Characterization of fractionated phenolic resins used in photoresists

Novolak resins fractionated using a liquid/liquid centrifugal fractionation/separation technique were characterized and contrasted to resins fractionated by conventional methods. The new fractionation method resulted in higher yields of resins with comparable or improved functional properties when used in i‐line sensitive (365 nm) photoresist formulations. By characterizing the isolateld resin and the lower molecular weight (Mw) fractions removed during fractionation, a better understanding of the structural differences associated with the fractionation processes could be determined. The potential for improving the fractionation/separation process and for obtaining higher yields and more consistent resists with the resins was identified.     

Privacy preserving data mining: A noise addition framework using a novel clustering technique

During the whole process of data mining (from data collection to knowledge discovery) various sensitive data get exposed to several parties including data collectors, cleaners, preprocessors, miners and decision makers. The exposure of sensitive data can potentially lead to breach of individual privacy. Therefore, many privacy preserving techniques have been proposed recently. In this paper we present a framework that uses a few novel noise addition techniques for protecting individual privacy while maintaining a high data quality. We add noise to all attributes, both numerical and categorical. We present a novel technique for clustering categorical values and use it for noise addition purpose. A security analysis is also presented for measuring the security level of a data set.     

Week Ahead Electricity Power and Price Forecasting Using Improved DenseNet-121 Method

In the Smart Grid (SG) residential environment, consumers change their power consumption routine according to the price and incentives announced by the utility, which causes the prices to deviate from the initial pattern. Thereby, electricity demand and price forecasting play a significant role and can help in terms of reliability and sustainability. Due to the massive amount of data, big data analytics for forecasting becomes a hot topic in the SG domain. In this paper, the changing and non-linearity of consumer consumption pattern complex data is taken as input. To minimize the computational cost and complexity of the data, the average of the feature engineering approaches includes: Recursive Feature Eliminator (RFE), Extreme Gradient Boosting (XGboost), Random Forest (RF), and are upgraded to extract the most relevant and significant features. To this end, we have proposed the DensetNet-121 network and Support Vector Machine (SVM) ensemble with Aquila Optimizer (AO) to ensure adaptability and handle the complexity of data in the classification. Further, the AO method helps to tune the parameters of DensNet (121 layers) and SVM, which achieves less training loss, computational time, minimized overfitting problems and more training/test accuracy. Performance evaluation metrics and statistical analysis validate the proposed model results are better than the benchmark schemes. Our proposed method has achieved a minimal value of the Mean Average Percentage Error (MAPE) rate i.e., 8% by DenseNet-AO and 6% by SVM-AO and the maximum accurateness rate of 92% and 95%, respectively.     

Constraint effects on probabilistic analysis of cracks in ductile solids

This paper presents a method for evaluating constraint effects on probabilistic elastic–plastic analysis of cracks in ductile solids. It is based on fracture parameters J and Q , correlation between Q and J– resistance curve of the material, and J -tearing theory for predicting fracture initiation and instability in cracked structures. Based on experimental data from small-scale fracture specimens, correlation equations were developed for fracture toughness at crack initiation and the slope of the J– resistance curve as a function of constraint condition. The random parameters may involve crack geometry, tensile and fracture toughness properties of the material, and applied loads. Standard reliability methods were applied to predict probabilistic fracture response and reliability of cracked structures. The results suggest that crack-tip constraints have little effect on the probability of crack initiation. However, the probability of fracture instability can be significantly reduced when constraint effects are taken into account. Hence, for a structure where some amount of stable crack-growth can be tolerated, crack-tip constraints should be considered for probabilistic fracture-mechanics analysis.     

Integer Representation and Counting in the Bit Probe Model

We examine the problem of integer representation in a nearly minimal number of bits so that the increment and the decrement (and indeed the addition and the subtraction) operations can be performed using few bit inspections and fewer bit changes. The model of computation we considered is the bit probe model, where the complexity measure counts only the bitwise accesses to the data structure. We present several efficient data structures to represent integer that use a logarithmic number of bit inspections and a constant number of bit changes per operation. The most space-efficient data structure uses only one extra bit. We also present an extension to our data structure to support efficient addition and subtraction, where the larger value is replaced by the result, while retaining the same asymptotic bounds for the increment and the decrement operations.     

Microstructural,Thermal, Crystallization,and Water Absorption Properties of Films Prepared from Never-Dried and Freeze-Dried Cellulose Nanocrystals

In this paper, the microstructural, optical, thermal, crystallization, and water absorption properties of films prepared from never-dried (ND) and freeze-dried (FD) cellulose nanocrystals (CNCs) are reported. Morphology of the ND CNCs reveals a needle-like structure, while after freeze-drying, they show a flake-like morphology. Microstructural analysis of ND and FD CNCs are further studied via small angle X-ray scattering to probe interactions. ND CNCs yield a transparent film with a low surface roughness (14 ± 4 nm), while the FD CNC film evidence a significant reduction of their transparency due to their higher surface roughness (134 ± 20 nm). Although Fourier transform infrared spectroscopy and energy-dispersive X-ray spectroscopy analyses reveal no chemical change occurs during the freeze-drying process, yet a more intense thermal degradation profile is observed for FD CNC film, probably due to the higher oxygen ingress within the gaps created between the stacked flakes. This, in turn, results in a greater loss of crystallinity at a higher temperature (300 °C) compared to the ND CNC film. A rapid decrease in water contact angle of the FD CNC film proves that the morphology of flakes and their orientation within the film has a strong influence in increasing water absorption capacity.     

A novel data-driven methodology for fault detection and dynamic risk assessment

This paper presents a novel methodology for dynamic risk analysis, integrating the multivariate data-based process monitoring and logical dynamic failure prediction model. This concept for dynamic risk analysis is comprised of the fault assessment and dynamic failure prognosis modules. A combination of the naïve Bayes classifier, Bayesian network, and event tree analysis is utilized to manifest the concept. The naïve Bayes classifier is used for fault detection and diagnosis; it also generates a multivariate probability for a fault class in each time-step, which is used for dynamic failure prognosis by different paths a fault can lead a process to failure. The proposed framework has been applied to two process systems: a binary distillation column and the RT 580 experimental setup in four fault scenarios, and it is found the developed technique can effectively monitor the process and predict the failure.     

Low surface area nanosilica from an agricultural biomass for fabrication of dental nanocomposites

This study aimed to obtain nanosilica with desirable characteristics from an agricultural biomass waste using an organic acid. The effect of the feed rate of the precipitant and the mixing speed on the morphology and characteristics of nanosilica from rice husk for use as fillers in dental nanocomposites has been explored. The feed rate showed considerable effects on the agglomeration and the size of the silica nanoparticles. At a feed rate of 0.2 ml/min, the particles were bigger and highly agglomerated with a mean particle size of 261 nm. The mean particle sizes for the feed rates of 1 ml/min and 5 ml/min were 213 nm and 174 nm, respectively, exhibiting a decrease in the mean particle size with increasing feed rate. The shape of the silica nanoparticles depended on the mixing speed and it was possible to obtain spherical, dense, low surface area silica particles suitable for use in the fabrication of dental nanocomposites using this simple technique.     

A novel atmospheric freeze dryer using simultaneous application of subzero and hot air streams using a vortex chiller

The objective of this research was to develop an atmospheric freeze-drying (AFD) system for heat-sensitive materials. A laboratory scale AFD system consisting of a vortex chiller was designed and fabricated. A vortex tube was used to convert compressed air into a subzero and a hot air stream. The system used the simultaneous application of subzero air stream and hot air carrier gas coupled with radiant heat input for drying heat-sensitive materials. A parametric evaluation of this drying system was performed. Comparison of drying characteristics and the physical quality (e.g., color, shape, size) with vacuum freeze and traditional AFD was also performed. Results revealed that the proposed system is a suitable and viable alternative without compromising the quality compared to traditional freeze-drying system.