Search results

In plastic reconstruction surgeries, total auricular reconstruction for microtia is a real challenge. Presently, autogenous costal cartilage and MEDPOR are the chosen materials but none can satisfy the requirements of orthopaedic operation. The purpose of this paper is to examine how to fabricate an ear scaffold with a good shape.

A new approach to form the auricle framework is described. CT scan data of the patient's contralateral “good ear” are used to generate a 3D reconstruction model of the new ear. This model is then imported into rapid prototyping (RP) software to slice. The sliced data drive the fused deposition modeling (FDM) machine to build the ear framework layer by layer. Based on the actual shape of the computer model, FDM technology produces a real feel ear framework to match the size of the opposite good ear.

An artificial human ear was built using FDM technology based on CT images. The auricular framework with polyurethane was a porous structure with good flexibility and biocompatibility. After implanting into the mouse, a real life human ear appeared on the back of the mouse. The experiment indicated that this method provided an efficient way to macrotia reconstruction.

The freeform fabrication technique combined with CT image reconstruction could provide an efficient way to produce a porous structure and solve the framework carving problem in microtia reconstruction.

The purpose of this paper is to explore the possibility of an enhanced continuous liquid interface production (CLIP) with a porous track-etched membrane as the oxygen-permeable window, which is prepared by irradiating polyethylene terephthalate membranes with accelerated heavy ions.

Experimental approaches are carried out to characterize printing parameters of resins with different photo-initiator concentrations by a photo-polymerization matrix, to experimentally observe and theoretically fit the oxygen inhibition layer thickness during printing under conditions of pure oxygen and air, respectively, and to demonstrate the enhanced CLIP processes by using pure oxygen and air, respectively.

Owing to the high permeability of track-etched membrane, CLIP process is demonstrated with printing speed up to 800 mm/h in the condition of pure oxygen, which matches well with the theoretically predicted maximum printing speed at difference light expose. Making a trade-off between printing speed and surface quality, maximum printing speed of 470 mm/h is also obtained even using air. As the oxygen inhibition layer created by air is thinner than that by pure oxygen, maximum speed cannot be simply increased by intensifying the light exposure as the case with pure oxygen.

CLIP process is capable of building objects continuously instead of the traditional layer-by-layer manner, which enables tens of times improvement in printing speed. This work presents an enhanced CLIP process by first using a porous track-etched membrane to serve as the oxygen permeable window, in which a record printing speed up to 800 mm/h using pure oxygen is demonstrated. Owing to the high permeability of track-etched membrane, continuous process at a speed of 470 mm/h is also achieved even using air instead of pure oxygen, which is of significance for a compact robust high-speed 3D printer.

The purpose of this paper is to examine the macroscopic and microscopic fields of fiber suspensions in the non‐isothermal situations, also to examine the effect of fiber on this non‐isothermal system.

Control equations are coupled and simultaneously solved by collocated finite volume method on fully triangular meshes.

Temperature dependence and wall temperature have significant effect on both macroscopic and microscopic fields of fiber suspensions. Moreover, the influence of fiber on the non‐isothermal system is similar to that of the isothermal system.

This is the first time that the microstructures of both molecules and fibers are presented in the non‐isothermal condition and it is hoped that the results will provide more insight into the microscopics of complex flows.

Additive manufacturing (AM), a method used in the nuclear, space and racing industries, allows the creation of customized titanium alloy scaffolds with highly defined external shape and internal structure using rapid prototyping as supporting external structures within which bone tissue can grow. AM allows porous tantalum parts with mechanical properties close to that of bone tissue to be obtained.

In this paper, porous tantalum structures with different scan distance were fabricated by AM using laser multi-layer micro-cladding.

Porous tantalum samples were tested for resistance to compressive force and used scanning electron microscope to reveal the morphology of before and after compressive tests. Their structure and mechanical properties of these porous Ta structures with porosity in the range of 35.48 to 50 per cent were investigated. The porous tantalum structures have comparable compressive strength 56 ∼ 480 MPa, and elastic modulus 2.8 ∼ 9.0GPa, which is very close to those of human spongy bone and compact bone.

This paper does not demonstrate the implant results.

It can be used as implant material for the repair bone.

It can be used for fabrication of other porous materials.

This paper system researched the scan distance on how to influence the mechanical properties of fabricated porous tantalum structures.

As a major agricultural province in central China, Hubei Province mainly carries out the peasant household credit investigation system through rural credit cooperatives (RCCs). The purpose of this paper is to evaluate the efficiency of the peasant household credit investigation system in Hubei RCCs and provide some evidence and ideas to the formulation of relevant policies.

First, this paper briefly reviews the related literature of the efficiency in credit investigation system; second, the paper gives a brief description of the data envelopment analysis (DEA) model and designs the indicators for efficiency evaluation; third, the paper accounts data sources and processing methods; and finally, the paper performs the empirical analysis and draws a conclusion.

The paper finds that the efficiency of the resource allocation in both regions is unoptimistic, the general efficiency is somewhat low, and it shows the trend of declination. The efficiency of pure technology in two regions represents the adverse trend. Every year's average scale efficiency in both regions is higher than the pure technology efficiency.

The main contributions of this paper include the first use of DEA model to practically evaluate the efficiency of credit investigation system based on 54 samples of RCCs in Hubei Province and the horizontal and vertical comparisons of the results. The conclusions of this paper not only make the efficiency of credit investigation system in the province's 54 credit cooperatives comparable but also has a great application value to the actual decision‐making departments in formulating credit policies, and each credit cooperative in further building the credit investigation system according to its own conditions. Moreover, it has certain reference value to other similar studies.

Predicting highly cited papers can enable an evaluation of the potential of papers and the early detection and determination of academic achievement value. However, most highly cited paper prediction studies consider early citation information, so predicting highly cited papers by publication is challenging. Therefore, the authors propose a method for predicting early highly cited papers based on their own features.

This research analyzed academic papers published in the Journal of the Association for Computing Machinery (ACM) from 2000 to 2013. Five types of features were extracted: paper features, journal features, author features, reference features and semantic features. Subsequently, the authors applied a deep neural network (DNN), support vector machine (SVM), decision tree (DT) and logistic regression (LGR), and they predicted highly cited papers 1–3 years after publication.

Experimental results showed that early highly cited academic papers are predictable when they are first published. The authors’ prediction models showed considerable performance. This study further confirmed that the features of references and authors play an important role in predicting early highly cited papers. In addition, the proportion of high-quality journal references has a more significant impact on prediction.

Based on the available information at the time of publication, this study proposed an effective early highly cited paper prediction model. This study facilitates the early discovery and realization of the value of scientific and technological achievements.

The purpose of this paper is to investigate attributes that influence Airbnb customer experience by analysing online reviews from users staying in London. It presents a text mining approach to identify a set of broad themes from the textual reviews. It aims to highlight the customers’ changing perception of good quality of accommodations.

This paper analyses 169,666 reviews posted by Airbnb users who stayed in London from 2011 to 2015. Hierarchical clustering algorithms are used to group similar words into clusters based on their co-occurrence. Longitudinal analysis and seasonal analysis are conducted for a more coherent understanding of the Airbnb customer behaviour.

This paper provides empirical insights about how Airbnb users’ mindset of good quality of accommodations changes over a five-year timespan and in different seasons. While there are common attributes considered important throughout the years, exclusive attributes are discovered in particular years and seasons.

This paper is confined to Airbnb experiences in London. Researchers are encouraged to apply the proposed methodology to investigate Airbnb experiences in other cities and detect any change in customer perception of quality stay.

This paper offers implications for the prioritisation of customer concerns to design and improve services offerings and for alignment of services with customer expectations in the sharing economy.

This paper fulfils an identified need to examine the change in customer expectation across the timespan and seasons in the case of Airbnb. It also contributes by illustrating how big data can be used to uncover key attributes that facilitate the engagement with the sharing economy.

The purpose of this article is to understand the current research status and future development trends in the field of numerical simulation on rock mass grouting.

This article first searched the literature database (EI, Web of Science, CNKI, etc.) for keywords related to the numerical simulation of rock mass grouting to obtain the initial literature database. Then, from the initial database, several documents with strong relevance to the numerical simulation theme of rock mass grouting and high citation rate were selected; some documents from the references were selected as supplements, forming the sample database of this review study (a total of 90 articles). Finally, through sorting out the relationship among the literature, this literature review was carried out.

The numerical simulation of rock mass grouting is mainly based on the porous media model and the fractured media model. It has experienced the development process from Newtonian fluid to non-Newtonian fluid, from time-invariant viscosity to time-varying viscosity, and from generalized theoretical model to engineering application model. Based on this, this article summarizes four scientific problems that need to be solved in the future in this research field: the law of grout distribution at the cross fissures, the grout diffusion mechanism under multi-field coupling, more accurate grouting theoretical model and simulation technology with strong engineering applicability.

This research systematically analyzes the current research status and shortcomings of numerical simulation on rock mass grouting, summarizes four key issues in the future development of this research field and provides new ideas for the future research on numerical simulation on rock mass grouting.

Saturated core type superconducting fault current limiter (SFCL) can effectively limit the short-circuit current in power system. However, the high induced voltage will occur between the terminals of DC superconducting bias winding caused by the variation of magnetic flux linked by DC winding due to the increasing short-circuit current. The DC source may be damaged. Thus, the induced voltage should be considered in DC winding design. The paper aims to discuss these issues.

Three-dimensional finite element method coupled with electric circuit.

The short-circuit current flowing through AC windings and induced voltage of DC winding are analyzed by using three-dimensional finite element method coupled with electric circuit for a 220-kV three-phase SFCL. Several circuit elements, such as a capacitor connected with DC winding in parallel, an additional short-circuit winding wound around DC core column and an energy-released piezoresistor, are, respectively, used for induced voltage reduction. These methods aim to save magnetic coupled energy in DC winding, or oppose the variation of magnetic flux, or limit the voltage of DC winding by using a resistor with low resistance.

The different methods for reduction of induced voltage of superconducting DC winding are studied and discussed. The decreased induced voltage may benefit the safety of superconducting DC winding and the source.

In any organization there are main goals, with lots of projects designed to achieve these goals. It is important for any organization to determine how much these projects affect the achievement of these goals. The purpose of this paper is to develop a fuzzy multiple attribute-based group decision-support system (FMAGDSS) to evaluate projects’ performance in promoting the organization's goals utilizing simple additive weighting (SAW) algorithm and technique for order of preference by similarity to ideal solution (TOPSIS) algorithm. The proposed FMAGDSS deals with choosing the most appropriate fuzzy ranking algorithm for solving a given fuzzy multi attribute decision making (FMADM) problem with both qualitative and quantitative criteria (attributes), and uncertain judgments of decision makers.

In this paper, a FMAGDSS model is designed to determine scores and ranks of every project in promoting the organization's goals. In the first step of FMAGDSS model, all projects are assessed by experts based on evaluation criteria and the organization's goals. The proposed FMAGDSS model will then choose the most appropriate fuzzy ranking method to solve the given FMADM problem. Finally, a sensitivity analysis system is developed to assess the reliability of the decision-making process and provide an opportunity to analyze the impacts of “criteria weights” and “projects” performance’ on evaluating projects in achieving the organizations’ goals, and to assess the reliability of the decision-making process. In addition, a software prototype has been developed on the basis of FMAGDSS model that can be applied to solve every FMADM problem that needs to rank alternatives according to certain attributes.

The result of this study simplifies and accelerates the evaluation process. The proposed system not only helps organizations to choose the most efficient projects for sustainable development, but also helps them to assess the reliability of the decision-making process, and decrease the uncertainty in final decision caused by uncertain judgment of decision makers.

Future studies are suggested to expand this system to evaluate and rank the project proposals. To achieve this goal, the efficiency of the projects in line with organization's goals, should be predicted.

This study contributes to the relevant literature by proposing a FMAGDSS model to evaluate projects in promoting organization's goals. The proposed FMAGDSS has ability to choose the most appropriate fuzzy ranking algorithm to solve a given FMADM problem based on the type and the number of attributes and alternatives, considering the least computation and time consumption for ranking alternatives.