Dyeing of poly(lactic acid) fibres with synthesised novel heterocyclic disazo disperse dyes

Poly(lactic acid) (PLA) is the first melt‐processable, renewable, sustainable and biodegradable natural‐based synthetic fibre. It has a broad range of uses and combines ecological advantages with outstanding performance in textiles. PLA fibre, as an aliphatic polyester, can be dyed with disperse dyes. Apart from the limited number of commercial disperse dyes, disperse dye exhaustion on PLA is generally lower than that on  poly(ethylene terephthalate) (PET). In this study, new heterocyclic disazo disperse dyes, substituted with methyl, nitro and chloro groups at their ortho‐, meta‐ and para‐ positions, synthesised in our previous study, were applied to PLA and PET fibres to examine their dyeing performance, and colour fastness and dye exhaustion properties. Different shades of yellow, orange, reddish brown and brown were obtained. Most of the synthesised novel heterocyclic disazo disperse dyes exhibited good build‐up properties with high K/S levels on both fibres. Para‐ bonding substituent provided higher K/S values than meta‐ and ortho‐ positions for –NO₂ and –Cl substituents for both fibres. Overall, the most synthesised novel heterocyclic disazo disperse dyes in this study exhibited good build‐up properties with high K/S, exhaustion and wet fastness levels on both PLA and PET fibres.      

Flexible poly(styrene-ethylene-butadiene-styrene) hybrid nanofibers for bioengineering and water filtration applications

Among the thermoplastic elastomers that play important roles in the polymer industry due to their superior properties, styrene-based species and polyurethane block copolymers are of great interest. Poly(styrene-ethylene-butadiene-styrene) (SEBS) as a triblock copolymer seems to have the potential to meet many demands in different applications due to various industrial requirements where durability, biocompatibility, breaking elongation, and interfacial adhesion are important. In this study, the SEBS triblock copolymer was functionalized with natural (Satureja hortensis, SH) and synthetic (nanopowder, TiO₂) agents to obtain composite nanofibers by electrospinning and electrospraying methods for use in biomedical and water filtration applications. The results were compared with thermoplastic polyurethane (TPU) composite nanofibers, which are commonly used in these fields. Here, functionalized SEBS nanofibers exhibited antibacterial effect while at the same time improving cell viability. In addition, because of successful water filtration by using the SEBS composite nanofibers, the material may have a good potential to be used comparably to TPU for the application.     

Self-aligning planarization and passivation for integration applications in III-V semiconductor devices

This work reports an easy planarization and passivation approach for the integration of III-V semiconductor devices. Vertically etched III-V semiconductor devices typically require sidewall passivation to suppress leakage currents and planarization of the passivation material for metal interconnection and device integration. It is, however, challenging to planarize all devices at once. This technique offers wafer-scale passivation and planarization that is automatically leveled to the device top in the 1-3-/spl mu/m vicinity surrounding each device. In this method, a dielectric hard mask is used to define the device area. An undercut structure is intentionally created below the hard mask, which is retained during the subsequent polymer spinning and anisotropic polymer etch back. The spin-on polymer that fills in the undercut seals the sidewalls for all the devices across the wafer. After the polymer etch back, the dielectric mask is removed leaving the polymer surrounding each device level with its device top to atomic scale flatness. This integration method is robust and is insensitive to spin-on polymer thickness, polymer etch nonuniformity, and device height difference. It prevents the polymer under the hard mask from etch-induced damage and creates a polymer-free device surface for metallization upon removal of the dielectric mask. We applied this integration technique in fabricating an InP-based photonic switch that consists of a mesa photodiode and a quantum-well waveguide modulator using benzocyclobutene (BCB) polymer. We demonstrated functional integrated photonic switches with high process yield of >90%, high breakdown voltage of >25 V, and low ohmic contact resistance of /spl sim/10 /spl Omega/. To the best of our knowledge, such an integration of a surface-normal photodiode and a lumped electroabsorption modulator with the use of BCB is the first to be implemented on a single substrate.     

Palmprint Recognition by Applying Wavelet-Based Kernel PCA

This paper presents a wavelet-based kernel Principal Component Analysis (PCA) method by integrating the Daubechies wavelet representation of palm images and the kernel PCA method for palmprint recognition. Kernel PCA is a technique for nonlinear dimension reduction of data with an underlying nonlinear spatial structure. The intensity values of the palmprint image are first normalized by using mean and standard deviation. The palmprint is then transformed into the wavelet domain to decompose palm images and the lowest resolution subband coeffcients are chosen for palm representation. The kernel PCA method is then applied to extract non-linear features from the subband coeffcients. Finally, similarity measurement is accomplished by using weighted Euclidean linear distance-based nearest neighbor classifier. Experimental results on PolyU Palmprint Databases demonstrate that the proposed approach achieves highly competitive performance with respect to the published palmprint recognition approaches.     

Deep learning frameworks to learn prediction and simulation focused control system models

Deep learning (DL) methods have brought world-shattering breakthroughs, especially in computer vision and classification problems. Yet, the design and deployment of DL methods in time series prediction and nonlinear system identification applications still need more progress. In this paper, we present DL frameworks that are developed to provide novel approaches as solutions to the aforementioned engineering problems. The proposed DL frameworks leverage the advantages of autoencoders and long-short term memory network, which are known being data compression and recurrent structures, respectively, to design Deep Neural Networks (DNN) for modeling time series and nonlinear systems with high performance. We provide recommendations on how deep AEs and LSTMs should be utilized to end up with efficient Prediction-focused (Pf) and Simulation-focused (Sf) DNNs for time series and system identification problems. We present systematic learning methods for the DL frameworks that allow straightforward learning of Pf-DNN and Sf-DNN models in detail. To demonstrate the efficiency of the developed DNNs, we present various comparative results conducted on the benchmark and real-world datasets in comparison with their conventional, shallow, and deep neural network counterparts. The results clearly show that the deployment of the proposed DL frameworks results with DNNs that have high accuracy, even with a low dimensional feature vector.

     

Finite element analysis of combined forming processes by means of rate dependent ductile damage modelling

Sheet metal forming is an inherent part of todays production industry. A major goal is to increase the forming limits of classical deep-drawing processes. One possibility to achieve that is to combine the conventional quasi-static (QS) forming process with electromagnetic high-speed (HS) post-forming. This work focuses on the finite element analysis of such combined forming processes to demonstrate the improvement which can be achieved. For this purpose, a cooperation of different institutions representing different work fields has been established. The material characterization is based on flow curves and forming limit curves for low and high strain rates obtained by novel testing devices. Further experimental investigations have been performed on the process chain of a cross shaped cup, referring to both purely quasi-static and quasi-static combined with electromagnetic forming. While efficient mathematical optimization algorithms support the new viscoplastic ductile damage modelling to find the optimum parameters based on the results of experimental material characterization, the full process chain is studied by means of an electro-magneto-mechanical finite element analysis. The constitutive equations of the material model are integrated in an explicit manner and implemented as a user material subroutine into the commercial finite element package LS-DYNA.     

Revealing determinants of two‐phase dynamics of P53 network under gamma irradiation based on a reduced 2D relaxation oscillator model

This study proposes a two‐dimensional (2D) oscillator model of p53 network, which is derived via reducing the multidimensional two‐phase dynamics model into a model of ataxia telangiectasia mutated (ATM) and Wip1 variables, and studies the impact of p53‐regulators on cell fate decision. First, the authors identify a 6D core oscillator module, then reduce this module into a 2D oscillator model while preserving the qualitative behaviours. The introduced 2D model is shown to be an excitable relaxation oscillator. This oscillator provides a mechanism that leads diverse modes underpinning cell fate, each corresponding to a cell state. To investigate the effects of p53 inhibitors and the intrinsic time delay of Wip1 on the characteristics of oscillations, they introduce also a delay differential equation version of the 2D oscillator. They observe that the suppression of p53 inhibitors decreases the amplitudes of p53 oscillation, though the suppression increases the sustained level of p53. They identify Wip1 and P53DINP1 as possible targets for cancer therapies considering their impact on the oscillator, supported by biological findings. They model some mutations as critical changes of the phase space characteristics. Possible cancer therapeutic strategies are then proposed for preventing these mutations’ effects using the phase space approach.Inspec keywords: physiological models, cellular biophysics, cancer, difference equations, delays, enzymes, biochemistry, molecular biophysics, gamma‐rays, radiation therapyOther keywords: two‐phase dynamics model, P53 network, gamma irradiation, 2D relaxation oscillator model, ATM model, Wip1 variables, p53‐regulators, cell fate decision, excitable relaxation oscillator, Wip1 time delay, state‐dependent delay differential equation, cell cycle arrest, cell apoptosis, cancer therapies, Wip1 overexpression, Wip1 downregulation, ATM deficiency, Mdm2 overexpression, Mdm2 downregulation, mutation effects, phase space approach     

Improved gait recognition by multiple-projections normalization

Recognizing people by gait promises to be useful for identifying individuals from a distance; in this regard, improved techniques are under development. In this paper, an improved method for gait recognition is proposed. Binarized silhouette of a motion object is first represented by four 1-D signals that are the basic image features called the distance vectors. The distance vectors are differences between the bounding box and silhouette, and extracted using four projections to silhouette. Fourier Transform is employed as a preprocessing step to achieve translation invariant for the gait patterns accumulated from silhouette sequences that are extracted from the subjects’ walk in different speed and/or different time. Then, eigenspace transformation is applied to reduce the dimensionality of the input feature space. Support vector machine (SVM)-based pattern classification technique is then performed in the lower-dimensional eigenspace for recognition. The input feature space is alternatively constructed by using two different approaches. The four projections (1-D signals) are independently classified in the first approach. A fusion task is then applied to produce the final decision. In the second approach, the four projections are concatenated to have one vector and then pattern classification with one vector is performed in the lower-dimensional eigenspace for recognition. The experiments are carried out on the most well-known public gait databases: the CMU, the USF, SOTON, and NLPR human gait databases. To effectively understand the performance of the algorithm, the experiments are executed and presented as increasing amounts of the gait cycles of each person available during the training procedure. Finally, the performance of the proposed algorithm is comparatively illustrated to take into consideration the published gait recognition approaches.     

Multiobjective evolutionary clustering of Web user sessions: a case study in Web page recommendation

In this study, we experiment with several multiobjective evolutionary algorithms to determine a suitable approach for clustering Web user sessions, which consist of sequences of Web pages visited by the users. Our experimental results show that the multiobjective evolutionary algorithm-based approaches are successful for sequence clustering. We look at a commonly used cluster validity index to verify our findings. The results for this index indicate that the clustering solutions are of high quality. As a case study, the obtained clusters are then used in a Web recommender system for representing usage patterns. As a result of the experiments, we see that these approaches can successfully be applied for generating clustering solutions that lead to a high recommendation accuracy in the recommender model we used in this paper.