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The goal of this study is to determine the values of the process parameters that should be used during the machining of ceramic tile using the abrasive water jet (AWJ) process in order to achieve the lowest possible values for surface roughness and kerf taper angle.

In the present work, ceramic tile is processed by the AWJ process and experimental data were recorded using the RSM approach based Box–Behnken design matrix. The input process factors were water jet pressure, jet traverse speed, abrasive flow rate and standoff distance, to determine the surface roughness and kerf taper angle. ANOVA was used to check the adequacy of model and significance of process parameters. Further, the elite opposition-based learning grasshopper optimization (EOBL-GOA) algorithm was implemented to identify the simultaneous optimization of multiple responses of surface roughness and kerf taper angle in AWJ.

The suggested EOBL-GOA algorithm is suitable for AWJ of ceramic tile, as evidenced by the error rate of ±2 percent between experimental and predicted solutions. The surfaces were evaluated with an SEM to assess the quality of the surface generated with the optimal settings. As compared with initial setting of the SEM image, it was noticed that the bottom cut surface was nearly smooth, with less cracks, striations and pits in the improved optimal results of the SEM image. The results of the analysis can be used to control machining parameters and increase the accuracy of AWJed components.

The findings of this study present an innovative method for assessing the characteristics of the nontraditional machining processes that are most suited for use in industrial and commercial applications.

In the current era of Industry 4.0, the manufacturing industries are striving toward mass production with mass customization by considering human–robot collaboration. This study aims to propose the reconfiguration of assembly systems by incorporating multiple humans with robots using a human–robot task allocation (HRTA) to enhance productivity.

A human–robot task scheduling approach has been developed by considering task suitability, resource availability and resource selection through multicriteria optimization using the Linear Regression with Optimal Point and Minimum Distance Calculation algorithm. Using line-balancing techniques, the approach estimates the optimum number of resources required for assembly tasks operating by minimum idle time.

The task allocation schedule for a case study involving a punching press was solved using human–robot collaboration, and the approach incorporated the optimum number of appropriate resources to handle different types of proportion of resources.

This proposed work integrates the task allocation by human–robot collaboration and decrease the idle time of resource by integrating optimum number of resources.

In this work, a kind of Mannich base (C₂₁H₂₅NO) was synthesized with cinnamal aldehyde, acetophenone and diethylamine in a condensing reflux device based on the conventional method. Optimization of the inhibitor concentration was explored.

Spectral properties of this compound was investigated by FTIR, and its inhibition efficiency and mechanism on N80 steel in 20% hydrochloric acid solution were studied by weight loss measurement, electrochemical measurement (potentiodynamic polarization and electrochemical impedance spectroscopy) and surface analytical measurement (scanning electron microscope with energy dispersive spectrometer).

The results showed that the new inhibitor reduced the double-layer capacitance and increased the charge transfer resistance. The inhibition efficiency is 99.7% when the concentration of C21H25NO is 3%. The adsorption of C₂₁H₂₅NO on N80 steel surface in 20% HCl solution was found to be spontaneous and steady. Observed from the steel surface, an inhibition film was confirmed to be presented after adding inhibitor and successfully hindered the corrosive ions from reaching the bulk steel.

A new Mannich base (C₂₁H₂₅NO) was synthesized by cinnamal aldehyde, acetophenone and diethylamine for the corrosion prevention of N80 steel in 20% hydrochloric acid solution.

The purpose of this paper is to purify the wastewater in the garment industry.

The preparation of the calcium alginate (CA)/activated carbon (AC) composite membrane was achieved by vacuum freeze-drying and the cross-linking reaction between sodium alginate and CaCl₂. Effective parameters in the methylene blue (MB) adsorption such as temperature, dose, contact time and pH were discussed. The adsorption properties of the composite membrane were investigated by isotherm, kinetics and thermodynamic analysis. The adsorption equilibrium data were described by the adsorption isotherm Langmuir model and the Freundlich model. The pseudo-first-order, pseudo-second-order and intra-particle diffusion equations were selected to evaluate the kinetics. The thermodynamic study described that the adsorption reaction was spontaneous and exothermic.

The AC/CA membrane is an efficient and powerful adsorbent to remove MB in printing and dyeing wastewater, and provides a new idea for the selection of adsorption materials for industrial printing and dyeing wastewater.

The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials.

The paper contributes to its wider and convenientapplication in wastewater treatment.

Studies on the combination of CA and AC into adsorption membranes and for the removal of dyes from printing and dyeing wastewater have not been reported. A novel composite material is provided for treatment dyeing wastewater in garment production. The composite membrane research on CA and AC can provide new ideas for the research of these kinds of materials and contribute to its wider and convenient application in wastewater treatment.

The study aims to investigate the effect of four customer relationship management (CRM) dimensions, namely, customer orientation, customer relationship management organization, managing knowledge and CRM based technology, on customer satisfaction in the hospitality sector of Kashmir.

A survey instrument with a slight modification is adapted from literature and is exercised on the customers of three- and four-star hotels operating in Kashmir. A total of 176 responses received using systematic random sampling were subjected to exploratory factor and regression analyses to uncover the underlying relationships among dependent and independent variables.

The results revealed a significant and positive relationship between CRM dimensions, namely, customer orientation, managing knowledge and CRM organization on customer satisfaction. Though the results also indicate a significant positive effect of CRM-based technology on customer satisfaction, the magnitude of this effect is very weak. This suggests that hotel organizations use technology as a mere tool to store customer information only. Thus, CRM-based technology should be used by the hotels to analyze customer information and, subsequently, design customized products. This will unravel the full potential of the technology and lead to better customer satisfaction.

The findings of this study provide significant insights to the practitioners to understand the role of successfully implementing a CRM strategy. It reflects that establishing an effective CRM strategy helps managers in improving customer satisfaction and in maintaining a long-term relationship with customers to achieve the organizational goals. Thus, establishing an efficient and effective CRM strategy should be (one of) the key objectives for all hotel managers. Moreover, the hotels that successfully implement CRM strategy and manage customer knowledge properly will reap the rewards in terms of better customer loyalty and long-term sustainable profitability.

This study approaches the implementation of CRM strategy from a customer perspective with a specific focus on investigating the effect of four CRM dimensions on customer satisfaction in the hospitality sector. This will provide a novel impetus to the hotel managers to devise and manage a CRM strategy that leads to (better) customer satisfaction.

The purpose of this paper is to examine the batch adsorption system of a cationic dye (methylene blue, widely used in various sectors) on two adsorbents; ZSM-5 zeolite which was prepared with the molar composition: 0.2057 Na₂O-0.00266 Al₂O₃-SiO₂-0.68 (pyrrolidine)-40 H₂O-0.12 H₂SO₄.

By the hydrothermal synthesis method, and the purified clay and is analyzed by IR and DRX method.

For this, the authors conducted a parametric study of adsorption and effect of several important parameters on the adsorption of BM on the material used, in particular, the contact time (equilibrium is established after 120 min), different concentrations of adsorbents, different masses, the pH and temperature. The experiments demonstrated the crucial role of these parameters. A kinetic study was done and kinetic models were applied to the experimental results such as the pseudo-first order, pseudo-second order.

This work is original.

The flexible mode transitions, multiple power sources and system uncertainty lead to challenges for mode transition control of four-wheel-drive hybrid powertrain. Therefore, the purpose of this paper is to improve dynamic performance and fuel economy in mode transition process for four-wheel-drive hybrid electric vehicles (HEVs), overcoming the influence of system uncertainty.

First, operation modes and transitions are analyzed and then dynamic models during mode transition process are established. Second, a robust mode transition controller based on radial basis function neural network (RBFNN) is proposed. RBFNN is designed as an uncertainty estimator to approximate lumped model uncertainty due to modeling error. Based on this estimator, a sliding mode controller (SMC) is proposed in clutch slipping phase to achieve clutch speed synchronization, despite disturbance of engine torque error, engine resistant torque and clutch torque. Finally, simulations are carried out on MATLAB/Cruise co-platform.

Compared with routine control and SMC, the proposed robust controller can achieve better performance in clutch slipping time, engine torque error, vehicle jerk and slipping work either in nominal system or perturbed system.

The mode transition control of four-wheel-drive HEVs is investigated, and a robust controller based on RBFNN estimation is proposed. Compared results show that the proposed controller can improve dynamic performance and fuel economy effectively in spite of the existence of uncertainty.

Over the past decade, many research works in various disciplines have benefited from the endless ocean of people and their potentials (in the form of crowdsourcing) as an effective problem-solving strategy and computational model. But nothing interesting is ever completely one-sided. Therefore, when it comes to leveraging people's power, as the dark side of crowdsourcing, there are some possible threats that have not been considered as should be, such as recruiting black hat crowdworkers for organizing targeted adversarial intentions. The purpose of this paper is to draw more attention to this critical issue through investigation of its different aspects.

To delve into details of such malicious intentions, the related literature and previous researches have been studied. Then, four major typologies for adversarial crowdsourced attacks as well as some real-world scenarios are discussed and delineated. Finally, possible future threats are introduced.

Despite many works on adversarial crowdsourcing, there are only a few specific research studies devoted to considering the issue in the context of cyber security. In this regard, the proposed typologies (and addressed scenarios) for such human-mediated attacks can shed light on the way of identifying and confronting such threats.

To the best of the authors' knowledge, this the first work in which the titular topic is investigated in detail. Due to popularity and efficiency of leveraging crowds' intelligence and efforts in a wide range of application domains, it is most likely that adversarial human-driven intentions gain more attention. In this regard, it is anticipated that the present research study can serve as a roadmap for proposing defensive mechanisms to cope with such diverse threats.

The purpose of this paper is to carry out a detailed investigation to study the natural convection of a non-Newtonian nanofluid flow between two vertical parallel plates. In this study, sodium alginate has been taken as a base fluid and nanoparticles that added to it are copper and silver. Maxwell–Garnetts and Brinkman models are used to calculate the effective thermal conductivity and viscosity of nanofluid, respectively.

The authors used two methods in this study, namely, Galerkin’s method and homotopy perturbation method.

This paper investigates the velocity and temperature profile of nanofluid and the real fluid flow between two vertical parallel plates. The impacts of physical parameters such as nanofluid volume fraction and dimensionless non-Newtonian viscosity are discussed.

Coupled non-linear differential equations are solved for velocity and temperature. A model is proposed in such a way that the authors may get the solution of real fluid from the nanofluid by neglecting the nano term. The authors do not require a further calculation for real fluid problem.

The purpose of this paper is to propose a novel collaborative trend prediction method to estimate the status of trending topics by crowdsourcing the wisdom in web search engines. Government officials and decision makers can take advantage of the proposed method to effectively analyze various trending topics and make appropriate decisions in response to fast-changing national and international situations or popular opinions.

In this study, a crowdsourced-wisdom-based feature selection method was designed to select representative indicators showing trending topics and concerns of the general public. The authors also designed a novel prediction method to estimate the trending topic statuses by crowdsourcing public opinion in web search engines.

The authors’ proposed method achieved better results than traditional trend prediction methods and successfully predict trending topic statuses by using the crowdsourced wisdom of web search engines.

This paper proposes a novel collaborative trend prediction method and applied it to various trending topics. The experimental results show that the authors’ method can successfully estimate the trending topic statuses and outperform other baseline methods. To the best of the authors’ knowledge, this is the first such attempt to predict trending topic statuses by using the crowdsourced wisdom of web search engines.