Influence of local strain heterogeneity on high piezoelectricity in 0.5Ba(Zr0.2Ti0.8)O3−0.5(Ba0.7Ca0.3)TiO3 ceramics

Dielectric and mechanical spectroscopies have been used to investigate ferroelectric transitions and twin wall dynamics in the lead-free ceramic 0.5Ba(Zr0.2Ti0.8)O3−0.5(Ba0.7Ca0.3)TiO3 (abbreviated as BZT-50BCT), which is known to have a high piezoelectric coefficient (d33>545pC/N). Results from dynamical mechanical analysis in the frequency range 0.2–20 Hz and resonant ultrasound spectroscopy in the frequency range ∼0.1–1.2MHz confirm the existence of three phase transitions with falling temperature, at ∼360K (cubic-tetragonal), ∼304K (tetragonal-orthorhombic), and ∼273K (orthorhombic-rhombohedral). In comparison with BaTiO3, however, the transitions are marked by rounded rather than sharp minima in the shear modulus. The pattern of acoustic loss is also quite different from that shown by BaTiO3 in having a broad interval of high loss at low temperatures, consistent with a spectrum of relaxation times for interactions of ferroelastic twin walls. Differences in the dielectric properties also suggest more relaxor like characteristics for BZT-50BCT. It is proposed that the overall pattern of behavior is significantly influenced by strain heterogeneity at a local length scale in the perovskite structure due to the substitution of cations with different ionic radii. The existence of this strain heterogeneity and its influence on the elastic behavior near the transition points could be contributory factors to the development of adaptive nanoscale microstructures and enhanced piezoelectric properties

Coupling between phase transitions and glassy magnetic behaviour in Heusler alloy Ni 50 Mn 34 In 8 Ga 8

Abstract: The transition sequence in the Heusler alloy Ni50Mn34In8Ga8 has been determined from measurements of elasticity, heat flow and magnetism to be paramagnetic austenite → paramagnetic martensite → ferromagnetic martensite at ∼335 and ∼260 K, respectively, during cooling. The overall pattern of elastic stiffening/softening and acoustic loss is typical of a system with bilinear coupling between symmetry breaking strain and the driving structural/electronic order parameter, and a temperature interval below the transition point in which ferroelastic twin walls remain mobile under the influence of external stress. Divergence between zero-field-cooling and field-cooling determinations of DC magnetisation below ∼220 K indicates that a frustrated magnetic glass develops in the ferromagnetic martensite. An AC magnetic anomaly which shows Vogel–Fulcher dynamics in the vicinity of ∼160 K is evidence of a further glassy freezing process. This coincides with an acoustic loss peak and slight elastic stiffening that is typical of the outcome of freezing of ferroelastic twin walls. The results suggest that local strain variations associated with the ferroelastic twin walls couple with local moments to induce glassy magnetic behaviour

Coupling between phase transitions and glassy magnetic behaviour in Heusler Alloy Ni50Mn34In8Ga8

The transition sequence in the Heusler alloy Ni50Mn34In8Ga8 has been determined from measurements of elasticity, heat flow and magnetism to be paramagnetic austenite → paramagnetic martensite → ferromagnetic martensite at ~335 and ~260 K, respectively, during cooling. The overall pattern of elastic stiffening/softening and acoustic loss is typical of a system with bilinear coupling between symmetry breaking strain and the driving order parameter in a temperature interval below the transition point in which ferroelastic twin walls remain mobile under the influence of external stress. Divergence between zero-field-cooling (ZFC) and field-cooling (FC) determinations of DC magnetisation below ~220 K indicates that a frustrated magnetic glass develops in the ferromagnetic martensite. An AC magnetic anomaly which shows Vogel-Fulcher dynamics in the vicinity of ~160 K is evidence of a further glassy freezing process. This coincides with an acoustic loss peak and slight elastic stiffening that is typical of the outcome of freezing of ferroelastic twin walls. The results indicate that local strain variations associated with the ferroelastic twin walls couple with local moments to induce glassy magnetic behaviour

LncRNAs: the bridge linking RNA and colorectal cancer.

Long noncoding RNAs (lncRNAs) are transcribed by genomic regions (exceeding 200 nucleotides in length) that do not encode proteins. While the exquisite regulation of lncRNA transcription can provide signals of malignant transformation, lncRNAs control pleiotropic cancer phenotypes through interactions with other cellular molecules including DNA, protein, and RNA. Recent studies have demonstrated that dysregulation of lncRNAs is influential in proliferation, angiogenesis, metastasis, invasion, apoptosis, stemness, and genome instability in colorectal cancer (CRC), with consequent clinical implications. In this review, we explicate the roles of different lncRNAs in CRC, and the potential implications for their clinical application

An Unusual Topological Structure of the HIV-1 Rev Response Element

SummaryNuclear export of unspliced and singly spliced viral mRNA is a critical step in the HIV life cycle. The structural basis by which the virus selects its own mRNA among more abundant host cellular RNAs for export has been a mystery for more than 25 years. Here, we describe an unusual topological structure that the virus uses to recognize its own mRNA. The viral Rev response element (RRE) adopts an “A”-like structure in which the two legs constitute two tracks of binding sites for the viral Rev protein and position the two primary known Rev-binding sites ∼55 Å apart, matching the distance between the two RNA-binding motifs in the Rev dimer. Both the legs of the “A” and the separation between them are required for optimal RRE function. This structure accounts for the specificity of Rev for the RRE and thus the specific recognition of the viral RNA

Scalable and accurate deep learning for electronic health records

Predictive modeling with electronic health record (EHR) data is anticipated to drive personalized medicine and improve healthcare quality. Constructing predictive statistical models typically requires extraction of curated predictor variables from normalized EHR data, a labor-intensive process that discards the vast majority of information in each patient's record. We propose a representation of patients' entire, raw EHR records based on the Fast Healthcare Interoperability Resources (FHIR) format. We demonstrate that deep learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization. We validated our approach using de-identified EHR data from two U.S. academic medical centers with 216,221 adult patients hospitalized for at least 24 hours. In the sequential format we propose, this volume of EHR data unrolled into a total of 46,864,534,945 data points, including clinical notes. Deep learning models achieved high accuracy for tasks such as predicting in-hospital mortality (AUROC across sites 0.93-0.94), 30-day unplanned readmission (AUROC 0.75-0.76), prolonged length of stay (AUROC 0.85-0.86), and all of a patient's final discharge diagnoses (frequency-weighted AUROC 0.90). These models outperformed state-of-the-art traditional predictive models in all cases. We also present a case-study of a neural-network attribution system, which illustrates how clinicians can gain some transparency into the predictions. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios, complete with explanations that directly highlight evidence in the patient's chart.Comment: Published version from https://www.nature.com/articles/s41746-018-0029-

Research on optimization of warehouse layout based on SLP theory-- Take Deppon Express Warehouse for example

This paper takes Deppon Express Company as an example to study the optimization of the layout of the store warehouse under the standardized operation mode. First of all, it Analyzes warehouse layout present situation of the express company, and gets the problems of the warehouse layout of the company, for example, the layout of the lack of scientific, as well as part of the operational areas of position arrangement is not reasonable and unreasonable action path which leads to the whole business operation efficiency is low. Finally, thus using SLP to solve those problems, and put forward the company store aisle warehouse layout optimization strategy of management according to the comprehensive relations preliminary to the data warehouse layout optimization figure

A Comparative Study of Robust MPC and Stochastic MPC of Wind Power Generation System

In this paper, a complete comparison analysis of two advanced control algorithms, namely robust model predictive control (MPC) and stochastic MPC, is performed in order to optimize the operation of a wind power generation system (WPGS). The power maximization often conflicts with the mechanical load experienced by the turbine in the full-load region (i.e., the higher the power extracted, the higher the load) under the wind speed disturbance, thereby leading to high maintenance cost resulting from the fatigue damage. Thus, a typical 5 MW wind turbine operating in a high-speed region is considered to guarantee system security and economy. The robust MPC is designed by utilizing the min–max framework to track steady-state optimum operating reference trajectory with the deterministic constraint of output power, while the stochastic MPC is constructed by incorporating the invariant set theory to also ensure the system security subjecting to the probabilistic constraint of output power. The relation between the constraints and the implications on optimal performance are also studied. Comprehensive simulations on a mechanism model and FAST simulator are carried out to demonstrate the validation of the two control methods under various scenarios. It is discovered that when wind speed in the near future can be predicted and utilized in controller design, the stochastic MPC can effectively reduce the maintenance cost by suppressing the constraint violation rate compared to robust MPC with a similar energy utilization due to the incorporation of the stochastic characteristics of wind speed

Research on Spraying Quality Prediction Algorithm for Automated Robot Spraying Based on KHPO-ELM Neural Network

In the intelligent transformation of spraying operations, the investigation into the robotic spraying process holds significant importance. The spraying process, however, falls within the realm of experience-driven technology, characterized by high complexity, diverse parameters, and coupling effects. Moreover, the quality of manual spraying processes relies entirely on manual experience. Thus, the crux of the intelligent transformation of spraying robots lies in establishing a mapping model between the spraying process and the resultant spraying quality. To address the challenge of intelligently transforming empirical spraying processes and achieving the mapping from the spraying process to spraying quality, an algorithm employing an enhanced extreme learning machine-based neural network is proposed for predicting spraying process parameters with respect to the evaluation index of spraying quality. In this approach, an algorithmic model based on the Extreme Learning Machine (ELM) neural network is initially constructed utilizing five spraying process parameters: spraying speed, spraying height, spraying width pressure, atomization pressure, and oil spraying pressure. Two spraying quality evaluation indexes, namely average film thickness at the center point and surface roughness, are also incorporated. Subsequently, the prediction neural network is optimized using the K-means improved predator optimization algorithm (KHPO) to enhance the model’s prediction accuracy. This optimization step aims to improve the efficiency of the model in predicting spraying quality based on the specified process parameters. Finally, data collection and model validation for the spraying quality prediction algorithm are conducted using a designed robotic automated waterborne paint spraying experimental system. The experimental results demonstrate a significant reduction in the prediction error of the KHPO-ELM neural network model for the average film thickness center point, showcasing a decrease of 61.95% in comparison to the traditional ELM neural network and 50.81% in comparison to the BP neural network. Likewise, the improved neural network model yields a 2.31% decrease in surface roughness prediction error compared to the traditional ELM neural network and a substantial 54.0% reduction compared to the BP neural network. Consequently, the KHPO-ELM neural network, incorporating the prediction algorithm, effectively facilitates the prediction of multi-spraying process parameters for the center point of average film thickness and surface roughness in automated robot spraying. Notably, the prediction algorithm exhibits a commendable level of accuracy in these predictions