Grid Search for Predicting Coronary Heart Disease by Tuning Hyper-Parameters

Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years. Coronary cardiovascular (CHD) is a kind of heart and blood vascular disease. Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders. Implementing Grid Search Optimization (GSO) machine training models is therefore a useful way to forecast the sickness as soon as possible. The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate. Three models with a cross-validation approach do the required task. Feature Selection based on the use of statistical and correlation matrices for multivariate analysis. For Random Search and Grid Search models, extensive comparison findings are produced utilizing retrieval, F1 score, and precision measurements. The models are evaluated using the metrics and kappa statistics that illustrate the three models’ comparability. The study effort focuses on optimizing function selection, tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification. Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.     

Electrochemical tuning induced oxygen vacancies for the photoluminescence enhancement

Oxygen vacancies(OVs) can greatly influence the properties of luminescent materials, however, finding a facile way of controlling the specific defect remains challenging. The traditional methods usually require either high temperature or long period of time. Here, we demonstrate an electrochemical strategy to implant OVs in as serious of oxides including Li₂GeZnO₄(LZGO), Li₂GeZnO₄:Mn²⁺(LZGO:Mn) and LiGa₅O₈:Cr³⁺(LGO:Cr). The photoluminescence intensity of all these oxides is increased by 43%, 36% and 38% respectively. Our electrochemical strategy not only exhibits facile advantage in tuning the OVs in the lattice of luminescent material, but also provides a method with general efficacy which should be benificial for many other correlated applications.     

气候环境实验室地面结冰云雾模拟控制系统设计

飞机结冰会引起飞机气动特性、动力系统性能改变,甚至造成飞行事故。地面结冰云雾模拟设施作为一种模拟飞机飞行结冰条件的重要设备,常用于飞机防除冰系统功能验证。针对气候环境实验室结冰云雾试验条件需要,在对地面结冰云雾模拟系统进行研究的基础上,对适用于气候环境实验室的结冰云雾模拟系统进行了设计。对系统关键设计技术、系统功能及组成进行了分析,并对控制系统方案进行了详细阐述。试验表明,控制系统人机界面友好,运行可靠,能够满足液态水含量、雾滴直径参数的精确调节,产生符合试验所需的结冰环境。     

Hierarchical Ni-BDC coated FeOOH nanosheets: A coordination tuning synergistic electrocatalyst with enhanced activity for water oxidation

Hierarchical composites represent a class of efficient electrocatalysts for renewable energy storage and conversion technologies owing to the porous structure and additional exposure of metal sites. Herein, a Ni-based metal organic frameworks (MOFs) (marked as Ni-BDC, BDC stands for 1,4-benzenedicarboxylic acid) nanosheet is successfully fabricated on hydroxyl iron oxide (FeOOH) array with carbon fiber cloth (CFC) as substrate. Benefit from the coordination tuning synergistic effect of the distinct chemical composition and the hierarchical structure for fast mass transportation, the as-obtained FeOOH@Ni-BDC illustrates excellent catalytic ability for electrochemical water oxidation with low overpotential of 270 mV to reach 10 mA/cm² current and good durability in alkaline electrolyte. The novelty of this work lies in the modulation of electronic structure of the FeOOH with Ni-BDC through coordination effect to enhance the activity of the hierarchical composite electrocatalyst. This work is expected to guide the preparation of efficient electrocatalyst for new type alternative energy sources exploitation in near future.     

Neural network based non-invasive method to detect anemia from images of eye conjunctiva

Detection of anemia can be done by examining the hemoglobin concentration level in the blood using complete blood count, which is an invasive, time-consuming, and costly technique. Preliminary methods for detecting anemia include examining the color of the palpebral conjunctiva, which is a non-invasive method, but color perception may vary from person to person. This study aims to develop a computerized non-invasive technique for anemia detection. We propose a novel machine learning model using the artificial neural network to detect anemic patients from the images of eye conjunctiva. Since limited and small dataset has been used in the earlier approaches, this may cause over fitting of the model. We have improved the number of available training images using image augmentation techniques. To standardize a non-invasive method, we have used computer vision algorithms for preprocessing and feature extraction. This article derives the backpropagation rules mathematically for adjusting the weights for the proposed neural network model. After hyper parameter tuning and using the mathematically derived backpropagation rules, the model was able to achieve the best accuracy of 97.00% with sensitivity 99.21% and specificity 95.42% on the created dataset.     

Two-stage optimization for machine learning workflow

Machine learning techniques play a preponderant role in dealing with massive amount of data and are employed in almost every possible domain. Building a high quality machine learning model to be deployed in production is a challenging task, from both, the subject matter experts and the machine learning practitioners.For a broader adoption and scalability of machine learning systems, the construction and configuration of machine learning workflow need to gain in automation. In the last few years, several techniques have been developed in this direction, known as AutoML.In this paper, we present a two-stage optimization process to build data pipelines and configure machine learning algorithms. First, we study the impact of data pipelines compared to algorithm configuration in order to show the importance of data preprocessing over hyperparameter tuning. The second part presents policies to efficiently allocate search time between data pipeline construction and algorithm configuration. Those policies are agnostic from the metaoptimizer. Last, we present a metric to determine if a data pipeline is specific or independent from the algorithm, enabling fine-grain pipeline pruning and meta-learning for the coldstart problem.     

Environmental Control of Triplet Emission in Donor–Bridge–Acceptor Organometallics

Carbene‐metal‐amides (CMAs) are a promising family of donor–bridge–acceptor molecular charge‐transfer (CT) emitters for organic light‐emitting diodes. A universal approach is demonstrated to tune the energy of their CT emission. A blueshift of up to 210 meV is achievable in solid state via dilution in a polar host matrix. The origin of this shift has two components: constraint of thermally‐activated triplet diffusion, and electrostatic interactions between guest and polar host. This allows the emission of mid‐green CMA archetypes to be tuned to sky blue without chemical modifications. Monte‐Carlo simulations based on a Marcus‐type transfer integral successfully reproduce the concentration‐ and temperature‐dependent triplet diffusion process, revealing a substantial shift in the ensemble density of states in polar hosts. In gold‐bridged CMAs, this shift does not lead to a significant change in luminescence lifetime, thermal activation energy, reorganization energy, or intersystem crossing rate. These discoveries offer new insight into coupling between the singlet and triplet manifolds in CMA materials, revealing a dominant interaction between states of CT character. The same approach is employed using materials which have been chemically modified to alter the energy of their CT state directly, shifting the emission of sky‐blue chromophores into the practical blue range.     

Regulating the Interfacial Electronic Coupling of Fe2N via Orbital Steering for Hydrogen Evolution Catalysis

The capability of manipulating the interfacial electronic coupling is the key to achieving on-demand functionalities of catalysts. Herein, it is demonstrated that the electronic coupling of Fe₂N can be effectively regulated for hydrogen evolution reaction (HER) catalysis by vacancy-mediated orbital steering. Ex situ refined structural analysis reveals that the electronic and coordination states of Fe₂N can be well manipulated by nitrogen vacancies, which impressively exhibit strong correlation with the catalytic activities. Theoretical studies further indicate that the nitrogen vacancy can uniquely steer the orbital orientation of the active sites to tailor the electronic coupling and thus benefit the surface adsorption capability. This work sheds light on the understanding of the catalytic mechanism in real systems and could contribute to revolutionizing the current catalyst design for HER and beyond.     

Tuning of Active Disturbance Rejection Control with application to power plant furnace regulation

Active Disturbance Rejection Control (ADRC) is emerging as a promising solution in dealing with the unmeasurable disturbances and unknown uncertainties, which are treated in a lumped manner and augmented as an extended state variable. Subsequently, an extended state observer (ESO) is designed to estimate and cancel the combined uncertain term in real time, modifying the uncertain plant to behave like a nominal model consisting of integrators. In the original ADRC formulation, the plant model is assumed to be of delay-free and its order is assumed to be equal to that of the real plant. However, a low-order ADRC is preferred and received a wide acceptance in practice because of its simplicity. Currently, the feasibility of such practice is not clearly revealed as well as its potential dangers. To this end, this paper analyzes the control mechanism from the perspective of the modified plant, which, in turn, would give guidance to parameter tuning. The effect of each parameter on the compensation efficiency and stability conditions of the modified plant is analyzed, based on which a complete tuning procedure for ADRC is developed where the initial settings is derived from the existing PI controller parameters. Finally, the proposed tuning method is experimentally used for a furnace pressure regulation of a 1000MW power plant, validating the feasibility of the low-order ADRC, even in the absence of both dynamic model and the information on the model order.     

A Real Time Self-Tuning Motion Controller for Mobile Robot Systems

This paper proposes an intelligent controller for motion control of robotic systems to obtain high precision tracking without the need for a real-time trial and error method. In addition, a new self-tuning algorithm has been developed based on both the ant colony algorithm and a fuzzy system for real-time tuning of controller parameters. Simulations and experiments using a real robot have been addressed to demonstrate the success of the proposed controller and validate the theoretical analysis. Obtained results confirm that the proposed controller ensures robust performance in the presence of disturbances and parametric uncertainties without the need for adjustment of control law parameters by a trial and error method.