Colossal Reversible Barocaloric Effects in a Plastic Crystal Mediated by Lattice Vibrations and Ion Diffusion.

Solid-state methods for cooling and heating promise a sustainable alternative to current compression cycles of greenhouse gases and inefficient fuel-burning heaters. Barocaloric effects (BCE) driven by hydrostatic pressure (p) are especially encouraging in terms of large adiabatic temperature changes (|ΔT| ≈ 10 K) and isothermal entropy changes (|ΔS| ≈ 100 J K-1 kg-1). However, BCE typically require large pressure shifts due to irreversibility issues, and sizeable |ΔT| and |ΔS| seldom are realized in a same material. Here, the existence of colossal and reversible BCE in LiCB11H12 is demonstrated near its order-disorder phase transition at ≈380 K. Specifically, for Δp ≈ 0.23 (0.10) GPa, |ΔSrev| = 280 (200) J K-1 kg-1 and |ΔTrev| = 32 (10) K are measured, which individually rival with state-of-the-art BCE figures. Furthermore, pressure shifts of the order of 0.1 GPa yield huge reversible barocaloric strengths of ≈2 J K-1 kg-1 MPa-1. Molecular dynamics simulations are performed to quantify the role of lattice vibrations, molecular reorientations, and ion diffusion on the disclosed BCE. Interestingly, lattice vibrations are found to contribute the most to |ΔS| while the diffusion of lithium ions, despite adding up only slightly to the entropy change, is crucial in enabling the molecular order-disorder phase transition.

Deep transfer learning for IDC breast cancer detection using fast AI technique and Sqeezenet architecture.

One of the most effective approaches for identifying breast cancer is histology, which is the meticulous inspection of tissues under a microscope. The kind of cancer cells, or whether they are cancerous (malignant) or non-cancerous, is typically determined by the type of tissue that is analyzed by the test performed by the technician (benign). The goal of this study was to automate IDC classification within breast cancer histology samples using a transfer learning technique. To improve our outcomes, we combined a Gradient Color Activation Mapping (Grad CAM) and image coloring mechanism with a discriminative fine-tuning methodology employing a one-cycle strategy using FastAI techniques. There have been lots of research studies related to deep transfer learning which use the same mechanism, but this report uses a transfer learning mechanism based on lightweight Squeeze Net architecture, a variant of CNN (Convolution neural network). This strategy demonstrates that fine-tuning on Squeeze Net makes it possible to achieve satisfactory results when transitioning generic features from natural images to medical images.

Topological Data analysis of Ion Migration Mechanism.

Topological data analysis based on persistent homology has been applied to the molecular dynamics simulation for the fast ion-conducting phase (α-phase) of AgI to show its effectiveness on the ion migration mechanism analysis. Time-averaged persistence diagrams of α-AgI, which quantitatively record the shape and size of the ring structures in the given atomic configurations, clearly showed the emergence of the four-membered rings formed by two Ag and two I ions at high temperatures. They were identified as common structures during the Ag ion migration. The averaged potential energy change due to the deformation of the four-membered ring during Ag migration agrees well with the activation energy calculated from the conductivity Arrhenius plot. The concerted motion of two Ag ions via the four-membered ring was also successfully extracted from molecular dynamics simulations by our approach, providing new insight into the specific mechanism of the concerted motion.

Effective Image Processing and Segmentation-Based Machine Learning Techniques for Diagnosis of Breast Cancer.

Breast cancer is the second leading cause of death among women, behind only heart disease. However, despite the high incidence and mortality rates associated with breast cancer, it is still unclear as to what is responsible for its development in the first place. The prevention of breast cancer is not possible with any of the current available methods. Patients who are diagnosed and treated for breast cancer at an early stage have a better chance of having a successful treatment and recovery. In the field of breast cancer detection, digital mammography is widely acknowledged to be a highly effective method of detecting the disease early on. We may be able to improve early detection of breast cancer with the use of image processing techniques, thereby boosting our chances of survival and treatment success. This article discusses a breast cancer image processing and machine learning framework that was developed. The input data set for this framework is a sequence of mammography images, which are used as input data. The CLAHE approach is then utilized to improve the overall quality of the photographs by means of image processing. It is called contrast restricted adaptive histogram equalization (CLAHE), and it is an improvement on the original histogram equalization technique. This aids in the removal of noise from photographs while simultaneously improving picture quality. The segmentation of images is the next step in the framework's development. An image is divided into distinct portions at this point because the pixels are labeled at this step. This assists in the identification of objects and the delineation of boundaries. To categorize these preprocessed images, techniques such as fuzzy SVM, Bayesian classifier, and random forest are employed, among others.

A Novel Approach to Classifying Breast Cancer Histopathology Biopsy Images Using Bilateral Knowledge Distillation and Label Smoothing Regularization.

Breast cancer is the most common invasive cancer in women and the second main cause of cancer death in females, which can be classified benign or malignant. Research and prevention on breast cancer have attracted more concern of researchers in recent years. On the other hand, the development of data mining methods provides an effective way to extract more useful information from complex databases, and some prediction, classification, and clustering can be made according to the extracted information. The generic notion of knowledge distillation is that a network of higher capacity acts as a teacher and a network of lower capacity acts as a student. There are different pipelines of knowledge distillation known. However, previous work on knowledge distillation using label smoothing regularization produces experiments and results that break this general notion and prove that knowledge distillation also works when a student model distils a teacher model, i.e., reverse knowledge distillation. Not only this, but it is also proved that a poorly trained teacher model trains a student model to reach equivalent results. Building on the ideas from those works, we propose a novel bilateral knowledge distillation regime that enables multiple interactions between teacher and student models, i.e., teaching and distilling each other, eventually improving each other's performance and evaluating our results on BACH histopathology image dataset on breast cancer. The pretrained ResNeXt29 and MobileNetV2 models which are already tested on ImageNet dataset are used for "transfer learning" in our dataset, and we obtain a final accuracy of more than 96% using this novel approach of bilateral KD.

Breast Cancer Calcifications: Identification Using a Novel Segmentation Approach.

Breast cancer is a strong risk factor of cancer amongst women. One in eight women suffers from breast cancer. It is a life-threatening illness and is utterly dreadful. The root cause which is the breast cancer agent is still under research. There are, however, certain potentially dangerous factors like age, genetics, obesity, birth control, cigarettes, and tablets. Breast cancer is often a malignant tumor that begins in the breast cells and eventually spreads to the surrounding tissue. If detected early, the illness may be reversible. The probability of preservation diminishes as the number of measurements increases. Numerous imaging techniques are used to identify breast cancer. This research examines different breast cancer detection strategies via the use of imaging techniques, data mining techniques, and various characteristics, as well as a brief comparative analysis of the existing breast cancer detection system. Breast cancer mortality will be significantly reduced if it is identified and treated early. There are technological difficulties linked to scans and people's inconsistency with breast cancer. In this study, we introduced a form of breast cancer diagnosis. There are different methods involved to collect and analyze details. In the preprocessing stage, the input data picture is filtered by using a window or by cropping. Segmentation can be performed using k-means algorithm. This study is aimed at identifying the calcifications found in bosom cancer in the last phase. The suggested approach is already implemented in MATLAB, and it produces reliable performance.

Mixed alkali-ion transport and storage in atomic-disordered honeycomb layered NaKNi2TeO6.

Honeycomb layered oxides constitute an emerging class of materials that show interesting physicochemical and electrochemical properties. However, the development of these materials is still limited. Here, we report the combined use of alkali atoms (Na and K) to produce a mixed-alkali honeycomb layered oxide material, namely, NaKNi2TeO6. Via transmission electron microscopy measurements, we reveal the local atomic structural disorders characterised by aperiodic stacking and incoherency in the alternating arrangement of Na and K atoms. We also investigate the possibility of mixed electrochemical transport and storage of Na+ and K+ ions in NaKNi2TeO6. In particular, we report an average discharge cell voltage of about 4 V and a specific capacity of around 80 mAh g-1 at low specific currents (i.e., < 10 mA g-1) when a NaKNi2TeO6-based positive electrode is combined with a room-temperature NaK liquid alloy negative electrode using an ionic liquid-based electrolyte solution. These results represent a step towards the use of tailored cathode active materials for "dendrite-free" electrochemical energy storage systems exploiting room-temperature liquid alkali metal alloy materials.

Colossal barocaloric effects in the complex hydride Li[Formula: see text]B[Formula: see text]H[Formula: see text].

Traditional refrigeration technologies based on compression cycles of greenhouse gases pose serious threats to the environment and cannot be downscaled to electronic device dimensions. Solid-state cooling exploits the thermal response of caloric materials to changes in the applied external fields (i.e., magnetic, electric and/or mechanical stress) and represents a promising alternative to current refrigeration methods. However, most of the caloric materials known to date present relatively small adiabatic temperature changes ([Formula: see text] to 10 K) and/or limiting irreversibility issues resulting from significant phase-transition hysteresis. Here, we predict by using molecular dynamics simulations the existence of colossal barocaloric effects induced by pressure (isothermal entropy changes of [Formula: see text] J K[Formula: see text] kg[Formula: see text]) in the energy material Li[Formula: see text]B[Formula: see text]H[Formula: see text]. Specifically, we estimate [Formula: see text] J K[Formula: see text] kg[Formula: see text] and [Formula: see text] K for a small pressure shift of P = 0.1 GPa at [Formula: see text] K. The disclosed colossal barocaloric effects are originated by a fairly reversible order-disorder phase transformation involving coexistence of Li[Formula: see text] diffusion and (BH)[Formula: see text] reorientational motion at high temperatures.

Role of divalent cation (Ba) substitution in the Li+ ion conductor LiTi2(PO4)3: a molecular dynamics study.

The derivatives of LiTi2(PO4)3 are promising electrolytes for solid-state batteries. An extensive molecular dynamics study is performed employing a refined set of potential parameters to understand the influence of Ba substitution on Li+ ion conductivity in Bax/2Li1-xTi2(PO4)3 (0.0 ≤ x ≤ 0.83). The refined set of potential parameters reveals the structural and dynamical properties of Bax/2Li1-xTi2(PO4)3 which are consistent with experimental results. In the presence of Ba2+, the system endures a persistent competition between the generation of vacant Li-sites and blocking of Li+ ion paths. The diffusivity of Li+ ions enhances with x and increases one order of magnitude higher at x = 0.67, where the creation of vacant Li-sites mainly drives the diffusion. This trend is similar to the experimental report. However, for x > 0.67 compositions, the blocking of the Li+ ion path dominates in the presence of immobile Ba2+ ions, resulting in a reduction of Li+ ion diffusion. The present study also proposes an ordered substitution of Ba2+ ions at crystallographically identified Li1-sites, where an extra Li-site generation is identified at higher compositions. In this case, the vacancy strongly dominates over Li+ ion path blocking, resulting in the possibility to achieve even higher Li+ ion diffusion. The creation of extra Li-sites and mechanism of Li+-ion transport are studied systematically with varying compositions. Further insight into Li+ ion transport is gained by constructing a three-dimensional density map and determining the free energy barrier and clustering of Li+ ion probability density. And the factors affecting the cation diffusion are also systematically investigated.