Predicting Paediatric Brain Disorders from MRI Images Using Advanced Deep Learning Techniques.

The problem at hand is the significant global health challenge posed by children's diseases, where timely and accurate diagnosis is crucial for effective treatment and management. Conventional diagnosis techniques are typical, use tedious processes and generate inaccurate results since they are executed by human beings and cause delays in treatment that can be fatal. Considering these and other shortcomings there exists a need to have more efficient and accurate solutions based on artificial intelligence.

Phase transitions, Dirac and Weyl semimetal states in MnGeBiTe.

Using angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT), an experimental and theoretical study of changes in the electronic structure (dispersion dependencies) and corresponding modification of the energy band gap at the Dirac point (DP) for topological insulator (TI) [Formula: see text] have been carried out with gradual replacement of magnetic Mn atoms by non-magnetic Ge atoms when concentration of the latter was varied from 10% to 75%. It was shown that when Ge concentration increases, the bulk band gap decreases and reaches zero plateau in the concentration range of 45-60% while trivial surface states (TrSS) are present and exhibit an energy splitting of 100 and 70 meV in different types of measurements. It was also shown that TSS disappear from the measured band dispersions at a Ge concentration of about 40%.

Dynamic In-Plane Compression and Fracture Growth in a Quasi-Isotropic Carbon-Fiber-Reinforced Polymer Composite.

This study presents an experimental investigation of the quasi-static and dynamic behavior of a quasi-isotropic carbon-fiber-reinforced composite subjected to in-plane compressive loading. The experiments were performed at strain rates ranging from 4×10-5 to ∼1200 s-1 to quantifythe strain-rate-dependent response, failure propagation, and damage morphology using advanced camera systems. Fiber bridging, kink band formation, dominance of interlaminar failure, and inter-fiber failure fracture planes are evidenced through post-mortem analysis.

Advanced deep transfer learning techniques for efficient detection of cotton plant diseases.

Introduction: Cotton, being a crucial cash crop globally, faces significant challenges due to multiple diseases that adversely affect its quality and yield. To identify such diseases is very important for the implementation of effective management strategies for sustainable agriculture. Image recognition plays an important role for the timely and accurate identification of diseases in cotton plants as it allows farmers to implement effective interventions and optimize resource allocation.

Experience With Dabigatran on Rate of Portal Vein Thrombosis Recanalization, Disease Progression and Survival.

Background And Aims: We assessed clinical, procoagulant and genetic risk factors and clinical outcomes in dabigatran-treated patients with non-tumoural acute and acute-on-chronic portal vein thrombosis (PVT).

Methods: Patients with a new diagnosis of non-tumoural acute and acute-on-chronic PVT between January 2021 and January 2024 (aged ≥ 18 years) in those without/with cirrhosis (Child-Pugh (CP)-A/B/C ≤ 10) were started on dabigatran and followed and compared with those on vitamin K antagonist (VKA) and untreated individuals.

Results: Dabigatran was prescribed in 119 patients with PVT type 1 (61, 51.