Seismic performance evaluation of hybrid coupled shear wall system with shear and flexural fuse-type steel coupling beams

Replaceable flexural and shear fuse-type coupling beams are used in hybrid coupled shear wall(HCSW)systems,enabling concrete buildings to be promptly recovered after severe earthquakes.This study aimed to analytically evaluate the seismic behavior of flexural and shear fuse beams situated in short-,medium-and high-rise RC buildings that have HCSWs.Three building groups hypothetically located in a high seismic hazard zone were studied.A series of 2D nonlinear time history analyses was accomplished in OpenSees,using the ground motion records scaled at the design basis earthquake level.It was found that the effectiveness of fuses in HCSWs depends on various factors such as size and scale of the building,allowable rotation value,inter-story drift ratio,residual drift quantity,energy dissipation value of the fuses,etc.The results show that shear fuses better meet the requirements of rotations and drifts.In contrast,flexural fuses dissipate more energy,but their sectional stiffness should increase to meet other requirements.It was concluded that adoption of proper fuses depends on the overall scale of the building and on how associated factors are considered.

Residual stress modeling of mitigated fused silica damage sites with CO_(2)laser annealing

A numerical model based on measured fictive temperature distributions is explored to evaluate the residual stress fields of CO_(2)laser-annealed mitigated fused silica damage sites.The proposed model extracts the residual strain from the differences in thermoelastic contraction of fused silica with different fictive temperatures from the initial frozen-in temperatures to ambient temperature.The residual stress fields of mitigated damage sites for the CO_(2)laser-annealed case are obtained by a finite element analysis of equilibrium equations and constitutive equations.The simulated results indicate that the proposed model can accurately evaluate the residual stress fields of laser-annealed mitigated damage sites with a complex thermal history.The calculated maximum hoop stress is in good agreement with the reported experimental result.The estimated optical retardance profiles from the calculated radial and hoop stress fields are consistent with the photoelastic measurements.These results provide sufficient evidence to demonstrate the suitability of the proposed model for describing the residual stresses of mitigated fused silica damage sites after CO_(2)laser annealing.

Alkylene-functionality in bridged and fused nitrogen-rich poly-cyclic energetic materials:Synthesis,structural diversity and energetic properties

From the standpoint of chemical structures,the organic backbones of energetic materials can be classified into aromatic rings,nonaromatic rings,and open chains.Although the category of aromatic energetic compounds exhibits several advantages in the regulation of energetic properties,the nonaromatic heterocycles,assembling nitramino explosophores with simple alkyl bridges,still have prevailed in benchmark materials.The methylene bridge plays a pivotal role in the constructions of the classic nonaromatic heterocycle-based energetic compounds,e.g.,hexahydro-1,3,5-trinitro-1,3,5-triazine(RDX)and octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine(HMX),whereas ethylene bridge is the core moiety of state-of-the-art explosive 2,4,6,8,10,12-hexanitro-2,4,6,8,10,12-hexaazaisowurtzitane(CL-20).In this context,it is of great interest to employ simple and practical bridges to assemble aromatic and nonaromatic nitrogen-rich heterocycles,thereby expanding the structural diversity of energetic materials,e.g.,bridged and fused nitrogen-rich poly-heterocycles.Furthermore,alkyl-bridged poly-heterocycles highlight the potential for the open chain type of energetic materials.In this review,the development of alkyl bridges in linking nitrogen-rich heterocycles is presented,and the perspective of the newly constructed energetic backbones is summarized for the future design of advanced energetic materials.

Research on aiming methods for small sample size shooting tests of two-dimensional trajectory correction fuse

The longitudinal dispersion of the projectile in shooting tests of two-dimensional trajectory corrections fused with fixed canards is extremely large that it sometimes exceeds the correction ability of the correction fuse actuator.The impact point easily deviates from the target,and thus the correction result cannot be readily evaluated.However,the cost of shooting tests is considerably high to conduct many tests for data collection.To address this issue,this study proposes an aiming method for shooting tests based on small sample size.The proposed method uses the Bootstrap method to expand the test data;repeatedly iterates and corrects the position of the simulated theoretical impact points through an improved compatibility test method;and dynamically adjusts the weight of the prior distribution of simulation results based on Kullback-Leibler divergence,which to some extent avoids the real data being"submerged"by the simulation data and achieves the fusion Bayesian estimation of the dispersion center.The experimental results show that when the simulation accuracy is sufficiently high,the proposed method yields a smaller mean-square deviation in estimating the dispersion center and higher shooting accuracy than those of the three comparison methods,which is more conducive to reflecting the effect of the control algorithm and facilitating test personnel to iterate their proposed structures and algorithms.;in addition,this study provides a knowledge base for further comprehensive studies in the future.

Identification of Stability Domains for Flow Parameters in Fused Filament Fabrication Using Acoustic Emission

In Fused Filament Fabrication(FFF),the state of material flow significantly influences printing outcomes.However,online monitoring of these micro-physical processes within the extruder remains challenging.The flow state is affected by multiple parameters,with temperature and volumetric flow rate(VFR)being the most critical.The study explores the stable extrusion of flow with a highly sensitive acoustic emission(AE)sensor so that AE signals generated by the friction in the annular region can reflect the flow state more effectively.Nevertheless,the large volume and broad frequency range of the data present processing challenges.This study proposes a method that initially selects short impact signals and then uses the Fast Kurtogram(FK)to identify the frequency with the highest kurtosis for signal filtration.The results indicate that this approach significantly enhances processing speed and improves feature extraction capabilities.By correlating AE characteristics under various parameters with the quality of extruded raster beads,AE can monitor the real-time state of material flow.This study offers a concise and efficient method for monitoring the state of raster beads and demonstrates the potential of online monitoring of the flow states.

Numerical simulation and printability analysis of fused deposition modeling with dual-temperature control

Ideal tissue engineering scaffolds need interconnected pores and high porosity to enable cell survival,migration,proliferation,and differentiation.However,obtaining a high-resolution structure is difficult with traditional one-temperature control fused deposition modeling(FDM).In this study,we propose a dual-temperature control method to improve printability.A numerical model is developed in which the viscosity is a function of temperature and shear rate to study the influence of two different temperature control modes.Quantitative tests are used to assess filament formation and shape fidelity,including one-dimensional filament printing,deposition at corners,fusion,and collapse.By using dual-temperature control,the width of the deposited poly(ε-caprolactone)filament is reduced to 50μm.The comparative results of both the experimental method and numerical simulation suggest that the dual-temperature control FDM can manufacture spatially arranged constructs and presents a promising application in tissue engineering。

eFuse器件的电迁移三维有限元仿真

应用有限元分析软件建立了电可编程熔丝(eFuse)器件的三维有限元模型,通过离子流通量散度法和最小原子浓度法对eFuse器件的电迁移熔断过程进行了多物理场耦合有限元仿真,仿真结果能够较好地拟合器件的实际熔断效果。通过仿真对比了不同阴极面积和不同编程电压条件下的电迁移过程及熔断效果。结果表明,更大的阴极面积能够提高熔丝局部的温度梯度,从而提高熔断效率;更高的编程电压能够提供更高的电流密度和温度,从而加速电迁移的发生并增大了eFuse熔断区的面积。提出了一种具有外部辅助加热功能的eFuse器件结构,并在不同条件下进行了电迁移熔断仿真,结果表明该结构能够显著提高eFuse器件局部的离子流通量散度,从而提高eFuse存储单元的熔断效率和编程良率。

A Fused Machine Learning Approach for Intrusion Detection System

The rapid growth in data generation and increased use of computer network devices has amplified the infrastructures of internet.The interconnectivity of networks has brought various complexities in maintaining network availability,consistency,and discretion.Machine learning based intrusion detection systems have become essential to monitor network traffic for malicious and illicit activities.An intrusion detection system controls the flow of network traffic with the help of computer systems.Various deep learning algorithms in intrusion detection systems have played a prominent role in identifying and analyzing intrusions in network traffic.For this purpose,when the network traffic encounters known or unknown intrusions in the network,a machine-learning framework is needed to identify and/or verify network intrusion.The Intrusion detection scheme empowered with a fused machine learning technique(IDS-FMLT)is proposed to detect intrusion in a heterogeneous network that consists of different source networks and to protect the network from malicious attacks.The proposed IDS-FMLT system model obtained 95.18%validation accuracy and a 4.82%miss rate in intrusion detection.

Multi-Objective Optimization of Fused Deposition Modeling for Mechanical Properties of Biopolymer Parts Using the Grey-Taguchi Method

The urgent need to develop customized functional products only possible by 3D printing had realized when faced with the unavailability of medical devices like surgical instruments during the coronavirus-19 disease and the ondemand necessity to perform surgery during space missions.Biopolymers have recently been the most appropriate option for fabricating surgical instruments via 3D printing in terms of cheaper and faster processing.Among all 3D printing techniques,fused deposition modelling(FDM)is a low-cost and more rapid printing technique.This article proposes the fabrication of surgical instruments,namely,forceps and hemostat using the fused deposition modeling(FDM)process.Excellent mechanical properties are the only indicator to judge the quality of the functional parts.The mechanical properties of FDM-processed parts depend on various process parameters.These parameters are layer height,infill pattern,top/bottom pattern,number of top/bottom layers,infill density,flow,number of shells,printing temperature,build plate temperature,printing speed,and fan speed.Tensile strength and modulus of elasticity are chosen as evaluation indexes to ascertain the mechanical properties of polylactic acid(PLA)parts printed by FDM.The experiments have performed through Taguchi’s L27orthogonal array(OA).Variance analysis(ANOVA)ascertains the significance of the process parameters and their percent contributions to the evaluation indexes.Finally,as a multiobjective optimization technique,grey relational analysis(GRA)obtains an optimal set of FDM process parameters to fabricate the best parts with comprehensive mechanical properties.Scanning electron microscopy(SEM)examines the types of defects and strong bonding between rasters.The proposed research ensures the successful fabrication of functional surgical tools with substantial ultimate tensile strength(42.6 MPa)and modulus of elasticity(3274 MPa).

A novel energetic framework with the combination of 5,6-fused triazolo-triazine and nitropyrazole-tetrazole for energy-stability balanced explosive

In recent years,the introduction of fused rings own high density and low sensitivity has promoted the development of energetic materials.However,the development of energetic compounds containing fused and bridged rings by introducing multiple nitrogen heterocycles at different sites of fused rings is still difficult to progress,which seriously limits the emergence of advanced energetic compounds.In this study,a series of energetic materials choosing different nitrogen rich heterocycles at the vacancies of the fused ring,i.e.,neutral compound 5,6 and their ionic derivatives(compounds 7-12)were designed and synthesized.Compounds 5 and 6 were further confirmed by single crystal X-ray diffraction,while the crystal analysis and theoretical calculations were carried out to explore the relationship between crystal structure and physicochemical properties.All of the newly synthesized compounds(5-12)are insensitive to mechanical stimulation(IS>40 J;FS≥342 N)and they own the high detonation velocity(D:8322-9075 m/s).Notably,hydrazine salt 11 own the higher detonation velocity(9075 m/s)and powder density(1.83 g/cm^(3)),but exhibits lower sensitivity(IS>40 J)than the classical energetic compound RDX(8795 m/s,1.80 g/cm^(3),7.5 J).It is obvious that the combination of 5,6-fused triazolo-triazine and nitropyrazole-tetrazole may be a new energetic skeleton for synthesising the heterocyclic compounds with balanced energy-stability.

A Hybrid Deep Fused Learning Approach to Segregate Infectious Diseases

Humankind is facing another deadliest pandemic of all times in history,caused by COVID-19.Apart from this challenging pandemic,World Health Organization(WHO)considers tuberculosis(TB)as a preeminent infectious disease due to its high infection rate.Generally,both TB and COVID-19 severely affect the lungs,thus hardening the job of medical practitioners who can often misidentify these diseases in the current situation.Therefore,the time of need calls for an immediate and meticulous automatic diagnostic tool that can accurately discriminate both diseases.As one of the preliminary smart health systems that examine three clinical states(COVID-19,TB,and normal cases),this study proposes an amalgam of image filtering,data-augmentation technique,transfer learning-based approach,and advanced deep-learning classifiers to effectively segregate these diseases.It first employed a generative adversarial network(GAN)and Crimmins speckle removal filter on X-ray images to overcome the issue of limited data and noise.Each pre-processed image is then converted into red,green,and blue(RGB)and Commission Internationale de l’Elcairage(CIE)color spaces from which deep fused features are formed by extracting relevant features using DenseNet121 and ResNet50.Each feature extractor extracts 1000 most useful features which are then fused and finally fed to two variants of recurrent neural network(RNN)classifiers for precise discrimination of threeclinical states.Comparative analysis showed that the proposed Bi-directional long-short-term-memory(Bi-LSTM)model dominated the long-short-termmemory(LSTM)network by attaining an overall accuracy of 98.22%for the three-class classification task,whereas LSTM hardly achieved 94.22%accuracy on the test dataset.

Modelling a Fused Deep Network Model for Pneumonia Prediction

Deep Learning(DL)is known for its golden standard computing paradigm in the learning community.However,it turns out to be an extensively utilized computing approach in the ML field.Therefore,attaining superior outcomes over cognitive tasks based on human performance.The primary benefit of DL is its competency in learning massive data.The DL-based technologies have grown faster and are widely adopted to handle the conventional approaches resourcefully.Specifically,various DL approaches outperform the conventional ML approaches in real-time applications.Indeed,various research works are reviewed to understand the significance of the individual DL models and some computational complexity is observed.This may be due to the broader expertise and knowledge required for handling these models during the prediction process.This research proposes a holistic approach for pneumonia prediction and offers a more appropriate DL model for classification purposes.This work incorporates a novel fused Squeeze and Excitation(SE)block with the ResNet model for pneumonia prediction and better accuracy.The expected model reduces the human effort during the prediction process and makes it easier to diagnose it intelligently as the feature learning is adaptive.The experimentation is carried out in Keras,and the model’s superiority is compared with various advanced approaches.The proposed model gives 90%prediction accuracy,93%precision,90%recall and 89%F1-measure.The proposed model shows a better trade-off compared to other approaches.The evaluation is done with the existing standard ResNet model,GoogleNet+ResNet+DenseNet,and different variants of ResNet models.

Effect of Fibre Size on Mechanical Properties and Surface Roughness of PLA Composites by Using Fused Deposition Modelling (FDM)

Naturalfibre as a reinforcing agent has been widely used in many industrial applications.Nevertheless,several factors need to be considered,such as the size and weight percentage of thefibre used in binding.Using fused deposition modelling(FDM),this factor was investigated by varying the size of naturalfibre as the responding variable with afixed weight percentage of kenaffibre.The process of modifying the naturalfibre in terms of size might increase the dispersion of kenaffibre in the polymer matrix and increase the adhesion bonding between thefibre and matrix of composites,subsequently improving the interfacial bonding between these two phases.In this paper,the effect offibre size was evaluated by performing the mechanical test,Scanning Electron Micrograph(SEM)to observe the morphology of the composites,and also by surface analysis.The surface roughness was visualised using a 3D profilometer and thefigure was illustrated as colour shading in the image.The composite withfibre size≤100μm displayed better tensile andflexural strength,compared to other sizes.In conclusion,by reducing the size of thefibre,the composites could develop high strength performance for industrial applications.

Development and Evaluation of Recycled Polypropylene and Bean Pod Powder Composite Biomaterial for Fused Filament Fabrication

Approximately 450 million tons of plastic and agricultural waste are produced each year in the world. Only a small portion of this plastic waste is recycled, and a small portion of this agricultural waste is used as fuel or fertilizer, and the rest of this waste is left in the environment or is burned, resulting in environmental and air pollution. For proper disposal, plastic and agricultural waste can be used in the manufacture of composites as raw materials. In this study, we had evaluated the use of bean pod powder (BPp) was used as natural reinforcing filler in recycled polypropylene (rPP) based composites. BPp/rPP composite filaments were developed using the extrusion method and the samples were printed by Fused Filament Fabrication (FFF). Composites with rPP matrix containing different weight fractions of BPp (5%, 10% and 15%) were fabricated to observe and compare the mechanical properties (tensile, flexural, and compressive strength) of the filament composites. In addition, the filament surface was analyzed for roughness and particle size of bean pod powder. The results established that BPp/rPP composites exhibited better tensile, flexural, and compressive strength than rPP and pure PP. By adding 5 wt% BPp, the tensile strength of rPP increased from 20.4 MPa to 22.8 MPa. The highest flexural strength (15.05 MPa) was obtained at 5 wt% BPp among all composites and the highest compressive strength (24.5 MPa), was obtained at 10 wt% BPp. Therefore, it can be concluded that by carefully selecting the ratio of BPp to bean pod powder, it is therefore possible to positively influence the mechanical properties of the resulting composite.

浅析计算机专业人才培养模式与创新创业教育融合

加强高校计算机专业人才培养模式改革,以应对快速变化的科技环境并推动社会进步。计算机专业人才培养模式与创新创业教育相融合,包括实践导向、多学科交叉合作、创业意识与商业技能培养。实践导向强调提供实际机会和项目,培养解决问题的能力和创新能力。与其他领域的合作可以拓宽学生的知识视野和培养跨领域合作能力。创业意识和商业技能培养方面,着重于市场分析培训、商业模式设计学习和财务规划训练。这种融合不仅有利于计算机专业人才掌握技术知识,还培养了其创新创业素养和能力,为科技行业的发展和创新作出贡献。

Characterization of 3D Printed Poly(3-Hydroxybutyric-Co-3-Hydroxyvalerate) by Fused Granular Fabrication through Thermal and Mechanical Analyses

Poly[R-3-hydroxybutyrate-co-(R-3-hydroxyvalerate)] (PHBVs) copolymers are promising biopolymers, which could substitute petroleum-based plastics for various applications. PHB and PHBV pellets were processed on a customized 3D printer via Fused Granular Manufacturing (FGM) approach modified with a Mahor screw extruder. To anticipate the behaviour of PHBVs when transformed using conventional thermo-mechanical shaping processes, thermal and mechanical analyses were carried out in order to better understand the effect of annealing temperature on their crystallization behaviour and mechanical properties of PHB polymer and PHBV copolymer. The objectives of the present work were to propose an experimental strategy to study the melting and crystallization events, crystalline structure changes, and mechanical performances of both PHB homopolymer and PHBV copolymer according to identical thermal annealing treatments. A monitoring of 3D printed PHB and PHBV structures was achieved by coupling Differential Scanning Calorimetry (DSC) and tensile tests. .

配电网低频非线性振荡引发PT保险熔断的机理及抑制措施

电缆的接入使配电网中性点不接地系统对地电容电流增大,产生由单相接地故障引发的低频非线性振荡,导致电磁式电压互感器(potential transformer,PT)保险熔断,严重影响系统安全运行。首先,分析了低频非线性振荡导致PT保险熔断的原理,并基于ATP⁃EMTP平台对10 kV中性点不接地系统进行了仿真计算,计算表明,低频非线性振荡最大过电压小于2.0 p.u.,故障0.1 s后电流接近熔断器熔断电流,系统对地电容增大、零序电阻减小,保险熔断风险增大;然后,仿真验证了4PT接线和PT高压侧中性点串接压敏电阻抑制措施的有效性,并计算了零序电压测量误差;最后,建立10 kV系统的低频非线性振荡试验平台,验证仿真的有效性,对提高配电网PT运行安全性具有参考价值。

企业重罪合规改革的理论基础与制度建构

企业重罪合规改革在刑法上面临的主要争议是正当化事由尚不明晰,以及是否违反罪刑法定、罪责刑相适应等刑法的基本原则。应当肯定的是,企业重罪合规改革具有充分的理论基础与重要的现实意义。在改革过程中,重罪合规应当划分为事前重罪合规与事后重罪合规两个方向。在组织体责任模式下,企业在事前制定有效的合规计划,并有效落实合规计划可以排除单位责任;企业事后合规义务的履行可以熔断企业刑事责任,进而带来刑罚上的从宽处罚,并解决罪责刑不相适应的问题。为了能够使企业合规改革在遵循罪刑法定原则的前提下得到充分落实,立法机关应当对《刑法》第30条、第31条以及分则有关条款进行修正,并由司法机关制定关于企业合规有效性的司法解释,以确保其在实践中的应用。

Optimization of Extrusion Process Parameters of Recycled High-Density Polyethylene-Thermoplastic Starch Composite for Fused Filament Fabrication

High-density poly-ethylene (HDPE) is a nonbiodegradable recyclable plastic which is widely utilized in single use packaging applications. Consequently, it constitutes a significant amount of plastic waste found in landfills. From literature, it has been shown that parts produced using composites of HDPE with carbohydrate-based polymers, such as thermoplastic starch (TPS), experience mechanical degradation through hydrolytic degradation process. The possible utilization of recycled-HDPE (rHDPE) and TPS composite in nonconventional manufacturing processes such as Fused filament fabrication (FFF) has however not been explored. This study explores the potential application of rHDPE and TPS composites in FFF and optimizes the extrusion process parameters used in rHDPE-TPS filament production process. Taguchi method was utilized to analyze the extrusion process. The extrusion process parameters studied were the spooling speed, extrusion speed and the extrusion temperatures. The response variable studied was the filament diameter. In this research, the maximum TPS content achieved during filament production was 40 wt%. This filament was however challenging to use in FFF printers due to frequent nozzle clogging. Printing was therefore done with filaments that contained 0 - 30 wt% TPS. The experimental results showed that the most significant parameter in extrusion process was the spooling speed, followed by extrusion speed. Extrusion temperature had the least significant influence on the filament diameter. It was observed that increase in TPS content resulted in reduced warping and increased rate of hydrolytic degradation. Mechanical properties of printed parts were investigated and the results showed that increasing TPS content resulted in reduction in tensile strength, reduction in compression strength and increase in stiffness. The findings of this research provide valuable insights to plastic recycling industries and researchers regarding the utilization of recycled HDPE and TPS composites as substitute materials in FFF.

基于机器学习耦合模型预测FDM零件的表面粗糙度

熔融沉积工艺(FDM)制造的零件表面粗糙度高,不仅影响了零件外观,还降低了性能。采用响应面实验设计,研究了层高(A)、填充密度(B)、喷嘴温度(C)、床层温度(D)和打印速度(E)对聚乳酸(PLA)零件表面粗糙度的影响。同时,将遗传算法(GA)与决策树(DT)、人工神经元网络(ANN)两种机器学习模型相结合,预测了零件的表面粗糙度。结果表明,A、B、C和E是显著影响零件表面粗糙度的主效应,A×B、A×C、A×E、B×C、B×E、C×E是影响显著的交互效应。GA+DT耦合模型预测PLA零件表面粗糙度的准确性更高,预测值与实验值的相关系数(R2)、均方误差(MSE)和平均绝对误差(MAE)分别为0.952、0.132和0.234,优于GA+ANN的0.823、1.561和1.759。GA+DT模型的预测值与实验值的Pearson相关系数为0.984,而GA+ANN模型仅为0.903,这表明GA+DT模型在预测PLA零件表面粗糙度时准确度更高。