Enhancing ransomware defense: deep learning-based detection and family-wise classification of evolving threats.

Ransomware is a type of malware that locks access to or encrypts its victim's files for a ransom to be paid to get back locked or encrypted data. With the invention of obfuscation techniques, it became difficult to detect its new variants. Identifying the exact malware category and family can help to prepare for possible attacks.

Adaptation of potato cultivars to phosphorus variability and enhancement of phosphorus efficiency by Bacillus subtilis.

Background: Plants utilize a variety of mechanisms to adapt to fluctuations in phosphorus (P) availability. Potatoes, in comparison to other crops, often display reduced phosphorus use efficiency (PUE) due to their underdeveloped root systems; therefore, understanding the mechanisms underlying PUE is critical for improving it. This study aimed to evaluate the morphological and physiological responses of potatoes to different P levels, with a focus on root system alterations and PUE.

4-Dimensional Cardiac Modelling of Multi-phase Computed Tomography for Predicting Outcomes Following Transcatheter Aortic Valve Replacement.

Background: Multi-phase Computed Tomography Angiography (mpCTA) is routinely performed prior to transcatheter aortic valve replacement (TAVR) to determine eligibility and enable pre-procedural planning. Incremental prognostic value may be realized from full-cycle, multi-phase reconstructions to assess the contractile health of cardiac chambers. This study aimed to assess the feasibility of 4D chamber modelling of the left ventricle (LV) to support 3D minimum principal strain (3DminPS) based predictions of clinical outcomes following TAVR.

A novel deep learning model for predicting marine pollution for sustainable ocean management.

Climate change has become a major source of concern to the global community. The steady pollution of the environment including our waters is gradually increasing the effects of climate change. The disposal of plastics in the seas alters aquatic life.

Navigating ambiguity: A novel neutrosophic cubic shapley normalized weighted Bonferroni Mean aggregation operator with application in the investment environment.

The Neutrosophic Cubic Shapley Normalized Bonferroni (NC-SNWBM) method represents a cutting-edge approach to decision making theory, combining three distinct mathematical frameworks the neutrosophic cubic sets (NCS), Shapley values, and the Bonferroni aggregation operator. This innovative method addresses the challenges posed by uncertainty, vagueness, and imprecision in decision-making (DM) processes, offering a comprehensive and versatile tool for handling complex and dynamic scenarios. Neutrosophic cubic sets offers a strong platform to handle ambiguous and vague data due to three components Membership Grade (MG), Non-Membership Grade (NMG) and Indeterminancy Grade (IG) in data.