Thermal Conductivity in Biphasic Silicon Nanowires.

The work unravels the previously unexplored atomic-scale mechanism involving the interaction of phonons with crystal homointerfaces. Silicon nanowires with engineered isotopic content and crystal phases were chosen for this investigation. Crystal polytypism, manifested by the presence of both diamond cubic and rhombohedral phases within the same nanowire, provided a testbed to study the impact of phase homointerfaces on phonon transport.

ALFREDO: Active Learning with FeatuRe disEntangelement and DOmain adaptation for medical image classification.

State-of-the-art deep learning models often fail to generalize in the presence of distribution shifts between training (source) data and test (target) data. Domain adaptation methods are designed to address this issue using labeled samples (supervised domain adaptation) or unlabeled samples (unsupervised domain adaptation). Active learning is a method to select informative samples to obtain maximum performance from minimum annotations.

Low-Temperature ALD of SbO /Sb Te Multilayers with Boosted Thermoelectric Performance.

Nanoscale superlattice (SL) structures have proven to be effective in enhancing the thermoelectric (TE) properties of thin films. Herein, the main phase of antimony telluride (Sb Te ) thin film with sub-nanometer layers of antimony oxide (SbO ) is synthesized via atomic layer deposition (ALD) at a low temperature of 80 °C. The SL structure is tailored by varying the cycle numbers of Sb Te and SbO .

Colon Disease Diagnosis with Convolutional Neural Network and Grasshopper Optimization Algorithm.

This paper presents a robust colon cancer diagnosis method based on the feature selection method. The proposed method for colon disease diagnosis can be divided into three steps. In the first step, the images' features were extracted based on the convolutional neural network.

Physiotherapy research on low back pain in the Indian context: A bibliometric analysis using the Scopus database.

Background: Low back pain (LBP) is a common musculoskeletal condition that necessitates public health concerns. It also attracts considerable research interest among physiotherapists.

Objective: This study conducted a bibliometric analysis to reveal the affinity of Indian physiotherapists toward research on LBP using the Scopus database.

Demystifying Supervised Learning in Healthcare 4.0: A New Reality of Transforming Diagnostic Medicine.

The global healthcare sector continues to grow rapidly and is reflected as one of the fastest-growing sectors in the fourth industrial revolution (4.0). The majority of the healthcare industry still uses labor-intensive, time-consuming, and error-prone traditional, manual, and manpower-based methods.

Bone Fracture Detection Using Deep Supervised Learning from Radiological Images: A Paradigm Shift.

Bone diseases are common and can result in various musculoskeletal conditions (MC). An estimated 1.71 billion patients suffer from musculoskeletal problems worldwide.

Attention UW-Net: A fully connected model for automatic segmentation and annotation of chest X-ray.

Background And Objective: Automatic segmentation and annotation of medical image plays a critical role in scientific research and the medical care community. Automatic segmentation and annotation not only increase the efficiency of clinical workflow, but also prevent overburdening of radiologists. The objective of this work is to improve the accuracy and give a probabilistic map for automatic annotation from small data set to reduce the use of tedious and prone to error manual annotations from chest X-rays.

Identity verification using palm print microscopic images based on median robust extended local binary pattern features and k-nearest neighbor classifier.

Automatic identity verification is one of the most critical and research-demanding areas. One of the most effective and reliable identity verification methods is using unique human biological characteristics and biometrics. Among all types of biometrics, palm print is recognized as one of the most accurate and reliable identity verification methods.