Electrolytic Ni-P and Ni-P-Cu Coatings on PCM-Loaded Expanded Graphite for Enhanced Battery Thermal Management with Mechanical Properties.

This study addresses the thermal management challenge in battery systems by enhancing phase change material composites with Ni-P and Ni-P-Cu coatings on phase change material/expanded graphite structures. Traditional phase change materials are limited by low thermal conductivity and mechanical stability, which restricts their effectiveness in high-demand applications. Unlike previous studies, this work integrates Ni-P and Ni-P-Cu coatings to significantly improve both the thermal conductivity and mechanical strength of phase change material/expanded graphite composites, filling a crucial gap in battery thermal management solutions.

Impact of Cell Design Parameters on Mechanical Properties of 3D-Printed Cores for Carbon Epoxy Sandwich Composites.

The introduction of 3D printing technology has broadened manufacturing possibilities, allowing the production of complex cellular geometries, including auxetic and curved plane structures, beyond the standard honeycomb patterns in sandwich composite materials. In this study, the effects of cell design parameters, such as cell geometry (honeycomb and auxetic) and cell size (cell thickness and width), are examined on acrylonitrile butadiene styrene (ABS) core materials produced using fusion deposition modeling (FDM). They are produced as a result of the epoxy bonding of carbon epoxy prepreg composite materials to the surfaces of core materials.

Landscape and prognostic significance of oncogene drivers in metastatic castration sensitive prostate cancer.

Background: Tumor suppressors are well known drivers of cancer invasion and metastasis in metastatic castration sensitive prostate cancer (mCSPC). However, oncogenes are also known to be altered in this state, however the frequency and prognosis of these alterations are unclear. Thus, we aimed to study the spectrum of oncogene mutations in mCSPC and study the significance of these alteration on outcomes.

A novel approach to antimicrobial resistance: Machine learning predictions for carbapenem-resistant Klebsiella in intensive care units.

This study was conducted at Kocaeli University Hospital in Turkey and aimed to predict carbapenem-resistant Klebsiella pneumoniae infection in intensive care units using the Extreme Gradient Boosting (XGBoost) algorithm, a form of artificial intelligence. This was a retrospective case-control study involving 289 patients, including 159 carbapenem-resistant and 130 carbapenem-susceptible individuals as controls. The model's predictive analysis combined a diverse range of demographic, clinical, and laboratory data, resulting in an average accuracy of 83.