Machinability of a nickel aluminide intermetallic alloy

This article reports the results of an experimental study on the machinability of a nickel aluminide intermetallic alloy. Machining was conducted at various speeds, and results indicate low material removal rates. Chips collected for each test run were measured for thickness for shear angle calculation and were subsequently observed microscopically. Chip segmentation was observed microscopically, and a fluctuation in the shear angle was evidenced. A parameter characterizing the severity of the machining process, the chip reduction coefficient (K), was calculated from chip thickness measurements. The calculated values ofK were found to be low, indicating a low severity of the metalcutting process. This, however, is in contract with the observed low rates of metal removal and low tool life. Thus, conventional metalcutting characterization parameters require re- examination in terms of machining high-strength materials.     

The effects of silicon on the properties of tantalum

The microalloying of tantalum with silicon has been effectively used on a commercial scale. However, data on effects of microalloying on processibility, microstructure and mechanical properties are not readily available. To develop some data, tantalum powder with various levels of silicon (0–50 ppm) was processed under identical conditions. Sheet samples were then annealed at different temperatures and evaluated via microstructural, chemical and mechanical tests. The recrystallization temperature was found to increase with silicon content, most probably as a result of the presence of an intermetallic (Ta₃Si) phase. The mechanical properties of unrecrystallized tantalum are, as expected, significantly different from those of recrystallized tantalum.     

Collaborative jamming and collaborative defense in cognitive radio networks

In cognitive radio networks, cognitive nodes operate on a common pool of spectrum where they opportunistically access and use parts of the spectrum not being used by others. Though cooperation among nodes is desirable for efficient network operations and performance, there might be some malicious nodes whose objective could be to hinder communications and disrupt network operations. The absence of a central authority or any policy enforcement mechanism makes these kinds of open-access network more vulnerable and susceptible to attacks.In this paper, we analyze a common form of denial-of-service attack, i.e., collaborative jamming. We consider a network in which a group of jammers tries to jam the channels being used by legitimate users who in turn try to evade the jammed channels. First, we compute the distribution of the jamming signal that a node experiences by considering a random deployment of jammers. Then, we propose different jamming and defending schemes that are employed by the jammers and legitimate users, respectively. In particular, we model and analyze the channel availability when the legitimate users randomly choose available channels and the jammers jam different channels randomly. We propose a multi-tier proxy-based cooperative defense strategy to exploit the temporal and spatial diversity for the legitimate secondary users in an infrastructure-based centralized cognitive radio network. Illustrative results on spectrum availability rates show how to improve resiliency in cognitive radio networks in the presence of jammers.     

Reduced complexity two stage vector quantization

We address the issue of complexity for vector quantization (VQ) of wide-band speech LSF (line spectrum frequency) parameters. The recently proposed switched split VQ (SSVQ) method provides better rate–distortion (R/D) performance than the traditional split VQ (SVQ) method, even at the requirement of lower computational complexity, but at the expense of much higher memory. We develop the two stage SVQ (TsSVQ) method, by which we gain both the memory and computational advantages and still retain good R/D performance. The proposed TsSVQ method uses a full dimensional quantizer in its first stage for exploiting all the higher dimensional coding advantages and then, uses an SVQ method for quantizing the residual vector in the second stage so as to reduce the complexity. We also develop a transform domain residual coding method in this two stage architecture such that it further reduces the computational complexity. To design an effective residual codebook in the second stage, variance normalization of Voronoi regions is carried out which leads to the design of two new methods, referred to as normalized two stage SVQ (NTsSVQ) and normalized two stage transform domain SVQ (NTsTrSVQ). These two new methods have complimentary strengths and hence, they are combined in a switched VQ mode which leads to the further improvement in R/D performance, but retaining the low complexity requirement. We evaluate the performances of new methods for wide-band speech LSF parameter quantization and show their advantages over established SVQ and SSVQ methods.     

Emotion Recognition From Facial Expressions and Its Control Using Fuzzy Logic

This paper presents a fuzzy relational approach to human emotion recognition from facial expressions and its control. The proposed scheme uses external stimulus to excite specific emotions in human subjects whose facial expressions are analyzed by segmenting and localizing the individual frames into regions of interest. Selected facial features such as eye opening, mouth opening, and the length of eyebrow constriction are extracted from the localized regions, fuzzified, and mapped onto an emotion space by employing Mamdani-type relational models. A scheme for the validation of the system parameters is also presented. This paper also provides a fuzzy scheme for controlling the transition of emotion dynamics toward a desired state. Experimental results and computer simulations indicate that the proposed scheme for emotion recognition and control is simple and robust, with good accuracy.     

A class of new transforms tailored for the hypergeometric series

A new class of transforms, tailored for the hypergeometric series, is proposed and tested on a number of series. From a knowledge of a given number of terms of a sequence, these transforms reproduce the functions with a better accuracy than the Levin-like transforms. Though there exists a correlation between the approximative power of a rational approximant and its ability to predict the leading terms of a series, there are exceptions to this, especially in the case of divergent series. The new transforms can, in most cases, predict a number of extra terms not used in the construction of the approximants.     

Physicochemical properties of calcific deposits isolated from porcine bioprosthetic heart valves removed from patients following 2-13 years function

The purpose of this study was to characterize the physicochemical properties of calcific deposits that cause the failure of tissue-derived heart valve bioprostheses. This was done in an effort to understand the mechanism of pathologic biomineralization in the cardiovascular system and potentially prevent deterioration of bioprostheses. Calcific deposits taken from 10 failed bioprosthetic valves that had been implanted in patients for 2-13 years were characterized by chemical analysis, x-ray diffraction, FTIR spectroscopy, scanning electron microscopy, polarized light microscopy, and solubility measurements. The combined results identified the biomineral as an apatitic calcium phosphate salt with substantial incorporation of sodium, magnesium and carbonate. The average Ca/PO4 ratio for this "young" pathologic biomineral was approximately 1.3, considerably lower than approximately 1.7 found in mature atherosclerotic plaque biomineral and mature skeletal biomineral, both of which approximate hydroxyapatite in composition. Deproteinated calcific deposits from bioprostheses had thermodynamic solubilities comparable to those of both atherosclerotic plaque, typical pathologic biomineral and hydrolyzed octacalcium phosphate (OCP, Ca4H(PO4)3 x 2.5 H2O), a proposed precursor phase to biomineral apatite. This later finding, together with chemical composition and structural details of the bioprosthetic deposits themselves, supports a mechanism of cardiovascular calcification in which OCP plays a crucial role in the formation of the final apatitic phase. This suggests an approach toward prevention of bioprosthetic tissue calcification through control of the formation of the kinetically favored OCP precursor and/or its transformation into bioapatite.     

A case of paracetamol retard poisoning with fatal outcome

A case of fatal paracetamol (acetaminophen) poisoning in a 26-year-old woman who developed liver necrosis is described. The patient reported having ingested 11 g of a slow-release paracetamol preparation and a certain amount of alcohol and benzodiazepine. An unknown dose of phenobarbital (phenemal) had been ingested 24 hours previously, leading to a serum phenobarbital concentration of 0.195 mmol/l at the time of admission. The patient was initially treated with N-acetylcysteine intravenously. This treatment was discontinued after five hours due to a serum paracetamol concentration of 0.49 mmol/l, well below the "treatment line" paracetamol concentration of 0.8 mmol/l six hours after ingestion. Possible aggravating factors are discussed, including sustained high serum concentration of paracetamol due to the slow-release preparation and enzyme induction caused from concomitant phenobarbital and alcohol intake, as well as benzodiazepines displacing paracetamol from liver enzymes. These factors make serum paracetamol concentrations undependable; the importance of continuing N-acetylcysteine treatment in spite of "safe" serum concentrations in the above cases is stressed.     

Carbohydrate molecular weight profiling, sequence, linkage, and branching data: ES-MS and CID

This section summarizes several strategies for a more complete understanding of carbohydrate structure with a focus on glycolipids and glycoprotein glycans. The techniques include periodate oxidation to impart greater molecular specificity to isomeric glycans, methylation to improve sensitivity and the information content within CID spectra, electrospray for "soft" and efficient ionization, and CID to obtain structural detail. The lipophilicity of the products following derivatization contributes to product cleanup by solvent extraction and enhances sensitivity during ES. When combined with CID information, this yields sequence, linkage, and branching information. Oxidation and reduction preceding methylation augments CID analysis with an altered structure that can be profiled at the same sensitivity. Within the context of established motifs, these contrasting profiles corroborate glycan structure and specifically identify isobaric elements transparent in the initial profile. An earlier report indicating ring-opening fragments were essentially absent in low-energy collisions of methylated and natriated oligosaccharides contrasts our observations. However, as this report used a methylated oligomer containing an internal N-acetylhexose as an illustration, the conclusion is plausible (cf., Figure 9). The poor ionization efficiency of FAB and the high matrix background limit the dynamic range in the CID spectrum and, thereby, the ability to unambiguously identify weaker peaks. It would be expected that high-energy CID affords a broader range of fragment types, including ring-opening fragments. In terms of a structural methodology, this is ambivalent since the increase in fragmentation pathways also applies to small molecule eliminations which are usually less informative. In ES-CID-MS, the carbohydrate chemist has a powerful new tool in hand for structural elucidations that can be conducted at the low-picomole level. Parallel developments can be expected to continue for other ionization methods, in particular matrix-assisted desorption/ionization on linear and reflectron time of flight mass spectrometers, and improvement in the performance and sensitivity of high-resolution mass analyzers through the use of focal plane detectors and more sophisticated hardware and software for Fourier transform ion cyclotron resonance mass measurements. These have, as yet, only begun to be applied to carbohydrate structural analysis but should add still more versatility to experimental design in the future.     

Generating Time-Series Data Using Generative Adversarial Networks for Mobility Demand Prediction

The increasing penetration rate of electric kickboard vehicles has been popularized and promoted primarily because of its clean and efficient features. Electric kickboards are gradually growing in popularity in tourist and education-centric localities. In the upcoming arrival of electric kickboard vehicles, deploying a customer rental service is essential. Due to its free-floating nature, the shared electric kickboard is a common and practical means of transportation. Relocation plans for shared electric kickboards are required to increase the quality of service, and forecasting demand for their use in a specific region is crucial. Predicting demand accurately with small data is troublesome. Extensive data is necessary for training machine learning algorithms for effective prediction. Data generation is a method for expanding the amount of data that will be further accessible for training. In this work, we proposed a model that takes time-series customers’ electric kickboard demand data as input, pre-processes it, and generates synthetic data according to the original data distribution using generative adversarial networks (GAN). The electric kickboard mobility demand prediction error was reduced when we combined synthetic data with the original data. We proposed Tabular-GAN-Modified-WGAN-GP for generating synthetic data for better prediction results. We modified The Wasserstein GAN-gradient penalty (GP) with the RMSprop optimizer and then employed Spectral Normalization (SN) to improve training stability and faster convergence. Finally, we applied a regression-based blending ensemble technique that can help us to improve performance of demand prediction. We used various evaluation criteria and visual representations to compare our proposed model’s performance. Synthetic data generated by our suggested GAN model is also evaluated. The TGAN-Modified-WGAN-GP model mitigates the overfitting and mode collapse problem, and it also converges faster than previous GAN models for synthetic data creation. The presented model’s performance is compared to existing ensemble and baseline models. The experimental findings imply that combining synthetic and actual data can significantly reduce prediction error rates in the mean absolute percentage error (MAPE) of 4.476 and increase prediction accuracy.