A group of new fluoroaniline Schiff bases (3a–3f) were synthesized and structurally characterized by various spectroscopic techniques such as ¹H-NMR, LC-MS and FT-IR spectral studies. All compounds were evaluated for in vitro antibacterial activity. Compounds exhibited good to moderate antibacterial activity. Compound 3f (Zone of Inhibition = 10.08±0.06 µM) was found to be the most active one, and comparable to the standard Streptomycin (IC50 = 15.95±0.08 µM). The compounds having chloro substituent exhibit good membrane damage property against Methicillin-resistant Staphylococcus aureus (MRSA) confirmed by SEM analysis. Structure-activity relationship (SAR) was rationalized by looking at the varying structural features of the molecules.

The efficacy of stainless-steel micro-particles on friction stir processed aluminium-based matrix composite (ABMC) was studied using tribological, mechanical and structural analysis tools. The stainless-steel powder (17-4PH) of average size 45 – 90 µm was used as the reinforcement particle. The parametric values employed during the fabrication of ABMC- AA7075-T651/17-4PH were the rotational speed of 1500 rpm and travel speed of 20 mm/min while the plunge depth and tilt angles used were respectively 0.3 mm and 3 degrees. Tribological study was carried out under the influence of dry sliding condition with varying loads of 20 N and 50 N using tribometer while the scanning electron microscope (SEM) was used to capture the wear track. Structural analysis was examined with the aid of x-ray diffraction (XRD). The tensile strengths of the fabricated ABMC were also tested and the fracture surfaces were studied using SEM analysis. The results from the study revealed that at higher loading of 50 N, the wear performance was significantly improved for the fabricated aluminium composite- AA7075-T651/17-4PH when compare with lower loading of 20 N. The tensile properties for the ABMC were also improved under the influence of the stainless steel microparticles. There was structural improvement in ABMC wherein the value for crystallite size was lowest while micro-strain, dislocation density, as well as full width at half maximum (FWHM), had the highest values over the FSPed AA7075-T651 and the parent material. The examined fractured surface of the fabricated composite was dominated with fine, network and equiaxed dimples with cup and cone attributes confirming superb interfacial bonding and that the failure mode was ductile.

Cracks are one of the main causes of structural failure and they develop in the structures due to various reasons such as fatigue, temperature variation, excessive load, cyclic load, environmental effects, impact loading etc. Thus, structural health monitoring is necessary to avoid risks, damages and failures. So, in order to avoid an extensive failure or accident, the early prognosis of crack in structures is necessary. Visual inspection and some non-destructive testing (NDT) methods for detection of crack are difficult as it requires time, expenses and are quite inefficient. So the alternative methods are motivated to be developed. In this study, vibration analysis, finite element analysis (FEA) and an alternative way which is artificial neural network (ANN) is used to predict, detect and identify the damages in structures. It is found that the theoretical, experimental, finite element analysis and artificial neural network have good accuracy in predicting the crack characteristics.

This paper proposes a framework to develop a deterministic model for the valuation, selection and grading (ranking) of e-suppliers by using Modified Distance Based Approach (MDBA), which has not been used earlier in e-supplier selection. The e-supplier selection system performs a major part for the successful running of any supply chain. Thus, for effective running of any supply chain, it is necessary to build a system for the selection of e-supplier. Building such a decision support system software is important for the development of any decision support system efficiently with reduced cost, time and effort. The current research is based on 8 criteria and 52 sub-criteria by giving equal weightage to all of them. In this study, the major criteria are disintegrated into small sub-criteria. To validate the results obtained through the proposed distance based approximation method, the results are compared with other methodologies. Finally, with the illustration of the example problem, the applicability of the developed model is described.

The paper deals with the performance analysis and optimization for Carbonated Soft Drink Glass Bottle (CSDGB) filling system of a beverage plant using Particle Swarm Optimization (PSO) approach. The CSDGB system consists of seven main subsystems arranged in series namely Uncaser, Bottle Washer, Electronic Inspection Station, Filling Machine, Crowner, Coding Machine and Case Packer. Considering exponential distribution for probable failures and repairs, mathematical modeling is performed using Markov Approach (MA). The differential equations have been derived on the basis of probabilistic approach using transition diagram. These equations are solved using normalizing condition and recursive method to drive out the steady state availability expression of the system i.e. system’s performance criterion. The performance optimization of system has been carried out by varying the number of particles and number of generations. It has been observed that the maximum availability of 90.27% is achieved at flock size of 55 and 90.84% at 300th generation. Thus, findings of the paper will be useful to the plant management for execution of proper maintenance decisions.

An appropriate sequence of assembly operations increases the productivity and enhances product quality there by decrease the overall cost and manufacturing lead time. Achieving such assembly sequence is a complex combinatorial optimization problem with huge search space and multiple assembly qualifying criteria. The purpose of the current research work is to develop an intelligent strategy to obtain an optimal assembly sequence subjected to the assembly predicates. This paper presents a novel hybrid artificial intelligent technique, which executes Artificial Immune System (AIS) in combination with the Genetic Algorithm (GA) to find out an optimal feasible assembly sequence from the possible assembly sequence. Two immune models are introduced in the current research work: (1) Bone marrow model for generating possible assembly sequence and reduce the system redundancy and (2) Negative selection model for obtaining feasible assembly sequence. Later, these two models are integrated with GA in order to obtain an optimal assembly sequence. The proposed AIS-GA algorithm aims at enhancing the performance of AIS by incorporating GA as a local search strategy to achieve global optimum solution for assemblies with large number of parts. The proposed algorithm is implemented on a mechanical assembly composed of eleven parts joined by several connectors. The method is found to be successful in achieving global optimum solution with less computational time compared to traditional artificial intelligent techniques.

This paper suggests an advanced hybrid multi output optimization technique by applying weighted principal component analysis (WPCA) incorporated with response surface methodology (RSM). This investigation has been carried out through a case study in CNC turning of Aluminum alloy 63400 for surface roughness (Ra) and tool vibration (db) optimization. Primarily, input parameters such as spindle speed (N), feed rate (S) and depth of cut (t) are designed for experiment by using RSM Box-Behnken methodology. The aluminum alloy workpieces are machined by using coated carbide tool (inserts) in dry environment. Secondly, the empirical model for the responses as the functions of cutting parameters are obtained through RSM technique and the adequacy of the models have been checked using analysis of variance (ANOVA). Finally, the process parameters are optimized using WPCA technique. The confirmatory experiment has been performed using optimized result and it reveals that multiple response performance index (MPI) value was increased by 0.2908 from initial setting. The increases in MPI value indicates that the aforesaid optimization methodology is suitably acceptable for multi response optimization for turning process.

In India, higher education institutions (HEIs) are realizing the value of green interaction, which includes using social media (S.M.) in an environmentally responsible manner. However, there needs to be more consistency on the adoption's present status, the particular tactics used, and the advantages that follow. The current research investigates the various aspects of sustainable communication and its benefits in HEIs throughout India. Through a questionnaire approach, the research enrolled learners across India. The study model's conceptual structure was validated, and its assumptions were proven through the implementation of a quantitative survey. The number of accepted samples was calculated using a conceptual model, and data analysis was performed using the structural equation model (SEM). There were 500 respondents in the study, ranging in age and academic degree and from different institutions of higher learning. The age range of 280 women and 220 men was 80% between 19 and 28. Respondents employed prominent S.M. sites (Twitter, LinkedIn, and Facebook) with purpose and had robust computer abilities. The results demonstrated that inspiration for employing S.M., S.M. characteristics, and information exchange had a favorable impact on students' opinions of user-friendliness and the benefits of digital learning platforms, raising their adoption of them. The research strategy in this investigation delivers suggestions for additional exploration into how HEIs in India may maximize the advantages and utilization of digital learning systems and can be an efficient structure for similar study ventures.

Automatic guided vehicle system (AGVs) plays a vital role in material handling operations for a flexible manufacturing system (FMS).Optimum AGVs fleet size selection is one of the most sig-nificant decisions in effective design and control of automated material handling system. The fleet size estimation and optimization of AGVs requires an in-depth understanding of the various factors that AGVs in the FMS relies on. In this paper, an investigation for fleet size optimization of AGVs in different layouts of FMS by application of the analytical method and grey wolf optimization al-gorithm (GWO) is carried out. Layout design is one of the significant factors for optimization of AGV’s fleet size in any FMS. Results yield from analytical and grey wolf optimization algorithm are compared and validated for the different sizes of FMS layouts by computational experiments.

Lamotrigine (LMT), chemically known as [6-(2,3-dichlorophenyl)-1,2,4-triazine-3,5-diamine], is a broad spectrum antiepileptic drug, used as monotherapy and as an adjunct with other antiepileptic drugs for treatment of partial and generalized toxic-clonic seizures. It is used to treat neurological lesions and as a tranquilizer. A selective electrochemical membrane sensor has been developed and validated for determination of LMT. The membrane constructed using LMT and molybdophosphoric acid in THF and PVC is applicable for the detection of 5 × 10^-4 to 9 × 10^-3 M LMT in the pH range between 4.6 and 5.8 with the Nernstian slope of 57.14±1 mV/decade. The regression coefficient value of 0.9932 showed a good linear correlation between the concentrations of LMT and measured cell potentials. The limits of detection (LOD) and quantification (LOQ) values for the fabricated sensor were 1.3 × 10^-5 and 4 × 10^-5 M LMT, respectively. Various experimental conditions were optimized to reach the effective performance characteristics of the sensor. The effect of various cations, anions and organic species on the performance of sensor was studied by following standard-addition procedure. The results revealed no such variations due to presence of foreign ions or species. The fabricated sensor was subjected to validation to check accuracy, precision, robustness and ruggedness. The mean accuracy for determination of LMT was found to be 99.16%. The developed sensor was successfully used to determine LMT in tablets and in spiked human urine.