Studies on the hot corrosion of a nickel-base superalloy,Udimet 700

The hot corrosion of a nickel-base superalloy, Udimet 700, has been studied in the temperature range 900–950°C. The effect of the amount of Na ₂SO₄ on the corrosion kinetics was determined. Large weight gains and severe corrosion were associated with two different modes of degradation: (1) formation of large, interconnected sulfides beneath the external scale, and (2) formation of a Na₂MoO₄-MoO₃ melt. The corrosion due to formation of the Na₂MoO₄-MoO₃ melt occurred for all the salt-coating thicknesses, whereas, large sulfides were formed only for the heavier coatings of Na₂SO₄. The formation of Na₂MoO₄-MoO₃ melt required an induction period, and the length of the induction period was observed to be a function of the amount of Na₂SO₄ and of temperature.Work funded under NASA Grant NCC 3-43.     

DECODE: a new method for discovering clusters of different densities in spatial data

When clusters with different densities and noise lie in a spatial point set, the major obstacle to classifying these data is the determination of the thresholds for classification, which may form a series of bins for allocating each point to different clusters. Much of the previous work has adopted a model-based approach, but is either incapable of estimating the thresholds in an automatic way, or limited to only two point processes, i.e. noise and clusters with the same density. In this paper, we present a new density-based cluster method (DECODE), in which a spatial data set is presumed to consist of different point processes and clusters with different densities belong to different point processes. DECODE is based upon a reversible jump Markov Chain Monte Carlo (MCMC) strategy and divided into three steps. The first step is to map each point in the data to its mth nearest distance, which is referred to as the distance between a point and its mth nearest neighbor. In the second step, classification thresholds are determined via a reversible jump MCMC strategy. In the third step, clusters are formed by spatially connecting the points whose mth nearest distances fall into a particular bin defined by the thresholds. Four experiments, including two simulated data sets and two seismic data sets, are used to evaluate the algorithm. Results on simulated data show that our approach is capable of discovering the clusters automatically. Results on seismic data suggest that the clustered earthquakes, identified by DECODE, either imply the epicenters of forthcoming strong earthquakes or indicate the areas with the most intensive seismicity, this is consistent with the tectonic states and estimated stress distribution in the associated areas. The comparison between DECODE and other state-of-the-art methods, such as DBSCAN, OPTICS and Wavelet Cluster, illustrates the contribution of our approach: although DECODE can be computationally expensive, it is capable of identifying the number of point processes and simultaneously estimating the classification thresholds with little prior knowledge.     

Ionic transport in the (Agl∶AgCl) mixed-system

Ionic mobility, conductivity, number of charge carriers and transference number are reported for annealed and rapidly quenched [xAgl(1–x)AgCl] mixed systems, these results are compared with Agl. Of these three materials the quenched mixed-system had the highest Ag⁺ ion conductivity. Further, preliminary studies are reported where the high conductivity system (i.e. quenched 0.75 Agl0.25 AgCl) has been used as host material for the preparation of Ag⁺ ion conducting glasses and composites. Detailed thermal/phase diagram studies have also been carried out on the quenched composition. The conductivity enhancement in the quenched system is attributed to the formation of new disordered phases and the introduction of amorphisity, the simple space-charge model applicable to the annealed system failed to explain these results.     

Fabricating Strong and Stiff Bioplastics from Whole Spirulina Cells

Since the 1950s, 8.3 billion tonnes (Bt) of virgin plastics have been produced, of which around 5 Bt have accumulated as waste in oceans and other natural environments, posing severe threats to entire ecosystems. The need for sustainable bio-based alternatives to traditional petroleum-derived plastics is evident. Bioplastics produced from unprocessed biological materials have thus far suffered from heterogeneous and non-cohesive morphologies, which lead to weak mechanical properties and lack of processability, hindering their industrial integration. Here, a fast, simple, and scalable process is presented to transform raw microalgae into a self-bonded, recyclable, and backyard-compostable bioplastic with attractive mechanical properties surpassing those of other biobased plastics such as thermoplastic starch. Upon hot-pressing, the abundant and photosynthetic algae spirulina forms cohesive bioplastics with flexural modulus and strength in the range 3–5 GPa and 25.5–57 MPa, respectively, depending on pre-processing conditions and the addition of nanofillers. The machinability of these bioplastics, along with self-extinguishing properties, make them promising candidates for consumer plastics. Mechanical recycling and fast biodegradation in soil are demonstrated as end-of-life options. Finally, the environmental impacts are discussed in terms of global warming potential, highlighting the benefits of using a carbon-negative feedstock such as spirulina to fabricate plastics.     

Learning Noise-Assisted Robust Image Features for Fine-Grained Image Retrieval

Fine-grained image search is one of the most challenging tasks in computer vision that aims to retrieve similar images at the fine-grained level for a given query image. The key objective is to learn discriminative fine-grained features by training deep models such that similar images are clustered, and dissimilar images are separated in the low embedding space. Previous works primarily focused on defining local structure loss functions like triplet loss, pairwise loss, etc. However, training via these approaches takes a long training time, and they have poor accuracy. Additionally, representations learned through it tend to tighten up in the embedded space and lose generalizability to unseen classes. This paper proposes a noise-assisted representation learning method for fine-grained image retrieval to mitigate these issues. In the proposed work, class manifold learning is performed in which positive pairs are created with noise insertion operation instead of tightening class clusters. And other instances are treated as negatives within the same cluster. Then a loss function is defined to penalize when the distance between instances of the same class becomes too small relative to the noise pair in that class in embedded space. The proposed approach is validated on CARS-196 and CUB-200 datasets and achieved better retrieval results (85.38% recall@1 for CARS-196% and 70.13% recall@1 for CUB-200) compared to other existing methods.     

Intrusion detection and prevention system for an IoT environment

Internet of Things (IoT) security is the act of securing IoT devices and networks. IoT devices, including industrial machines, smart energy grids, and building automation, are extremely vulnerable. With the goal of shielding network systems from illegal access in cloud servers and IoT systems, Intrusion Detection Systems (IDSs) and Network-based Intrusion Prevention Systems (NBIPSs) are proposed in this study. An intrusion prevention system is proposed to realize NBIPS to safeguard top to bottom engineering. The proposed NBIPS inspects network activity streams to identify and counteract misuse instances. The NBIPS is usually located specifically behind a firewall, and it provides a reciprocal layer of investigation that adversely chooses unsafe substances. Network-based IPS sensors can be installed either in an inline or a passive model. An inline sensor is installed to monitor the traffic passing through it. The sensors are installed to stop attacks by blocking the traffic using an IoT signature-based protocol.     

Reliable before-fabrication forecasting of MEMS piezoresistive pressure sensor: mathematical modelling and numerical simulation

Microsystem Technologies - Micro-mechanical systems (MEMS) based piezoresistive pressure sensors have significant importance in several pressure sensor devices in real world, i.e., aviation, IoT...     

Experimental deformation analysis of an adhesively bonded multi-material joint for marine applications

The strength and deformation of full-scale adhesively bonded multi-material joints is studied in this paper. Four joints with a thick layer of methyl methacrylate adhesive (MMA) have been manufactured in shipyard conditions. In two specimens, cracks have been introduced at steel–adhesive and composite–adhesive interfaces. One cracked and one un-cracked specimen were subjected to quasi-static tensile testing; the two remaining specimens were stepwise loaded/unloaded with increasing load until failure. The strain in the adhesive layers was measured with digital image correlation (DIC). This showed a predominant shear deformation and dissimilar shear strain patterns for different bond lines. Fibre Bragg (FBG) sensors were used to monitor strains at steel and composite constituents and to detect the onset and evolution of damage in the un-cracked specimen. Strains measured by FBG sensors correspond well with DIC results at nearby regions. All specimens failed by delamination of the composite panel near the composite–adhesive interface.     

Multi‐response optimization and empirical modeling of cardiopulmonary responses during manual lifting tasks

An experimental study involving five independent lifting variables (namely weight, lift frequency, horizontal distance from the object, operator, and vertical distance) and their interactions was conducted using the Taguchi L₂₇ design. Two responses—heart rate and oxygen uptake—were measured during each trial. To optimize both responses simultaneously, the signal‐to‐noise ratio was normalized to obtain a multi‐response signal‐to‐noise (MRSN) ratio for each trial. The MRSN ratio was analyzed using analysis of variance to identify the optimal condition in which both responses were optimized at the same time. Lift frequency was found to be the most significant factor, and operator and horizontal distance were found to be least significant. The empirical modeling equation was developed and can be used to predict the response values during experimental trials involving similar work conditions. © 2010 Wiley Periodicals, Inc.