Naturally functionalized silk as useful material for photonic applications

Silk is a natural fibre obtained from the Bombyx mori silkworm cocoons that can be used in a wide range of fields thanks to its inherent multifunctionality.Post-production steps are necessary to impart colour to the fibres to employ the material for optics and photonic applications, such as in fluorescence-based optofluidic devices in lab-on-a-chip realization.Here we present an intrinsically greener dyeing approach for fabricating naturally functionalized silk, where highly-fluorescent organic dyes with lasing properties are in vivo up-taken by silkworms once introduced in the artificial diet. A detailed photoluminescence spectroscopy investigation is implemented to test whether the dyes are effectively incorporated within the silk proteins, in correlation with the silkworm gland positions where proteins extraction is held. Light amplification characteristics are demonstrated in silk extracted from glands of silkworm fed with artificial diet doped with Rhodamine B dye.     

Real-time administration of tool sharing and best matching to enhance assembly lines balanceability and flexibility

Collaboration has been found in previous studies on the design of assembly lines to be a useful mechanism. In this study, the focus is on a collaborative assembly (CA) framework, inspired by the design principles of CCT, the Collaborative Control Theory, to improve balanceability and flexibility of assembly lines through tool sharing (TS) among idle and bottleneck workstations. TS is widely practiced in advanced assembly facilities to reduce cost and improve consistency and standardization in assembly and in assembly-and-test utilities, relying often on real time control. The framework developed here addresses the systems design aspect of Mechatronics, covering the planning, execution, and control mechanisms. Planning includes assembly line balancing (ALB) and initial TS decisions, made continually by solving a bi-objective mixed-integer program (BOMIP). A collaborative multi-agent system (CMAS) enhanced with a TS-best matching (BM) protocol is developed to execute the plan, control the process, and modify the TS decisions, considering dynamic changes in the system’s operations. Experiments show that the new CA framework significantly outperforms classic approaches (i.e., ALB without TS-BM) in terms of cycle time, utilization of tools, and balanceability. In addition, the control mechanism is proven to augment the line’s flexibility against the inherent uncertainties of assembly processes, compared to the previously developed static CA frameworks.     

Complex concrete structures: Merging existing casting techniques with digital fabrication

Over the course of the 20th century, architectural construction has gone through intense innovation in its material, engineering and design, radically transforming the way buildings were and are conceived. Technological and industrial advances enabled and challenged architects, engineers and constructors to build increasingly complex architectural structures from concrete. Computer-aided design and manufacturing (CAD/CAM) techniques have, more recently, rejuvenated and increased the possibilities of realizing ever more complex geometries. Reinforced concrete is often chosen for such structures as almost any shape can be achieved when placed into a formwork. However, most complex forms generated with these digital design tools bear little relation to the default modes of production used in concrete construction today. A large gap has emerged between the possibilities offered by the digital technology in architectural design and the reality of the building industry, where actually no efficient solutions exist for the production of complex concrete structures. This paper presents construction methods that unfold their full potential by linking digital design, additive fabrication and material properties and hence allow accommodating the construction of complex concrete structures. The emphasis is set on the on-going research project Smart Dynamic Casting (SDC) where advanced material design and robotic fabrication are interconnected in the design and fabrication process of complex concrete structures. The proposed fabrication process is belonging to an emerging architectural phenomenon defined first as Digital Materiality by Gramazio & Kohler (2008) or more recently as Material Ecologies by Neri Oxman  [1].     

A systematic methodology for optimal mixture design in an integrated biorefinery

Biomass is a sustainable source of energy which can be utilised to produce value-added products such as biochemical products and biomaterials. In order to produce a sustainable supply of such value-added products, an integrated biorefinery is required. An integrated biorefinery is a processing facility that integrates multiple biomass conversion pathways to produce value-added products. To date, various biomass conversion pathways are available to convert biomass into a wide range of products. Due to the large number of available pathways, various systematic screening tools have been developed to address the process design aspect of an integrated biorefinery. Process design however, is often inter-linked with product design as it is important to identify the optimal molecule (based on desired product properties) prior to designing its optimal production routes. In cases where the desired product properties cannot be met by a single component chemical product, a mixture of chemicals would be required. In this respect, product and process design decisions would be a challenging task for an integrated biorefinery. In this work, a novel two-stage optimisation approach is developed to identify the optimal conversion pathways in an integrated biorefinery to convert biomass into the optimal mixtures in terms of target product properties. In the first stage, the optimal mixture is designed via computer-aided molecular design (CAMD) technique. CAMD technique is a reverse engineering approach which predicts the molecules with optimal properties using property prediction models. Different classes of property models such as group contribution (GC) models and quantitative structure property relationship (QSPR) are adapted in this work. The main component of the mixture is first determined from the target product properties. This is followed by the identifying of additive components to form an optimal mixture with the main component based on the desired product properties. Once the optimal mixture is determined, the second stage identifies the optimal conversion pathways via superstructural mathematical optimisation approach. With such approach, the optimal conversion pathways can be determined based on different optimisation objectives (e.g. highest product yield, lowest environmental impact etc.). To illustrate the proposed methodology, a case study on the design of fuel additives as a mixture of different molecules from palm-based biomass is presented. With the developed methodology, optimal fuel additives are designed based on optimal target properties. Once the optimal fuel additives are designed, the optimal conversion pathways in terms of highest product yield and economic performance that convert biomass into the optimal fuel additives are identified.     

Discovery of path-attribute dependency in manufacturing environments: A process mining approach

The more knowledge industrial practitioners detain of their production processes, the more they are capable of performing process improvements. Nonetheless, there may exist process characteristics and dependencies that are not easily extractable from business models, such as routing dependent attributes. This paper introduces an algorithm-driven framework to establish whether process path decisions influence the attributes in non-direct sequences, e.g., deploying machine A instead of machine B affects the % of rejected parts on the process, 4 stages down the line. This problem is shown to bears similarities with sequential pattern mining problems. The basis of the solution framework relies on process mining and data mining techniques. The approach proposed is applied on a real industrial log, unveiling deficiencies in the system and providing further improvement recommendations.     

A review on modeling of solar photovoltaic systems using artificial neural networks,fuzzy logic,genetic algorithm and hybrid models

The uncertainty associated with modeling and performance prediction of solar photovoltaic systems could be easily and efficiently solved by artificial intelligence techniques. During the past decade of 2009 to 2019, artificial neural network (ANN), fuzzy logic (FL), genetic algorithm (GA) and their hybrid models are found potential artificial intelligence tools for performance prediction and modeling of solar photovoltaic systems. In addition, during this decade there is no extensive review on applicability of ANN, FL, GA and their hybrid models for performance prediction and modeling of solar photovoltaic systems. Therefore, this article focuses on extensive review on design, modeling, maximum power point tracking, fault detection and output power/efficiency prediction of solar photovoltaic systems using artificial intelligence techniques of the ANN, FL, GA and their hybrid models. In addition, the selected articles on the solar radiation prediction using ANN, FL, GA and their hybrid models are also summarized. Total of 122 articles are reviewed and summarized in the present review for the period of 2009 to 2019 with 90 articles in the field of {ANN, FL, GA and their hybrid models} + solar photovoltaic systems and 32 articles in the field of {ANN, FL, GA and their hybrid models} + solar radiation. The review shows the suitability and reliability of ANN, FL, GA and hybrid models for accurate prediction of the solar radiation and the performance characteristics of solar photovoltaic systems. In addition, this review presents the guidance for the researchers and engineers in the field of solar photovoltaic systems to select the suitable prediction tool for enhancement of the performance characteristics of the solar photovoltaic systems and the utilization of the available solar radiation.     

Optimized techniques for driving and control of the switched reluctance motor to improve efficiency

This work presents modeling, driving and classical speed control techniques for the switched reluctance motor. The aim is to improve the computational model, the control response and the machine efficiency. A parametric regression model was used to find the inductance profile of the switched reluctance motor and from the new inductance profile model. The drive and control techniques are shown: (i) with speed control acting on the excitation voltage and fixed switching angles, (ii) with speed control acting on the switching angles and fixed excitation voltage and (iii) with speed control acting on the excitation voltage, in this case, with dynamic switching angles and controller parameters. The inductance profile is represented by expression and inserted into the machine computer model, allowing greater precision and low computational cost. The speed control acting on the excitation voltage with dynamic controller parameters and dynamic switching angles allowed: (i) shorter response time for a wide range of control, (ii) higher efficiency, (iii) low computational cost and (iv) simplified implementation and maintenance. The techniques proposed in this work obtained precision of the computational model with respect to the system (in workbench) and optimized parameters in a wide range of the speed control, allowing an improvement of switched reluctance motor efficiency.     

具有光照鲁棒的图像匹配方法

针对现有的基于局部特征的图像匹配算法对光照变化敏感、匹配正确率低等问题，提出一种具有光照鲁棒性的图像匹配算法。首先使用实时对比保留去色（RTCP）算法灰度化图像，然后利用对比拉伸函数模拟不同光照变换对图像的影响从而提取抗光照变换特征点，最后采用局部强度顺序模式建立特征点描述符，根据待匹配图像局部特征点描述符的欧氏距离判断是否为成对匹配点。在公开数据集上，所提算法与尺度不变特征变换（SIFT）算法、加速鲁棒特征（SURF）算法、"风"（KAZE）算法和ORB算法在匹配速度和匹配正确率上进行了对比实验。实验结果表明：随着图像亮度差异的增加，SIFT算法、SURF算法、"风"（KAZE）算法和ORB算法匹配正确率下降迅速，所提算法下降缓慢并且正确率均高于80%；所提算法特征点检测较慢和描述符维数较高，平均耗时为23.47 s，匹配速度不及另外四种算法，但匹配质量却远超过它们。对实时性要求不高的系统中，所提算法可以克服光照变化对图像匹配造成的影响。     

Tris(dithiocarbamato)iron(III) complexes as precursors for iron sulfide nanocrystals and iron sulfide-hydroxyethyl cellulose composites

Iron(III) complexes of dimethyldithiocarbamate and imidazolyl dithiocarbamate were synthesized and characterized by elemental analyses, FTIR, UV–VIS and the ligands by NMR spectroscopy. The complexes were thermolysed as single molecule precursors at 180°C to prepare octadecylamine (ODA) capped iron sulfide nanocrystals and iron sulfide-hydroxyethyl cellulose (HEC) composites. UV–VIS, PL, FTIR, P-XRD, HRTEM, FESEM and EDS were used to characterize the iron sulfide nanocrystals and corresponding HEC nanocomposites. XRD confirmed iron sulfide nanocrystal (NP1) from dimethyldithiocarbamate to be hexagonal pyrrhotite-5H, Fe₉S₁₀ crystalline phase while iron sulfide nanocrystals (NP2) from imidazolyl dithiocarbamate is in pyrrhotite, Fe₁₁S₁₂ crystalline phase. TEM images show that the iron sulfide nanocrystals have particle sizes in the range 24–32?nm for NP1 and 18–25?nm for NP2 iron sulfide nanocrystals. The optical band gaps of the iron sulfide nanocrystals obtained from Tauc plots are 3.83 and 4.16?eV for NP1 and NP2, respectively. IR spectra, FESEM surface morphology and EDS spectra of iron sulfide/HEC composites confirmed dispersion of the iron sulfide nanocrystals within the hydroxyethyl cellulose (HEC) matrix.     

Expression of Main Toll-Like Receptors in Patients with Different Types of Colorectal Polyps and Their Relationship with Gut Microbiota

Abnormal activation of Toll-like receptor (TLRs) signaling can result in colon cancer development. The aim of this study was to investigate the expression of important TLRs in different histological types of colorectal polyps and evaluate their relationship with intestinal microbiota. The expression levels of TLR2, 3, 4, and 5 were analyzed in intestinal biopsy specimens of 21 hyperplastic polyp (HP), 16 sessile serrated adenoma (SSA), 29 tubular adenoma (TA), 21 villous/tubulovillous (VP/TVP) cases, and 31 normal controls. In addition, selected gut bacteria including Streptococcus bovis, Enterococcus faecalis, Enterotoxigenic Bacteroides fragilis (ETBF), Fusobacterium nucleatum, Porphyromonas spp., Lactobacillus spp., Roseburia spp., and Bifidobacterium spp. were quantified in fecal samples using absolute qRT PCR, and, finally, the association between TLRs and these gut microbiota- was evaluated by Spearman’s correlation coefficient. Higher expression of TLR2 and TLR4 in VP/TVP and TA, and lower expression levels of TLR3 and TLR5 in all type of polyps were observed. The differences in TLR expression patterns was not only dependent on the histology, location, size, and dysplasia grade of polyps but also related to the intestinal microbiota patterns. TLR2 and TLR4 expression was directly associated with the F. nucleatum, E. faecalis, S. bovis, Porphyromonas, and inversely to Bifidobacterium, Lactobacillus, and Roseburia quantity. Furthermore, TLR3 and TLR5 expression was directly associated with Bifidobacterium, Roseburia, and Lactobacillus quantity. Our results suggest a possible critical role of TLRs during colorectal polyp progression. An abnormal regulation of TLRs in relation to gut microbial quantity may contribute to carcinogenesis.