Plasmid DNA-loaded asymmetrically porous membrane for guided bone regeneration

Although bone defects can be restored spontaneously,bone reconstruction with sufficient strength and volume continues to be a challenge in clinical practices.In recent years,the use of a variety of biomaterials with bioactivity has been attempted to compensate for this limitation.Herein,we fabricated a pDNA(encoding for BMP-2)-loaded asymmetrically porous polycaprolactone(PCL)/Pluronic F127 membrane as a bioactive guided bone regeneration(GBR)membrane,using a modified immersion-precipitation method.It was observed that the GBR membrane allows continuous release of pDNA for more than20 weeks.The pDNA was sufficiently transfected into human bone marrow stem cells(h BMSCs)without significant cytotoxicity and the gene-transfected cells showed prolonged synthesis of BMP-2.From in vitro osteogenic differentiation and in vivo animal studies,the effective induction of osteogenic differentiation of h BMSCs and enhanced bone regeneration by the pDNA-loaded asymmetrically porous PCL/Pluronic F127 membrane was observed,suggesting that the pDNA-loaded membrane as a bioactive GBR membrane can be an alternative therapeutic technique for effective bone regeneration.     

Incremental face recognition for large-scale social network services

Due to the rapid growth of social network services such as Facebook and Twitter, incorporation of face recognition in these large-scale web services is attracting much attention in both academia and industry. The major problem in such applications is to deal efficiently with the growing number of samples as well as local appearance variations caused by diverse environments for the millions of users over time. In this paper, we focus on developing an incremental face recognition method for Twitter application. Particularly, a data-independent feature extraction method is proposed via binarization of a Gabor filter. Subsequently, the dimension of our Gabor representation is reduced considering various orientations at different grid positions. Finally, an incremental neural network is applied to learn the reduced Gabor features. We apply our method to a novel application which notifies new photograph uploading to related users without having their ID being identified. Our extensive experiments show that the proposed algorithm significantly outperforms several incremental face recognition methods with a dramatic reduction in computational speed. This shows the suitability of the proposed method for a large-scale web service with millions of users.     

A Reliable Communication Protocol for Multiregion Mobile Agent Environments

A mobile agent system is regarded as an attractive technology when developing distributed applications. However, mobility makes it more difficult to trace agents. It is also more complex for agents to communicate with each other in a reliable manner. Therefore, a reliable communication protocol is necessary to control and monitor mobile agents and deliver messages between them. In this paper, a new Reliable Communication Protocol (RCP) is proposed for a multiregion mobile agent computing environment. RCP is implemented on the ODDUGI mobile agent system. Analysis and evaluation show that RCP fulfills the following design goals: reliability, asynchrony, timeliness, location dependency, scalability, and communication cost.     

Service-oriented architecture based on biometric using random features and incremental neural networks

We propose a service-oriented architecture based on biometric system where training and classification tasks are used by millions of users via internet connection. Such a large-scale biometric system needs to consider template protection, accuracy and efficiency issues. This is a challenging problem since there are tradeoffs among these three issues. In order to simultaneously handle these issues, we extract both global and local features via controlling the sparsity of random bases without training. Subsequently, the extracted features are fused with a sequential classifier. In the proposed system, the random basis features are not stored for security reason. The non-training based on feature extraction followed by a sequential learning contributes to computational efficiency. The overall accuracy is consequently improved via an ensemble of classifiers. We evaluate the performance of the proposed system using equal error rate under a stolen-token scenario. Our experimental results show that the proposed method is robust over severe local deformation with efficient computation for simultaneous transactions. Although we focus on face biometrics in this paper, the proposed method is generic and can be applied to other biometric traits.     

N-division output coding method applied to face recognition

Most research on face recognition has focused on representation of face appearances rather than the classifiers. For robust classification performance, we need to adopt elaborate classifiers. Output coding is suitable for this purpose because it can allow online learning. In this paper, we propose an N-division output coding method. In the experiments we demonstrate such properties as problem complexity, margin of separation, machine relevance and the recognition performance among different output coding methods.     

Double S-Shaped Polarization – Voltage Curve and Negative Capacitance from Al2O3-Hf0.5Zr0.5O2-Al2O3 Triple-Layer Structure

The negative capacitance (NC) effect, recently discovered in a fluorite-based ferroelectric thin film, has attracted great attention as a rescue to overcome the scaling limitations of the conventional memory and logic devices of highly integrated circuits. The NC effect manifesting an S-shaped polarization–voltage (P–V) curve is initially interpreted by a 1-dimensional Landau Ginzburg Devonshire (LGD) model. However, a series of recent studies have found that this effect can also be explained by the inhomogeneous stray field energy (ISE) model. In this study, by extending the ISE model in the ferroelectric (FE)-dielectric (DE) layered structure, an analytical model that considers the influence of the interfacial screening charge distribution is presented. This model showed that the NC effect in the FE-DE heterostructure can be manifested in various forms other than a single S-shaped P–V curve. In particular, a double S-shaped P–V curve is expected from the fully compensated anti-parallel domain structure, confirmed experimentally in the actual Al₂O₃/(Hf_0.5Zr_0.5)O₂/Al₂O₃ triple-layer structure. Furthermore, to reveal the origin of the double S-shaped P–V curve, a multidomain LGD model is presented. It is confirmed that this phenomenon is attributed to the evolution of inhomogeneous stray field energy.     

Automated detection of panic disorder based on multimodal physiological signals using machine learning

We tested the feasibility of automated discrimination of patients with panic disorder (PD) from healthy controls (HCs) based on multimodal physiological responses using machine learning. Electrocardiogram (ECG), electrodermal activity (EDA), respiration (RESP), and peripheral temperature (PT) of the participants were measured during three experimental phases: rest, stress, and recovery. Eleven physiological features were extracted from each phase and used as input data. Logistic regression (LoR), k-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), and multilayer perceptron (MLP) algorithms were implemented with nested cross-validation. Linear regression analysis showed that ECG and PT features obtained in the stress and recovery phases were significant predictors of PD. We achieved the highest accuracy (75.61%) with MLP using all 33 features. With the exception of MLP, applying the significant predictors led to a higher accuracy than using 24 ECG features. These results suggest that combining multimodal physiological signals measured during various states of autonomic arousal has the potential to differentiate patients with PD from HCs.     

3D Shape-Morphing Display Enabled by Electrothermally Responsive,Stiffness-Tunable Liquid Metal Platform with Stretchable Electroluminescent Device

3D displays are of great interest as next-generation displays by providing intensified realism of 3D visual information and haptic perception. However, challenges lie in implementing 3D displays due to the limitation of conventional display manufacturing technologies that restrict the dimensional scaling of their forms beyond the 2D layout. Furthermore, on account of the inherent static mechanical properties of constituent materials, the current display form factors can hardly achieve robust and complex 3D structures, thereby hindering their diversity in morphologies and applications. Herein, a versatile shape-morphing display is presented that can reconfigure its shape into various complex 3D structures through electrothermal operation and firmly maintain its morphed states without power consumption. To achieve this, a shape-morphing platform, which is composed of a low melting point alloy (LMPA)-graphene nanoplatelets (GNPs)-elastomer composite, is integrated with a stretchable electroluminescent (EL) device. The LMPA in the composite, the key material for variable stiffness, imparts shape memory property and forms conductive pathways with GNPs enabling rapid electrothermal actuation. The stretchable EL device provides reliable illumination in 3D shape implementations. Experimental studies and proof-of-concept demonstrations show the potential of the shape-morphing display, opening new opportunities for 3D art displays, transformative wearable electronics, and visio-tactile automotive interfaces.     

Large-scale synthesis of CuS nanoparticles for photothermal materials using high-concentration Cu complex ion precursor

Copper sulfide (CuS), a copper-deficient p-type semiconductor material, has been widely utilized due to its unique optical properties, low toxicity, and cost-effectiveness. Although many studies have been conducted on synthesizing CuS nanoparticles, harsh synthetic conditions and low yield must be solved. This study presents a new methodology that can synthesize CuS nanoparticles in large quantities at room temperature and pressure using high-concentration Cu complex ion precursors. This methodology is based on the theory that the critical nucleus radius and the critical nucleation free energy decrease as the concentration of the precursor increases to synthesize a large number of nanoparticles by applying low energy. In addition, it is possible to minimize the aggregation of nanoparticles, which is a problem of nanoparticles synthesized at a high precursor concentration through complex ions in the solution. We synthesized nanoparticles by controlling the precursor concentration from 0.1 to 2.5 M to confirm the effect of the precursor concentration on the size, shape, and yield of nanoparticles. As the precursor concentration increased, the particle size decreased, and the yield improved. The CuS nanoparticles synthesized at the highest concentration had a size of about 17 nm without a strong agglomeration and a yield of about 213.9 g/L. Furthermore, the nanoparticles showed excellent photothermal performance due to their high near-infrared absorption. When about 0.1 g of the nanoparticles were irradiated with a Xenon lamp and an 808 nm laser, the maximum temperatures and rising rates were 53.7°C and 172.1°C and 13.8°C/mg and 33°C/mg, respectively. The excellent photothermal properties of CuS nanoparticles suggest the potential of this material for various applications.     

General Blending Approach of Fundamental Cold Flow Models for an Almost Complete Cold Flow Model in Terms of Tensile Test

The importance of two major issues in obtaining an almost complete cold flow model (ACCFM) from a tensile test, that is, the necking conditions (including the Considère condition and tensile strength) and several selected representative flow points in both pre- and postnecking strain hardening regions, is emphasized. It is shown using the AISI 1010 steel for automatic multistage cold forging (AISI 1010 equivalent) that all the traditional fundamental flow models (FFMs) such as the Ludwik, Voce, Hollomon, and Swift models cannot simultaneously satisfy the two major issues. A general blended flow model (BFM) is proposed, which is a linear combination of known basis blending functions satisfying the necking conditions. The number of basis blending functions is unconstrained. The constants of the linear combination, called blending coefficients, are calculated by a set of linear equations to meet the selected representative flow points. Numerous combinations of basis flow functions are tried. It has been shown that there is no interpolation case among all the obtained BFMs but that there are a few ACCFM-acceptable cases among them, which are based on the extrapolation of the basis blending functions.