FSL-BM: Fuzzy Supervised Learning with Binary Meta-Feature for Classification

This paper introduces a novel real-time Fuzzy Supervised Learning with Binary Meta-Feature (FSL-BM) for big data classification task. The study of real-time algorithms addresses several major concerns, which are namely: accuracy, memory consumption, and ability to stretch assumptions and time complexity. Attaining a fast computational model providing fuzzy logic and supervised learning is one of the main challenges in the machine learning. In this research paper, we present FSL-BM algorithm as an efficient solution of supervised learning with fuzzy logic processing using binary meta-feature representation using Hamming Distance and Hash function to relax assumptions. While many studies focused on reducing time complexity and increasing accuracy during the last decade, the novel contribution of this proposed solution comes through integration of Hamming Distance, Hash function, binary meta-features, binary classification to provide real time supervised method. Hash Tables (HT) component gives a fast access to existing indices; and therefore, the generation of new indices in a constant time complexity, which supersedes existing fuzzy supervised algorithms with better or comparable results. To summarize, the main contribution of this technique for real-time Fuzzy Supervised Learning is to represent hypothesis through binary input as meta-feature space and creating the Fuzzy Supervised Hash table to train and validate model.Comment: FICC201

Evolving evidence on a link between the ZMYM3 exceptionally long GA-STR and human cognition

The human X-linked zinc finger MYM-type protein 3 (ZMYM3) contains the longest GA-STR identified across protein-coding gene 5� UTR sequences, at 32-repeats. This exceptionally long GA-STR is located at a complex string of GA-STRs with a human-specific formula across the complex as follows: (GA)8-(GA)4-(GA)6-(GA)32 (ZMYM3-207 ENST00000373998.5). ZMYM3 was previously reported among the top three genes involved in the progression of late-onset Alzheimer�s disease. Here we sequenced the ZMYM3 GA-STR complex in 750 human male subjects, consisting of late-onset neurocognitive disorder (NCD) as a clinical entity (n = 268) and matched controls (n = 482). We detected strict monomorphism of the GA-STR complex, except of the exceptionally long STR, which was architecturally skewed in respect of allele distribution between the NCD cases and controls F (1, 50) = 12.283; p = 0.001. Moreover, extreme alleles of this STR at 17, 20, 42, and 43 repeats were detected in seven NCD patients and not in the control group (Mid-P exact = 0.0003). A number of these alleles overlapped with alleles previously found in schizophrenia and bipolar disorder patients. In conclusion, we propose selective advantage for the exceptional length of the ZMYM3 GA-STR in human, and its link to a spectrum of diseases in which major cognition impairment is a predominant phenotype. © 2020, The Author(s)

Ionic liquids at electrified interfaces

Until recently, “room-temperature” (<100–150 °C) liquid-state electrochemistry was mostly electrochemistry of diluted electrolytes(1)–(4) where dissolved salt ions were surrounded by a considerable amount of solvent molecules. Highly concentrated liquid electrolytes were mostly considered in the narrow (albeit important) niche of high-temperature electrochemistry of molten inorganic salts(5-9) and in the even narrower niche of “first-generation” room temperature ionic liquids, RTILs (such as chloro-aluminates and alkylammonium nitrates).(10-14) The situation has changed dramatically in the 2000s after the discovery of new moisture- and temperature-stable RTILs.(15, 16) These days, the “later generation” RTILs attracted wide attention within the electrochemical community.(17-31) Indeed, RTILs, as a class of compounds, possess a unique combination of properties (high charge density, electrochemical stability, low/negligible volatility, tunable polarity, etc.) that make them very attractive substances from fundamental and application points of view.(32-38) Most importantly, they can mix with each other in “cocktails” of one’s choice to acquire the desired properties (e.g., wider temperature range of the liquid phase(39, 40)) and can serve as almost “universal” solvents.(37, 41, 42) It is worth noting here one of the advantages of RTILs as compared to their high-temperature molten salt (HTMS)(43) “sister-systems”.(44) In RTILs the dissolved molecules are not imbedded in a harsh high temperature environment which could be destructive for many classes of fragile (organic) molecules

An imidazole-based benzilic-dicationic ionic liquid performance in 1.0 M HCl solution to mitigate the mild steel degradation: electrochemical noise/impedance investigation

The corrosion prevention capability of an imidazole-based benzilic-dicationic ionic liquid labeled 1,4 -di [1′ –methylene- 3′- methyl imidazolium bromide]- benzene (DMIBr2) over mild steel in hydrochloric acid was investigated through experimental and theoretical studies. The excellent corrosion inhibitor ability of DMIBr2 was demonstrated using a new concept in noise resistance and spectral noise resistance interrelation to adequately eliminate the trend of electrochemical noise (EN) signals, which is in line with electrochemical impedance spectroscopy findings. EN also revealed that DMIBr2 mitigated both localized and general mild steel corrosion, as evidenced by SEM analysis. Potentiodynamic polarization suggested DMIBr2 can be described as a mixed-type corrosion inhibitor with cathodic control predominance in 1.0 M HCl. Langmuir adsorption isotherm was considered to interpret the DMIBr2 adsorption behavior on mild steel. Furthermore, according to quantum chemical calculations, the probable protonation process in the acid can affect the functionalities of different DMIBr2 components

Si-containing 3D cage-functionalized graphene oxide grafted with Ferrocene for high-performance supercapacitor application: An experimental and theoretical study

In this work, graphene oxide sheets were functionalized with Octa(aminopropyl)silsesquioxane. Then, Octa(aminopropyl)silsesquioxane-functionalized graphene oxide (GO-Amine-SSQ) was grafted with Ferrocene through Friedel-Craft reaction. Structural properties of the prepared composite (GO-Amine-SSQ-Fc) were analyzed by XPS, FT-IR, XRD, Raman, SEM, TEM, and BET tests. Results confirmed the successful synthesis and high porosity. Next, the electrochemical properties of GO-Amine-SSQ-Fc were characterized by CV, GCD, and EIS techniques in the 3E system. The GO-Amine-SSQ-Fc electrode showed a specific capacitance of 574 F g−1 at 1 A g−1, retention capacitance of 90.1% after 10,000 charge-discharge cycles, low resistance, and efficient diffusion of ions. After confirming the excellent electrochemical performance of this electrode, a symmetric supercapacitor system (GO-Amine-SSQ-Fc//GO-Amine-SSQ-Fc) was tested by CV and GCD techniques, to determine practical application of system. GO-Amine-SSQ-Fc//GO- Amine-SSQ-Fc system recorded a specific capacitance of 304 F g−1 at 0.5 A g−1, retention capacitance of 92.5% over 10,000 charge-discharge cycles, and specific energy of 10.14 Wh kg−1 at a specific power of 500 W kg−1. Also, the results of computational methodology show that the interaction of SSQ, Fc and GO layer in GO-Amine-SSQ-Fc composite, makes it effective as an electrode material for supercapacitors. This excellent performance, as a result of the unique structure of Amine-SSQ groups and the superior electrochemical behavior of Ferrocene groups, suggests that GO-Amine-SSQ-Fc composite has great potential for energy storage devices

Key factors affecting graphene oxide transport in saturated porous media

This study focuses on the transport in porous media of graphene oxide nanoparticles (GONP) under conditions similar to those applied in the generation of in-situ reactive zones for groundwater remediation (i.e. GO concentration of few tens of mg/l, stable suspension in alkaline solution). The experimental tests evaluated the influence on GO transport of three key factors, namely particle size (300–1200 nm), concentration (10–50 mg/L), and sand size (coarse to fine). Three sources of GONP were considered (two commercial and one synthesized in the laboratory). Particles were stably dispersed in water at pH 8.5 and showed a good mobility in the porous medium under all experimental conditions: after injection of 5 pore volumes and flushing, the highest recovery was around 90%, the lowest around 30% (only for largest particles in fine sand). The particle size was by far the most impacting parameter, with increasing mobility with decreasing size, even if sand size and particle concentration were also relevant. The source of GONP showed a minor impact on the mobility. The transport test data were successfully modeled using the advection-dispersion-deposition equations typically applied for spherical colloids. Experimental and modeling results suggested that GONP, under the explored conditions, are retained due to both blocking and straining, the latter being relevant only for large particles and/or fine sand. The findings of this study play a key role in the development of an in-situ groundwater remediation technology based on the injection of GONP for contaminant degradation or sorption. Despite their peculiar shape, GONP behavior in porous media is comparable with spherical colloids, which have been more studied by far. In particular, the possibility of modeling GONP transport using existing models ensures that they can be applied also for the design of field-scale injections of GONP, similarly to other particles already used in nanoremediation

HMIC: Hierarchical medical image classification, a deep learning approach

© 2020 by the authors. Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper outlines an approach that is different from the current medical image classification tasks that view the issue as multi-class classification. We performed a hierarchical classification using our Hierarchical Medical Image classification (HMIC) approach. HMIC uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. For testing our performance, we use biopsy of the small bowel images that contain three categories in the parent level (Celiac Disease, Environmental Enteropathy, and histologically normal controls). For the child level, Celiac Disease Severity is classified into 4 classes (I, IIIa, IIIb, and IIIC)