Homogenization Construction of Three-Level Teaching Bases for General Practice Medicine Under the Internet+Model

With the continuous expansion of medical student enrollment,the number of clinical teaching bases is gradually increasing.However,there are significant differences in clinical teaching management models and teaching levels among different bases.Most clinical teaching bases have incomplete teaching management systems,inadequate teaching management institutions,insufficient teaching personnel,and inadequate implementation of teaching rules and regulations.This article combines the construction practice of three-level clinical teaching base of the General Medicine College and the First Affiliated Hospital of Xi’an Medical University.We establish a standardized management system for the three-level clinical teaching base;implement a teaching supervision system and strengthen the monitoring of teaching quality;adopt multiple evaluations to test the effectiveness of clinical teaching implementation;explore the path of homogenization construction of teaching bases in terms of unified teacher training,promoting the development of teacher teaching abilities with equal quality and excellence,and providing a reference for improving the quality of medical talent training.

Hyperparameter Tuning for Deep Neural Networks Based Optimization Algorithm

For training the present Neural Network(NN)models,the standard technique is to utilize decaying Learning Rates(LR).While the majority of these techniques commence with a large LR,they will decay multiple times over time.Decaying has been proved to enhance generalization as well as optimization.Other parameters,such as the network’s size,the number of hidden layers,drop-outs to avoid overfitting,batch size,and so on,are solely based on heuristics.This work has proposed Adaptive Teaching Learning Based(ATLB)Heuristic to identify the optimal hyperparameters for diverse networks.Here we consider three architec-tures Recurrent Neural Networks(RNN),Long Short Term Memory(LSTM),Bidirectional Long Short Term Memory(BiLSTM)of Deep Neural Networks for classification.The evaluation of the proposed ATLB is done through the various learning rate schedulers Cyclical Learning Rate(CLR),Hyperbolic Tangent Decay(HTD),and Toggle between Hyperbolic Tangent Decay and Triangular mode with Restarts(T-HTR)techniques.Experimental results have shown the performance improvement on the 20Newsgroup,Reuters Newswire and IMDB dataset.

SRResNet Performance Enhancement Using Patch Inputs and Partial Convolution-Based Padding

Due to highly underdetermined nature of Single Image Super-Resolution(SISR)problem,deep learning neural networks are required to be more deeper to solve the problem effectively.One of deep neural networks successful in the Super-Resolution(SR)problem is ResNet which can render the capability of deeper networks with the help of skip connections.However,zero padding(ZP)scheme in the network restricts benefits of skip connections in SRResNet and its performance as the ratio of the number of pure input data to that of zero padded data increases.In this paper.we consider the ResNet with Partial Convolution based Padding(PCP)instead of ZP to solve SR problem.Since training of deep neural networks using patch images is advantageous in many aspects such as the number of training image data and network complexities,patch image based SR performance is compared with single full image based one.The experimental results show that patch based SRResNet SR results are better than single full image based ones and the performance of deep SRResNet with PCP is better than the one with ZP.

Lateral interaction by Laplacian‐based graph smoothing for deep neural networks

Lateral interaction in the biological brain is a key mechanism that underlies higher cognitive functions.Linear self‐organising map(SOM)introduces lateral interaction in a general form in which signals of any modality can be used.Some approaches directly incorporate SOM learning rules into neural networks,but incur complex operations and poor extendibility.The efficient way to implement lateral interaction in deep neural networks is not well established.The use of Laplacian Matrix‐based Smoothing(LS)regularisation is proposed for implementing lateral interaction in a concise form.The authors’derivation and experiments show that lateral interaction implemented by SOM model is a special case of LS‐regulated k‐means,and they both show the topology‐preserving capability.The authors also verify that LS‐regularisation can be used in conjunction with the end‐to‐end training paradigm in deep auto‐encoders.Additionally,the benefits of LS‐regularisation in relaxing the requirement of parameter initialisation in various models and improving the classification performance of prototype classifiers are evaluated.Furthermore,the topologically ordered structure introduced by LS‐regularisation in feature extractor can improve the generalisation performance on classification tasks.Overall,LS‐regularisation is an effective and efficient way to implement lateral interaction and can be easily extended to different models.

Microstructural image based convolutional neural networks for efficient prediction of full-field stress maps in short fiber polymer composites

The increased demand for superior materials has highlighted the need of investigating the mechanical properties of composites to achieve enhanced constitutive relationships.Fiber-reinforced polymer composites have emerged as an integral part of materials development with tailored mechanical properties.However,the complexity and heterogeneity of such composites make it considerably more challenging to have precise quantification of properties and attain an optimal design of structures through experimental and computational approaches.In order to avoid the complex,cumbersome,and labor-intensive experimental and numerical modeling approaches,a machine learning(ML)model is proposed here such that it takes the microstructural image as input with a different range of Young’s modulus of carbon fibers and neat epoxy,and obtains output as visualization of the stress component S11(principal stress in the x-direction).For obtaining the training data of the ML model,a short carbon fiberfilled specimen under quasi-static tension is modeled based on 2D Representative Area Element(RAE)using finite element analysis.The composite is inclusive of short carbon fibers with an aspect ratio of 7.5that are infilled in the epoxy systems at various random orientations and positions generated using the Simple Sequential Inhibition(SSI)process.The study reveals that the pix2pix deep learning Convolutional Neural Network(CNN)model is robust enough to predict the stress fields in the composite for a given arrangement of short fibers filled in epoxy over the specified range of Young’s modulus with high accuracy.The CNN model achieves a correlation score of about 0.999 and L2 norm of less than 0.005 for a majority of the samples in the design spectrum,indicating excellent prediction capability.In this paper,we have focused on the stage-wise chronological development of the CNN model with optimized performance for predicting the full-field stress maps of the fiber-reinforced composite specimens.The development of such a robust and efficient algorithm would significantly reduce the amount of time and cost required to study and design new composite materials through the elimination of numerical inputs by direct microstructural images.

Honey Badger Algorithm Based Clustering with Routing Protocol for Wireless Sensor Networks

Wireless sensor network(WSN)includes a set of self-organizing and homogenous nodes employed for data collection and tracking applications.It comprises a massive set of nodes with restricted energy and processing abilities.Energy dissipation is a major concern involved in the design of WSN.Clustering and routing protocols are considered effective ways to reduce the quantity of energy dissipation using metaheuristic algorithms.In order to design an energy aware cluster-based route planning scheme,this study introduces a novel Honey Badger Based Clustering with African Vulture Optimization based Routing(HBAC-AVOR)protocol for WSN.The presented HBAC-AVOR model mainly aims to cluster the nodes in WSN effectually and organize the routes in an energy-efficient way.The presented HBAC-AVOR model follows a two stage process.At the initial stage,the HBAC technique is exploited to choose an opti-mal set of cluster heads(CHs)utilizing afitness function involving many input parameters.Next,the AVOR approach was executed for determining the optimal routes to BS and thereby lengthens the lifetime of WSN.A detailed simulation analysis was executed to highlight the increased outcomes of the HBAC-AVOR protocol.On comparing with existing techniques,the HBAC-AVOR model has outperformed existing techniques with maximum lifetime.

Base Station Energy Management in 5G Networks Using Wide Range Control Optimization

The traffic activity offifth generation(5G)networks demand for new energy management techniques that is dynamic deep and longer duration of sleep as compared to the fourth generation(4G)network technologies that demand always for varied control and data signalling based on control base station(CBS)and data base station(DBS).Hence,this paper discusses the energy management in wireless cellular networks using wide range of control for twice the reduction in energy conservation in non-standalone deployment of 5G network.As the new radio(NR)based 5G network is configured to transmit signal blocks for every 20 ms,the proposed algorithm implements withstanding capacity of on or off based energy switching,which in-turn operates in wide range control by carrying out reduced computational complexity.The proposed Wide range of control for base station in green cellular network using sleep mode for switch(WGCNS)algorithm toon and off the base station will work in heavy load with neighbouring base station.For reducing the overhead duration in air,heuristic versions of the algorithm are proposed at the base station.The algorithm operates based on the specification with suggested protocol-level to give best amount of energy savings.The proposed algorithm reduces 40%to 83%of residual energy based on the traffic pattern of the urban scenario.

Genetics Based Compact Fuzzy System for Visual Sensor Network

As a component of Wireless Sensor Network(WSN),Visual-WSN(VWSN)utilizes cameras to obtain relevant data including visual recordings and static images.Data from the camera is sent to energy efficient sink to extract key-information out of it.VWSN applications range from health care monitoring to military surveillance.In a network with VWSN,there are multiple challenges to move high volume data from a source location to a target and the key challenges include energy,memory and I/O resources.In this case,Mobile Sinks(MS)can be employed for data collection which not only collects information from particular chosen nodes called Cluster Head(CH),it also collects data from nearby nodes as well.The innovation of our work is to intelligently decide on a particular node as CH whose selection criteria would directly have an impact on QoS parameters of the system.However,making an appropriate choice during CH selection is a daunting task as the dynamic and mobile nature of MSs has to be taken into account.We propose Genetic Machine Learning based Fuzzy system for clustering which has the potential to simulate human cognitive behavior to observe,learn and understand things from manual perspective.Proposed architecture is designed based on Mamdani’s fuzzy model.Following parameters are derived based on the model residual energy,node centrality,distance between the sink and current position,node centrality,node density,node history,and mobility of sink as input variables for decision making in CH selection.The inputs received have a direct impact on the Fuzzy logic rules mechanism which in turn affects the accuracy of VWSN.The proposed work creates a mechanism to learn the fuzzy rules using Genetic Algorithm(GA)and to optimize the fuzzy rules base in order to eliminate irrelevant and repetitive rules.Genetic algorithmbased machine learning optimizes the interpretability aspect of fuzzy system.Simulation results are obtained using MATLAB.The result shows that the classification accuracy increase along with minimizing fuzzy rules count and thus it can be inferred that the suggested methodology has a better protracted lifetime in contrast with Low Energy Adaptive Clustering Hierarchy(LEACH)and LEACHExpected Residual Energy(LEACH-ERE).

Improved Monarchy Butterfly Optimization Algorithm (IMBO): Intrusion Detection Using Mapreduce Framework Based Optimized ANU-Net

The demand for cybersecurity is rising recently due to the rapid improvement of network technologies.As a primary defense mechanism,an intrusion detection system(IDS)was anticipated to adapt and secure com-puting infrastructures from the constantly evolving,sophisticated threat land-scape.Recently,various deep learning methods have been put forth;however,these methods struggle to recognize all forms of assaults,especially infrequent attacks,because of network traffic imbalances and a shortage of aberrant traffic samples for model training.This work introduces deep learning(DL)based Attention based Nested U-Net(ANU-Net)for intrusion detection to address these issues and enhance detection performance.For this IDS model,the first data preprocessing is carried out in three stages:duplication elimi-nation,label transformation,and data normalization.Then the features are extracted and selected based on the Improved Flower Pollination Algorithm(IFPA).The Improved Monarchy Butterfly Optimization Algorithm(IMBO),a new metaheuristic,is used to modify the hyper-parameters in ANU-Net,effectively increasing the learning rate for spatial-temporal information and resolving the imbalance problem.Through the use of parallel programming,the MapReduce architecture reduces computation complexity while signifi-cantly accelerating processing.Three publicly available data sets were used to evaluate and test the approach.The investigational outcomes suggest that the proposed technique can more efficiently boost the performances of IDS under the scenario of unbalanced data.The proposed method achieves above 98%accuracy and classifies various attacks significantly well compared to other classifiers.

Geographic Drone-based Route Optimization Approach for Emergency Area Ad-Hoc Network

Wireless sensor Mobile ad hoc networks have excellent potential in moving and monitoring disaster area networks on real-time basis.The recent challenges faced in Mobile Ad Hoc Networks(MANETs)include scalability,localization,heterogeneous network,self-organization,and self-sufficient operation.In this background,the current study focuses on specially-designed communication link establishment for high connection stability of wireless mobile sensor networks,especially in disaster area network.Existing protocols focus on location-dependent communications and use networks based on typically-used Internet Protocol(IP)architecture.However,IP-based communications have a few limitations such as inefficient bandwidth utilization,high processing,less transfer speeds,and excessive memory intake.To overcome these challenges,the number of neighbors(Node Density)is minimized and high Mobility Nodes(Node Speed)are avoided.The proposed Geographic Drone Based Route Optimization(GDRO)method reduces the entire overhead to a considerable level in an efficient manner and significantly improves the overall performance by identifying the disaster region.This drone communicates with anchor node periodically and shares the information to it so as to introduce a drone-based disaster network in an area.Geographic routing is a promising approach to enhance the routing efficiency in MANET.This algorithm helps in reaching the anchor(target)node with the help of Geographical Graph-Based Mapping(GGM).Global Positioning System(GPS)is enabled on mobile network of the anchor node which regularly broadcasts its location information that helps in finding the location.In first step,the node searches for local and remote anticipated Expected Transmission Count(ETX),thereby calculating the estimated distance.Received Signal Strength Indicator(RSSI)results are stored in the local memory of the node.Then,the node calculates the least remote anticipated ETX,Link Loss Rate,and information to the new location.Freeway Heuristic algorithm improves the data speed,efficiency and determines the path and optimization problem.In comparison with other models,the proposed method yielded an efficient communication,increased the throughput,and reduced the end-to-end delay,energy consumption and packet loss performance in disaster area networks.

基于改进SqueezeNet算法的VBE设备电路板元件失效识别研究

在直流输电系统中,换流阀阀基电子VBE(valve base electronics)设备的稳定运作对维护直流系统安全至关重要。传统的阀基电子设备电路板(VBE板)元件失效检测方法依赖于耗时的人工检查或基于规则的自动化系统,这些方法通常检测效率低下且准确性有限。针对该问题,提出一种基于改进的SqueezeNet深度学习模型的VBE板元件失效区域识别方法。通过引入深度可分离卷积和残差连接,所提改进SqueezeNet模型旨在提高元件失效检测的准确性,同时降低计算资源的需求。在VBE板元件失效数据集上的实验结果表明,所提方法在元件失效检测准确率和运算效率方面均优于传统方法和标准SqueezeNet模型,准确率达到了95.27%,比原模型高出4.45%。不仅提升了VBE板元件失效检测的效率和准确性,而且为电力系统中类似设备的元件失效诊断提供了新的技术参考。

Base Pressure Control with Semi-Circular Ribs at Critical Mach Number

When better fuel-air mixing in the combustion chamber or a reduction in base drag are required in vehicles,rockets,and aeroplanes,the base pressure control is activated.Controlling the base pressure and drag is necessary in both scenarios.In this work,semi-circular ribs with varying diameters(2,4,and 6 mm)positioned at six distinct positions(0.5D,1D,1.5D,2D,3D,and 4D)inside a square duct with a side of 15 mm are proposed as an efficient way to apply the passive control technique.In-depth research is done on optimising rib size for various rib sites.According to this study,the base pressure rises as rib height increases.Furthermore,the optimal location for the semi-circular ribs with a diameter of 2 mm is at 0.5D.The 1D location appears to be optimal for the 4 mm size as well.For the 6 mm size,however,the 4D position fills this function.

Prediction of Damping Capacity Demand in Seismic Base Isolators via Machine Learning

Base isolators used in buildings provide both a good acceleration reduction and structural vibration control structures.The base isolators may lose their damping capacity over time due to environmental or dynamic effects.This deterioration of them requires the determination of the maintenance and repair needs and is important for the long-termisolator life.In this study,an artificial intelligence prediction model has been developed to determine the damage and maintenance-repair requirements of isolators as a result of environmental effects and dynamic factors over time.With the developed model,the required damping capacity of the isolator structure was estimated and compared with the previously placed isolator capacity,and the decrease in the damping property was tried to be determined.For this purpose,a data set was created by collecting the behavior of structures with single degrees of freedom(SDOF),different stiffness,damping ratio and natural period isolated from the foundation under far fault earthquakes.The data is divided into 5 different damping classes varying between 10%and 50%.Machine learning model was trained in damping classes with the data on the structure’s response to random seismic vibrations.As a result of the isolator behavior under randomly selected earthquakes,the recorded motion and structural acceleration of the structure against any seismic vibration were examined,and the decrease in the damping capacity was estimated on a class basis.The performance loss of the isolators,which are separated according to their damping properties,has been tried to be determined,and the reductions in the amounts to be taken into account have been determined by class.In the developed prediction model,using various supervised machine learning classification algorithms,the classification algorithm providing the highest precision for the model has been decided.When the results are examined,it has been determined that the damping of the isolator structure with the machine learning method is predicted successfully at a level exceeding 96%,and it is an effective method in deciding whether there is a decrease in the damping capacity.

Role of micro-Ca/In alloying in tailoring the microstructural characteristics and discharge performance of dilute Mg-Bi-Sn-based alloys as anodes for Mg-air batteries

The influence of micro-Ca/In alloying on the microstructural charac teristics,electrochemical behaviors and discharge properties of extruded dilute Mg-0.5Bi-0.5Sn-based(wt.%)alloys as anodes for Mg-air batteries are evaluated.The grain size and texture intensity of the Mg-Bi-Sn-based alloys are significantly decreased after the Ca/In alloying,particularly for the In-containing alloy.Note that,in addition to nanoscale Mg_(3)Bi_(2)phase,a new microscale Mg_(2)Bi_(2)Ca phase forms in the Ca-containing alloy.The electrochemical test results demonstrate that Ca/In micro-alloying can enhance the electrochemical activity.Using In to alloy the Mg-Bi-Sn-based alloy is effective in restricting the cathodic hydrogen evolution(CHE)kinetics,leading to a low self-corrosion rate,while severe CHE occurred after Ca alloying.The micro-alloying of Ca/In to Mg-Bi-Sn-based alloy strongly deteriorates the compactness of discharge products film and mitigates the"chunk effect"(CE),hence the cell voltage,anodic efficiency as well as discharge capacity are greatly improved.The In-containing alloy exhibits outstanding discharge performance under the combined effect of the modified microstructure and discharge products,thus making it a potential anode material for primary Mg-air battery.

RoBGP:A Chinese Nested Biomedical Named Entity Recognition Model Based on RoBERTa and Global Pointer

Named Entity Recognition(NER)stands as a fundamental task within the field of biomedical text mining,aiming to extract specific types of entities such as genes,proteins,and diseases from complex biomedical texts and categorize them into predefined entity types.This process can provide basic support for the automatic construction of knowledge bases.In contrast to general texts,biomedical texts frequently contain numerous nested entities and local dependencies among these entities,presenting significant challenges to prevailing NER models.To address these issues,we propose a novel Chinese nested biomedical NER model based on RoBERTa and Global Pointer(RoBGP).Our model initially utilizes the RoBERTa-wwm-ext-large pretrained language model to dynamically generate word-level initial vectors.It then incorporates a Bidirectional Long Short-Term Memory network for capturing bidirectional semantic information,effectively addressing the issue of long-distance dependencies.Furthermore,the Global Pointer model is employed to comprehensively recognize all nested entities in the text.We conduct extensive experiments on the Chinese medical dataset CMeEE and the results demonstrate the superior performance of RoBGP over several baseline models.This research confirms the effectiveness of RoBGP in Chinese biomedical NER,providing reliable technical support for biomedical information extraction and knowledge base construction.

Leveraging base-pair mammalian constraint to understand genetic variation and human disease

Thousands of genomic regions have been associated with heritable human diseases,but attempts to elucidate biological mechanisms are impeded by an inability to discern which genomic positions are functionally important.Evolutionary constraint is a powerful predictor of function,agnostic to cell type or disease mechanism.Single-base phyloP scores from 240 mammals identified 3.3%of the human genome as significantly constrained and likely functional.We compared phyloP scores to genome annotation,association studies,copy-number variation,clinical genetics findings,and cancer data.Constrained positions are enriched for variants that explain common disease heritability more than other functional annotations.Our results improve variant annotation but also highlight that the regulatory landscape of the human genome still needs to be further explored and linked to disease.

Prediction of Lubricant Physicochemical Properties Based on Gaussian Copula Data Expansion

The composition of base oils affects the performance of lubricants made from them.This paper proposes a hybrid model based on gradient-boosted decision tree(GBDT)to analyze the effect of different ratios of KN4010,PAO40,and PriEco3000 component in a composite base oil system on the performance of lubricants.The study was conducted under small laboratory sample conditions,and a data expansion method using the Gaussian Copula function was proposed to improve the prediction ability of the hybrid model.The study also compared four optimization algorithms,sticky mushroom algorithm(SMA),genetic algorithm(GA),whale optimization algorithm(WOA),and seagull optimization algorithm(SOA),to predict the kinematic viscosity at 40℃,kinematic viscosity at 100℃,viscosity index,and oxidation induction time performance of the lubricant.The results showed that the Gaussian Copula function data expansion method improved the prediction ability of the hybrid model in the case of small samples.The SOA-GBDT hybrid model had the fastest convergence speed for the samples and the best prediction effect,with determination coefficients(R^(2))for the four indicators of lubricants reaching 0.98,0.99,0.96 and 0.96,respectively.Thus,this model can significantly reduce the model’s prediction error and has good prediction ability.

Laboratory Evaluation of Performance Recovery of Aged SBS-modified Asphalt Using Regenerants with Different Base Oil Components

In this study,the regenerative effects of different regenerants on aged SBS-modified asphalt from different perspectives were investigated,including their conventional properties,viscoelastic behavior,creep-related properties,and micromorphology.Base oils composed of different proportions of aromatic and saturated hydrocarbons as well as the styrene-butadiene-styrene(SBS)restorer were used to prepare the regenerants.The results showed that the components of the base oil of the regenerant played a crucial role in determining the characteristics and performance of the recycled SBSmodified asphalt.Regenerants containing a high proportion of aromatics dissolved the hard segment in the SBS restorer,thereby delaying the effect of a reduction in the regenerants on the performance of the aged asphalts at a high temperature.Regenerants containing a high proportion of saturates dissolved the soft segment in the SBS restorer to enhance the lowtemperature performance of the recycled asphalts.In addition,the stress sensitivity of the recycled asphalts increased with the fraction of aromatics in the regenerant.As the aromatic content of the base oil components of the regenerants increased and their saturate content decreased,the state of dispersion of the SBS phase in the recycled SBS-modified asphalts improved.The optimal content of aromatics in the base oil of the regenerants should be set in the range of 33%to 47%to ensure the adequate performance of the recycled asphalts and a high efficiency of the SBS restorer.

Buckling Optimization of Curved Grid Stiffeners through the Level Set Based Density Method

Stiffened structures have great potential for improvingmechanical performance,and the study of their stability is of great interest.In this paper,the optimization of the critical buckling load factor for curved grid stiffeners is solved by using the level set based density method,where the shape and cross section(including thickness and width)of the stiffeners can be optimized simultaneously.The grid stiffeners are a combination ofmany single stiffenerswhich are projected by the corresponding level set functions.The thickness and width of each stiffener are designed to be independent variables in the projection applied to each level set function.Besides,the path of each single stiffener is described by the zero iso-contour of the level set function.All the single stiffeners are combined together by using the p-norm method to obtain the stiffener grid.The proposed method is validated by several numerical examples to optimize the critical buckling load factor.

Investigation of the Behavior of a Photovoltaic Cell under Concentration as a Function of the Temperature of the Base and a Variable External Magnetic Field in 3D Approximation

The photovoltaic (PV) cell performances are connected to the base photogenerated carriers charge. Some studies showed that the quantity of the photogenerated carriers charge increases with the increase of the solar illumination. This situation explains the choice of concentration PV cell (C = 50 suns) in this study. However, the strong photogeneration of the carriers charge causes a high heat production by thermalization, collision and carriers charge braking due to the electric field induced by concentration gradient. This heat brings the heating of the PV cell base. That imposes the taking into account of the temperature influence in the concentrator PV cell operation. Moreover, with the proliferation of the magnetic field sources in the life space, it is important to consider its effect on the PV cell performances. Thus, when magnetic field and base temperature increase simultaneously, we observe a deterioration of the photovoltage, the electric power, the space charge region capacity, the fill factor and the conversion efficiency. However the photocurrent increases when the base temperature increases and the magnetic field strength decreases. It appears an inversion phenomenon in the evolution of the electrical parameters as a function of magnetic field for the values of magnetic field B> 4×10-4 T.