Hydrogen Yield from Low Temperature Steam Reforming of Ethanol

Low temperature steam reforming of ethanol in the temperature range of 200–360°C was studied to maximize the production of H₂. The optimum reaction conditions in presence of a suitable catalyst can produce mainly the desired products H₂ and CO₂. Cu/Al₂O₃ catalysts with six different concentrations ranging from 0 to 10 wt.% Mn, were prepared, characterized and studied for the ethanol-steam reforming reaction. Maximum ethanol conversion of 60.7% and hydrogen yield of 3.74 (mol H₂ / mol ethanol converted) were observed at 360°C for catalyst with 2.5 wt.% Mn loading.     

Occurrence of wax esters and 1-O-alkyl-2,3-diacylglycerols in goat epididymal sperm plasma membrane

Two unusual lipid classes were detected by thin-layer chromatography in the neutral lipids derived from goat cauda-epididymal sperm plasma membrane. The lipids were identified as wax esters and 1-O-alkyl-2,3-diacylglycerols based on chromatographic properties, identity of their hydrolysis products, and infrared/¹H nuclear magnetic resonance spectral evidence. The membrane containedca. 3 and 5 μg/mg protein of wax esters and alkyldiacylglycerols, respectively. The relative proportions of wax esters and alkyldiacylglycerols in the total neutral lipids were 1.5% and 2.4%, respectively. The lipids contained fatty acids with chain lengths of C₁₄ to C₂₂. The major fatty acids of the wax esters were 14∶0, 16∶0, 16∶1ω7, 18∶0 and 18∶1ω9. The fatty acids in alkyldiacylglycerol were 16∶0, 18∶0, 22∶5ω3 and 22∶6ω3. Alkyldiacylglycerol was particularly rich in docosahexaenoic acid 22∶6ω3) representing 30% of the total fatty acids. The alcohols of wax ester were all saturated with C₂₀–C₂₉ carbon chains. The deacylated products derived from alkyldiacylglycerols were identified as hexadecyl, octadecyl and octadec-9′-enyl glycerol ethers.     

Dependence of coal liquefaction behaviour on coal characteristics. 6. Product distributions from coals treated in a continuous-flow reactor

Further experimentation with coals in a continuous-flow reactor system at 440 and 455°C confirms the significance of the previous classification of a set of coals by cluster analysis. The greatest yields of distillable products tend to be given by coals of high sulphur content in the middle of the range of the high-volatile bituminous rank classes. Yet the lowest yields of preasphaltene (toluene-insoluble material) tend to be given by coals of high rank and medium sulphur content. The group of low sulphur, low-rank coals (mostly Rocky Mountain province), in a solvent different from that used for the other groups, showed poor conversion and poor distillate yield at the higher temperature. It is concluded that this coal/solvent combination is particularly prone to retrogressive reactions, including coking.     

Fabrication and characterization of electrospun psyllium husk-based nanofibers for tissue regeneration

The present study reports for first time the blending of psyllium husk (PH) powder/gelatin (G) in the polymer-rich composition of polyvinyl alcohol (PVA) to make an electrospinnable solution. The composite was prepared in 3 different ratios viz., 100% (wt/wt) (PVA + PH), 75% + 25% (PVA + 75PH + 25G) (wt/wt) and 50% + 50% (PVA + 50PH + 50G) (wt/wt) in 6% PVA solution. Optimum electrospinning parameters were evaluated for all the prepared blends. The fabricated nanofibers were characterized by scanning electron microscopy (SEM), attenuated total reflectance-Fourier transform infrared, differential scanning calorimetry, porosity percentage, and fiber orientation using ImageJ software. A qualitative in vitro degradation study at room temperature is supported by SEM images. The cellular interactions were characterized by MTT assay of NIH-3T3 fibroblast cells for 2 and 4 days with an optimum cell growth of >50% by fourth day of culture and long-term cultivation of L929-RFP cells was observed for 10 days. The nanofibers were formed in the range of 49–600 nm. PVA + 75PH + 25G when cultured with L929-RFP cells exhibited highest fluorescence intensity and thus supported cellular proliferation significantly. Based on the results obtained from various analyses, we anticipate that fabricated psyllium-based nanofiber can be used as a promising candidate for wound healing and other biomedical applications.     

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.     

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.