Intelligent Deep Convolutional Neural Network Based Object Detection Model for Visually Challenged People

Artificial Intelligence (AI) and Computer Vision (CV) advancements have led to many useful methodologies in recent years, particularly to help visually-challenged people. Object detection includes a variety of challenges, for example, handling multiple class images, images that get augmented when captured by a camera and so on. The test images include all these variants as well. These detection models alert them about their surroundings when they want to walk independently. This study compares four CNN-based pre-trained models: Residual Network (ResNet-50), Inception v3, Dense Convolutional Network (DenseNet-121), and SqueezeNet, predominantly used in image recognition applications. Based on the analysis performed on these test images, the study infers that Inception V3 outperformed other pre-trained models in terms of accuracy and speed. To further improve the performance of the Inception v3 model, the thermal exchange optimization (TEO) algorithm is applied to tune the hyperparameters (number of epochs, batch size, and learning rate) showing the novelty of the work. Better accuracy was achieved owing to the inclusion of an auxiliary classifier as a regularizer, hyperparameter optimizer, and factorization approach. Additionally, Inception V3 can handle images of different sizes. This makes Inception V3 the optimum model for assisting visually challenged people in real-world communication when integrated with Internet of Things (IoT)-based devices.     

Characterization of paper mill sludge as a renewable feedstock for sustainable hydrogen and biofuels production

Paper and pulp mills generate substantial quantities of cellulose-rich sludge materials that are disposed in landfills at a large scale. For sustainability purposes, sludge materials can be bioprocessed to produce renewable fuels and useful chemicals. The enzymatic hydrolysis of cellulose is the process bottleneck that affects the conversion economics directly by using zero-cost raw materials. In order to study and optimize the process, the characteristics of the sludge raw materials should be first evaluated. In this work, sludge samples were obtained from paper mills located at different locations in Wisconsin and Minnesota. Part of the sludge samples was washed (de-ashed) with hydrochloric acid while the other part remained unwashed. The samples were subjected to multiple spectroscopic analyses techniques to evaluate the morphological properties of cellulose fibers and to estimate the total structural carbohydrate content. The results showed that the de-ashing process changed some fiber characteristics and cellulose crystallinity structure in all sludge samples. Sludge sample A (obtained from Kraft pulp and recycled paper mill region) showed a high percentage of fiber, with crystalline cellulose, compared to the other two sludge samples suggesting that sludge A is a valuable source to make value-added products. Aspen Plus mass and energy calculations performed in view of the ‘zero’ cost and the reliable supply of sludge raw materials producing 2 mol H₂/mol glucose. Moreover, the results showed that extracting crystalline cellulose from these sludge samples is more profitable than crystalline cellulose made from the other lignocellulosic feedstocks. The results reported here showed that the utilization of these sludge materials would be an economically attractive and promising alternative for the production of hydrogen.     

Techno-economic analysis and a novel assessment technique of paper mill sludge conversion to bioethanol toward sustainable energy production

A novel bioprocess design to convert paper mill sludge (PMS) to biofuels is proposed in this work. The design utilizes cellulosic fiber recovered from the PMS via optimized de-ashing (HCl washing) step. This work specifically provided a technical and economic analysis of paper mill sludge conversion into biofuel production using a novel protocol. The protocol is based on scanning electron microscopy (SEM) analysis to assess the quality of the contained cellulose prior to further processing. The results are crucially important to determine the suitability of the PMS feedstock to produce biofuels. SEM analysis was employed as a preliminary screening tool to evaluate sludge digestibility and conversion. The SEM characterization technique established a direct relationship between the fiber morphology, presence of crystals salts and sugar yield after enzymatic hydrolysis. Substantial structural changes were observed before and after de-ashing the sludge samples, leading to a correlation between the surface morphology and the washing step. The results suggested that de-ashing changes the surface morphology and upon analysis, increased the sugar yield up to about 86% as opposed to 2.2% in sludge sample A as an example. The PMS conversion into biofuel was simulated using Aspen PLUS and compared to a similar process using corn stover as feedstock. The simulation results showed it is 20% cheaper to produce bioethanol from PMS compared to corn stover. The simulation revealed less energy demand by around 13 320 MJ/h compared to that when corn stover was used.     

Alkaline earth metal oxides on γ-Al2O3 supported Co catalyst and their application to mercaptan oxidation

γ-Al₂O₃-supported alkaline earth metal oxide samples with different MgO, CaO, SrO and BaO loadings have been prepared by impregnating γ-Al₂O₃ with alkaline earth metal nitrate solutions and then calcining at 773 K and 1,023 K. The resultant samples have been characterized by XRD, EDS coupled with SEM, CO₂-TPD and BET. After preparation of the γ-Al₂O₃-supported alkaline earth metal oxide samples and impregnation with the liquid catalyst LCPs30, supplied by Axens, the catalytic performance of these catalysts was evaluated in the mercaptan oxidation reaction. Results showed that magnesium base oxide is formed at 773 K and base oxides of calcium, barium and strontium are formed at 1,023 K. Catalysts with higher mole ratios have higher conversions and the basicity increases with increasing base oxide loading in the samples. Furthermore, the conversion of mercaptans increases with increasing atomic number of the alkaline earth metal, excluding MgO, which has the highest conversion compared to the other base oxides.     

Sailfish Optimizer with EfficientNet Model for Apple Leaf Disease Detection

Recent developments in digital cameras and electronic gadgets coupled with Machine Learning (ML) and Deep Learning (DL)-based automated apple leaf disease detection models are commonly employed as reasonable alternatives to traditional visual inspection models. In this background, the current paper devises an Effective Sailfish Optimizer with EfficientNet-based Apple Leaf disease detection (ESFO-EALD) model. The goal of the proposed ESFO-EALD technique is to identify the occurrence of plant leaf diseases automatically. In this scenario, Median Filtering (MF) approach is utilized to boost the quality of apple plant leaf images. Moreover, SFO with Kapur's entropy-based segmentation technique is also utilized for the identification of the affected plant region from test image. Furthermore, Adam optimizer with EfficientNet-based feature extraction and Spiking Neural Network (SNN)-based classification are employed to detect and classify the apple plant leaf images. A wide range of simulations was conducted to ensure the effective outcomes of ESFO-EALD technique on benchmark dataset. The results reported the supremacy of the proposed ESFO-EALD approach than the existing approaches.     

Engineering Aptamer Switches for Multifunctional Stimulus-Responsive Nanosystems

Although RNA and DNA are best known for their capacity to encode biological information, it has become increasingly clear over the past few decades that these biomolecules are also capable of performing other complex functions, such as molecular recognition (e.g., aptamers) and catalysis (e.g., ribozymes). Building on these foundations, researchers have begun to exploit the predictable base-pairing properties of RNA and DNA in order to utilize nucleic acids as functional materials that can undergo a molecular “switching” process, performing complex functions such as signaling or controlled payload release in response to external stimuli including light, pH, ligand-binding and other microenvironmental cues. Although this field is still in its infancy, these efforts offer exciting potential for the development of biologically based “smart materials”. Herein, ongoing progress in the use of nucleic acids as an externally controllable switching material is reviewed. The diverse range of mechanisms that can trigger a stimulus response, and strategies for engineering those functionalities into nucleic acid materials are explored. Finally, recent progress is discussed in incorporating aptamer switches into more complex synthetic nucleic acid-based nanostructures and functionalized smart materials.     

Gelation of Amylopectin without Long Range Order

Weak amylopectin gels have been formed directly following dissolution of waxy maize granules. Rheological characterisation of these gels has been carried out as a function of stress and frequency. The gels are visually clear and homogeneous, and environmental scanning electron microscopy shows no trace of the original granules remaining. It is postulated that the origin of the gel network lies in pairwise aggregation of outer chains of the amylopectin molecules, presumably via double helix formation. However, these pairs of chain segments do not aggregate to develop crystallinity.     

Synthesis and Characterization of Water-Based Epoxy-Acrylate/Graphene Oxide Decorated with Fe3O4 Nanoparticles Coatings and Its Enhanced Anticorrosion Properties

ABSTRACT

In this article, water-based epoxy–acrylate (EP/AC) emulsion coatings and its nanocomposites with Fe₃O₄ nanoparticles, graphene oxide nanosheets and graphene oxide which was decorated with Fe₃O₄ nanoparticles were synthesized. This was executed by means of a pre-emulsion seeded semi-batch emulsion polymerization handle with a blend of conventional anionic surfactant (Polyalkylene glycol ether sulfate, ammonium salt-EXOSEL 20 S) and typical nonionic type emulsifier (Octylphenol polyoxyethylene ether OP-10) for emulsion polymerization (schematic 1). Prepared nanoparticles were investigated with different analyzing methods like FT-IR, XRD, SEM-EDAX and TGA. The impact of the distinctive nanostructures in an (EP/AC) resin, on coated steel panels with the features of corrosion conservation, was investigated by the test of electrochemical impedance spectroscopy (EIS) and potentiodynamic polarization. Morphology of surface and thermal behavior of nanocomposite tests were executed using surface filtering electron microscopy (FE-SEM) and thermal gravimetric analysis (TGA). Different analyses resulted from nanostructures and nanocomposites approved very good dispersion of fillers in nanocomposite beads. Besides, they indicate the effective role of nanoparticles especially graphene oxide which was decorated with Fe₃O₄ nanoparticles in corrosion resistance of water-based EP/AC coatings and other properties of coated instances.