Significance of low water—solid ratio and temperature on the physico-mechanical characteristics of hydrates of tricalcium aluminate

Hydration characteristics are reported of tricalcium aluminate studied at 20 and 80 °C using water-solid ratios of 0.2 and 1.0. Hydration products were subjected to differential thermal analysis, thermogravimetric analysis, scanning electron microscopy, length change, porosity and pore size distribution, and micro-hardness measurements. Hydration proceeds faster at 80 °C than at 20 °C and the cubic C₃AH₆ phase is detected in the first few seconds. At 20 °C hydration occurs with the initial formation of the hexagonal phases and expansion is continuous. At higher temperatures and a water-solid ratio of 0.2, after an initial small expansion, the dimensional change is low. The product at 80 °C develops more than fourfold the hardness value developed at 20 °C. After 2 days of hydration, porosity, per cent C₃AH₆ and microhardness are 15.2%, 83% and 38.9 kg/mm²; corresponding values for the product obtained at 20 °C are 21.5%, 75% and 9.1 kg/mm². Microstructural examination of the material formed at 80 °C indicates a closely welded and continuous network of the cubic phase. These results reveal that at a low water-solid ratio and higher temperatures the formation of the cubic phase from C₃A results in an enhancement of strength.     

Experiments on slider lubricant interactions and lubricant transfer using TFC sliders

Future magnetic storage density targets (>4 Tb/in. ²) require subnanometer physical clearances that pose a tremendous challenge to the head disk interface (HDI) design. A detailed understanding of slider-lubricant interactions at small clearances and contact is important to not only address magnetic spacing calibration and long term HDI reliability but also to meet additional challenges imposed by future recording architectures such as heat assisted magnetic recording (HAMR). In this work, the behavior of the disk lubricant is investigated through controlled tests using TFC sliders which are actuated to proximity (i.e. backoff) and into contact (i.e. overpush) on one specific half of the disk per rotation by synchronization with the spindle index. Observations for lubricant distribution in contact tests (i.e. overpush) reveal an accumulation of lubricant on the disk near the onset of contact suggesting a migration of lubricant from the slider to the disk as the slider approaches the disk. Experiments also reveal that there is a similar deposition of lubricant even in the absence of contact for backoff tests. Furthermore, light contact tests result in significant lubricant rippling and depletion with associated slider dynamics. The lubricant rippling frequencies correlate well with the slider’s vibration frequencies. Interestingly, strong overpush may lead to stable slider dynamics (for certain air bearing designs) that is also associated with noticeably lower lubricant distribution (compared to the light contact case), and the greatest lubricant changes are observed only at the onset and the end of contact. This paper reveals the complex nature of slider-lubricant interactions under near-contact and contact conditions, and it highlights the need for further studies on the topic to help design a HDI for recording architectures of the future.     

Physico-mechanical characteristics of hydrating tetracalcium aluminoferrite system at low water: Solid ratios

Tetracalcium aluminoferrite (C₄AF) mixed with gypsum in amounts of 0, 5, 10, 20 and 30% was hydrated at 25 or 80°C, either as discs (formed at pressures of 140 and 690 MPa to obtain an effective water:solid ratio of 0.08 and 0.13, respectively) or in powder form at water:cement ratios of 0.5 and 1.0. Hydration products formed at periods ranging from a few minutes to 7 days were identified and estimated. The sequence of hydrate formation and the accompanying length changes were also determined. The formation of hexagonal hydrate, cubic hydrate, ettringite, low sulphoaluminate series and their interconversions was primarily dependent on gypsum content, temperature, and initial water:solid (w/s) ratio. The study indicates that ettringite need not necessarily be a precursor of the formation of low sulphoaluminate hydrate when hydration is carried out at a very low w/s ratio at 80°C. The microhardness and porosity characteristics of the hydrated products were compared after 2 days of hydration. The relative contribution of low sulphoaluminate, ettringite, and other hydrated phases on strength development were assessed.     

Europium(III) Triflate‐Catalyzed Trofimov Synthesis of Polyfunctionalized Pyrroles

The synthesis of polyfunctionalized pyrroles by reaction of a ketoxime with dimethyl acetylenedicarboxylate using europium(III) triflate as the catalyst is described.     

A machine learning approach for imputation and anomaly detection in IoT environment

The problem of anomaly and attack detection in IoT environment is one of the prime challenges in the domain of internet of things that requires an immediate concern. For example, anomalies and attacks in IoT environment such as scan, malicious operation, denial of service, spying, data type probing, wrong setup, malicious control can lead to failure of an IoT system. Datasets generated in an IoT environment usually have missing values. The presence of missing values makes the classifier unsuitable for classification task. This article introduces (a) a novel imputation technique for imputation of missing data values (b) a classifier which is based on feature transformation to perform classification (c) imputation measure for similarity computation between any two instances that can also be used as similarity measure. The performance of proposed classifier is studied by using imputed datasets obtained through applying Kmeans, F-Kmeans and proposed imputation methods. Experiments are also conducted by applying existing and proposed classifiers on the imputed dataset obtained using proposed imputation technique. For experimental study in this article, we have used an open source dataset named distributed smart space orchestration system publicly available from Kaggle. Experiment results obtained are also validated using Wilcoxon non-parametric statistical test. It is proved that the performance of proposed approach is better when compared to existing classifiers when the imputation process is performed using F-Kmeans and K-Means imputation techniques. It is also observed that accuracies for attack classes scan, malicious operation, denial of service, spying, data type probing, wrong setup are 100% while it is 99% for malicious control attack class when the proposed imputation and classification technique are applied.     

SRIHASS - a similarity measure for discovery of hidden time profiled temporal associations

Mining and visualization of time profiled temporal associations is an important research problem that is not addressed in a wider perspective and is understudied. Visual analysis of time profiled temporal associations helps to better understand hidden seasonal, emerging, and diminishing temporal trends. The pioneering work by Yoo and Shashi Sekhar termed as SPAMINE applied the Euclidean distance measure. Following their research, subsequent studies were only restricted to the use of Euclidean distance. However, with an increase in the number of time slots, the dimensionality of a prevalence time sequence of temporal association, also increases, and this high dimensionality makes the Euclidean distance not suitable for the higher dimensions. Some of our previous studies, proposed Gaussian based dissimilarity measures and prevalence estimation approaches to discover time profiled temporal associations. To the best of our knowledge, there is no research that has addressed a similarity measure which is based on the standard score and normal probability to find the similarity between temporal patterns in z-space and retains monotonicity. Our research is pioneering work in this direction. This research has three contributions. First, we introduce a novel similarity (or dissimilarity) measure, SRIHASS to find the similarity between temporal associations. The basic idea behind the design of dissimilarity measure is to transform support values of temporal associations onto z-space and then obtain probability sequences of temporal associations using a normal distribution chart. The dissimilarity measure uses these probability sequences to estimate the similarity between patterns in z-space. The second contribution is the prevalence bound estimation approach. Finally, we give the algorithm for time profiled associating mining called Z-SPAMINE that is primarily inspired from SPAMINE. Experiment results prove that our approach, Z-SPAMINE is computationally more efficient and scalable compared to existing approaches such as Naïve, Sequential and SPAMINE that applies the Euclidean distance.     

Buckling and vibration of rectangular composite viscoelastic sandwich plates under thermal loads

The free vibration and damping characteristics of plates consisting of composite stiff-layers and an isotropic viscoelastic core were studied under thermal loads using finite element method. The temperature dependence of viscoelastic core properties and effects of pre-stresses are taken into account. The inherent composite damping and damping due to viscoelastic layer are compared. Parametric study is conducted with fiber angle, ply lay-up, and core thickness as parameters. The results show some interesting phenomenon like shifting of modes with temperature.     

ASTRA - A Novel interest measure for unearthing latent temporal associations and trends through extending basic gaussian membership function

Time profiled association mining is one of the important and challenging research problems that is relatively less addressed. Time profiled association mining has two main challenges that must be addressed. These include addressing i) dissimilarity measure that also holds monotonicity property and can efficiently prune itemset associations ii) approaches for estimating prevalence values of itemset associations over time. The pioneering research that addressed time profiled association mining is by J.S. Yoo using Euclidean distance. It is widely known fact that this distance measure suffers from high dimensionality. Given a time stamped transaction database, time profiled association mining refers to the discovery of underlying and hidden time profiled itemset associations whose true prevalence variations are similar as the user query sequence under subset constraints that include i) allowable dissimilarity value ii) a reference query time sequence iii) dissimilarity function that can find degree of similarity between a temporal itemset and reference. In this paper, we propose a novel dissimilarity measure whose design is a function of product based gaussian membership function through extending the similarity function proposed in our earlier research (G-Spamine). Our approach, MASTER (Mining of Similar Temporal Associations) which is primarily inspired from SPAMINE uses the dissimilarity measure proposed in this paper and support bound estimation approach proposed in our earlier research. Expression for computation of distance bounds of temporal patterns are designed considering the proposed measure and support estimation approach. Experiments are performed by considering naïve, sequential, Spamine and G-Spamine approaches under various test case considerations that study the scalability and computational performance of the proposed approach. Experimental results prove the scalability and efficiency of the proposed approach. The correctness and completeness of proposed approach is also proved analytically.