A novel analytical solution of a fractional diffusion problem by homotopy analysis transform method

In this study, we present the homotopy analysis transform method for finding solution of fractional diffusion-type equations. We can attain these equations by substituting a first-order time derivative by a fractional-order derivative in regular diffusion equation. We add some examples in order to illustrate the usefulness and efficiency of our novel proposed technique for fractional diffusion equations.     

Heterogeneous data analysis using a mixture of Laplace models with conjugate priors

The development of flexible parametric classes of probability models in Bayesian analysis is a very popular approach. This study is designed for heterogeneous population for a two-component mixture of the Laplace probability distribution. When a process initially starts, the researcher expects that the failure components will be very high but after some improvement/inspection it is assumed that the failure components will decrease sufficiently. That is why in such situation the Laplace model is more suitable as compared to the normal distribution due to its fatter tails behaviour. We considered the derivation of the posterior distribution for censored data assuming different conjugate informative priors. Various kinds of loss functions are used to derive these Bayes estimators and their posterior risks. A method of elicitation of hyperparameter is discussed based on a prior predictive approach. The results are also compared with the non-informative priors. To examine the performance of these estimators we have evaluated their properties for different sample sizes, censoring rates and proportions of the component of the mixture through the simulation study. To highlight the practical significance we have included an illustrative application example based on real-life mixture data.     

Preparation and in vitro evaluation of novel cross‐linked chondroitin sulphate nanoparticles by aluminium ions for encapsulation of green tea flavonoids

Chondroitin sulphate is a sulphated glycosaminoglycan biopolymer composed over 100 individual sugars. Chondroitin sulphate nanoparticles (NPs) loaded with catechin were prepared by an ionic gelation method using AlCl₃ and optimised for polymer and cross‐linking agent concentration, curing time and stirring speed. Zeta potential, particle size, loading efficiency, and release efficiency over 24 h (RE₂₄ %) were evaluated. The surface morphology of NPs was investigated by scanning electron microscopy and their thermal behaviour by differential scanning calorimetric. Antioxidant effect of NPs was determined by chelating activity of iron ions. The cell viability of mesenchymal stem cells was determined by 3‐[4, 5‐dimethylthiazol‐2‐yl]‐2, 5‐diphenyl tetrazolium bromide assay and the calcification of osteoblasts was studied by Alizarin red staining. The optimised NPs showed particle size of 176 nm, zeta potential of −20.8 mV, loading efficiency of 93.3% and RE₂₄ % of 80.6%. The chatechin loaded chondroitin sulphate NPs showed 70‐fold more antioxidant activity, 3‐fold proliferation effect and higher calcium precipitation in osteoblasts than free catechin.Inspec keywords: nanoparticles, encapsulation, biomedical materials, particle size, nanofabrication, nanomedicine, electrokinetic effects, cellular biophysics, polymer blends, molecular biophysics, molecular configurations, biochemistry, curing, surface morphology, scanning electron microscopy, differential scanning calorimetry, dyes, precipitationOther keywords: in vitro evaluation, cross‐linked chondroitin sulphate nanoparticles, aluminium ions, nanoparticles, green tea flavonoids, sulphated glycosaminoglycan biopolymer, sugars, catechin, ionic gelation method, cross‐linking agent concentration, curing time, size 176 nm, time 24 h, calcium precipitation, 3‐fold proliferation effect, antioxidant activity, chatechin loaded chondroitin sulphate NPs, Alizarin red staining, osteoblasts, calcification, 3‐[4,5‐dimethylthiazol‐2‐yl]‐2,5‐diphenyl tetrazolium bromide assay, mesenchymal stem cells, cell viability, chelating activity, differential scanning calorimetry, thermal behaviour, scanning electron microscopy, surface morphology, release efficiency, loading efficiency, particle size, zeta potential, stirring speed     

Vector Based Genetic Algorithm to optimize predictive analysis in network security

A new Intrusion Detection System (IDS) for network security is proposed making use of a Vector-Based Genetic Algorithm (VBGA) inspired by evolutionary approaches. The novelty in the algorithm is to represent chromosomes as vectors and training data as matrices. This approach allows multiple pathways to calculate fitness function out of which one particular methodology is used and tested. The proposed method uses the overlap of the matrices with vector chromosomes for model building. The fitness of the chromosomes is calculated from the comparison of true and false positives in test data. The algorithm is flexible to train the chromosomes for one particular attack type or to detect the maximum number of attacks. The VBGA has been tested on two datasets (KDD Cup-99 and CTU-13). The proposed algorithm gives high detection rate and low false positives as compared to traditional Genetic Algorithm. A detailed comparative analysis is given of proposed VBGA with the traditional string-based genetic algorithm on the basis of accuracy and false positive rates. The results show that vector based genetic algorithm provides a significant improvement in detection rates keeping false positives at minimum.     

High Gain Solution-Processed Carbon-Free BiSI Chalcohalide Thin Film Photodetectors

Chalcohalide semiconductors are an emergent class of materials for optoelectronics. Here, the first work on BiSI chalcohalide thin film photodetectors (PDs) is presented. An entirely new method for the fabrication of bismuth chalcohalide thin films (BiOI and BiSI) is developed. This method circumvents the use of any ligands or counter ions during fabrication and provides highly pure thin films free of carbon residues and other contaminants. When integrated into lithographically patterned lateral PDs these BiSI thin films show outstanding performances and high stability. The direct ≈1.55 eV bandgap of BiSI perfectly accommodates optical sensing over the full visible spectrum. The responsivity (R) of the BiSI PDs reaches 62.1 A W⁻¹, which is the best value reported to date across chalcohalide materials of any type. The BiSI PDs display remarkable sensitivity to low light levels, supporting a broad operational detectivity ≈10¹² Jones over four decades in light intensity, with a peak specific detectivity (D*) of 2.01 × 10¹³ Jones. The dynamics of photocurrent generation are demonstrated to be dominated by photoconductive gain. These results cement BiSI as an exciting candidate for high performance photodetector applications and encourage ongoing work in BiSX (X = Cl, Br, I) materials for optoelectronics.     

A Hybrid Approach for Network Intrusion Detection

Due to the widespread use of the internet and smart devices, various attacks like intrusion, zero-day, Malware, and security breaches are a constant threat to any organization's network infrastructure. Thus, a Network Intrusion Detection System (NIDS) is required to detect attacks in network traffic. This paper proposes a new hybrid method for intrusion detection and attack categorization. The proposed approach comprises three steps to address high false and low false-negative rates for intrusion detection and attack categorization. In the first step, the dataset is preprocessed through the data transformation technique and min-max method. Secondly, the random forest recursive feature elimination method is applied to identify optimal features that positively impact the model's performance. Next, we use various Support Vector Machine (SVM) types to detect intrusion and the Adaptive Neuro-Fuzzy System (ANFIS) to categorize probe, U2R, R2U, and DDOS attacks. The validation of the proposed method is calculated through Fine Gaussian SVM (FGSVM), which is 99.3% for the binary class. Mean Square Error (MSE) is reported as 0.084964 for training data, 0.0855203 for testing, and 0.084964 to validate multiclass categorization.     

Sheet forming of flax reinforced polypropylene composites using vacuum assisted oven consolidation (VAOC)

Fibre reinforced plastics are widely used in different industry sectors since the past half century. Considerable research has been carried out on natural fibres as reinforcements because of their light weight, good mechanical properties, biodegradability, sustainability and higher stiffness per unit mass compared to glass fibres. This paper develops a knowledge base for the manufacture of vacuum assisted fibre reinforced thermoplastic composites inside a simple oven from sheets of the fibre and the thermoplastic. The compaction behaviour of the reinforcements used was investigated and used to target a range of achievable fibre volume fractions to manufacture the composites. The reinforcements were also characterized on the basis of their transverse permeability to quantify the ease of polymer flow through the reinforcements. An experimental setup was designed to measure the relevant processing parameters during the experiment. Thickness, pressure and temperature of the composites were monitored with applied vacuum pressure and oven temperature during the experiments. The mould temperature inside the oven was first increased to the melting temperature of the thermoplastic, held at that temperature for a certain time period and then cooled subsequently. The application of compaction pressure and the holding time was varied to study its effect on consolidation quality. The achieved fibre volume fraction of the manufactured composites was calculated by first using the average thickness of the composites and then using the density of the composite. The cross-section of the laminates was examined under an optimal microscope to evaluate the quality of consolidation. Tensile and short beam shear tests were performed to assess structural integrity, strength and stiffness of the composites. The results show that this manufacturing technique can produce composites at expected fibre volume fractions under vacuum. The cooling rate, applied temperature, pressure and sheet layup were found to have considerable effects on the fibre volume fraction. It was also found that the mechanical properties of natural fibre reinforced thermoplastic composites (NFRTS) do not change appreciably even after modifying the process so as to reduce the manufacturing time.     

Facile CO2 Separation in Composite Membranes

CO₂ emission from anthropogenic sources has raised worldwide environmental concerns and hence proficient energy paradigm has tilted towards CO₂ capture. Membrane technology is one of the efficient technologies for CO₂ separation since it is environmentally friendly, inexpensive, and offers high surface areas. Various approaches are discussed to improve membrane performance focusing mainly on permeability and selectivity parameters. Different types of fillers are incorporated to reach the Robeson's upper bound curve. In this review, polymer‐inorganic nanocomposite membranes for the separation of CO₂, CH₄, and N₂ from various gas mixtures are comprehensively discussed. Metal organic frameworks (MOFs) and ionic liquid (ILs) mixed‐matrix membranes are also considered.     

Fabrication and modelling of the macro-mechanical properties of cross-ply laminated fibre-reinforced polymer composites using artificial neural network

The mechanical behaviour of fibre-reinforced polymer composites (FRPCs) is considered very complex due to many factors such as composition, material type, manufacturing process and end user applications. This article presents the mechanical properties and artificial neural network (ANN) modelling results of cross-ply laminated FRPCs. Twenty composite samples were fabricated by varying the number of layers of carbon fibre and glass fibre as reinforcement and polyphenylene sulphide and high-density polyethylene as matrix. Mechanical properties were measured in terms of flexural modulus, hardness, impact and transverse rupture strength. Multilayer feed-forward backpropagation ANN approach was used to predict the mechanical properties by using material type, composition and number of reinforcement and matrix layers as input variables. From 20 data patterns, 16 were used for network training and remaining 4 were used to test the models. Furthermore, trend analysis was also performed to understand the influence of inputs on developed models. It is evident from the ANN prediction results that there is good correlation between predicted and actual values within acceptable mean absolute error. The outcomes of this research will help to reduce cost and time by eliminating tedious composite property measurements and to fabricate tailored composites meeting application requirements.