Accuracy of acetabular cup positioning in robotic-assisted total hip arthroplasty: a CT-based evaluation.

Introduction: Robot-assisted instrumentation during total hip arthroplasty (THA) has the potential to improve acetabular cup positioning. This study aimed to evaluate the precision of robotic-arm-assisted THA (rTHA) and assess whether the system can provide accurate cup positioning comparable to conventional THA (cTHA).

Methods: A single-center prospective cohort study consisting of 151 patients who underwent THA (108 rTHA and 43 cTHA).

Exploring the electrochemical performance of layered BiSe hexagonal platelets as the anode material for lithium-ion batteries.

The escalating need for lithium-ion batteries (LIBs), driven by their expanding range of applications in our daily lives, has led to a surge in interest in metal selenides as potential anode materials. Among them, BiSe stands out as a promising anode material for LIBs due to its unique layered structure. Herein, we explored hexagonally structured layered BiSe platelets synthesized using the solvothermal method.

Starch-Assisted Synthesis of BiS Nanoparticles for Enhanced Dielectric and Antibacterial Applications.

Starch [(CHO) ]-stabilized bismuth sulfide (BiS) nanoparticles (NPs) were synthesized in a single-pot reaction using bismuth nitrate pentahydrate (Bi(NO)·5HO) and sodium sulfide (NaS) as precursors. BiS NPs were stable over time and a wide band gap of 2.86 eV was observed.

Compensation of the Stress Gradient in Physical Vapor Deposited AlScN Films for Microelectromechanical Systems with Low Out-of-Plane Bending.

Thin film through-thickness stress gradients produce out-of-plane bending in released microelectromechanical systems (MEMS) structures. We study the stress and stress gradient of AlScN thin films deposited directly on Si. We show that AlScN cantilever structures realized in films with low average film stress have significant out-of-plane bending when the AlScN material is deposited under constant sputtering conditions.

AMMU: A survey of transformer-based biomedical pretrained language models.

Transformer-based pretrained language models (PLMs) have started a new era in modern natural language processing (NLP). These models combine the power of transformers, transfer learning, and self-supervised learning (SSL). Following the success of these models in the general domain, the biomedical research community has developed various in-domain PLMs starting from BioBERT to the latest BioELECTRA and BioALBERT models.