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Three-dimensional (3D) bioprinting is an advanced technology to fabricate artificial 3D tissue constructs containing cells and hydrogels for tissue engineering and regenerative medicine. Nanocomposite reinforcement endows hydrogels with superior properties and tailored functionalities. A broad range of nanomaterials, including silicon-based, ceramic-based, cellulose-based, metal-based, and carbon-based nanomaterials, have been incorporated into hydrogel networks with encapsulated cells for improved performances. This review emphasizes the recent developments of cell-laden nanocomposite bioinks for 3D bioprinting, focusing on their reinforcement effects and mechanisms, including viscosity, shear-thinning property, printability, mechanical properties, structural integrity, and biocompatibility. The cell-material interactions are discussed to elaborate on the underlying mechanisms between the cells and the nanomaterials. The biomedical applications of cell-laden nanocomposite bioinks are summarized with a focus on bone and cartilage tissue engineering. Finally, the limitations and challenges of current cell-laden nanocomposite bioinks are identified. The prospects are concluded in designing multi-component bioinks with multi-functionality for various biomedical applications. STATEMENT OF SIGNIFICANCE: 3D bioprinting, an emerging technology of additive manufacturing, has been one of the most innovative tools for tissue engineering and regenerative medicine. Recent developments of cell-laden nanocomposite bioinks for 3D bioprinting, and cell-materials interactions are the subject of this review paper. The reinforcement effects and mechanisms of nanocomposites on viscosity, printability and biocompatibility of bioinks and 3D printed scaffolds are addressed mainly for bone and cartilage tissue engineering. It provides detailed information for further designing and optimizing multi-component bioinks with multi-functionality for specialized biomedical applications.

Wound healing is a dynamic process that involves a series of molecular and cellular events aimed at replacing devitalized and missing cellular components and/or tissue layers. Recently, extracellular vesicles (EVs), naturally cell-secreted lipid membrane-bound vesicles laden with biological cargos including proteins, lipids, and nucleic acids, have drawn wide attention due to their ability to promote wound healing and tissue regeneration. However, current exploitation of EVs as therapeutic agents is limited by their low isolation yields and tedious isolation processes. To circumvent these challenges, bioinspired cell-derived nanovesicles (CDNs) that mimic EVs were obtained by shearing mesenchymal stem cells (MSCs) through membranes with different pore sizes. Physical characterisations and high-throughput proteomics confirmed that MSC-CDNs mimicked MSC-EVs. Moreover, these MSC-CDNs were efficiently uptaken by human dermal fibroblasts and demonstrated a dose-dependent activation of MAPK signalling pathway, resulting in enhancement of cell proliferation, cell migration, secretion of growth factors and extracellular matrix proteins, which all promoted tissue regeneration. Of note, MSC-CDNs enhanced angiogenesis in human dermal microvascular endothelial cells in a 3D PEG-fibrin scaffold and animal model, accelerating wound healing in vitro and in vivo. These findings suggest that MSC-CDNs could replace both whole cells and EVs in promoting wound healing and tissue regeneration. Agency for Science, Technology and Research (A*STAR) Ministry of Education (MOE) Nanyang Technological University Published version This work was supported by the National University of Singapore (NanoNash Program A-0004336-00-00 & A-0008504- 00-00, Singapore), and Nanyang Technological University, Singapore (grant number 001487-00001). Giorgia Pastorin. would also like to thank the Industry Alignment FunddPre-Positioning (IAF-PP) grant (A20G1a0046 and R-148-000-307-305/A0004345-00-00). This work was also supported by the Singapore Ministry of Education, under its Singapore Ministry of Education Academic Research Fund Tier 1 (10051 - MOE AcRF Tier 1: Thematic Call 2020) from Bertrand Czarny. Jiong-Wei Wang would like to thank the National University of Singapore NanoNASH Program (NUHSRO/2020/002/NanoNash/LOA) and the National University of Singapore Yong Loo Lin School of Medicine Nanomedicine Translational Research Program (NUHSRO/ 2021/034/TRP/09/Nanomedicine). Authors would also like to thank for the financial supports from Agency for Science, Technology, and Research (A*STAR, Singapore) Advanced Manufacturing and Engineering Individual Research Grant (AME IRG) (Project ID: A1883c0013, Singapore).

Although anatomical hepatectomy (AH) is widely used in the treatment of hepatocellular carcinoma (HCC), the prognosis is still unsatisfactory. The present study aimed to evaluate the survival benefit of adjuvant transcatheter arterial chemoembolization (TACE) for patients with HCC after AH. A total of 832 patients were stratified into with adjuvant TACE (443, 53.2%) and without adjuvant TACE group (389, 46.8%) AH. Propensity score matching (PSM) was performed to control for confounding factors, and multivariable Cox regression was performed to determine the independent risk factors. After PSM, the results showed that the adjuvant TACE group had better overall survival (OS) and recurrence-free survival (RFS). Among the patients with tumor recurrence, adjuvant TACE was associated with a high rate of early-stage tumor at recurrence, a lower recurrence rate around the frontal margin and extrahepatic metastases, and a higher rate of receiving curative treatment. Multivariable Cox regression analysis showed that adjuvant TACE was an independent prognostic factor for OS (HR 0.673, P = 0.001) and RFS (HR 0.650, P = 0.001). Patients with HCC after AH can benefit from postoperative adjuvant TACE. Therefore, adjuvant TACE should be considered for patients with a high risk of recurrence.

Product appearance is one of the crucial factors that influence consumers’ purchase decisions. The attractiveness of product appearance is mainly determined by the inherent aesthetics of the design composition related to the arrangement of visual design elements. Hence, it is critical to study and improve the arrangement of visual design elements for product appearance design. Strategies that apply aesthetic design principles to assist designers in effectively arranging visual design elements are widely acknowledged in both academia and industry. However, applying aesthetic design principles relies heavily on the designer’s perception and experience, while it is rather challenging for novice designers. Meanwhile, it is hard to measure and quantify design aesthetics in designing artefacts when designers refer to existing successful designs. In this regard, this study aims to introduce a method that assists designers in applying aesthetic design principles to improve the attractiveness of product appearance. Furthermore, formulas for aesthetic measurement based on aesthetic design principles are also developed, and it makes an early attempt to provide quantified aesthetic measurements of design artefacts. A case study on camera design was conducted to demonstrate the merits of the proposed method where the improved strategies for the camera appearance design offer insights for concept generation in product appearance design based on aesthetic design principles.

This chapter presents the basic concepts of aesthetic needs and product specifications in conceptual design. The user aesthetics needs are considered one of the significant determinants in increasing user satisfaction. In this regard, the importance of establishing product appearance specifications to identify user aesthetic needs is discussed. A method is introduced to demonstrate the significance of considering aesthetic and emotional needs when establishing product appearance specifications in product conceptual design. To improve appearance specifications based on aesthetic experiences collected from users and designers, an approach using fuzzy logic is proposed and illustrated by a case study of digital camera design.

Energy-efficient manufacturing is playing an important role in addressing worldwide problems like air pollution, climate change, and energy crisis. Despite of the close relationship between process planning and scheduling, existing research efforts often consider energy efficiency-related issues by treating them separately and sequentially. This may cause various problems such as unbalanced resource allocation and infeasible production solutions, thus limiting their potential application. To advance the current state of the art in this area, this paper investigates energy-efficient integration of process planning and scheduling. Specifically, a mixed-integer linear programming model is firstly developed to formulate this problem mathematically. Subsequently, to solve more realistic large-sized problems, a heuristic-based two-stage approach is proposed. Stage 1 and Stage 2 aim at minimizing the total tardiness and reducing the total energy consumption, respectively. A realistic case study shows that the proposed two-stage approach works more efficiently and effectively than a modified genetic algorithm from existing literature. Through instructive modification of the solution space, the proposed approach can effectively reduce the total tardiness and total energy consumption; meanwhile the machining cost of individual process plans can also be kept at low level throughout the searching process. Results show that the proposed approach can allocate the machining resources in a more reasonable manner. As such, this paper may be a valuable supplement to existing efforts aiming at developing energy-efficient manufacturing techniques.

Multiple common databases were integrated to analyze the expression status and prognostic meaning ofThe transcription of

ObjectiveThis study aimed to investigate the role and potential regulatory mechanism of citron kinase (CIT) in esophageal squamous cell carcinoma (ESCC).MethodsCitron kinase (CIT) expression in ESCC tissues was analyzed based on the microarray dataset GSE20347, and CIT expression in ESCC cell lines was analyzed. Eca-109 cells were lentivirally transfected with shRNA-CIT (LV-shCIT) to knock down CIT, followed by investigation of cell proliferation and apoptosis. Nude mouse xenograft experiments were performed to evaluate the tumorigenicity of CIT-knockdown Eca-109 cells. Microarray analysis of Eca-109 cells transfected with LV-shCIT or LV-shNC and subsequent Ingenuity Pathway Analysis (IPA) were performed to identify CIT-related differentially expressed genes (DEGs) and signaling pathways. Furthermore, the expression of key DEGs was validated using the clinical samples of ESCC.ResultsCitron kinase (CIT) was highly expressed in ESCC tissues and cell lines. Knockdown of CIT suppressed Eca-109 cell proliferation and promoted apoptosis in vitro. Moreover, CIT knockdown significantly reduced tumorigenicity of Eca-109 cells in vivo. Microarray and IPA analysis showed that signaling by the Rho family GTPases pathway was significantly activated, and CIT intrinsically interacted with the protein kinase AMP-activated catalytic subunit alpha 1 (PRKAA1), sequestosome 1 (SQSTM1), and interleukin 6 (IL6). Notably, the expression levels of PRKAA1 and SQSTM1 were upregulated in ESCC tissues, while the IL6 expression was downregulated.ConclusionOur findings confirm that CIT functions as an oncogene in ESCC. CIT may contribute to ESCC development by upregulating PRKAA1 and SQSTM1 as well as downregulating IL6. Citron kinase may serve as a promising therapeutic target for ESCC.

Modern manufacturing enterprises are consuming a considerable amount of energy every year. Improving energy efficiency will not only benefit the enterprises economically, but also help the world to overcome various problems such as energy crisis and air pollution. To achieve this, an accurate energy consumption model is essential. The main objective of this paper is to develop an improved cutting power-based energy consumption model for general end milling process. The proposed model consists of an idle part due to auxiliary components and spindle rotation, and an additional part due to cutting workpiece materials. The first part is modelled as a function of spindle rotation speed, and the other part is considered proportional to the cutting power. Experiments under various milling conditions have demonstrated the effectiveness and efficacy of the proposed model. Comparative studies show that the proposed model is more accurate than other models. Although calibrated from slotting experiments when cutting aluminium alloy, the proposed model is applicable for general milling process. Partial-immersion milling experiments show that the prediction error of the proposed model is as low as 1.74%. When workpiece material changes to titanium alloy, its performance remains decent, with low prediction error of 2.81%. This reveals its capability to provide reliable estimation for different workpiece materials. As such, it could help avoid tedious model calibration, thus saving time, material, and energy. Finally, the energy efficiency of general end milling process is investigated through numerical experiments with the proposed model. By revealing the relationship between energy consumption and various cutting parameters, the proposed model could serve as an excellent platform towards energy-efficient manufacturing/cleaner production.

Additively manufactured lattice structures enable the realisation of light-weight, multi-functional, structures. For example, lattices can be used for high stiffness and buckling resistance in sandwich structures or as support material for additive manufacturing. Topology optimisation and additive manufacturing are two technologies that allow the design, optimisation and manufacture of complex lattice designs. In this work, a new lattice optimisation methodology is presented that tailors the size, shape and orientation of individual lattice trusses in three-dimensional space by using principal strain fields obtained from topology optimisation. This new method of generating functionally graded lattices is shown both numerically and experimentally to be capable of generating lattice structures with greatly improved stiffness and strength when compared to lattice structures with a uniform lattice infill. Upper and lower relative density thresholds and minimum truss member sizes are included in the optimisation workflow to ensure that the optimised lattice designs are compatible with additive manufacturing process constraints. The functional grading method is also shown to be capable of generating conformal lattice structures in three dimensions, even for complex loading conditions and arbitrary volume boundaries.

3D printing and particularly fused deposition modelling (FDM) is widely used for prototyping and fabricating low-cost customised parts. However, present fused deposition modelling 3D printers have limited nozzle condition monitoring techniques to minimize nozzle clogging errors. Nozzle clogging is one of the significant process errors in fused deposition modelling 3D printers, and it affects the quality of prototyped parts in terms of mechanical properties and geometrical accuracy. This paper proposes a dynamic model for current-based nozzle condition monitoring in fused deposition modelling, which is briefly described as follows. First, all the process forces in filament extrusion of the fused deposition modelling were identified and derived theoretically, and theoretical equations of the feed rolling forces and flow-through-nozzle forces were derived. In addition, the effect of the nozzle clogging on the current of extruding motor were identified. Second, based on the proposed dynamic model, current-based nozzle condition monitoring method was proposed. Next, sets of experiments on FDM machine using polylactic acid (PLA) material were carried out to verify the proposed theoretical model, and the results were analysed and evaluated. Findings of the present study indicate that nozzle clogging in FDM 3D printing can be monitored by sensing the current of the filament extruding motor. The proposed model can be used efficiently for monitoring nozzle clogging conditions in fused deposition modelling 3D printers as it is based on the fundamental process modelling.

Drop-on-demand (DOD) bioprinting has been widely used in tissue engineering due to its high-throughput efficiency and cost effectiveness. However, this type of bioprinting involves challenges such as satellite generation, too-large droplet generation, and too-low droplet speed. These challenges reduce the stability and precision of DOD printing, disorder cell arrays, and hence generate further structural errors. In this paper, a multi-objective optimization (MOO) design method for DOD printing parameters through fully connected neural networks (FCNNs) is proposed in order to solve these challenges. The MOO problem comprises two objective functions: to develop the satellite formation model with FCNNs; and to decrease droplet diameter and increase droplet speed. A hybrid multi-subgradient descent bundle method with an adaptive learning rate algorithm (HMSGDBA), which combines the multi-subgradient descent bundle (MSGDB) method with Adam algorithm, is introduced in order to search for the Pareto-optimal set for the MOO problem. The superiority of HMSGDBA is demonstrated through comparative studies with the MSGDB method. The experimental results show that a single droplet can be printed stably and the droplet speed can be increased from 0.88 to 2.08 m·s−1 after optimization with the proposed method. The proposed method can improve both printing precision and stability, and is useful in realizing precise cell arrays and complex biological functions. Furthermore, it can be used to obtain guidelines for the setup of cell-printing experimental platforms. Keywords: Drop-on-demand printing, Inkjet printing, Gradient descent multi-objective optimization, Fully connected neural networks

Background Mesenchymal stem cells (MSCs) can migrate to tissue injury sites where they can induce multipotential differentiation and anti-inflammation effects to treat tissue injury. When traditional therapeutic methods do not work, MSCs are considered to be one of the best candidates for cell therapy. MSCs have been used for treating several injury- and inflammation-associated diseases, including liver cirrhosis. However, the therapeutic effect of MSCs is limited. In some cases, the anti-inflammatory function of naïve MSCs is not enough to rescue tissue injury. Methods Carbon tetrachloride (CCl4) was used to establish a mouse liver cirrhosis model. Enhanced green fluorescence protein (EGFP) and hepatocyte nuclear factor-4α (HNF-4α) overexpression adenoviruses were used to modify MSCs. Three weeks after liver injury induction, mice were injected with bone marrow MSCs via their tail vein. The mice were then sacrificed 3 weeks after MSC injection. Liver injury was evaluated by measuring glutamic-pyruvic transaminase (ALT) and glutamic oxalacetic transaminase (AST) levels. Histological and molecular evaluations were performed to study the mechanisms. Results We found that HNF-4α-overexpressing MSCs had a better treatment effect than unmodified MSCs on liver cirrhosis. In the CCl4-induced mouse liver injury model, we found that HNF-4α-MSCs reduced inflammation in the liver and alleviated liver injury. In addition, we found that HNF-4α promoted the anti-inflammatory effect of MSCs by enhancing nitric oxide synthase (iNOS) expression, which was dependent on the nuclear factor kappa B (NF-κB) signalling pathway. Conclusions MSCs overexpressing HNF-4α exerted good therapeutic effects against mouse liver cirrhosis due to an enhanced anti-inflammatory effect. Gene modification is likely a promising method for improving the effects of cell therapy. Electronic supplementary material The online version of this article (10.1186/s13287-019-1260-7) contains supplementary material, which is available to authorized users.

3D printing and particularly fused filament fabrication is widely used for prototyping and fabricating low-cost customized parts. However, current fused filament fabrication 3D printers have limited nozzle condition monitoring techniques to minimize nozzle clogging errors. Nozzle clogging is one of the most significant process errors in current fused filament fabrication 3D printers, and it affects the quality of the prototyped parts in terms of geometry tolerance, surface roughness, and mechanical properties. This paper proposes a nozzle condition monitoring technique in fused filament fabrication 3D printing using a vibration sensor, which is briefly described as follows. First, a bar mount that supports the liquefier in fused filament fabrication extruder was modeled as a beam excited by a system of process forces. The boundary conditions were identified, and the applied forces were analyzed for Direct and Bowden types of fused filament fabrication extruders. Second, a new 3D printer with a fixed extruder and a moving platform was designed and built for conducting nozzle condition monitoring experiments. Third, nozzle clogging was simulated by reducing the nozzle extrusion temperature, which caused partial solidification of the filament around inner walls of the nozzle. Fourth, sets of experiments were performed by measuring the vibrations of a bar mount during extrusion of polylactic acid, acrylonitrile butadiene styrene, and SemiFlex filaments via Direct and Bowden types of fused filament fabrication extruders. Findings of the current study show that nozzle clogging in fused filament fabrication 3D printers can be monitored using an accelerometer sensor by measuring extruder’s bar mount vibrations. The proposed technique can be used efficiently for monitoring nozzle clogging in fused filament fabrication 3D printers as it is based on the fundamental process modeling.

Background Patients with lung adenocarcinoma (LUAD) have high mortality rate and poor prognosis. The LUAD cells display increased aerobic glycolysis, which generates energy required for their survival and proliferation. Deregulation of Wnt/β-catenin signaling pathway induces the metabolism switching and oncogenesis in tumor cells. RING finger protein 115 (RNF115) is an E3 ligase for ubiquitin-mediated degradation. Although the oncogenic functions of RNF115 have been revealed in breast tumor cells, the effect of RNF115 on lung cancer is still not clear. Methods RNF115 expression and its correlation with the features of LUAD patients were analyzed by using public database and our own cohort. The functions of RNF115 in proliferation and energy metabolism in LUAD cells were explored by downregulating or upregulating RNF115 expression. Results We demonstrated that RNF115 was overexpressed in LUAD tissues and its expression was positively correlated with the poor overall survival of LUAD patients. Moreover, RNF115 overexpression inhibited LUAD cell apoptosis and promoted cellular proliferation and metabolism in LUAD cells. On the contrary, RNF115 knockdown displayed reverse effects. Furthermore, the underlying mechanism of the biological function of RNF115 in LUAD was through regulating Wnt/β-catenin pathway via ubiquitination of adenomatous polyposis coli (APC). Conclusion The current study reveals a close association between RNF115 expression and prognostic conditions in LUAD patients and the oncogenic roles of RNF115 in LUAD at the first time. These findings may help establish the foundation for the development of therapeutics strategies and clinical management for lung cancer in future.

Background and Aim: The resection margin (RM) status and microscopic vascular invasion (MVI) are known prognostic factors for intrahepatic cholangiocarcinoma (ICC). An enhanced understanding of their impact on long-term prognosis is required to improve oncological outcomes.Methods: We reviewed data on 711 consecutive patients who underwent curative liver resection for hepatitis B virus–related ICC. The different impact of the RM status (narrow, Results: The 1-, 3-, and 5-year OS rates were 67.6%, 42.5% and 33.2% in wide RM & & MVI (-), 58.0%, 36.1% and 26.5% in narrow RM & MVI (-), 51.0%, 27.0% and 24.3% in wide RM & MVI (+), and 39.0%, 20.4% and 14.3% in narrow RM & MVI (+) (p < 0.001).The 1-, 3-, and 5-year RFS rates were 60.0%, 40.2% and 28.7% in wide RM & MVI (-), 45.2%, 34.3% and 24.2% in narrow RM & MVI (-), 40.0%, 18.5% and 12.3% in wide RM & MVI (+), and 28.2%, 11.5% and 9.8% in narrow RM & MVI (+) (p < 0.001). Multivariate analysis showed that RM & MVI were independent risk factors for the OS and RFS. Conclusions: Combined analysis of RM and MVI can better stratify the risks of postoperative death and recurrence in patients with HBV-related ICC, which may help subsequent adjuvant therapy and follow-up.

Overlapping melting trajectories and partially-melted powders result in poor surface morphology and high surface roughness values (Ra = ~13.34 μm) of selective laser melted (SLMed) Ti6Al4V alloy. Secondary processing of SLMed components is thus an essential finishing operation to produce functional SLMed parts in precision engineering. This paper investigates the effects of laser scanning strategies (0°, 67.5° and 90° laser scanning schemes) and machining surfaces (top and front surfaces) on the machining performance of SLMed Ti6Al4V alloy in milling, compared with that of annealed ASTM B265 Ti6Al4V alloy. High degree of anisotropy of SLMed Ti6Al4V alloy is reflected in cutting force, surface morphology and surface roughness on different machining surfaces. The machining anisotropy is dominated by the anisotropy of microstructure and mechanical properties of SLMed Ti6Al4V alloy, where the anisotropy weakens following the sequence of 0°, 90° and 67.5° SLMed samples. It is verified that high cutting speed can improve machining anisotropy features of SLMed titanium alloy. The chip shape of SLMed Ti6Al4V alloy is a typical conical spiral chip, and the bending degree and length of chips produced by SLMed Ti6Al4V alloy are larger than those produced by annealed Ti6Al4V alloy.

This paper aims to improve the surface quality of 316L stainless steel parts manufactured by selective laser melting (SLM) using dry mechanical-electrochemical polishing (DMECP). DMECP is an advanced surface finishing method combining the advantages of both mechanical and electrochemical polishing techniques in a more environmentally friendly manner. In this paper, the SLM process-related defects causing poor surface quality are analysed first. The material removal mechanism of DMECP is investigated to continuously remove the oxide layers formed during polishing. Surface morphology and roughness evolution under different polishing conditions are characterised. The top surface roughness can be reduced by over 91% from 8.72 μm to 0.75 μm compared to side surface by over 93% from 12.10 to 0.80 μm. The material removal on the top surface is more efficient than that on the side surface under the same polishing condition. The secondary defects formed during polishing can be removed using mechanical polishing mode. The chemical element composition of the polished surface exhibits almost identical content to the initial 316L powders. Compared with the initial dark and rough surfaces, the results validate the capability of DMECP as an effective tool to improve the SLM surface quality and achieve a mirror finish.

Stress shielding is one of the main problems that lead to bone resorption and revision surgery after implantation. Most of the commercially available metallic non-porous bone implants have a much greater stiffness than the native human bones and are prone to cause stress-shielding. With an open cell structure and intricate architecture, hyperbolic minimal surfaces offer several advantages such as less stress concentration, high permeability and high surface area to volume ratio, thus providing an ideal environment for cell adhesion, migration, and proliferation. This paper explores the use of porous bone implant design based on Triply Periodic Minimal Surfaces (TPMS) which is additively manufactured with ceramic material (Alumina) using Lithography-based Ceramics Manufacturing (LCM) technology. A total of 12 different primitive surface structure unit cells with pore size in the range of 500–1000 μm and porosity above 50% were considered. This is one of the earliest studies reporting the 3D printing of TPMS-based structures using ceramic material. Our results suggest that the choice of material and a porous TPMS-based design led to fabrication of structures with a much lesser compressive modulus comparable with the native bone and hence could potentially be adopted for bone implant design to mitigate the stress-shielding effect.

Lightweight lattice structures are an important class of cellular structures with high potentials for multi-functional applications. Considering load-bearing requirements, truss buckling is one of the main failure mechanisms for low density and slender lattice structures. Critical buckling loads can be increased by modifying the profile of a truss. In this paper, we present a shape design method to optimize the critical buckling loads for lattice cores with free-form trusses. The free-form truss is represented by Fourier series and implicit surfaces, having smooth truss diameter variations and truss joints. The optimized truss profile is obtained by solving a parametric shape optimization problem with Fourier series coefficients as design variables. The method is used for designing optimized 1D columns and 3D Kagome lattice cores for sandwich panels. The numerical results predict 26.8% and 20.4% improvements of the critical buckling loads for 1D columns and 3D Kagome lattice cores compared to their uniform counterparts of the same mass, respectively. The optimized structures include complex smooth and curved geometries that are well suited for additive manufacturing because of the greater design freedom. Finally, the initial and optimized lattice cores are additively manufactured and tested. The experimental results validate the effectiveness of the proposed method. Keywords: Additive manufacturing, Shape design, Buckling failure, Kagome lattice core, Free-form truss

For negative Poisson's ratio (NPR) cellular structures, most previous research focus on the design of unit cells, and then repeat the unit cell to construct uniform cellular structures. However, there is a disadvantage that these structures do not have much design freedom to achieve high-level functions, such as performing a desired deformation. As a solution, an evolutionary design method is proposed to develop nonuniform cellular structures. To conduct this method, the design domain is divided into finite unit cells with tunable Poisson's ratio (PR). With a given objective deformation, the value of each unit cell's PR is optimized using evolutionary algorithm (EA). In order to reduce the computational cost of the algorithm, discrete cosine transform (DCT) is applied to encode the structure for evolving. Considering the geometrical complexity of the optimized nonuniform cellular structures, additive manufacturing (AM) is chosen to build them physically. Both two-dimensional (2D) and three-dimensional (3D) design cases were developed and analyzed to validate the proposed method. The computational and experimental results showed good conformation with each other. Most importantly, this novel design method brings huge potential to NPR cellular structures with high-level functions and much wider applications. Keywords: Nonuniform cellular structure, Poisson's ratio, Evolutionary algorithm, Re-entrant structure, Additive manufacturing

Object proposal generation, as a preprocessing technique, has been widely used in current object detection pipelines to guide the search of objects and avoid exhaustive sliding window search across images. Current object proposals are mostly based on low-level image cues, such as edges and saliency. However, objectness is possibly a high-level semantic concept showing whether one region contains objects. This paper presents a framework utilizing fully convolutional networks (FCNs) to produce object proposal positions and bounding box location refinement with Support Vector Machine (SVM) to further improve proposal localization. Experiments on the PASCAL VOC 2007 show that using high-level semantic object proposals obtained by FCN, the object recall can be improved. An improvement in detection mean average precision is also seen when using our proposals in the Fast R-convolutional neural network framework. In addition, we also demonstrate that our method shows stronger robustness when introduced to image perturbations, e.g., blurring, JPEG compression, and salt and pepper noise. Finally, the generalization capability of our model (trained on the PASCAL VOC 2007) is evaluated and validated by testing on PASCAL VOC 2012 validation set, ILSVRC 2013 validation set, and MS COCO 2014 validation set.

Functionally graded lattice core structures show a gradual and localised variation in their mechanical properties with the aim of improving structural performance whilst minimising weight. We present a novel approach to generate optimised functionally graded lattice core structures. Firstly, topology optimisation is performed to return the optimal core density distribution to minimise the structure's compliance subject to a mass constraint. A series of isostatic lines are then constructed with respect to the local principal stresses to generate a lattice structure spatially graded with respect to lattice cell size, aspect ratio and orientation. To validate this novel approach, optimisation is performed on a sandwich core structure subject to three point bending. Experimental tests confirm the greatly improved stiffness and strength properties (101% and 172% respectively) of the core as a result of spatially grading the lattice cells when compared to a benchmark core with uniform cell size of the same density. The new approach also significantly outperforms lattice structures with graded diameters as optimised by state-of-the-art commercial software packages. Non-dimensional core performance indices are formulated to express the relative specific stiffness and strength properties of the core for the three design approaches. Keywords: Topology optimisation, Lattice structures, Finite element analysis, Functionally graded materials, Sandwich structures

Biocompatible tissue engineering (TE) scaffolds were used to closure the leakage area in gastro intestinal (GI) tract as patches; they could induce the regeneration of defected tissue and degraded steadily. However, some disadvantages, such as small and uncontrolled pore size, hinder the application of existed TE scaffolds. In this study, PCL/F127 composite scaffolds were fabricated through electrohydrodynamic jetting (E-jetting) technique. And the E-jetted scaffolds were cultured with esophageal fibroblast cells (the most important cell type in wound healing process) to evaluate their biocompatibility. The results show that E-jetted PCL/F127 scaffold has potential to be an alternative GI tract regeneration tool.

Surface roughness is often considered as one of the most important technical requirements for a machined part in production. Traditional selection of conservative process parameters neither guarantees part surface quality nor attains high metal removal rates. An accurate prediction and effective control of surface roughness is of vital importance for improving machining efficiency and reducing machining cost. In this paper, a novel model is presented for predicting surface roughness when slot milling Al-7075. The model is developed using a hybrid approach combining analytical calculation of specific cutting energy consumption (SCEC) and empirical characterization of the relationship between the surface roughness and SCEC. The proposed model has been validated by experiments under various cutting conditions. Compared to Taguchi methodology for predicting surface roughness, the proposed model is more accurate and reliable. From a novel viewpoint of SCEC, the model may provide valuable information regarding the effects of the cutting parameters on the surface roughness. Besides, the implementation of this model is straightforward, thus providing great potential for surface roughness control in real production.

Conductivity is a desirable property of an ideal nerve guide conduit (NGC) that is being considered for peripheral nerve regeneration. Most of the conductive polymers reported in use for fabrication of tissue engineering scaffolds such as polypyrrole (PPy), polyaniline, polythiophene, and poly(3,4-ethylenedioxythiophene) are non-biodegradable and possess weak mechanical properties to be fabricated into 3D structures. In this study, a biodegradable and conductive block copolymer of PPy and Polycaprolactone (PPy-b-PCL) was used to fabricate 3D porous NGCs using a novel electrohydrodynamic jet 3D printing process which offers superior control over fiber diameter, pore size, porosity, and fiber alignment. PCL/PPy scaffolds with three different concentrations of PPy-b-PCL (0.5, 1, and 2% v/v) were fabricated as a mesh (pore size 125 ± 15 μm) and the effect of incorporation of PPy-b-PCL on mechanical properties, biodegradability, and conductivity of the NGCs were studied. The mechanical properties of the scaffolds decreased with the addition of PPy-b-PCL which aided the ability to fabricate softer scaffolds that are closer to the properties of the native human peripheral nerve. With increasing concentrations of PPy-b-PCL, the scaffolds displayed a marked increase in conductivity (ranging from 0.28 to 1.15 mS/cm depending on concentration of PPy). Human embryonic stem cell-derived neural crest stem cells (hESC-NCSCs) were used to investigate the impact of PPy-b-PCL based conductive scaffolds on the growth and differentiation to peripheral neuronal cells. The hESC-NCSCs were able to attach and differentiate to peripheral neurons on PCL and PCL/PPy scaffolds, in particular the PCL/PPy (1% v/v) scaffolds supported higher growth of neural cells and a stronger maturation of hESC-NCSCs to peripheral neuronal cells. Overall, these results suggest that PPy-based conductive scaffolds have potential clinical value as cell-free or cell-laden NGCs for peripheral neuronal regeneration.

Bioprinting is increasingly being used for fabrication of engineered tissues for regenerative medicine, drug testing, and other biomedical applications. The success of this technology lies with the development of suitable bioinks and hydrogels that are specific to the intended tissue application. For applications such as neural tissue engineering, conductivity plays an important role in determining the neural differentiation and neural tissue regeneration. Although several conductive hydrogels based on metal nanoparticles (NPs) such as gold and silver, carbon-based materials such as graphene and carbon nanotubes and conducting polymers such as polypyrrole (PPy) and polyaniline were used, they possess several disadvantages. The long-term cytotoxicity of metal nanoparticles (NPs) and carbon-based materials restricts their use in regenerative medicine. The conductive polymers, on the other hand, are non-biodegradable and possess weak mechanical properties limiting their printability into three-dimensional constructs. The aim of this study is to develop a biodegradable, conductive, and printable hydrogel based on collagen and a block copolymer of PPy and polycaprolactone (PCL) (PPy-block-poly(caprolactone) [PPy-b-PCL]) for bioprinting of neural tissue constructs. The printability, including the influence of the printing speed and material flow rate on the printed fiber width; rheological properties; and cytotoxicity of these hydrogels were studied. The results prove that the collagen/PPy-b-PCL hydrogels possessed better printability and biocompatibility. Thus, the collagen/PPy-b-PCL hydrogels reported this study has the potential to be used in the bioprinting of neural tissue constructs for the repair of damaged neural tissues and drug testing or precision medicine applications.

Stainless steel 304L (SS304L) has been selected as the material for canisters for spent fuel storage from three nuclear power plants in Taiwan. A crucial issue is extending the spent fuel storage safety standards of the canisters. The anti-saline corrosion abilities of three thermal spray coatings (i.e., Al, ZnAl, and 625 Inconel alloys) on the SS304L were evaluated by immersion in 3.5 wt % aqueous NaCl and with 0.025 g/cm2 NaCl deposition at 80 &deg, C and 80% relative humidity (RH) for 1000 h. The pristine thermal spray coatings were examined using the pull-off adhesion test to understand the adhesion strength, and Vickers hardness was measured for the mechanical properties of the three coatings. Confocal laser scanning microscopy (CLSM) was used to identify the porosities of the coatings. Furthermore, the surfaces of the specimens before and after corrosion were investigated using scanning electron microscopy (SEM), X-ray diffraction (XRD), and energy dispersive spectroscopy (EDS). The composition and distribution of the oxide layers formed on the coating surfaces during corrosion were evaluated. Electrochemical measurement was also performed with the polarization method to quantify the corrosion property of the three thermal spray coatings. The results showed that the corrosion rate of Al coating was lowest from the Tafel analysis after the 1000 h corrosion test in 3.5% aqueous NaCl. In contrast, the corrosion rate of Inconel 625 was lowest after 1000 h of the NaCl deposition corrosion test in a controlled environment. Therefore, the ZnAl thermal spray coating is a potential protection layer, keeping in mind economic considerations, of SS304L for anti-corrosion in saline environments.