Multi-innovation auto-constructed least squares identification for 4 DOF ship manoeuvring modelling with full-scale trial data

This research is concerned with the problem of 4 degrees of freedom (DOF) ship manoeuvring identification modelling with the full-scale trial data. To avoid the multi-innovation matrix inversion in the conventional multi-innovation least squares (MILS) algorithm, a new transformed multi-innovation least squares (TMILS) algorithm is first developed by virtue of the coupling identification concept. And much effort is made to guarantee the uniformly ultimate convergence. Furthermore, the auto-constructed TMILS scheme is derived for the ship manoeuvring motion identification by combination with a statistic index. Comparing with the existing results, the proposed scheme has the significant computational advantage and is able to estimate the model structure. The illustrative examples demonstrate the effectiveness of the proposed algorithm, especially including the identification application with full-scale trial data.     

Ga-doped IZO films obtained by magnetron sputtering as transparent conductors for visible and solar applications

C-axis textured thin films of gallium-doped indium zinc oxide (GIZO) with a 2% ratio of Ga/Zn, were obtained via RF-magnetron sputtering with high transparency and electrical conductivity. A Box-Behnken response surface design was used to evaluate the effects of the deposition parameters (In₂O₃ target power, deposition time, and substrate temperature) on the chemical composition, optical, electrical, and structural properties of the GIZO films. The optical constants and the electrical properties were obtained using optical models. The GIZO stoichiometry, and therefore the In/Zn atomic ratio, affected the crystallinity, crystalline parameters, band gap, and charge carrier mobility of the GIZO films. The charge carrier density was related to the change in the crystalline parameters of the hexagonal structure and the In/Zn atomic ratio. The best electrical conductivity values (1.75?×?10³ Ω^?1 cm^?1) were obtained for GIZO films with In/Zn ratio ≥?1. Several figures of merit (FOM) defined for the visible and solar regions were comparatively used to select the optimal In/Zn atomic ratio that provided the best balance between the conductivity and the transparency. The optimal In/Zn ratio was in a range of 0.85–0.90 for the GIZO films.     

Digital image correlation strain measurement of thick adherend shear test specimen joined with an epoxy film adhesive

Structural bonding and bonded repairs of composite materials become more and more important. Understanding the strain within the bondline leads to suitable bonding design. For new design approaches the strain distribution within the bondline has to be analyzed. Thus, often finite element analysis (FE) are used. However, a huge challenge is the availability of reliable material properties for the adhesives and their validation. Previous work has shown that it is possible to measure the small displacements resulting within thin epoxy film adhesives using high resolution digital image correlation (DIC). In this work a 2D DIC setup with a high resolution consumer camera is used to visualize the strain distribution within the bondline over the length of the joint as well as over the adhesive thickness. Therefore, single lap joints with thick aluminum adherends according to ASTM D 5656 are manufactured and tested. Local 2D DIC strain measurements are performed and analyzed. Two different camera setups are used and compared. The evaluation provides reliable material data and enables a look insight the bondline. The results of the full field strain data measured with DIC are compared with numerical simulations. Thus, material models as well as chosen parameters for the adhesive are validated. Compared to extensometers, giving only point-wise information for fixed measuring points, the DIC allows a virtual point-wise inspection along the complete bondline. Furthermore, it allows measuring close to the bondline to reduce the influence of adherend deformation.     

PIXE study on recovery of making-technology of Chinese Longquan celadon made in the Southern Song Dynasty (1127–1279 CE)

Proton induced X-ray emission (PIXE) technique and factor analysis were used to study the recovery of making-technology of Chinese Longquan celadon made in the late Southern Song Dynasty (1127–1279 CE). Based on factor analysis, for the raw material for body and the recipe for glaze, there is a close but slightly different relationship between these Longquan celadons unearthed from the core area of Dayao and the non-core area, despite all the Longquan celadons belong to the high-calcium-glaze system. The chemical compositions of most of the tentative body and glaze samples are very close to those of Longquan celadons unearthed from the core area of Dayao and the non-core area. This study can provide a guidance for recovery of making-technology of Longquan celandon.     

致密油藏注天然气提高采收率研究现状

随着我国石油需求量不断增加,致密油藏的开发也愈发重要。如何高效开发致密油藏是一项重点难点,其中致密油藏注天然气提高采收率是一个极具潜力的研究方向。因此,本文着重介绍以天然气作为能量补充介质在国内外的研究现状和应用现状,并且从两相特征等方面总结了理论研究中的一些机理,对致密油藏注天然气提高采收率的发展前景进行了一定的展望。     

Matrilin-3-Primed Adipose-Derived Mesenchymal Stromal Cell Spheroids Prevent Mesenchymal Stromal-Cell-Derived Chondrocyte Hypertrophy

Adipose-derived mesenchymal stromal cells (Ad-MSCs) are a promising tool for articular cartilage repair and regeneration. However, the terminal hypertrophic differentiation of Ad-MSC-derived cartilage is a critical barrier during hyaline cartilage regeneration. In this study, we investigated the role of matrilin-3 in preventing Ad-MSC-derived chondrocyte hypertrophy in vitro and in an osteoarthritis (OA) destabilization of the medial meniscus (DMM) model. Methacrylated hyaluron (MAHA) (1%) was used to encapsulate and make scaffolds containing Ad-MSCs and matrilin-3. Subsequently, the encapsulated cells in the scaffolds were differentiated in chondrogenic medium (TGF-β, 1–14 days) and thyroid hormone hypertrophic medium (T3, 15–28 days). The presence of matrilin-3 with Ad-MSCs in the MAHA scaffold significantly increased the chondrogenic marker and decreased the hypertrophy marker mRNA and protein expression. Furthermore, matrilin-3 significantly modified the expression of TGF-β2, BMP-2, and BMP-4. Next, we prepared the OA model and transplanted Ad-MSCs primed with matrilin-3, either as a single-cell suspension or in spheroid form. Safranin-O staining and the OA score suggested that the regenerated cartilage morphology in the matrilin-3-primed Ad-MSC spheroids was similar to the positive control. Furthermore, matrilin-3-primed Ad-MSC spheroids prevented subchondral bone sclerosis in the mouse model. Here, we show that matrilin-3 plays a major role in modulating Ad-MSCs’ therapeutic effect on cartilage regeneration and hypertrophy suppression.     

Operational time-series data modeling via LSTM network integrating principal component analysis based on human experience

Today’s information technologies involve increasingly intelligent systems, which come at the cost of increasingly complex equipment. Modern monitoring systems collect multi-measuring-point and long-term data which make equipment health prediction a “big data” problem. It is difficult to extract information from such condition monitoring data to accurately estimate or predict health statuses. Deep learning is a powerful tool for big data processing that is widely utilized in image and speech recognition applications, and can also provide effective predictions in industrial processes. This paper proposes the Long Short-term Memory Integrating Principal Component Analysis based on Human Experience (HEPCA-LSTM), which uses operational time-series data for equipment health prognostics. Principal component analysis based on human experience is first conducted to extract condition parameters from the condition monitoring system. The long short-term memory (LSTM) framework is then constructed to predict the target status. Finally, a dynamic update of the prediction model with incoming data is performed at a certain interval to prevent any model misalignment caused by the drifting of relevant variables. The proposed model is validated on a practical case and found to outperform other prediction methods. It utilizes a powerful deep learning analysis method, the LSTM, to fully process big condition monitoring series data; it effectively extracts the features involved with human experience and takes dynamic updates into consideration.     

Intelligent Information‐Centric Networking routing scheme under imprecise information

Information‐Centric Networking (ICN) has been accepted to overcome some weaknesses of the current Internet architecture, showing that “what is being exchanged” is more important than “who are exchanging information.” Given the inadequate considerations on Quality of Service (QoS) and energy saving in ICN routing, we propose in this paper a routing algorithm to enhance the two aspects. At first, on one hand, Cauchy distribution is used as a fuzzy model to evaluate users' QoS requirements, such as bandwidth, delay, and error rate; on the other hand, we formulate energy saving problem to evaluate the green quality of routing algorithm. Then, we design a link selection approach by considering QoS and energy saving, which belongs to a multi‐objective decision problem resolved by intelligent drops algorithm. Finally, we implement the proposed algorithm and compare it with the famous adaptive forwarding mechanism in terms of some significant metrics, and the experimental results reveal that the proposed algorithm is more efficient.     

Smart firefighting construction in China: Status,problems, and reflections

“Smart firefighting” construction as a part of the “smart city” has been a concern of the public security and fire agencies at all levels. In this study, the status, problems, and reflections of “smart firefighting” construction in China are discussed. A recent survey indicates that China has launched its smart firefighting construction and initially created a new perspective on its regional smart firefighting work based on three main aspects: intelligent disaster perception by Internet of Things (IoT) construction, intelligent disaster prevention by big data construction, and intelligent disaster disposal by emergency rescue platform construction. However, the current smart firefighting construction in China still has some prominent problems such as the data interconnectivity and normalized management of various platforms, the extensibility of smart firefighting platforms, and the intelligent level of smart firefighting researches, which need to be solved urgently. Therefore, we argue that smart firefighting construction in China should establish data interconnectivity, industrial normalized management, 2D/3D geographic information interaction and extension, high-integration fire protection theory, and many other aspects in the near future and truly realize firefighting visualization and efficient data applications for 4D time space. This study could provide valuable reference for smart firefighting and smart city construction.