Customized scaffolds for large bone defects using 3D‑printed modular blocks from 2D‑medical images

Additive manufacturing(AM)has revolutionized the design and manufacturing of patient-specific,three-dimensional(3D),complex porous structures known as scaffolds for tissue engineering applications.The use of advanced image acquisition techniques,image processing,and computer-aided design methods has enabled the precise design and additive manufacturing of anatomically correct and patient-specific implants and scaffolds.However,these sophisticated techniques can be timeconsuming,labor-intensive,and expensive.Moreover,the necessary imaging and manufacturing equipment may not be readily available when urgent treatment is needed for trauma patients.In this study,a novel design and AM methods are proposed for the development of modular and customizable scaffold blocks that can be adapted to fit the bone defect area of a patient.These modular scaffold blocks can be combined to quickly form any patient-specific scaffold directly from two-dimensional(2D)medical images when the surgeon lacks access to a 3D printer or cannot wait for lengthy 3D imaging,modeling,and 3D printing during surgery.The proposed method begins with developing a bone surface-modeling algorithm that reconstructs a model of the patient’s bone from 2D medical image measurements without the need for expensive 3D medical imaging or segmentation.This algorithm can generate both patient-specific and average bone models.Additionally,a biomimetic continuous path planning method is developed for the additive manufacturing of scaffolds,allowing porous scaffold blocks with the desired biomechanical properties to be manufactured directly from 2D data or images.The algorithms are implemented,and the designed scaffold blocks are 3D printed using an extrusion-based AM process.Guidelines and instructions are also provided to assist surgeons in assembling scaffold blocks for the self-repair of patient-specific large bone defects.

Fixed-Time Sliding Mode Control With Varying Exponent Coefficient for Modular Reconfigurable Flight Arrays

The modular system can change its physical structure by self-assembly and self-disassembly between modules to dynamically adapt to task and environmental requirements. Recognizing the adaptive capability of modular systems, we introduce a modular reconfigurable flight array(MRFA) to pursue a multifunction aircraft fitting for diverse tasks and requirements,and investigate the attitude control and the control allocation problem by using the modular reconfigurable flight array as a platform. First, considering the variable and irregular topological configuration of the modular array, a center-of-mass-independent flight array dynamics model is proposed to allow control allocation under over-actuated situations. Secondly, in order to meet the stable, fast and accurate attitude tracking performance of the MRFA, a fixed-time convergent sliding mode controller with state-dependent variable exponent coefficients is proposed to ensure fast convergence rate both away from and near the system equilibrium point without encountering the singularity. It is shown that the controller also has fixed-time convergent characteristics even in the presence of external disturbances. Finally,simulation results are provided to demonstrate the effectiveness of the proposed modeling and control strategies.

Modularized and Parametric Modeling Technology for Finite Element Simulations of Underground Engineering under Complicated Geological Conditions

The surrounding geological conditions and supporting structures of underground engineering are often updated during construction,and these updates require repeated numerical modeling.To improve the numerical modeling efficiency of underground engineering,a modularized and parametric modeling cloud server is developed by using Python codes.The basic framework of the cloud server is as follows:input the modeling parameters into the web platform,implement Rhino software and FLAC3D software to model and run simulations in the cloud server,and return the simulation results to the web platform.The modeling program can automatically generate instructions that can run the modeling process in Rhino based on the input modeling parameters.The main modules of the modeling program include modeling the 3D geological structures,the underground engineering structures,and the supporting structures as well as meshing the geometric models.In particular,various cross-sections of underground caverns are crafted as parametricmodules in themodeling program.Themodularized and parametric modeling program is used for a finite element simulation of the underground powerhouse of the Shuangjiangkou Hydropower Station.This complicatedmodel is rapidly generated for the simulation,and the simulation results are reasonable.Thus,this modularized and parametric modeling program is applicable for three-dimensional finite element simulations and analyses.

"Five steps four quadrants" modularized en bloc dissection technique for accessing hepatic hilum lymph nodes in laparoscopic pancreaticoduodenectomy

BACKGROUND Although en bloc dissection of hepatic hilum lymph nodes has many advantages in radical tumor treatment,the feasibility and safety of this approach for laparo-scopic pancreaticoduodenectomy(LPD)require further clinical evaluation and investigation.AIM To explore the application value of the"five steps four quadrants"modularized en bloc dissection technique for accessing hepatic hilum lymph nodes in LPD patients.METHODS A total of 52 patients who underwent LPD via the"five steps four quadrants"modularized en bloc dissection technique for hepatic hilum lymph nodes from April 2021 to July 2023 in our department were analyzed retrospectively.The patients'body mass index(BMI),preoperative laboratory indices,intraoperative variables and postoperative complications were recorded.The relationships between preoperative data and intraoperative lymph node dissection time and blood loss were also analyzed.RESULTS Among the 52 patients,36 were males and 16 were females,and the average age was 62.2±11.0 years.There were 26 patients with pancreatic head cancer,16 patients with periampullary cancer,and 10 patients with distal bile duct cancer.The BMI was 22.3±3.3 kg/m²,and the median total bilirubin(TBIL)concentration was 57.7(16.0-155.7)µmol/L.All patients successfully underwent the"five steps four quadrants"modularized en bloc dissection technique without lymph node clearance-related complications such as postoperative bleeding or lymphatic leakage.Correlation analysis revealed significant associations between preoperative BMI(r=0.3581,P=0.0091),TBIL level(r=0.2988,P=0.0341),prothrombin time(r=0.3018,P=0.0297)and lymph node dissection time.Moreover,dissection time was significantly correlated with intraoperative blood loss(r=0.7744,P<0.0001).Further stratified analysis demonstrated that patients with a preoperative BMI≥21.9 kg/m²and a TIBL concentration≥57.7μmol/L had significantly longer lymph node dissection times(both P<0.05).CONCLUSION The"five steps four quadrants"modularized en bloc dissection technique for accessing the hepatic hilum lymph node is safe and feasible for LPD.This technique is expected to improve the efficiency of hepatic hilum lymph node dissection and shorten the learning curve;thus,it is worthy of further clinical promotion and application.

Illumination Adaptive Identification Algorithm of a Reconfigurable Modular Robot

Reconfigurable modular robots feature high mobility due to their unconstrained connection manners.Inspired by the snake multi-joint crawling principle,a chain-type reconfigurable modular robot(CRMR)is designed,which could reassemble into various configurations through the compound joint motion.Moreover,an illumination adaptive modular robot identification(IAMRI)algorithm is proposed for CRMR.At first,an adaptive threshold is applied to detect oriented FAST features in the robot image.Then,the effective detection of features in non-uniform illumination areas is achieved through an optimized quadtree decomposition method.After matching features,an improved random sample consensus algorithm is employed to eliminate the mismatched features.Finally,the reconfigurable robot module is identified effectively through the perspective transformation.Compared with ORB,MA,Y-ORB,and S-ORB algorithms,the IAMRI algorithm has an improvement of over 11.6%in feature uniformity,and 13.7%in the comprehensive indicator,respectively.The IAMRI algorithm limits the relative error within 2.5 pixels,efficiently completing the CRMR identification under complex environmental changes.

Design and Development of Small Modular Courses for“Education-Training Integration”in Vocational Colleges:A Case Study

Vocational education plays a vital role in the development of skilled technical professionals and the advancement of the economy.However,the emphasis on campus education often neglects the importance of external training,hindering the overall development of vocational education.This study aims to address this issue by exploring the design and development of small modular courses that integrate training and education in vocational colleges,focusing on the mechanics course as a case study.The research methods employed in this study include an in-depth analysis of enterprise training needs,the development of digital teaching resources utilizing the finite element method(FEM),and the implementation of a small modular course integrating education and training.The data analysis reveals positive outcomes in terms of learners’comprehension and engagement with complex mechanics formulas through the use of stress nephograms and other digital resources.This research provides a new perspective on curriculum design and offers insights into the integration of training and education in vocational colleges.The findings underscore the significance of incorporating innovative teaching methodologies and digital resources in enhancing the quality and relevance of vocational education,ultimately contributing to the cultivation of skilled professionals and the growth of the vocational education sector.

Modular flexible“Tetris”microsatellite platform based on sandwich assembly mode

In this paper,a flexible modular“Tetris”microsatellite platform is studied to implement the rapid integration and assembly of microsatellites.The proposed microsatellite platform is fulfilled based on a sandwich assembly mode which consists of the isomorphic module structure and the standard mechanical-electric-data-thermal interfaces.The advantages of the sandwich assembly mode include flexible reconfiguration and efficient assembly.The prototype of the sandwich assembly mode is built for verifying the performance and the feasibility of the proposed mechanical-electric-data-thermal interfaces.Finally,an assembly case is accomplished to demonstrate the validity and advantages of the proposed“Tetris”microsatellite platform.

Accident source term and radiological consequences of a small modular reactor

Considering the growing global demand for energy and the need for countries to achieve climate goals,there is an increasing global interest in small modular reactors(SMRs)and their applications.Accident source term and radiological consequence evaluations of SMRs are key components of nuclear and radiation safety reviews,which affect the site,exclusion area(EAB),and low population zone outer boundaries.Based on the design characteristics of the SMR and accident analysis results,a theoretical model of a whole-core fuel cladding damage accident was constructed to study the radioactivity released into the environment and its consequences.The accident source term and radiation dose calculation models were established to analyze the released amounts of radionuclides and the total effective dose affecting individuals at the site boundary.The results showed that the amount of radionuclides released into the environment after a whole-core fuel cladding damage accident reached 10^(14) Bq,among which the release amount of ^(133)Xe was the largest.The total effective dose at the site boundary 30 days after the accident was 8.65 mSv.The highest total effective dose affecting individuals occurred to the east-north-east.The results of the accident source term and radiological consequence provide technical support for site boundary dose assessments and reviews of SMRs.

Product Specification Analysis for Modular Product Design Using Big Sales Data

Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers’requirements of product specification combinations.To better facilitate decision-making of modular product design,correlations among specifications and components originated from customers’conscious and subconscious preferences can be investigated by using big data on product sales.This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data.The correlations of the product specifications are determined by analyzing the collected product sales data.By building the relations between the product components and specifications,a matrix for measuring the correlation among product components is formed for component clustering.Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster.A case study of electric vehicles illustrates the application of the proposed method.

Investigation of the Severity of Modular Construction Adoption Barriers with Large-Scale Group Decision Making in an Organization from Internal and External Stakeholder Perspectives

Modular construction as an innovativemethod aids the construction industry in transforming to off-site construction production with high efficiency and environmental friendliness.Despite the obvious advantages,the uptake of modular construction is not booming as expected.However,previous studies have investigated and summarized the barriers to the adoption ofmodular construction.In this research,a Large-ScaleGroupDecisionMaking(LSGDM)-based analysis is first made of the severity of barriers to modular construction adoption from the perspective of construction stakeholders.In addition,the Technology-Organization-Environment(TOE)framework is utilized to identify the barriers based on three contexts(technology,organization,and environment).The Technique for Order Preference by Similarity to Ideal Solution(TOPSIS)and LSGDM models are both implemented for the first time to analyze the severity of the barriers to modular adoption based on questionnaire results from internal and external stakeholders in an organization.Finally,in this research,in-depth insights into the severity of barriers are gained,providing a reference for construction organizations to manage modular adoption.

Modular Extremely Large-Scale Array Communication:Near-Field Modelling and Performance Analysis

This paper investigates the wireless communication with a novel architecture of antenna arrays,termed modular extremely large-scale array(XLarray),where array elements of an extremely large number/size are regularly mounted on a shared platform with both horizontally and vertically interlaced modules.Each module consists of a moderate/flexible number of array elements with the inter-element distance typically in the order of the signal wavelength,while different modules are separated by the relatively large inter-module distance for convenience of practical deployment.By accurately modelling the signal amplitudes and phases,as well as projected apertures across all modular elements,we analyse the near-field signal-to-noise ratio(SNR)performance for modular XL-array communications.Based on the non-uniform spherical wave(NUSW)modelling,the closed-form SNR expression is derived in terms of key system parameters,such as the overall modular array size,distances of adjacent modules along all dimensions,and the user's three-dimensional(3D)location.In addition,with the number of modules in different dimensions increasing infinitely,the asymptotic SNR scaling laws are revealed.Furthermore,we show that our proposed near-field modelling and performance analysis include the results for existing array architectures/modelling as special cases,e.g.,the collocated XL-array architecture,the uniform plane wave(UPW)based far-field modelling,and the modular extremely large-scale uniform linear array(XL-ULA)of onedimension.Extensive simulation results are presented to validate our findings.

Inverse stochastic resonance in modular neural network with synaptic plasticity

This work explores the inverse stochastic resonance(ISR) induced by bounded noise and the multiple inverse stochastic resonance induced by time delay by constructing a modular neural network, where the modified Oja’s synaptic learning rule is employed to characterize synaptic plasticity in this network. Meanwhile, the effects of synaptic plasticity on the ISR dynamics are investigated. Through numerical simulations, it is found that the mean firing rate curve under the influence of bounded noise has an inverted bell-like shape, which implies the appearance of ISR. Moreover, synaptic plasticity with smaller learning rate strengthens this ISR phenomenon, while synaptic plasticity with larger learning rate weakens or even destroys it. On the other hand, the mean firing rate curve under the influence of time delay is found to exhibit a decaying oscillatory process, which represents the emergence of multiple ISR. However, the multiple ISR phenomenon gradually weakens until it disappears with increasing noise amplitude. On the same time, synaptic plasticity with smaller learning rate also weakens this multiple ISR phenomenon, while synaptic plasticity with larger learning rate strengthens it. Furthermore, we find that changes of synaptic learning rate can induce the emergence of ISR phenomenon. We hope these obtained results would provide new insights into the study of ISR in neuroscience.

Prediction of NO_(x)concentration using modular long short-term memory neural network for municipal solid waste incineration

Air pollution control poses a major problem in the implementation of municipal solid waste incineration(MSWI).Accurate prediction of nitrogen oxides(NO_(x))concentration plays an important role in efficient NO_(x)emission controlling.In this study,a modular long short-term memory(M-LSTM)network is developed to design an efficient prediction model for NO_(x)concentration.First,the fuzzy C means(FCM)algorithm is utilized to divide the task into several sub-tasks,aiming to realize the divide-and-conquer ability for complex task.Second,long short-term memory(LSTM)neural networks are applied to tackle corresponding sub-tasks,which can improve the prediction accuracy of the sub-networks.Third,a cooperative decision strategy is designed to guarantee the generalization performance during the testing or application stage.Finally,after being evaluated by a benchmark simulation,the proposed method is applied to a real MSWI process.And the experimental results demonstrate the considerable prediction ability of the M-LSTM network.

Novel Double Modular Redundancy Based Fault-Tolerant FIR Filter for Image Denoising

In signal processing and communication systems,digital filters are widely employed.In some circumstances,the reliability of those systems is crucial,necessitating the use of fault tolerant filter implementations.Many strategies have been presented throughout the years to achieve fault tolerance by utilising the structure and properties of the filters.As technology advances,more complicated systems with several filters become possible.Some of the filters in those complicated systems frequently function in parallel,for example,by applying the same filter to various input signals.Recently,a simple strategy for achieving fault tolerance that takes advantage of the availability of parallel filters was given.Many fault-tolerant ways that take advantage of the filter’s structure and properties have been proposed throughout the years.The primary idea is to use structured authentication scan chains to study the internal states of finite impulse response(FIR)components in order to detect and recover the exact state of faulty modules through the state of non-faulty modules.Finally,a simple solution of Double modular redundancy(DMR)based fault tolerance was developed that takes advantage of the availability of parallel filters for image denoising.This approach is expanded in this short to display how parallel filters can be protected using error correction codes(ECCs)in which each filter is comparable to a bit in a standard ECC.“Advanced error recovery for parallel systems,”the suggested technique,can find and eliminate hidden defects in FIR modules,and also restore the system from multiple failures impacting two FIR modules.From the implementation,Xilinx ISE 14.7 was found to have given significant error reduction capability in the fault calculations and reduction in the area which reduces the cost of implementation.Faults were introduced in all the outputs of the functional filters and found that the fault in every output is corrected.

A New Fast Modular Arithmetic Method in Public Key Cryptography

Modular arithmetic is a fundamental operation and plays an important role in public key cryptosystem. A new method and its theory evidence on the basis of modular arithmetic with large integer modulus-changeable modulus algorithm is proposed to improve the speed of the modular arithmetic in the presented paper. For changeable modulus algorithm, when modular computation of modulo n is difficult, it can be realized by computation of modulo n-1 and n-2 on the perquisite of easy modular computations of modulo n-1 and modulo n-2. The conclusion is that the new method is better than the direct method by computing the modular arithmetic operation with large modulus. Especially, when computations of modulo n-1 and modulo n-2 are easy and computation of modulo n is difficult, this new method will be faster and has more advantages than other algorithms on modular arithmetic. Lastly, it is suggested that the proposed method be applied in public key cryptography based on modular multiplication and modular exponentiation with large integer modulus effectively

罗氏cobase602与modular e170电化学发光分析仪检测血清HBsAg阳性低值结果的对比分析

目的探讨乙肝表面抗原(HBsAg)阳性低值结果在不同检测系统间结果的相关性,为临床检验结果分析及报告的准确发布提供参考。方法选取经罗氏电化学发光检测仪Cobas e602检测的HBsAg结果在1~80 COI的标本243份,同时将标本在罗氏电化学发光检测仪Modular e170上进行检测,计算两组测定值的直线回归相关系数R^2值,计算各组别的阳性符合率。结果两个检测系统比对实验结果为R^2=0.933。根据测定值高低分为A,B,C,D和E组的阳性符合率分别为60.60%,92.72%,96.66%,96.66%和100%;男性组阳性符合率为87.66%,女性组阳性符合率为70.96%,男性患者阳性符合率大于女性患者(P<0.05)。结论两检测系统对于范围在1~80 COI的标本检测结果有相关性:其中测量值在10 COI以上标本的阳性符合率完全一致;测量值在1~10 COI的标本阳性符合率不一致,且随结果的减小而降低,在临床上可能导致有争议的结果,建议做进一步复查。

酶学指标检测验证罗氏Modular P800全自动生化分析仪性能的分析

目的通过检测酶学指标丙氨酸氨基转移酶(ALT)和天门冬氨酸氨基转移酶(AST)验证罗氏Modular P800全自动生化分析仪的分析性能。方法根据美国临床实验室标准协会(CLSI)系列文件(EP15-A2,EP6-A2)和其他相关文献的实验方案,对罗氏Modular P800全自动生化分析仪检测ALT、AST的精密度、正确度、线性、临床可报告范围、生物参考区间5大性能进行验证,参照昆涞Q-expertTM质控软件进行结果判断,并与厂商声明的性能或公认的质量标准进行比较。结果精密度均符合制造商性能标准;ALT与AST相对偏倚(SE%)均在临床可接受水平范围内;ALT、AST检测均呈一阶线性,线性范围分别为10.63~600U/L、4~800U/L;ALT和AST临床可报告范围分别为10.63~9600U/L、4~12800U/L;ALT、AST生物参考区间验证结果均符合要求。结论罗氏Modular P800全自动生化分析仪ALT、AST检测的主要分析性能符合质量目标要求。

基于模糊Modular神经网络的官厅水库及邻区的地震危险性估计

首先介绍了模糊Modular神经网络的原理、建模方法与仿真实验,然后利用该方法把一些常用的地震学指标作为神经网络的输入,未来50年最大震级则作为网络的期望输出,对官厅水库及邻区的地震活动进行学习与最大震级序列建模,进行危险性预测。通过分析,认为该方法在一定程度上具有学习、建模与外推预测泛化能力,具有很好的中长期地震危险性预测效果,可以作为中长期地震危险性分析的工具。

改进的模糊Modular神经网络在既有建筑可靠性鉴定中的应用

在Takagi-Sugeno模糊逻辑系统的基础上,提出了改进的模糊Modular神经网络模型(IF-MNN),并将该模型应用于既有建筑的可靠性鉴定。改进的模型是将传统的模糊Modular神经网络模型中的单输出改进为多输出。这种改进的多输入多输出的模糊Modular神经网络模型具有预测性能好、训练学习速度快的优点,它的系统门网络采用模糊C均值聚类算法代替K-means算法,专家网络的训练中引进了先进的Levenberg-Marquardt算法。在应用改进的模糊Modular神经网络模型对既有建筑进行可靠性鉴定的过程中,综合考虑了各主要因素对既有建筑可靠性鉴定等级的影响,并将经量化处理的影响因素作为网络的外部输入,将网络计算得到的4个输出值分别作为样本对应于不同可靠性等级的隶属度,建筑可靠性鉴定的最终评判等级为最大隶属度所对应的等级。训练和预测样本的计算结果证明了改进的模糊Modular神经网络模型在既有建筑可靠性鉴定中应用的可行性和有效性。

一种模糊Modular神经网络模型及其应用

将神经网络模糊系统与模糊C均值聚类法相结合 ,对模糊Modular神经网络进行研究 ,提出了该模糊神经网络模型的多输出结构及其学习算法 ,据此开发了模糊神经网络诊断系统 ,并将其用于某电源分系统的诊断分析 ,运行的结果表明 。