Data fusion and transfer learning empowered granular trust evaluation for Internet of Things

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.     

Establishment of a color tolerance for yarn-dyed fabrics from different color-depth yarns

Yarn-dyed fabric is often woven from warp and weft yarns in the same color depth to ensure a uniform color appearance. The difference in color depth between warp and weft tends to result in the uneven color of the yarn-dyed fabric. This article aims to establish a color tolerance for yarn-dyed fabric that can be woven with a qualified color appearance but from the warp and weft yarns in different color depths. A total of 27 yarn-dyed fabric samples in three color series (red, yellow, and blue) were evaluated by using the yarn-dyed fabric from warp and weft yarns in the same color depth of 2% (on weight of fabric, owf) as the standard. Visual assessment and instrumental measurement of color were carried out to establish the color tolerance ellipse that was defined as CMC (Color Measurement Committee) color differences (2:1) of no more than 1.00. It was found that the color strengths (K/S) and color differences (ΔE_CMC(2:1)) of these fabric samples for each color series had linear relationships with the color depths of warp and weft yarns. The color tolerance ellipses indicated that, even though the warp and weft yarns had an apparent color difference, they could be woven in fabrics with relatively uniform color appearance and meet the requirements for yarn-dyed fabric. This work provided valuable insight into the production of qualified yarn-dyed fabrics from unqualified dyed yarns.     

Comparative analysis of inbreeding parameters and runs of homozygosity islands in 2 Italian autochthonous cattle breeds mainly raised in the Parmigiano-Reggiano cheese production region

Reggiana and Modenese are autochthonous cattle breeds, reared in the North of Italy, that can be mainly distinguished for their standard coat color (Reggiana is red, whereas Modenese is white with some pale gray shades). Almost all milk produced by these breeds is transformed into 2 mono-breed branded Parmigiano-Reggiano cheeses, from which farmers receive the economic incomes needed for the sustainable conservation of these animal genetic resources. After the setting up of their herd books in 1960s, these breeds experienced a strong reduction in the population size that was subsequently reverted starting in the 1990s (Reggiana) or more recently (Modenese) reaching at present a total of about 2,800 and 500 registered cows, respectively. Due to the small population size of these breeds, inbreeding is a very important cause of concern for their conservation programs. Inbreeding is traditionally estimated using pedigree data, which are summarized in an inbreeding coefficient calculated at the individual level (F_PED). However, incompleteness of pedigree information and registration errors can affect the effectiveness of conservation strategies. High-throughput SNP genotyping platforms allow investigation of inbreeding using genome information that can overcome the limits of pedigree data. Several approaches have been proposed to estimate genomic inbreeding, with the use of runs of homozygosity (ROH) considered to be the more appropriate. In this study, several pedigree and genomic inbreeding parameters, calculated using the whole herd book populations or considering genotyping information (GeneSeek GGP Bovine 150K) from 1,684 Reggiana cattle and 323 Modenese cattle, were compared. Average inbreeding values per year were used to calculate effective population size. Reggiana breed had generally lower genomic inbreeding values than Modenese breed. The low correlation between pedigree-based and genomic-based parameters (ranging from 0.187 to 0.195 and 0.319 to 0.323 in the Reggiana and Modenese breeds, respectively) reflected the common problems of local populations in which pedigree records are not complete. The high proportion of short ROH over the total number of ROH indicates no major recent inbreeding events in both breeds. ROH islands spread over the genome of the 2 breeds (15 in Reggiana and 14 in Modenese) identified several signatures of selection. Some of these included genes affecting milk production traits, stature, body conformation traits (with a main ROH island in both breeds on BTA6 containing the ABCG2, NCAPG, and LCORL genes) and coat color (on BTA13 in Modenese containing the ASIP gene). In conclusion, this work provides an extensive comparative analysis of pedigree and genomic inbreeding parameters and relevant genomic information that will be useful in the conservation strategies of these 2 iconic local cattle breeds.     

Mixing enhancement mechanism of combined H2–Water jets in supersonic crossflows in a combustor with an expanded section

To obtain the mixing enhancement mechanism of H₂–Water combined jets in supersonic crossflows in a combustor with expanded section for rotating detonation ramjet, the flow field shape and spray structure were studied by experimental and numerical methods. The Eulerian–Lagrangian method was used to investigate the diffusion mechanism and H₂–Water interaction law of combined jets with different sequences. At the same time, high-speed photography and the schlieren technique were used to capture the flow field. The effects of jet pressure drop, orifice diameter, orifice spacing, incoming Mach number, and other parameters on the penetration depth of water jets were studied. The results of experiment and simulation show that using H₂–Water combined jets, the penetration depth of the jet spray can be greatly increased and the jet mixing effect can be significantly improved, which will contribute to the engine's ignition and stable combustion. In the case of pre-water/post-H₂, the penetration depth of the hydrogen jet is greater. In the case of pre-H₂/post-water, the hydrogen jet raises the water spray mainly by protecting the integrity of the water column.     

Deep reinforcement learning-based cooperative interactions among heterogeneous vehicular networks

Most real-world vehicle nodes can be structured into an interconnected network of vehicles. Through structuring these services and vehicle device interactions into multiple types, such internet of vehicles becomes multidimensional heterogeneous overlay networks. The heterogeneousness of the overlays makes it difficult for the overlay networks to coordinate with each other to improve their performance. Therefore, it poses an interesting but critical challenge to the effective analysis of heterogeneous virtual vehicular networks. A variety of virtual vehicular networks can be easily deployed onto the native network by applying the concept of SDN (Software Defined Networking). These virtual networks reflect their heterogeneousness due to their different performance goals, and they compete for the same physical resources of the underlying network, so that a sub-optimal performance of the virtual networks may be achieved. Therefore, we propose a Deep Reinforcement Learning (DRL) approach to make the virtual networks cooperate with each other through the SDN controller. A cooperative solution based on the asymmetric Nash bargaining is proposed for co-existing virtual networks to improve their performance. Moreover, the Markov Chain model and DRL resolution are introduced to leverage the heterogeneous performance goals of virtual networks. The implementation of the approach is introduced, and simulation results confirm the performance improvement of the latency sensitive, loss-rate sensitive and throughput sensitive heterogeneous vehicular networks using our cooperative solution.     

Stress distribution in thick-walled cylinder due to non-uniform radial pressure on the example of reinforcement corrosion in concrete

This paper presents an analytical solution to the non-uniform pressure on thick-walled cylinder. The formulation is based on the linear elasticity theory (plain strain) and stress function method. As an example, the proposed solution is used to model the stress distribution due to non-uniform steel reinforcement corrosion in concrete. The model is formulated considering different scenarios of corrosion pressure distribution. It is validated against the finite element model for different cases of non-uniform pressure distributions. The results show that the corrosion-induced cracks are likely to start just beyond the anodic zone. This is confirmed by the experimental tests on concrete cylinder exposed to non-uniform accelerated corrosion of steel reinforcement. The model can be effectively used to calculate the distribution of corrosion-induced stresses in concrete.     

基于新型光纤的传输技术探讨

随着互联网的高速发展，引发了网络流量、电信骨干网流量急速增长，使得网络容量的提升迫在眉睫。目前，100G 系统已经商用，超100G系统能够更有效地解决流量和网络带宽持续增长带来的压力，本文主要介绍新型单模光纤的特性以及探讨基于新型单模光纤的传输技术。     

A Novel Beam Search to Improve Neural Machine Translation for English-Chinese

Neural Machine Translation (NMT) is an end-to-end learning approach for automated translation, overcoming the weaknesses of conventional phrase-based translation systems. Although NMT based systems have gained their popularity in commercial translation applications, there is still plenty of room for improvement. Being the most popular search algorithm in NMT, beam search is vital to the translation result. However, traditional beam search can produce duplicate or missing translation due to its target sequence selection strategy. Aiming to alleviate this problem, this paper proposed neural machine translation improvements based on a novel beam search evaluation function. And we use reinforcement learning to train a translation evaluation system to select better candidate words for generating translations. In the experiments, we conducted extensive experiments to evaluate our methods. CASIA corpus and the 1,000,000 pairs of bilingual corpora of NiuTrans are used in our experiments. The experiment results prove that the proposed methods can effectively improve the English to Chinese translation quality.     

Assessment of the effectiveness of steel fibre reinforcement for the punching resistance of flat slabs by experimental research and design approach

The present paper deals with the experimental assessment of the effectiveness of steel fibre reinforcement in terms of punching resistance of centrically loaded flat slabs, and to the development of an analytical model capable of predicting the punching behaviour of this type of structures. For this purpose, eight slabs of 2550 × 2550 × 150 mm³ dimensions were tested up to failure, by investigating the influence of the content of steel fibres (0, 60, 75 and 90 kg/m³) and concrete strength class (50 and 70 MPa). Two reference slabs without fibre reinforcement, one for each concrete strength class, and one slab for each fibre content and each strength class compose the experimental program. All slabs were flexurally reinforced with a grid of ribbed steel bars in a percentage to assure punching failure mode for the reference slabs. Hooked ends steel fibres provided the unique shear reinforcement. The results have revealed that steel fibres are very effective in converting brittle punching failure into ductile flexural failure, by increasing both the ultimate load and deflection, as long as adequate fibre reinforcement is assured. An analytical model was developed based on the most recent concepts proposed by the fib Mode Code 2010 for predicting the punching resistance of flat slabs and for the characterization of the behaviour of fibre reinforced concrete. The most refined version of this model was capable of predicting the punching resistance of the tested slabs with excellent accuracy and coefficient of variation of about 5%.     

Corrosion resistance of Al–Cr-slag containing chromium–corundum refractories to slags with different basicity

Al–Cr slag is the solid waste generated by the smelting of Cr metal. It presents a range of environmental hazards. This study addressed the corrosion resistance of Al–Cr slag containing chromium–corundum refractories to slags with different basicity. Herein, we provide suggestions for the use of Cr–corundum of different basicity in kilns. Al–Cr slag, brown fused Al₂O₃, and chrome green were used as the raw materials, with pure calcium aluminate cement being used as a binder. The brick samples, prepared using different blends of chrome green and corundum, were fired at 1600?°C, and subsequently subjected to a slag corrosion test. After corrosion by slag of different basicity, the phase composition and microstructure of the sample were analyzed by X-ray diffraction, energy dispersive spectrometer and scanning electron microscopy. There were two major findings. First, Cr–corundum brick made from Al–Cr slag has a better slag corrosion resistance than that made from Cr₂O₃ and brown fused Al₂O₃. Second, Cr–corundum brick made from Al–Cr slag has superior corrosion resistance to slag with a CaO:SiO₂ ratio of 2:1.