Structural Characterization and Electrochemical Performance of AIF(3)-coated LiNi0.45Mn0.45Co0.10O2 as Cathode Materials for Lithium Ion Batteries

In this paper, layered-LiNi0.45Mn0.45Co0.10O2 was prepared by co-precipitation and solid state reaction methods. The crystal structures and morphology of the materials were investigated by X-ray diffraction (XRD) and scanning electron microscopy (SEM) techniques. The LiNi0.45Mn0.45Co0.10O2 sample delivered the initial discharge capacity of 167.2 mAh center dot g(-1) at 28 mA center dot g(-1) (0.1 C) in the range of 2.5 to 4.5 V, but the cycle-life performance was relatively bad, which could be remarkably improved by AlF3 surface coating. Electrochemical impedance spectroscopy (EIS) was used to understand the possible mechanisms of improved electrochemical properties by the AlF3 coating. The results demonstrate that these coating layers could protect the dissolution and corrosion of LiNi0.45Mn0.45Co0.10O2 cathode materials in electrolyte, stabilize its layered structure and reduce interface impedance. Therefore, the AlF3 coating technique is an effective method and tool to improve the electrochemical properties of LiNi0.45Mn0.45Co0.10O2 cathode material

Leveraging The Finite States of Emotion Processing to Study Late-Life Mental Health

Traditional approaches in mental health research apply General Linear Models (GLM) to describe the longitudinal dynamics of observed psycho-behavioral measurements (questionnaire summary scores). Similarly, GLMs are also applied to characterize relationships between neurobiological measurements (regional fMRI signals) and perceptual stimuli or other regional signals. While these methods are useful for exploring linear correlations among the isolated signals of those constructs (i.e., summary scores or fMRI signals), these classical frameworks fall short in providing insights into the comprehensive system-level dynamics underlying observable changes. Hidden Markov Models (HMM) are a statistical model that enable us to describe the sequential relations among multiple observable constructs, and when applied through the lens of Finite State Automata (FSA), can provide a more integrated and intuitive framework for modeling and understanding the underlying controller (the prescription for how to respond to inputs) that fundamentally defines any system, as opposed to linearly correlating output signals produced by the controller. We present a simple and intuitive HMM processing pipeline vcHMM (See Preliminary Data) that highlights FSA theory and is applicable for both behavioral analysis of questionnaire data and fMRI data. HMMs offer theoretic promise as they are computationally equivalent to the FSA, the control processor of a Turing Machine (TM) The dynamic programming Viterbi algorithm is used to leverage the HMM model. It efficiently identifies the most likely sequence of hidden states. The vcHMM pipeline leverages this grammar to understand how behavior and neural activity relate to depression

A hierarchical opportunistic screening model for osteoporosis using machine learning applied to clinical data and CT images

Background: Osteoporosis is a common metabolic skeletal disease and usually lacks obvious symptoms. Many individuals are not diagnosed until osteoporotic fractures occur. Bone mineral density (BMD) measured by dual-energy X-ray absorptiometry (DXA) is the gold standard for osteoporosis detection. However, only a limited percentage of people with osteoporosis risks undergo the DXA test. As a result, it is vital to develop methods to identify individuals at-risk based on methods other than DXA. Results: We proposed a hierarchical model with three layers to detect osteoporosis using clinical data (including demographic characteristics and routine laboratory tests data) and CT images covering lumbar vertebral bodies rather than DXA data via machine learning. 2210 individuals over age 40 were collected retrospectively, among which 246 individuals’ clinical data and CT images are both available. Irrelevant and redundant features were removed via statistical analysis. Consequently, 28 features, including 16 clinical data and 12 texture features demonstrated statistically significant differences (p < 0.05) between osteoporosis and normal groups. Six machine learning algorithms including logistic regression (LR), support vector machine with radial-basis function kernel, artificial neural network, random forests, eXtreme Gradient Boosting and Stacking that combined the above five classifiers were employed as classifiers to assess the performances of the model. Furthermore, to diminish the influence of data partitioning, the dataset was randomly split into training and test set with stratified sampling repeated five times. The results demonstrated that the hierarchical model based on LR showed better performances with an area under the receiver operating characteristic curve of 0.818, 0.838, and 0.962 for three layers, respectively in distinguishing individuals with osteoporosis and normal BMD. Conclusions: The proposed model showed great potential in opportunistic screening for osteoporosis without additional expense. It is hoped that this model could serve to detect osteoporosis as early as possible and thereby prevent serious complications of osteoporosis, such as osteoporosis fractures

Global trends and hotspots in the study of the effects of PM2.5 on ischemic stroke

Abstract Aim The objective of this study was to visually analyse global research trends and hotspots regarding the role of PM2.5 in ischemic stroke. Methods The Web of Science core collection database was used to search the literature on PM2.5 and ischemic stroke from 2006 to 2024. Visualization analysis was conducted using CiteSpace, VOSviewer, and an online bibliometric platform. Results The analysis comprises 190 articles published between 2006 and 2024 by 1229 authors from 435 institutions in 39 countries, across 78 journals. Wellenius GA has the highest number of published and cited papers. China has the highest number of papers, while Canada has the highest citation frequency. Capital Medical University published the highest number of papers, and Harvard University had the highest citation frequency for a single paper. The study investigated the impact of PM2.5 on ischemic stroke in three phases. The first phase analysed hospitalisation rates for correlations. The second phase utilised large-scale multi-cohort data from around the world. The third phase involved studying global exposure risk through machine learning and model construction. Currently, there is limited research on the mechanisms involved, and further in-depth investigation is required. Conclusion This paper presents a bibliometric analysis of the research framework and hotspots concerning the effect of PM2.5 on ischemic stroke. The analysis aims to provide a comprehensive understanding of this field for researchers. It is expected that research on the effect of PM2.5 on ischemic stroke will remain an important research topic in the future

The effects of quenching treatment and AlF3 coating on LiNi0.5Mn0.5O2 cathode materials for lithium-ion battery

Submicron layered LiNi0.5Mn0.5O2 was synthesized via a co-precipitation and solid-state reaction method together with a quenching process. The crystal structure and morphology of the materials were investigated by X-ray diffraction (XRD), Brunauer-Emmett and Teller (BET) surface area and scanning electron microscopy (SEM) techniques. It is found that LiNi0.5Mn0.5O2 material prepared with quenching methods has smooth and regular structure in submicron scale with surface area of 0.43 m(2) g(-1). The initial discharge capacities are 175.8 mAh g(-1) at 0.1 C (28 mA g(-1)) and 120.3 mAh g(-1) at 5.0 C (1400 mA g(-1)), respectively, for the quenched samples between 2.5 and 4.5 V. It is demonstrated that quenching method is a useful approach for the preparation of submicron layered LiNi0.5Mn0.5O2 cathode materials with excellent rate performance. in addition, the cycling performance of quenched-LiNi0.5Mn0.5O2 material was also greatly improved by AlF3 coating technique. (C) 2009 Elsevier B.V. All rights reserved.National Basic Research Program of China (973 Program) [2007CB209702]; National Natural Science Foundation of China [29925310, 20473060, 20021002]; construct program of the key discipline in Hunan province [2006-180

Critical Conditions for Wellbore Failure during CO₂-ECBM Considering Sorption Stress

Significant stress changes caused by sorption-induced swelling raise the coal wellbore failure potential, which directly impacts the safety and sustainability of CO2 enhanced coalbed methane (CO2-ECBM). Additionally, a mixture gas (CO2/N2) injection is recommended due to the sharp decline of permeability with pure CO2 injection. In this study, incorporating the impacts of mixture gas adsorption and poroelastic effects, a semi-analytical model of coal wellbore stability during mixture gas injection is proposed. Model results indicate that the stress field is significantly influenced by the boundary condition and sorption effect. In addition, parametric studies are performed to determine the influence of adsorption parameters, mechanical properties, and gas composition on the stress distribution and then on the wellbore failure index. Furthermore, mixture gas injection with a large proportion of CO2 or N2 both cause wellbore instability. Significant compressive hoop stress and shear failure are caused by the mixture gas injection with a large proportion of CO2. In contrast, the displacement of CH4 with weakly adsorptive N2 will result in less compressive and even tensile hoop stress, so shear or tensile failure may occur. Thus, mixture gas (including pure CO2/N2) injection must be controlled by coal wellbore failure, providing an accurate estimation of in-situ coal seams’ CO2 storage capacity from the perspective of wellbore stability

Serum GRP78 as a Tumor Marker and Its Prognostic Significance in Non-Small Cell Lung Cancers: A Retrospective Study

Introduction. Glucose-regulated protein 78 (78 kDa, GRP78), which is also known as immunoglobulin heavy chain binding protein (BIP), is a major chaperone in the endoplasmic reticulum (ER). The expression and clinical significance of GRP78 in the serum of non-small cell lung cancer patients have not yet been clearly described. The aims of the present study were to investigate the expression of GRP78 in the serum of non-small cell lung cancer patients, the relationships with clinicopathological parameters, and the potential implications for survival. Patients and Methods. A total of 163 peripheral blood samples from non-small cell lung cancer patients were prospectively collected at the Department of Thoracic Surgery, Fudan University Shanghai Cancer, China. Clinical characteristics data, including age, gender, stage, overall survival (OS) time, and relapse-free survival (RFS) time, were also collected. Serum GRP78 levels were measured using a commercially available ELISA kit. The associations between GRP78 levels and clinicopathological characteristics and survival were examined using Student’s t-test, Kaplan-Meier, or Cox regression analyses. Results. The mean ± standard error (SE) value of GRP78 was 326.5 ± 49.77 pg/mL. This level was significantly lower compared with the level in late-stage non-small cell lung cancer patients (1227 ± 223.6, p=0.0001). There were no significant correlations with the clinicopathological parameters. No significant difference was found between high GRP78 expression and low GRP78 expression with regard to RFS (p=0.1585). However, the OS of patients with higher GRP78 expression was significantly poorer (p=0.0334). Conclusions. GRP78 was expressed in non-small cell lung cancer patients and was highly enriched in late-stage lung cancer. GRP78 may have an important role in the carcinogenesis of non-small cell lung cancer and may be a prognostic marker for non-small cell lung cancer