肺部微小病灶术前定位方法的进展及新思路

肺部周围型微小病灶在手术中的定位比较困难。现有各种术前定位方法依然存在缺陷,应用有限,难以适应临床微小病灶日益增多的迫切需要。本文拟就目前的相关进展予以综述,并简介我们近期探索尝试的新方法。     

基于机器学习算法的NT-proBNP灰值患者心力衰竭判别模型评价

目的 应用机器学习算法构建氨基末端脑钠尿肽(N-terminal pro-brain natriuretic peptide,NT-proBNP)灰值患者心力衰竭判别模型并评价.方法 收集2013年1月至2018年12月在上海市浦东新区公利医院进行NT-proBNP 实验室检测的患者临床资料和实验室检测信息,数据清洗后...     

国医大师张震精气形神生命观及郁证治疗经验

精、气、神为人身三宝。精为生命活动的物质基础,气是构成人体的基本物质和动力来源,为形体之本,与精同为神的物质来源,且与血、津、液的生成密切相关。精、气、形、神四要素紧密联系,共同构成精气形神生命观。“一体两翼、疏调气机”学术思想由国医大师张震所创,在疏肝基础上兼顾脾肾,使气机调畅,是精气形神生命观的具体体现。针对郁证肝失疏泄、气机失调的核心病机,具体为疏调气机可调气,健脾温肾能养精,调和五脏以治形,气血调和以宁神。在郁证治疗中,将疏肝解郁、健脾温肾相结合,选取辛散之药及花药,具有调气、养精、治形、宁神的功效,疗效明显。     

Utilizing Tunnel Boring Machine (TBM)-Crushed Limestone as a Construction Material

Tunnel boring machine (TBM) materials are usually crushed powder obtained during tunnel excavations for subways and transportation networks. Huge quantities of crushed rock powder are generally treated as waste. This study is aimed at assessing proposed mixtures of TBM and granular material for use in construction. This approach will help in a greener environment and reduce the need for crushed aggregates used in sub-base and subgrade layers of pavements. Assessment is executed using dynamic and static strength tests, including the modulus of resilience and the California bearing ratio (CBR). The TBM-crushed material can be sorted and screened on site to optimize its use as a construction material. The blending ratios for the 3/8-inch aggregate (G1) to the material-passing sieve number 4 (P4) were found to influence the pavement design parameters. This study recommends sorting the TBM-crushed limestone by an on-site sieving operation. A guide to optimizing the quality of the material is suggested by blending 3/8-inch aggregate with the crushed limestone fine-powder material at a specified percentage ranging from 5 to 10% by weight. The stability and durability tests conducted on the TBM-crushed powder material confirmed its suitability as a pavement construction material for subgrade and sub-base layers. Modulus of resilience, CBR values and compressive strength tests were carried out for different suggested mixtures.     

Mixture Optimization of Cementitious Materials Using Machine Learning and Metaheuristic Algorithms: State of the Art and Future Prospects

The hybrid optimization of modern cementitious materials requires concrete to meet many competing objectives (e.g., mechanical properties, cost, workability, environmental requirements, and durability). This paper reviews the current literature on optimizing mixing ratios using machine learning and metaheuristic optimization algorithms based on past studies on varying methods. In this review, we first discuss the conventional methods for mixing optimization of cementitious materials. Then, the problem expression of hybrid optimization is discussed, including decision variables, constraints, machine learning algorithms for modeling objectives, and metaheuristic optimization algorithms for searching the best mixture ratio. Finally, we explore the development prospects of this field, including, expanding the database by combining field data, considering more influencing variables, and considering more competitive targets in the production of functional cemented materials. In addition, to overcome the limitation of the swarm intelligence-based multi-objective optimization (MOO) algorithm in hybrid optimization, this paper proposes a new MOO algorithm based on individual intelligence (multi-objective beetle antenna search algorithm). The development of computationally efficient robust MOO models will continue to make progress in the field of hybrid optimization. This review is adapted for engineers and researchers who want to optimize the mixture proportions of cementitious materials using machine learning and metaheuristic algorithms.     

Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques

A crucial factor in the efficient design of concrete sustainable buildings is the compressive strength (Cs) of eco-friendly concrete. In this work, a hybrid model of Gradient Boosting Regression Tree (GBRT) with grid search cross-validation (GridSearchCV) optimization technique was used to predict the compressive strength, which allowed us to increase the precision of the prediction models. In addition, to build the proposed models, 164 experiments on eco-friendly concrete compressive strength were gathered for previous researches. The dataset included the water/binder ratio (W/B), curing time (age), the recycled aggregate percentage from the total aggregate in the mixture (RA%), ground granulated blast-furnace slag (GGBFS) material percentage from the total binder used in the mixture (GGBFS%), and superplasticizer (kg). The root mean square error (RMSE) and coefficient of determination (R²) between the observed and forecast strengths were used to evaluate the accuracy of the predictive models. The obtained results indicated that—when compared to the default GBRT model—the GridSearchCV approach can capture more hyperparameters for the GBRT prediction model. Furthermore, the robustness and generalization of the GSC-GBRT model produced notable results, with RMSE and R² values (for the testing phase) of 2.3214 and 0.9612, respectively. The outcomes proved that the suggested GSC-GBRT model is advantageous. Additionally, the significance and contribution of the input factors that affect the compressive strength were explained using the Shapley additive explanation (SHAP) approach.     

Quality Quantification and Control via Novel Self-Growing Process-Quality Model of Parts Fabricated by LPBF Process

Laser Powder Bed Fusion (LPBF) presents a more extensive allowable design complexity and manufacturability compared with the traditional manufacturing processes by depositing materials in a layer-wised manner. However, the process variability in the LPBF process induces quality uncertainty and inconsistency. Specifically, the mechanical properties, e.g., tensile strength, are hard to be predicted and controlled in the LPBF process. Much research has recently been reported exploring the qualitative influence of single/two process parameters on tensile strength. In fact, mechanical properties are comprehensively affected by multiple correlated process parameters with unclear and complex interactions. Thus, the study on the quantitative process-quality model of the metal LPBF process is urgently needed to provide an enough-strength component via the metal LPBF process. Recent progress in artificial intelligence (AI) and machine learning (ML) provides new insight into quality prediction in terms of computational accuracy and speed. However, the predictive model quality through the traditional AL/ML is heavily determined by the training data size, and the experimental analysis can be expansive on LPBF. This paper explores the comprehensive effect of the tensile strength of 316L stainless-steel parts on LPBF and proposes a valid quantitative predictive model through a novel self-growing machine-learning framework. The self-growing framework can autonomously expand and classify the growing dataset to provide a high-accuracy prediction with fewer input data. To verify this predictive model of tensile strength, specimens manufactured by the LPBF process with different group process parameters (laser power, scanning speed, and hatch spacing) are collected. The experimental results validate the predicted tensile strengths within a less than 3% deviation.     

Are general circulation models obsolete?

Traditional general circulation models, or GCMs—that is, three-dimensional dynamical models with unresolved terms represented in equations with tunable parameters—have been a mainstay of climate research for several decades, and some of the pioneering studies have recently been recognized by a Nobel prize in Physics. Yet, there is considerable debate around their continuing role in the future. Frequently mentioned as limitations of GCMs are the structural error and uncertainty across models with different representations of unresolved scales and the fact that the models are tuned to reproduce certain aspects of the observed Earth. We consider these shortcomings in the context of a future generation of models that may address these issues through substantially higher resolution and detail, or through the use of machine learning techniques to match them better to observations, theory, and process models. It is our contention that calibration, far from being a weakness of models, is an essential element in the simulation of complex systems, and contributes to our understanding of their inner workings. Models can be calibrated to reveal both fine-scale detail and the global response to external perturbations. New methods enable us to articulate and improve the connections between the different levels of abstract representation of climate processes, and our understanding resides in an entire hierarchy of models where GCMs will continue to play a central role for the foreseeable future.     

Predicting the Durability of Solid Fired Bricks Using NDT Electroacoustic Methods

Historical buildings and monuments are largely made of brickwork. These buildings form the historical and artistic character of cities, and how we look after them is a reflection of our society. When assessing ceramic products, great emphasis is placed on their mechanical properties, whilst their durability is often neglected. However, the durability or resistance to weathering of masonry elements is just as important as their mechanical properties. Therefore, this work deals with predicting the durability of solid-fired bricks before they are used when reconstructing monuments and historical buildings. Durability prediction is assessed by identifying defects in the material’s internal structure. These faults may not be visible on the element’s surface and are difficult to detect. For this purpose, non-destructive electroacoustic methods, such as the resonant pulse method or the ultrasonic pulse method, were used. Based on an analysis of the initial and residual mechanical properties after freezing cycles, four durability classes of solid-fired bricks were determined. This work aimed to find a way to predict the durability (lifetime) of an anonymous solid-fired brick, expressed in terms of the number of freeze cycles the brick would last, based on non-destructive measurements.