基于Pro/Engineering的特征映射器的开发

针对特征映射方法的不足，提出了分层、分类实施特征映射的策略。以此为基础，运用Ｐｒｏ／Ｄｅｖｅｌｏｐ为工具，在Ｐｒｏ／Ｅｎｇｉｎｅｅｒｉｎｇ软件平台上开发一个特征映射器，该特征映射器可以实现零件的形状特征、尺寸特征、几何公差特征由设计域向制造域的映射，能够满足ＣＡＤ／ＣＡＰＰ集成的信息需求。

Pro/Engineering软件在“工程制图”课程教学中的应用

空间思维能力的培养是学生学习"工程制图"课程成败的关键。将三维设计软件Pro/Engineering引入"工程制图"课程教学中,可以帮助学生认识立体的构成及特点,认识视图与立体的关系,提高学生的空间想象力。教学实践证明,将Pro/Engineering三维设计软件引入到"工程制图"课程的教学中,可以提高教学质量,是切实可行的教学辅助手段。

“Pro/Engineering建模”技巧在工业设计中的作用

在工业设计行业和设计教育机构中 ,Pro/EN GINEER( Pro/E)软件的使用日益普及。 Pro/E的造型建模功能 ,影响设计师的“设计思维”,Pro/E建模在“设计思维”中的作用也显得日益重要 ,Pro/E建模在“设计思维”

Biomaterials and tissue engineering in traumatic brain injury:novel perspectives on promoting neural regeneration

Traumatic brain injury is a serious medical condition that can be attributed to falls, motor vehicle accidents, sports injuries and acts of violence, causing a series of neural injuries and neuropsychiatric symptoms. However, limited accessibility to the injury sites, complicated histological and anatomical structure, intricate cellular and extracellular milieu, lack of regenerative capacity in the native cells, vast variety of damage routes, and the insufficient time available for treatment have restricted the widespread application of several therapeutic methods in cases of central nervous system injury. Tissue engineering and regenerative medicine have emerged as innovative approaches in the field of nerve regeneration. By combining biomaterials, stem cells, and growth factors, these approaches have provided a platform for developing effective treatments for neural injuries, which can offer the potential to restore neural function, improve patient outcomes, and reduce the need for drugs and invasive surgical procedures. Biomaterials have shown advantages in promoting neural development, inhibiting glial scar formation, and providing a suitable biomimetic neural microenvironment, which makes their application promising in the field of neural regeneration. For instance, bioactive scaffolds loaded with stem cells can provide a biocompatible and biodegradable milieu. Furthermore, stem cells-derived exosomes combine the advantages of stem cells, avoid the risk of immune rejection, cooperate with biomaterials to enhance their biological functions, and exert stable functions, thereby inducing angiogenesis and neural regeneration in patients with traumatic brain injury and promoting the recovery of brain function. Unfortunately, biomaterials have shown positive effects in the laboratory, but when similar materials are used in clinical studies of human central nervous system regeneration, their efficacy is unsatisfactory. Here, we review the characteristics and properties of various bioactive materials, followed by the introduction of applications based on biochemistry and cell molecules, and discuss the emerging role of biomaterials in promoting neural regeneration. Further, we summarize the adaptive biomaterials infused with exosomes produced from stem cells and stem cells themselves for the treatment of traumatic brain injury. Finally, we present the main limitations of biomaterials for the treatment of traumatic brain injury and offer insights into their future potential.

Synthesis of Reviews on Auscultation, Approaches, and Methods for Engineering Structures

Topometric auscultation is used to monitor the durability of structures, measure deformations linked to the structure of a structure or to the movement of the ground over a part of the globe, set up warning systems, etc. It first appeared as a visual method and rapidly evolved through the various techniques used. Some of these techniques using topography are used in several fields (civil engineering, geodesy, topography, mechanics, nuclear engineering, hydraulics, physics, etc.). These topometric techniques have undergone major changes as a result of technological advances, growing needs in the monitoring of movements or deformations, increased requirements and new challenges. The methodology adopted depends on the measuring instrument used, the parameters to be estimated and access to the area to be measured. There are two types of methods: destructive and non-destructive. In addition to the visual method, they can also be classified as mechanical, physico-chemical, dynamometric, electrophysical and geometric. The estimated parameter varies according to the methodology adopted. It can be defined by coordinates, distances, potential, electrical resistance, etc.

Tissue engineering approaches for dental pulp regeneration:The development of novel bioactive materials using pharmacological epigenetic inhibitors

The drive for minimally invasive endodontic treatment strategies has shifted focus from technically complex and destructive root canal treatments towards more conservative vital pulp treatment.However,novel approaches to maintaining dental pulp vitality after disease or trauma will require the development of innovative,biologicallydriven regenerative medicine strategies.For example,cell-homing and cell-based therapies have recently been developed in vitro and trialled in preclinical models to study dental pulp regeneration.These approaches utilise natural and synthetic scaffolds that can deliver a range of bioactive pharmacological epigenetic modulators(HDACis,DNMTis,and ncRNAs),which are cost-effective and easily applied to stimulate pulp tissue regrowth.Unfortunately,many biological factors hinder the clinical development of regenerative therapies,including a lack of blood supply and poor infection control in the necrotic root canal system.Additional challenges include a need for clinically relevant models and manufacturing challenges such as scalability,cost concerns,and regulatory issues.This review will describe the current state of bioactive-biomaterial/scaffold-based engineering strategies to stimulate dentine-pulp regeneration,explicitly focusing on epigenetic modulators and therapeutic pharmacological inhibition.It will highlight the components of dental pulp regenerative approaches,describe their current limitations,and offer suggestions for the effective translation of novel epigenetic-laden bioactive materials for innovative therapeutics.

How can we improve the stability of organic solar cells from materials design to device engineering?

Among a promising photovoltaic technology for solar energy conversion,organic solar cells(OSCs)have been paid much attention,of which the power conversion efficiencies(PCEs)have rapidly surpassed over 20%,approaching the threshold for potential applications.However,the device stability of OSCs including storage stability,photostability and thermal stability,remains to be an enormous challenge when faced with practical applications.The major causes of device instability are rooted in the poor inherent properties of light-harvesting materials,metastable mor-phology,interfacial reactions and highly sensitive to external stresses.To get rid of theseflaws,a comprehensive review is provided about recent strategies and meth-ods for improving the device stability from active layers,interfacial layers,device engineering and encapsulation techniques for high-performance OSC devices.In the end,prospectives for the next stage development of high-performance devices with satisfactory long-term stability are afforded for the solar community.

Constructing a biofunctionalized 3D-printed gelatin/sodium alginate/chitosan tri-polymer complex scaffold with improvised biological andmechanical properties for bone-tissue engineering

Sodium alginate(SA)/chitosan(CH)polyelectrolyte scaffold is a suitable substrate for tissue-engineering application.The present study deals with further improvement in the tensile strength and biological properties of this type of scaffold to make it a potential template for bone-tissue regeneration.We experimented with adding 0%–15%(volume fraction)gelatin(GE),a protein-based biopolymer known to promote cell adhesion,proliferation,and differentiation.The resulting tri-polymer complex was used as bioink to fabricate SA/CH/GEmatrices by three-dimensional(3D)printing.Morphological studies using scanning electron microscopy revealed the microfibrous porous architecture of all the structures,which had a pore size range of 383–419μm.X-ray diffraction and Fourier-transform infrared spectroscopy analyses revealed the amorphous nature of the scaffold and the strong electrostatic interactions among the functional groups of the polymers,thereby forming polyelectrolyte complexes which were found to improve mechanical properties and structural stability.The scaffolds exhibited a desirable degradation rate,controlled swelling,and hydrophilic characteristics which are favorable for bone-tissue engineering.The tensile strength improved from(386±15)to(693±15)kPa due to the increased stiffness of SA/CH scaffolds upon addition of gelatin.The enhanced protein adsorption and in vitro bioactivity(forming an apatite layer)confirmed the ability of the SA/CH/GE scaffold to offer higher cellular adhesion and a bone-like environment to cells during the process of tissue regeneration.In vitro biological evaluation including the MTT assay,confocal microscopy analysis,and alizarin red S assay showed a significant increase in cell attachment,cell viability,and cell proliferation,which further improved biomineralization over the scaffold surface.In addition,SA/CH containing 15%gelatin designated as SA/CH/GE15 showed superior performance to the other fabricated 3D structures,demonstrating its potential for use in bone-tissue engineering.

In vitro investigations on the effects of graphene and graphene oxide on polycaprolactone bone tissue engineering scaffolds

Polycaprolactone(PCL)scaffolds that are produced through additive manufacturing are one of the most researched bone tissue engineering structures in the field.Due to the intrinsic limitations of PCL,carbon nanomaterials are often investigated to reinforce the PCL scaffolds.Despite several studies that have been conducted on carbon nanomaterials,such as graphene(G)and graphene oxide(GO),certain challenges remain in terms of the precise design of the biological and nonbiological properties of the scaffolds.This paper addresses this limitation by investigating both the nonbiological(element composition,surface,degradation,and thermal and mechanical properties)and biological characteristics of carbon nanomaterial-reinforced PCL scaffolds for bone tissue engineering applications.Results showed that the incorporation of G and GO increased surface properties(reduced modulus and wettability),material crystallinity,crystallization temperature,and degradation rate.However,the variations in compressive modulus,strength,surface hardness,and cell metabolic activity strongly depended on the type of reinforcement.Finally,a series of phenomenological models were developed based on experimental results to describe the variations of scaffold’s weight,fiber diameter,porosity,and mechanical properties as functions of degradation time and carbon nanomaterial concentrations.The results presented in this paper enable the design of three-dimensional(3D)bone scaffolds with tuned properties by adjusting the type and concentration of different functional fillers.

Metabolic engineering and genome editing strategies for enhanced lipid production in microalgae

Depleting global petroleum reserves and skyrocketing prices coupled with succinct supply have been a grave concern,which needs alternative sources to conventional fuels.Oleaginous microalgae have been explored for enhanced lipid production,leading towards biodiesel production.These microalgae have short life cycles,require less labor,and space,and are easy to scale up.Triacylglycerol,the primary source of lipids needed to produce biodiesel,is accumulated by most microalgae.The article focuses on different types of oleaginous microalgae,which can be used as a feedstock to produce biodiesel.Lipid biosynthesis in microalgae occurs through fatty acid synthesis and TAG synthesis approaches.In-depth discussions are held regarding other efficient methods for enhancing fatty acid and TAG synthesis,regulating TAG biosynthesis bypass methods,blocking competing pathways,multigene approach,and genome editing.The most potential targets for gene transformation are hypothesized to be a malic enzyme and diacylglycerol acyltransferase while lowering phosphoenolpyruvate carboxylase activity is reported to be advantageous for lipid synthesis.

Engineering electrolyte additives for stable zinc-based aqueous batteries:Insights and prospects

Zn-based aqueous batteries(ZABs) are gaining widespread popularity due to their low cost and high safety profile. However, the application of ZABs faces significant challenges, such as dendrite growth and parasitic reactions of metallic Zn anodes. Therefore, achieving high-energy–density ZABs necessitates addressing the fundamental thermodynamics and kinetics of Zn anodes. Various strategies are available to mitigate these challenges, with electrolyte additive engineering emerging as one of the most efficient and promising approaches. Despite considerable research in this field, a comprehensive understanding of the intrinsic mechanisms behind the high performance of electrolyte additives remains limited. This review aims to provide a detailed introduction to functional electrolyte additives and thoroughly explore their underlying mechanisms. Additionally, it discusses potential directions and perspectives in additive engineering for ZABs, offering insights into future development and guidelines for achieving high-performance ZABs.

Investigation into the Methodology and Implementation of Life Cycle Engineering under China’s Carbon Reduction Target in the Process Industry

The industrial sector is the primary source of carbon emissions in China.In pursuit of meeting its carbon reduction targets,China aims to promote resource consumption sustainability,reduce energy consumption,and achieve carbon neutrality within its processing industries.An effective strategy to promote energy savings and carbon reduction throughout the life cycle of materials is by applying life cycle engineering technology.This strategy aims to attain an optimal solution for material performance,resource consumption,and environmental impact.In this study,five types of technologies were considered:raw material replacement,process reengineering,fuel replacement,energy recycling and reutilization,and material recycling and reutilization.The meaning,methodology,and development status of life cycle engineering technology abroad and domestically are discussed in detail.A multidimensional analysis of ecological design was conducted from the perspectives of resource and energy consumption,carbon emissions,product performance,and recycling of secondary resources in a manufacturing process.This coupled with an integrated method to analyze carbon emissions in the entire life cycle of a material process industry was applied to the nonferrous industry,as an example.The results provide effective ideas and solutions for achieving low or zero carbon emission production in the Chinese industry as recycled aluminum and primary aluminum based on advanced technologies had reduced resource consumption and emissions as compared to primary aluminum production.

Precursor engineering enables high-performance all-inorganic CsPbIBr_(2) perovskite solar cells with a record efficiency approaching 13%

All-inorganic CsPbIBr_(2) perovskite has attracted widespread attention in photovoltaic and other optoelectronic devices because of its superior thermal stability.However,the deposition of high-quality solutionprocessed CsPbIBr_(2) perovskite films with large thicknesses remains challenging.Here,we develop a triple-component precursor(TCP) by employing lead bromide,lead iodide,and cesium bromide,to replace the most commonly used double-component precursor(DCP) consisting of lead bromide and cesium iodide.Remarkably,the TCP system significantly increases the solution concentration to 1.3 M,leading to a larger film thickness(~390 nm) and enhanced light absorption.The resultant CsPbIBr_(2) films were evaluated in planar n-i-p structured solar cells,which exhibit a considerably higher optimal photocurrent density of 11.50 mA cm^(-2) in comparison to that of DCP-based devices(10.69 mA cm^(-2)).By adopting an organic surface passivator,the maximum device efficiency using TCP is further boosted to a record efficiency of 12.8% for CsPbIBr_(2) perovskite solar cells.

A Comparative Study of Metaheuristic Optimization Algorithms for Solving Real-World Engineering Design Problems

Real-world engineering design problems with complex objective functions under some constraints are relatively difficult problems to solve.Such design problems are widely experienced in many engineering fields,such as industry,automotive,construction,machinery,and interdisciplinary research.However,there are established optimization techniques that have shown effectiveness in addressing these types of issues.This research paper gives a comparative study of the implementation of seventeen new metaheuristic methods in order to optimize twelve distinct engineering design issues.The algorithms used in the study are listed as:transient search optimization(TSO),equilibrium optimizer(EO),grey wolf optimizer(GWO),moth-flame optimization(MFO),whale optimization algorithm(WOA),slimemould algorithm(SMA),harris hawks optimization(HHO),chimp optimization algorithm(COA),coot optimization algorithm(COOT),multi-verse optimization(MVO),arithmetic optimization algorithm(AOA),aquila optimizer(AO),sine cosine algorithm(SCA),smell agent optimization(SAO),and seagull optimization algorithm(SOA),pelican optimization algorithm(POA),and coati optimization algorithm(CA).As far as we know,there is no comparative analysis of recent and popular methods against the concrete conditions of real-world engineering problems.Hence,a remarkable research guideline is presented in the study for researchersworking in the fields of engineering and artificial intelligence,especiallywhen applying the optimization methods that have emerged recently.Future research can rely on this work for a literature search on comparisons of metaheuristic optimization methods in real-world problems under similar conditions.

Evaluations of Chris-Jerry Data Using Generalized Progressive Hybrid Strategy and Its Engineering Applications

A new one-parameter Chris-Jerry distribution,created by mixing exponential and gamma distributions,is discussed in this article in the presence of incomplete lifetime data.We examine a novel generalized progressively hybrid censoring technique that ensures the experiment ends at a predefined period when the model of the test participants has a Chris-Jerry(CJ)distribution.When the indicated censored data is present,Bayes and likelihood estimations are used to explore the CJ parameter and reliability indices,including the hazard rate and reliability functions.We acquire the estimated asymptotic and credible confidence intervals of each unknown quantity.Additionally,via the squared-error loss,the Bayes’estimators are obtained using gamma prior.The Bayes estimators cannot be expressed theoretically since the likelihood density is created in a complex manner;nonetheless,Markov-chain Monte Carlo techniques can be used to evaluate them.The effectiveness of the investigated estimations is assessed,and some recommendations are given using Monte Carlo results.Ultimately,an analysis of two engineering applications,such as mechanical equipment and ball bearing data sets,shows the applicability of the proposed approaches that may be used in real-world settings.

Developing Lexicons for Enhanced Sentiment Analysis in Software Engineering:An Innovative Multilingual Approach for Social Media Reviews

Sentiment analysis is becoming increasingly important in today’s digital age, with social media being a significantsource of user-generated content. The development of sentiment lexicons that can support languages other thanEnglish is a challenging task, especially for analyzing sentiment analysis in social media reviews. Most existingsentiment analysis systems focus on English, leaving a significant research gap in other languages due to limitedresources and tools. This research aims to address this gap by building a sentiment lexicon for local languages,which is then used with a machine learning algorithm for efficient sentiment analysis. In the first step, a lexiconis developed that includes five languages: Urdu, Roman Urdu, Pashto, Roman Pashto, and English. The sentimentscores from SentiWordNet are associated with each word in the lexicon to produce an effective sentiment score. Inthe second step, a naive Bayesian algorithm is applied to the developed lexicon for efficient sentiment analysis ofRoman Pashto. Both the sentiment lexicon and sentiment analysis steps were evaluated using information retrievalmetrics, with an accuracy score of 0.89 for the sentiment lexicon and 0.83 for the sentiment analysis. The resultsshowcase the potential for improving software engineering tasks related to user feedback analysis and productdevelopment.

Structural engineering of Fe single-atom oxygen reduction catalyst with high site density and improved mass transfer

Fe-N-C catalysts are widely considered as promising non-precious-metal candidates for electrocatalytic oxygen reduction reaction(ORR),Yet despite their high catalytic activity through rational modulation,challenges remain in their low site density and unsatisfactory mass transfer structure.Herein,we present a structural engineering approach employing a soft-template coating strategy to fabricate a hollow and hierarchically porous N-doped carbon framework anchored with atomically dispersed Fe sites(FeNCh) as an efficient ORR catalyst.The combination of hierarchical porosity and high exterior surface area is proven crucial for exposing more active sites,which gives rise to a remarkable ORR performance with a half-wave potential of 0.902 V in 0.1 m KOH and 0.814 V in 0.1 m HClO_(4),significantly outperforming its counterpart with solid structure and dominance of micropores(FeNC-s).The mass transfer property is revealed by in-situ electrochemical impedance spectroscopy(EIS) measurement.The distribution of relaxation time(DRT) analysis is further introduced to deconvolve the kinetic and mass transport processes,which demonstrates an alleviated mass transport resistance for FeNC-h,validating the effectiveness of structural engineering.This work not only provides an effective structural engineering approach but also contributes to the comprehensive mass transfer evaluation on advanced electrocatalyst for energy conversion applications.

Building Custom Spreadsheet Functions with Python: End-User Software Engineering Approach

End-user computing empowers non-developers to manage data and applications, enhancing collaboration and efficiency. Spreadsheets, a prime example of end-user programming environments widely used in business for data analysis. However, Excel functionalities have limits compared to dedicated programming languages. This paper addresses this gap by proposing a prototype for integrating Python’s capabilities into Excel through on-premises desktop to build custom spreadsheet functions with Python. This approach overcomes potential latency issues associated with cloud-based solutions. This prototype utilizes Excel-DNA and IronPython. Excel-DNA allows creating custom Python functions that seamlessly integrate with Excel’s calculation engine. IronPython enables the execution of these Python (CSFs) directly within Excel. C# and VSTO add-ins form the core components, facilitating communication between Python and Excel. This approach empowers users with a potentially open-ended set of Python (CSFs) for tasks like mathematical calculations, statistical analysis, and even predictive modeling, all within the familiar Excel interface. This prototype demonstrates smooth integration, allowing users to call Python (CSFs) just like standard Excel functions. This research contributes to enhancing spreadsheet capabilities for end-user programmers by leveraging Python’s power within Excel. Future research could explore expanding data analysis capabilities by expanding the (CSFs) functions for complex calculations, statistical analysis, data manipulation, and even external library integration. The possibility of integrating machine learning models through the (CSFs) functions within the familiar Excel environment.

Analysis on the development of chemically-improved soil in railway engineering

Purpose-Explore the development trend of chemically-improved soil in railway engineering.Design/methodology/approach–In this paper,the technical standards home and abroad were analyzed.Laboratory test,field test and monitoring were carried out.Findings–The performance design system of the chemically-improved soil should be established.Originality/value–On the basis of the performance design,the test methods and standards for various properties of chemically-improved soil should be established to evaluate the improvement effect and control the engineering quality.

Accuracy comparison and improvement for state of health estimation of lithium-ion battery based on random partial recharges and feature engineering

State of health(SOH)estimation of e-mobilities operated in real and dynamic conditions is essential and challenging.Most of existing estimations are based on a fixed constant current charging and discharging aging profiles,which overlooked the fact that the charging and discharging profiles are random and not complete in real application.This work investigates the influence of feature engineering on the accuracy of different machine learning(ML)-based SOH estimations acting on different recharging sub-profiles where a realistic battery mission profile is considered.Fifteen features were extracted from the battery partial recharging profiles,considering different factors such as starting voltage values,charge amount,and charging sliding windows.Then,features were selected based on a feature selection pipeline consisting of filtering and supervised ML-based subset selection.Multiple linear regression(MLR),Gaussian process regression(GPR),and support vector regression(SVR)were applied to estimate SOH,and root mean square error(RMSE)was used to evaluate and compare the estimation performance.The results showed that the feature selection pipeline can improve SOH estimation accuracy by 55.05%,2.57%,and 2.82%for MLR,GPR and SVR respectively.It was demonstrated that the estimation based on partial charging profiles with lower starting voltage,large charge,and large sliding window size is more likely to achieve higher accuracy.This work hopes to give some insights into the supervised ML-based feature engineering acting on random partial recharges on SOH estimation performance and tries to fill the gap of effective SOH estimation between theoretical study and real dynamic application.