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The purpose of the study reported in this paper was to investigate the process for the preparation of carboxymethyl chitosan (CMCS) hydrogel and to characterize such a hydrogel via various analytical techniques.

The hydrogel in the aqueous solution was prepared by using CMCS as the raw material and glutaraldehyde as the crosslinking agent. The as-prepared CMCS hydrogel was characterized by transmission electron microscopy, scanning electron microscopy, Fourier transform infrared (FTIR) spectra, differential scanning calorimetry, X-ray diffraction (XRD) and ultraviolet-visible (UV-vis) spectra.

The CMCS hydrogel possessed a porous structure and the shape of the pore was irregular. Generally, the diameter of the pores ranged from 20 to 70 nm. The results from FTIR, UV-vis and XRD showed that there was no obvious difference between the structures of the CMCS hydrogel and CMCS powder.

The strength of the hydrogel is not high enough and the degree of swelling is relatively small. So, improving the strength and swelling degree of the hydrogel is necessary.

The CMCS hydrogel presented obvious hollow structures and its fabrication was processed absolutely in aqueous phase. Besides, it possessed low toxicity, good biocompatibility and biodegradability. So, the hydrogel will have potential applications in drug delivery and release, tissue engineering and other biomedical fields.

This paper is the first to present the relationship between the structures of the CMCS hydrogel and CMCS micromolecule, and it confirms that there is no fundamental difference between them.

Fixture layout design is concerned with immobilization of the workpiece (engine mount bracket) during machining such that the workpiece elastic deformation is reduced. The fixture holds the workpiece through the positioning of fixturing elements that causes the workpiece elastic deformation, in turn, leads to the form and dimensional errors and increased machining cost. The fixture layout has the major impact on the machining accuracy and is the function of the fixturing position. The position of the fixturing elements, key aspects, needed to be optimized to reduce the workpiece elastic deformation. The purpose of this study is to evaluate the optimized fixture layout for the machining of the engine mount bracket.

In this research work, using the finite element method (FEM), a model is developed in the MATLAB for the fixture-workpiece system so that the workpiece elastic deformation is determined. The artificial neural network (ANN) is used to develop an empirical model. The results of deformation obtained for different fixture layouts from FEM are used to train the ANN and finally the empirical model is developed. The model capable of predicting the deformation is embedded to the evolutionary optimization techniques, capable of finding local and global optima, to optimize the fixture layouts and to find the robust one.

For efficient optimization of the fixture layout parameters to obtain the least possible deformation, ant colony algorithm (ACA) and artificial bee colony algorithm (ABCA) are used and the results of deformation obtained from both the optimization techniques are compared for the best results.

A MATLAB-based FEM technique is able to provide solutions when the repeated modeling and simulations required i.e. modeling of fixture layouts (500 layouts) for every variation in the parameters requires individual modeling and simulation for the output requirement in any FEM-based software’s (ANSYS, ABACUS). This difficulty is reduced in this research. So that the MATLAB-based FEM modeling, simulation and optimization is carried out to determine the solutions for the optimized fixture layout to reach least deformation.

Many a time the practicability of the machining/mechanical operations are difficult to perform costly and time-consuming when more number of experimentations are required. To sort out the difficulties the computer-based automated solution techniques are highly required. Such kind of research over this study is presented for the readers.

A MATLAB-based FEM modeling and simulation technique is used to obtain the fixture layout optimization. ANN-based empirical model is developed for the fixture layout deformation that creates a hypothesis for the fixture layout system. ACA and ABCA are used for optimizing the fixture layout parameters and are compared for the best algorithm suited for the fixture layout system.

This paper aims to explore new variable separation solutions for a new generalized (3 + 1)-dimensional breaking soliton equation, construct novel nonlinear excitations and discuss their dynamical behaviors that may exist in many realms such as fluid dynamics, optics and telecommunication.

By means of the multilinear variable separation approach, variable separation solutions for the new generalized (3 + 1)-dimensional breaking soliton equation are derived with arbitrary low dimensional functions with respect to {y, z, t}. The asymptotic analysis is presented to represent generally the evolutions of rogue waves.

Fixing several types of explicit expressions of the arbitrary function in the potential field U, various novel nonlinear wave excitations are fabricated, such as hybrid waves of kinks and line solitons with different structures and other interesting characteristics, as well as interacting waves between rogue waves, kinks, line solitons with translation and rotation.

The paper presents that a variable separation solution with an arbitrary function of three independent variables has great potential to describe localized waves.

The roles of parameters in the chosen functions are ascertained in this study, according to which, one can understand the amplitude, shape, background and other characteristics of the localized waves.

The work provides novel localized waves that might be used to explain some nonlinear phenomena in fluids, plasma, optics and so on.

The study proposes a new generalized (3 + 1)-dimensional breaking soliton equation and derives its nonlinear variable separation solutions. It is demonstrated that a variable separation solution with an arbitrary function of three independent variables provides a treasure-house of nonlinear waves.

This study discusses the tracking trajectory issue of the exoskeleton under the bounded disturbance and designs an useful tracking trajectory control method to solve it. By using the proposed control method, the tracking error can be successfully convergence to the assigned boundary. Meanwhile, the chattering effect caused by the actuators is already reduced, and the tracking performance of the pneumatic artificial muscles (PAMs) elbow exoskeleton is improved effectively.

A prescribed performance sliding mode control method was developed in this study to fulfill the joint position tracking trajectory task on the elbow exoskeleton driven by two PAMs. In terms of the control structure, a dynamic model was built by conforming to the adaptive law to compensate for the time variety and uncertainty exhibited by the system. Subsequently, a super-twisting algorithm-based second-order sliding mode control method was subjected to the exoskeleton under the boundedness of external disturbance. Moreover, the prescribed performance control method exhibits a smooth prescribed function with an error transformation function to ensure the tracking error can be finally convergent to the pre-designed requirement.

From the theoretical perspective, the stability of the control method was verified through Lyapunov synthesis. On that basis, the tracking performance of the proposed control method was confirmed through the simulation and the manikin model experiment.

As revealed by the results of this study, the proposed control method sufficiently applies to the PAMs elbow exoskeleton for tracking trajectory, which means it has potential application in the actual robot-assisted passive rehabilitation tasks.

This paper aims to report a novel preparation method of ZnO particles with different structures and their photocatalytic activity.

ZnO powders are prepared by a facile, economical and environment-friendly aqueous solution route. X-ray diffraction, field emission scanning electron microscopy, energy dispersive spectroscopy, transmission electron microscopy and UV-vis diffuse reflectance spectra are used to characterize the products. Photocatalytic activity of the samples is evaluated by degradation of organic pollutant pentachlorophenol under UV-vis irradiation.

It is found that three-dimensional ZnO hierarchical structures can be prepared via aqueous solution route without using any template or structure-directing agent, and the alkalinity of reaction solution is the key factor. All the as-prepared ZnO products have good catalytic activity under UV-vis light irradiation.

This report presents a simple method for the preparation of ZnO particles with excellent photocatalytic activity. Experimental results could provide useful reference for the treatment of chlorophenols in the future.

The aim of this study was to investigate the impact of the transactive memory system (TMS) on green innovation and examine the mediation role of the social network at all hierarchical levels.

Three hypotheses were examined by performing regression analyses on survey data from manufacturing firms in China. Especially, the nested sets of data from 389 individual observations nested in 53 work teams, including individual level and collective level have been investigated.

The study results show that the TMS has a positive effect on green innovation. Furthermore, the results indicate that at the team level, structure holes' mediation in this relationship is stronger than degree centrality; at the individual level, weak ties mediation in the relationship of specialization and green innovation is stronger than strong ties, conversely, strong ties mediation in the relationship of credibility and green innovation is stronger than weak ties.

This study expands previous research by highlighting the significance of multilevel social network elements in the context of the TMS and sustainable development and enriches the present research on green innovation.

The aim of this paper is to provide an easy method of extrusion freeforming to fabricate microwave electromagnetic bandgap (EBG) crystals. EBG crystals are periodic dielectric structures that can block wave propagation and generate a bandgap. These crystals can be used in high capability antennae, electromagnetic wave semiconductors, microresonators, high‐reflectivity mirrors and polarizing beam splitters.

The effects of extrusion process parameters and paste characteristics were investigated. Finally, one‐period and two‐period woodpile EBG crystals with bandgaps in the frequency region of 90‐110 GHz were fabricated and the bandgap was measured.

The filament diameter is influenced by whether extrusion is carried out with or without a substrate and by the free fall‐distance from the nozzle. The quality of lattice structures is dependent on paste flow and properties. A ceramic paste with 60 vol. % (the fraction of ceramic powder based on solvent‐free polymer) was well suited to fabrication. The solvent content also influenced the fabrication. The experimental results show that under ∼12 per cent solvent mass fraction in the paste and relatively high extrusion ram velocity (more than 0.014 mm/s) at a pressure of 14 MPa, samples with high quality were fabricated.

This paper demonstrates that the rapid prototyping method of extrusion freeforming can be applied for the fabrication of EBG crystals from ceramic powders and the important factors which influence the product quality are identified.

The Indian power sector is dominated by coal. Environmental awareness and advances in techno-economic front have led to a slow but steady shift towards greener alternatives. The distributions of both fossil fuel resources and renewable energy potential are not uniform across the states. Paper attempts to answer how the states are performing in the sector and how the renewable energy and conventional resources are affecting the dynamics.

The authors employ a two-stage data envelopment analysis (DEA) to rank the performance of Indian states in the power sector. Multi-stage analysis opens up the DEA black-box through disaggregating power sector in two logical sub-sectors. The performance is evaluated from the point-of-view of policy formulating and implementing agencies. Further, an econometric analysis using seemingly unrelated regression equations (SURE) is conducted to estimate the determinants of total and industrial per-capita electricity consumption.

Efficiency scores obtained from the first phase of analysis happens to be a significant explanatory variable for power consumption. The growth in electricity consumption, which is necessary for economic wellbeing, is positively affected by both renewable and non-renewable sources; but conventional sources have a larger impact on per-capita consumption. Yet, the share of renewables in the energy mix has positive elasticity. Hence, the findings are encouraging, because development in storage technologies, falling costs and policy interventions are poised to give further impetus to renewable sources.

The study is one of the very few where entire spectrum of the Indian power sector is evaluated from efficiency perspective. Further, the second phase analysis gives additional relevant insights on the sector.

Academic groups are designed specifically for researchers. A group recommendation procedure is essential to support scholars’ research-based social activities. However, group recommendation methods are rarely applied in online libraries and they often suffer from scalability problem in big data context. The purpose of this paper is to facilitate academic group activities in big data-based library systems by recommending satisfying articles for academic groups.

The authors propose a collaborative matrix factorization (CoMF) mechanism and implement paralleled CoMF under Hadoop framework. Its rationale is collaboratively decomposing researcher-article interaction matrix and group-article interaction matrix. Furthermore, three extended models of CoMF are proposed.

Empirical studies on CiteULike data set demonstrate that CoMF and three variants outperform baseline algorithms in terms of accuracy and robustness. The scalability evaluation of paralleled CoMF shows its potential value in scholarly big data environment.

The proposed methods fill the gap of group-article recommendation in online libraries domain. The proposed methods have enriched the group recommendation methods by considering the interaction effects between groups and members. The proposed methods are the first attempt to implement group recommendation methods in big data contexts.

The proposed methods can improve group activity effectiveness and information shareability in academic groups, which are beneficial to membership retention and enhance the service quality of online library systems. Furthermore, the proposed methods are applicable to big data contexts and make library system services more efficient.

The proposed methods have potential value to improve scientific collaboration and research innovation.

The proposed CoMF method is a novel group recommendation method based on the collaboratively decomposition of researcher-article matrix and group-article matrix. The process indirectly reflects the interaction between groups and members, which accords with actual library environments and provides an interpretable recommendation result.

This paper introduces a novel method, Variance Rule-based Window Size Tracking (VR-WT), for deriving a sequence of estimation window sizes. This approach not only identifies structural change points but also ascertains the optimal size of the estimation window. VR-WT is designed to achieve accurate model estimation and is versatile enough to be applied across a range of models in various disciplines.

This paper proposes a new method named Variance Rule-based Window size Tracking (VR-WT), which derives a sequence of estimation window sizes. The concept of VR-WT is inspired by the Potential Scale Reduction Factor (PSRF), a tool used to evaluate the convergence and stationarity of MCMC.

Monte Carlo simulation study demonstrates that VR-WT accurately detects structural change points and select appropriate window sizes. The VR-WT is essential in applications where accurate estimation of model parameters and inference about their value, sign, and significance are critical. The VR-WT has also helped us understand shifts in parameter-based inference, ensuring stability across periods and highlighting how the timing and impact of market shocks vary across fields and datasets.

The first distinction of the VR-WT lies in its purpose and methodological differences. The VR-WT focuses on precise parameter estimation. By dynamically tracking window sizes, VR-WT selects flexible window sizes and enables the visualization of structural changes. The second distinction of VR-WT lies in its broad applicability and versatility. We conducted empirical applications across three fields of study: CAPM; interdependence analysis between global stock markets; and the study of time-dependent energy prices.