSTP). This is a unique use of the QuEChERS method for calculating these two compounds from aqueous matrixes with substantially greater outputs (20–25 samples per day) than typical Solid Phase Extraction (SPE) procedures. [ABSTRACT FROM AUTHOR]

Melanoma is an uncommon and dangerous type of skin cancer. Dermoscopic imaging aids skilled dermatologists in detection, yet the nuances between melanoma and non-melanoma conditions complicate diagnosis. Early identification of melanoma is vital for successful treatment, but manual diagnosis is time-consuming and requires a dermatologist with training. To overcome this issue, this article proposes an Optimized Attention-Induced Multihead Convolutional Neural Network with EfficientNetV2-fostered melanoma classification using dermoscopic images (AIMCNN-ENetV2-MC). The input pictures are extracted from the dermoscopic images dataset. Adaptive Distorted Gaussian Matched Filter (ADGMF) is used to remove the noise and maximize the superiority of skin dermoscopic images. These pre-processed images are fed to AIMCNN. The AIMCNN-ENetV2 classifies acral melanoma and benign nevus. Boosted Chimp Optimization Algorithm (BCOA) optimizes the AIMCNN-ENetV2 classifier for accurate classification. The proposed AIMCNN-ENetV2-MC is implemented using Python. The proposed approach attains an outstanding overall accuracy of 98.75%, less computation time of 98 s compared with the existing models., (© 2024. International Federation for Medical and Biological Engineering.)