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Individual applications of coir and hemp as reinforcement in composites have been exhaustively studied; however, their hybridization must also be investigated. In this context, this research investigates the utilization of coir and hemp fibers as reinforcements in a polyester-based hybrid composite system. The primary objective is to find out how these reinforcements affect the hybrid composites' mechanical (tensile, flexural, and impact) and thermo-gravimetric properties. To accomplish this, composite samples with varying weight proportions of coir and hemp fibers were fabricated, and extensive mechanical testing was performed. The findings from the tensile, flexural, and impact tests revealed an enhancement in the mechanical characteristics of the fabricated composites as the proportion of coir fiber grew and the proportion of hemp fiber reduced. The hybrid composite, containing 15% coir and 5% hemp fibers, had superior mechanical properties to the binary composite system. In addition, thermogravimetric analysis was performed to determine the thermal stability of the hybrid composites. Within a temperature range of 30 °C–800 °C, weight loss was observed, confirming the overall thermal resistance of the materials. Fourier Transform InfraRed Spectroscopy (FTIR) was used to determine the composite's chemical composition, revealing the presence of functional groups that contribute to the composite's performance. Utilizing Scanning Electron Microscopy (SEM), the surface morphology of hybrid composites was investigated, yielding valuable insights into the fiber-matrix interaction and composite structure. The results of this study demonstrate the potential of the coir/hemp/polyester hybrid composite as a lightweight material in a variety of industries.

In this study, a combination of “Cordia Dichotoma” natural fiber and “granite powder” as filler has been reinforced into epoxy resin making a hybrid composite. In this study, unused industrial waste obtained from the granite polishing industry, known as granite powder (GP) is being utilized for its usefulness as a reinforcement in polymer composite. Hybrid composite was prepared by reinforcing a constant Cordia Dichotoma fiber content of 20 wt. % and varying the granite powder weight (wt. %) percentages (0, 5, 10, 15 and 20) into epoxy matrix. Mechanical properties (tensile, flexural and impact) of hybrid composites were investigated. Following the results obtained, granite powder could be evidenced as a good filler material for the betterment of composite’s mechanical properties. Also, the ability of this filler material is proved in decreasing water absorption and chemical resistance. SEM morphology was performed to investigate the bonding and distribution of granite powder within the Cordia Dichotoma reinforced composite material. Besides, presence of chemical functional groups in the composite was traced by Infra-Red Spectroscopy. Also, Thermo-Gravimetric Analysis (TGA) was carried out and the composite was found to be thermally stable up to 455°C.

Function approximation is an important task in many different fields like economics, engineering, computing, classification, and forecasting. From a finite data set, the basic task of a function approximation method is to find a suitable relationship between variables and their corresponding responses. In the recent past, the improved neural networks including intuitive, interpretable correlated-contours fuzzy rules for classification tasks were proposed. However, the acquired data set can contain large volume of data and noise that degrades the classification ability of the model and increases the computational time. Thus, it is important to consider this problem which was not focused on recent existing works. Furthermore, there are also some neuron regularization issues in the second layer. To solve this issue in this proposed system Bat optimization based feature selection is proposed for optimal selection of features from the available dataset. Then classification is done by using enhanced neural network including intuitive and interpretable correlated-contours fuzzy rules (EC-FR). According to fuzzy rules extraction, an appropriate framework is built-in which similarity-based directional component of data partitioning and also a model to form cloud data is presented. Neurons weight and bias values are computed by adapting wavelet functions. Finally, parameters of the fuzzy neural networks are fine-tuned using the hybrid ant colony particle swarm optimization (HASO). Performance is evaluated primarily in accordance with the subsequent metrics like precision, recall, accuracy, and error rate.

Inorganic pollutants are the leading cause of water pollution, which harms human health and the environment. The most highly efficient nanoparticles with enhanced photocatalytic activity are semiconductor quantum dots (QDs). The heterogeneous photocatalysis using UV radiation on quantum dots is a fascinating technique for treating polluted sewage with a naturally available plant-based product. In this study, CdS QDs were synthesized using a facile green synthetic protocol in the presence of Coccinia grandis (CG) and Punica granatum (PG) fruit sap as capping and stabilizing agents. XRD, HR-TEM, FT-IR, UV-DRS, and photoluminescence spectroscopy were used to analyze the synthesized CdS QDs. The photocatalytic degradation activity of CdS quantum dots was performed against anionic and cationic dyes under UV light irradiation. The results revealed that PG-capped CdS QDs have the best photocatalytic activity compared to CG-capped CdS QDs.Further, the pseudo-first-order model is used to analyze the kinetics studies. Moreover, the electrochemical measurements of pure zinc and CdS QDs coated Zn (Zn/CdS QDs) plate were tested in three electrolytes: sodium chloride, potassium hydroxide, and hydrochloric acid, which demonstrated the corrosion inhibition ability of prepared CdS QDs. The electrochemical impedance spectra, Tafel plot, and in-situ SPM analysis depict the improving corrosion resistance of the Zn metal plate due to the prepared CdS QDs coatings. Based on the results, the prepared CdS QDs by the green method can be a potential candidate in the water purification and corrosion resistance field.

In this paper, we consider homogeneous and non-homogeneous second order linear fuzzy systems under granular differentiability. The concept of continuous n-dimensional fuzzy functions on the space of n-dimensional fuzzy numbers are introduced. Developed an algorithm for the solution of a non-homogeneous second order linear fuzzy system under granular differentiability. The proposed algorithm is applied to solve some well-known mechanical problems with fuzzy uncertainty.

Rate of Penetration (ROP) is one of the important factors influencing the drilling efficiency. Since cost recovery is an important bottom line in the drilling industry, optimizing ROP is essential to minimize the drilling operational cost and capital cost. Traditional the empirical models are not adaptive to new lithology changes and hence the predictive accuracy is low and subjective. With advancement in big data technologies, real- time data storage cost is lowered, and the availability of real-time data is enhanced. In this study, it is shown that optimization methods together with data models has immense potential in predicting ROP based on real time measurements on the rig. A machine learning based data model is developed by utilizing the offset vertical wells’ real time operational parameters while drilling. Data pre-processing methods and feature engineering methods modify the raw data into a processed data so that the model learns effectively from the inputs. A multi – layer back propagation neural network is developed, cross-validated and compared with field measurements and empirical models.

In this paper, we consider homogeneous and non-homogeneous system of first order linear fuzzy boundary value problems (SFOLBVPs) under granular differentiability. Using the concept of horizontal membership function, we introduced the notion of first order granular differentiability for n-dimensional fuzzy functions. We present granular integral and its properties. Theorems on the existence and uniqueness of solutions for homogeneous and non-homogeneous SFOLFBVPs are proved. We develop an algorithm for solution of non-homogeneous SFOLBVPs under granular differentiability. We provide some examples to illustrate the validity of the proposed algorithm.

Abstract This paper is devoted to studying the existence and uniqueness of solutions of fuzzy nabla dynamic equations on time scales under Hukuhara differentiability. We apply the Banach contraction mapping principle to establish existence and uniqueness solution for fuzzy nabla initial value problems on time scales and also obtain existence result using Ascoli’s theorem and Schauder’s fixed point theorem.

The mechanism of fat soluble bioactives on activation of AMPK, normalization of insulin and serum proteome on obesity is not yet clear. This study investigated the effect of wheat bran oil concentrate on adipokines, pro-inflammatory markers and transcription factors in relation to differentially expressed serum proteins using two-dimensional electrophoresis (2DE) approach-in high fat diet (HFC) fed C57BL6 mice.The HFC group exhibited a significant increase in body weight, insulin level and lipid profile. The ELISA and Western blot analysis of serum proteins of HFC group also exhibited increased level of inflammatory markers (TNF-α, TGF-β and interleukin-1β), tissue transcription factors (SREBP-2 & PPARγ) and were validated by gene expression and immunohistochemistry. However, the treatment with WBA 1 and 2% significantly lessened lipid droplets in 3T3-L1 cells. Reduced insulin level, down-regulated transcription factors and associated inflammatory cytokines by activating AMPK in HFC-fed mice. Furthermore, WBA treatment directly reversed differentially expressed recombinant proteins of KCTD family and Zinc finger protein 236 isoform.Bioactive fraction of WBA had beneficial effect on ameliorating obesity related complications mainly through phosphorylation of AMPK and inflammatory cytokines which was authenticated by proteomic analysis.

Abstract A one-dimensional numerical model is developed to simulate the nitrogen species in wastewater-applied agricultural field to investigate the effect of various isotherms of ammonium nitrogen and nitrate nitrogen. Results suggest that the ammonium nitrogen concentration reaches up to the depth of 40, 25 and 16 cm due to the linear, Freundlich and Langmuir isotherms of ammonium nitrogen, which is corresponding to the reduction in migration depth up to 37% by Freundlich isotherm and 55% by Langmuir isotherm as compared with linear isotherm. Similarly, the peak concentration of 25, 15 and 12 mg/l of nitrate nitrogen is observed during linear, Freundlich and Langmuir isotherms of NH4–N, respectively. Further, the results show that 4% peak concentration reduction is experienced for NO3–N due to the linear NH4–N sorption alone, 7% peak reduction is observed for NO3–N concentration by Freundlich NH4–N sorption alone, and 12.5% peak reduction is monitored for NO3–N concentration due to Langmuir NH4–N sorption alone compared with the combination of Freundlich NO3–N sorption. The numerical results suggest that the combination of Langmuir isotherm of NH4–N and Freundlich isotherm of NO3–N is identified as a best combination of isotherm scenario, which mitigates the NO3–N contamination in groundwater resources.

Vortex tube separates inlet pressurized gas into hot and cold streams to let out at the two ends. It is mainly used for spot cooling purposes. In the hitherto studies on vortex tube, either cold temperature or temperatures difference of outlet gas was considered the response variable. In this work, suggesting that vortex tube can also be used for heating applications, the two response variables viz., cold-outlet air temperature and hot-outlet air temperature are optimized simultaneously using Taguchi method and grey relational analysis. Various input controllable parameters / factors may be conceived, the values (levels) of whom may affect the response variables. Five controllable parameters with three levels each and L-27 Orthogonal Array are used for experimentation in the present work. Grey Relational Grades are computed. Except hot-tube Length, all the parameters considered are found significant from ANOVA table. Through confirmatory test, experimental results are validated.

Nickel ferrite nanoplatelets have been successfully synthesized by a simple microwave assisted combustion method using trisodium citrate as a fuel. The prepared sample was chemically and structurally characterized by different techniques and the magnetic behaviour was studied by field dependent magnetization measurement. The obtained results indicate that the prepared sample is phase pure nickel ferrite nanoplatelets having size in the range of 40–50 nm and it exhibits a soft ferromagnetic nature with saturation magnetization of 49 emu/g and coercivity of 167 G. Hence proposed method is a facile approach to obtain nickel ferrite nanoplatelets for broad spectrum of applications.

Research on nickel ferrite nanostructures has drawn a great interest because of its inherent chemical, physical and electronic properties. In this study, we have synthesized rhombohedron – like nickel ferrite nanostructure by a rapid microwave assisted combustion method using ethylenediamminetetraacetic acid as a chelating agent. X-ray diffraction, Fourier transform infrared spectrometer, transmission electron microscope and energy dispersive X-ray microanalyser were used to characterize the prepared sample. The magnetic behaviour was analysed by means of field dependent magnetization measurement which indicates that the prepared sample exhibits a soft ferromagnetic nature with saturation magnetization of 63.034 emu/g. This technique can be a potential method to synthesize novel nickel ferrite nanostructure with improved magnetic properties.

Sorption is one of the key processes that plays a major role in the transport of contaminants in fractured porous media. Extensive studies have been conducted on sorption isotherms in fracture matrix coupled system but studies pertaining to sorption in fractured porous media with fracture–skin are very limited. In this study, a numerical model is developed for analysing the influence of sorption intensities on velocity, macro dispersion coefficient and dispersivity using the method of spatial moments. Implicit finite difference numerical technique has been used to solve the coupled non-linear governing equations. A varying grid is adopted at the fracture and skin interface to capture the mass transfer at the interface. Results suggest that the role of non-linear sorption is dominant in comparison with that of advection and dispersion in deciding the final relative concentration within the fracture. The role of sorption partition coefficients is not always enhancing the mixing phenomena which lead to dilution of solutes. Furthermore, the role of sorption partition coefficients is extremely sensitive in the sense that the resultant magnitude of effective dispersivity may either get enhanced or mitigated depending on the magnitude of sorption partition coefficients.

This paper mainly deals with introducing and studying the properties of generalized nabla differentiability for fuzzy functions on time scales via Hukuhara difference. Further, we obtain embedding results on E n for generalized nabla differentiable fuzzy functions. Finally, we prove a fundamental theorem of a nabla integral calculus for fuzzy functions on time scales under generalized nabla differentiability. The obtained results are illustrated with suitable examples.

Domain ontology is used as a reliable source of knowledge in information retrieval systems such as question answering systems. Automatic ontology construction is possible by extracting concept relations from unstructured large-scale text. In this paper, we propose a methodology to extract concept relations from unstructured text using a syntactic and semantic probability-based Naïve Bayes classifier. We propose an algorithm to iteratively extract a list of attributes and associations for the given seed concept from which the rough schema is conceptualized. A set of hand-coded dependency parsing pattern rules and a binary decision tree-based rule engine were developed for this purpose. This ontology construction process is initiated through a question answering process. For each new query submitted, the required concept is dynamically constructed, and ontology is updated. The proposed relation extraction method was evaluated using benchmark data sets. The performance of the constructed ontology was evaluated using gold standard evaluation and compared with similar well-performing methods. The experimental results reveal that the proposed approach can be used to effectively construct a generic domain ontology with higher accuracy. Furthermore, the ontology construction method was integrated into the question answering framework, which was evaluated using the entailment method.

We provide a way to combine matrix Lyapunov systems with fuzzy rules to form a new fuzzy system called fuzzy dynamical matrix Lyapunov system, which can be regarded as a new approach to intelligent control. First, we study the controllability property of the fuzzy dynamical matrix Lyapunov system and provide a sufficient condition for its controllability with the use of fuzzy rule base. The significance of our result is that given a deterministic system and a fuzzy state with rule base, we can determine the rule base for the control. Further, we discuss the concept of observability and give a sufficient condition for the system to be observable. The advantage of our result is that we can determine the rule base for the initial value without solving the system.

The optimization of mining vehicle rear axle weld joints is a complex procedure. The designs for low-cycle fatigue with peak loads were analyzed using computational models and rig testing. On-road evaluation of high-cycle fatigue was performed with the entire vehicle. The iterative analysis is used in design optimization to minimize design cycle time. In sophisticated fatigue calculations with all affecting factors, FEM is utilized. In fatigue failures using road load data, the present deterministic method yields up to 80% correlations. Some critical failure spots were most likely missed owing to operational variations caused by the randomness and multiaxiality of load transfer in link mechanisms with non-linear elements. The fabricated rear axle housing joints are optimized by comparing damage state estimates using stochastic type and multiaxial equivalent loads obtained using Multibody Simulation. The damage state analysis as MBS input stimulus in FE weld models and bolted designs were compared. [ABSTRACT FROM AUTHOR]