Rolling contact fatigue behavior of sintered and hardened steels

Powder-metal-processed bearings and gears are finding increasing application because of their economical and technical advantages. The residual pores from the sintering operatives act as lubricant pockets and dampen sound and vibration. However, porosity also decreases the mechanical strength and reduces the life of components fabricated by powder processing relative to similar wrought components. The rolling contact fatigue behavior of sintered and heat treated steel rollers was investigated using a fatigue test machine designed and fabricated for that purpose. The powder-metal-processed and the wrought steel rollers that were tested had similar composition and hardness and were mated against wrought steel rollers of high hardness. The contact stress versus number of cycles to failure data showed that the wrought steel had a very high endurance limit under rolling contact fatigue compared to the sintered steels investigated. Rolling contact fatigue behavior was found to depend on the porosity present in the material. Large surface peeling failures and pitting type fatigue failures were observed in the sintered and hardened steels, while only pitting type failures were observed in the wrought steels     

A system for debugging via online tracing and dynamic slicing

Dynamic slicing is a promising trace based technique that helps programmers in the process of debugging. In order to debug a failed run, dynamic slicing requires the dynamic dependence graph (DDG) information for that particular run. The two major challenges involved in utilizing dynamic slicing as a debugging technique are the efficient computation of the DDG and the efficient computation of the dynamic slice, given the DDG. In this paper, we present an efficient debugger, which first computes the DDG efficiently while the program is executing; dynamic slicing is later performed efficiently on the computed DDG, on demand. To minimize program slowdown during the online computation of DDG, we make the design decision of not outputting the computed dependencies to a file, instead, storing them in memory in a specially allocated fixed size circular buffer. The size of the buffer limits the length of the execution history that can be stored. To maximize the execution history that can be maintained, we introduce optimizations to eliminate the storage of most of the generated dependencies, at the same time ensuring that those that are stored are sufficient to capture the bug. Experiments conducted on CPU‐intensive programs show that our optimizations are able to reduce the trace rate from 16 to 0.8 bytes per executed instruction. This enables us to store the dependence trace history for a window of 20 million executed instructions in a 16‐MB buffer. Our debugger is also very efficient, yielding slicing times of around a second, and only slowing down the execution of the program by a factor of 19 during the online tracing step. Using recently proposed architectural support for monitoring, we are also able to handle multithreaded programs running on multicore processors. Copyright © 2011 John Wiley & Sons, Ltd.     

Investigation of a Novel Condensation Aerosol Generator: Solute and Solvent Effects

Part I of this article presents results of an experimental study on gas-phase nucleation for three model solutes and their solvent, propylene glycol (PG), with variables being solute concentration and the nature of the solute substance. A single manifestation of an aerosol generator, which forms condensation aerosols by cooling of hot vapor issuing from an electrically heated, pumped capillary, is described and used for all experiments. The effects of solute concentration and solute type were studied for deoxycorticosterone (DOC), benzil (BZ), and phenyl salicylate (PhS). Suppression of homogeneous nucleation and occurrence of heterogeneous condensation of the solvent was observed at different solute concentrations for BZ, PhS, and DOC. The nature and concentration of the solute dissolved in the solvent was shown to determine the final particle size distribution of the condensed aerosol. In the case of the least volatile solute, DOC, solute aerosol and total aerosol size distributions were identical at low solute concentrations. A transitional concentration region then existed in which a bimodal solute aerosol was formed, followed at high concentrations by increasing separation of the solvent-dominated aerosol size distribution and that of the solute. In Part II of this article, the effect of DOC dissolution in different solvents was studied at fixed solute concentration. The effects of six glycol solvents--i.e., PG, ethylene glycol (EG), dipropylene glycol (DPG), diethylene glycol (DEG), triethylene glycol (TEG), and tetraethylene glycol (TetEG)--and three nonglycol solvents--i.e., dimethyl sulfoxide (DMSO), formamide (FORM), and oleyl alcohol (OA)--were studied, as these affected the resultant aerosol sizes. Suppression of total aerosol mass median aerodynamic diameter (MMAD) was observed on dissolution of 0.5% w/w DOC in each solvent, although suppression occurred to different extents. It was shown that the boiling point or volatility of the solvent used to dissolve the less volatile DOC had an effect on the final particle size distribution of the condensed aerosol and whether the aerodynamic size distributions for the solute and the total aerosol were the same or different.     

Enhancing biological hydrogen production through complementary microbial metabolisms

Hydrogen (H₂) production through biological routes is an eco-friendly process. Dark-fermentative or photosynthetic pathways have been widely studied to produce H₂. In these cases yields up to 3.8 mol H₂/mol glucose have been reported to be produced under dark-fermentative conditions by Enterobacter, Caldicellulosiruptor and Thermotoga spp., or through photo-fermentative route by bacteria such as Rhodopseudomonas spp. A combination of the two systems allows a 2-fold increase in H₂ yield. The need is to increase the efficiency of the whole process to yield 12 mol H₂/mol glucose. In this review, we propose that metabolic activities of bacteria may be complemented to use biowaste as feed for increasing the efficiency of H₂ production process. Further utilization of intermediates of these two processes for polyhydroxyalkanoate and methane production is likely to increase the feasibility of meeting the ever increasing energy demand.     

Wettability and interfacial phenomena in the liquid-phase bonding of refractory diboride ceramics: Recent developments

Joining and integration technologies are integral to manufacturing of components based on ultrahigh-temperature ceramics (UHTCs) such as transition metal diborides. Brazing is a particularly attractive joining technique because of its simplicity and versatility, but its use to join the UHTCs demands knowledge of the complex interplay among high-temperature wettability, interfacial reactions, and chemical and thermoelastic compatibilities. This paper summarizes the research and development activities carried out over the last two decades to characterize the wettability and interfacial phenomena in brazing of refractory diboride ceramics. The contact angle data of various metal alloys on diboride-based ceramics have been collected and critically evaluated in conjunction with an analysis of the chemistry and structure of the interface to understand the underlying mechanisms and phenomena that govern interface formation. It explores how solid–liquid interactions impact and are impacted by physical, chemical, and mechanical properties of joined materials. It also describes how this knowledge has been successfully utilized to create liquid-phase bonded diboride-based joints. The paper concludes with a summary of the current state of the art and highlights integration challenges and future research and technology development needs in the area.     

Protein-Metabolite Interactions: Discovery and Significance

Metabolites orchestrate cellular processes as either substrates, co-enzymes, inhibitors, or activators of cellular proteins such as enzymes and receptors. Although traditional biochemical and structural biology-based approaches have been successfully employed for the discovery of protein-metabolite interactions, they often fail to detect transient and low-affinity biomolecular relationships. Another limitation of these approaches is that they are performed under in vitro conditions lacking the physiological context. Recently developed mass spectrometry-based methodologies overcome both these shortcomings, and have resulted in the discovery of global protein-metabolite cellular interaction networks. Herein, we describe traditional and modern approaches for the discovery of protein-metabolite interactions, and discuss the impact of these discoveries on our understanding of cellular physiology and on drug development.     

An intelligent railway surveillance framework based on recognition of object and railway track using deep learning

Multimedia Tools and Applications - In high speed railways, the intelligent railway safety system is necessary to avoid the accidents due to collision between trains and obstacles on the railway...     

Covid-19 Forecasting with Deep Learning-based Half-binomial Distribution Cat Swarm Optimization

About 170 nations have been affected by the COvid VIrus Disease-19 (COVID-19) epidemic. On governing bodies across the globe, a lot of stress is created by COVID-19 as there is a continuous rise in patient count testing positive, and they feel challenging to tackle this situation. Most researchers concentrate on COVID-19 data analysis using the machine learning paradigm in these situations. In the previous works, Long Short-Term Memory (LSTM) was used to predict future COVID-19 cases. According to LSTM network data, the outbreak is expected to finish by June 2020. However, there is a chance of an over-fitting problem in LSTM and true positive; it may not produce the required results. The COVID-19 dataset has lower accuracy and a higher error rate in the existing system. The proposed method has been introduced to overcome the above-mentioned issues. For COVID-19 prediction, a Linear Decreasing Inertia Weight-based Cat Swarm Optimization with Half Binomial Distribution based Convolutional Neural Network (LDIWCSO-HBDCNN) approach is presented. In this suggested research study, the COVID-19 predicting dataset is employed as an input, and the min-max normalization approach is employed to normalize it. Optimum features are selected using Linear Decreasing Inertia Weight-based Cat Swarm Optimization (LDIWCSO) algorithm, enhancing the accuracy of classification. The Cat Swarm Optimization (CSO) algorithm’s convergence is enhanced using inertia weight in the LDIWCSO algorithm. It is used to select the essential features using the best fitness function values. For a specified time across India, death and confirmed cases are predicted using the Half Binomial Distribution based Convolutional Neural Network (HBDCNN) technique based on selected features. As demonstrated by empirical observations, the proposed system produces significant performance in terms of f-measure, recall, precision, and accuracy.     

Small-Molecule Inhibitors of METTL3, the Major Human Epitranscriptomic Writer

The RNA methylase METTL3 catalyzes the transfer of a methyl group from the cofactor S-adenosyl-L-methionine (SAM) to the N⁶ atom of adenine. We have screened a library of 4000 analogues and derivatives of the adenosine moiety of SAM by high-throughput docking into METTL3. Two series of adenine derivatives were identified in silico, and the binding mode of six of the predicted inhibitors was validated by protein crystallography. Two compounds, one for each series, show good ligand efficiency. We propose a route for their further development into potent and selective inhibitors of METTL3.