Trivalent rare-earth (RE(3+) = Eu(3+), Tb(3+)) ion activated KLa(MoO4)2 microspheres have been synthesized at 180 °C via a facile hydrothermal route without using any templates, surfactant, or other organic additives. X-ray diffraction (XRD), field emission-scanning electron microscopy (FE-SEM), photoluminescence (PL), and photoluminescent excitation spectra (PLE) were employed to characterize the samples. It is found that the pH value in the initial solution is responsible for crystal phase, shape determination and emission intensity of final products. The possible formation mechanism for products with uniform spheres has been presented. Furthermore, a systematic study on the photoluminescence of RE(3+) (RE(3+) = Eu(3+), Tb(3+)) doped KLa(MoO4)2 samples has been explored in order to obtain the multicolor tunable emission by varying the Tb(3+)/Eu(3+) ratio. The tunable luminescence may be potentially applied in fields such as solid state lighting and field emission displays.

The concept of corporate social responsibility (CSR) is an ever-evolving concept in the field of business management. Even in 2021, its boundaries are evolving and researchers are linking the concept of CSR to different variables to achieve different outcomes. However, the concept of CSR in the healthcare sector is not well-explored in prior literature. The current study is an application of social identity theory to induce electronic word-of-mouth (eWOM) from consumers for a specific brand, through its CSR engagement on social media (CSRS) and consumer-company identification (CCI) in the healthcare sector of an emerging economy. The data of the current survey were collected from different patients of four large hospitals in a large city through a self-administered questionnaire (paper-pencil technique). To validate different hypotheses of the current study, the authors employed the structural-equation-modeling (SEM) technique using AMOS software. The output of SEM analysis confirmed that CSRS positively influences eWOM, and CCI mediates this relationship. The findings of the current study will be helpful for policymakers in the healthcare industry to improve their understanding of CSRS and CCI, inducing eWOM through the lens of social identity theory.

Social media data analytics is the art of extracting valuable hidden insights from vast amounts of semi-structured and unstructured social media data to enable informed and insightful decision-making. Analysis of social media data has been applied for discovering patterns that may support urban planning decisions in smart cities. In this paper, Weibo social media data are used to analyze social-geographic human mobility in the CBD area of Shanghai to track citizen’s behavior. Our main motivation is to test the validity of geo-located Weibo data as a source for discovering human mobility and activity patterns. In addition, our goal is to identify important locations in people’s lives with the support of location-based services. The algorithms used are described and the results produced are presented using adequate visualization techniques to illustrate the detected human mobility patterns obtained by the large-scale social media data in order to support smart city planning decisions. The outcome of this research is helpful not only for city planners, but also for business developers who hope to extend their services to citizens.

Coverage path planning on a complex free-form surface is a representative problem that has been steadily investigated in path planning and automatic control. However, most methods do not consider many optimisation conditions and cannot deal with complex surfaces, closed surfaces, and the intersection of multiple surfaces. In this study, a novel and efficient coverage path-planning method is proposed that considers trajectory optimisation information and uses point cloud data for environmental modelling. First, the point cloud data are denoised and simplified. Then, the path points are converted into the rotation angle of each joint of the manipulator. A mathematical model dedicated to energy consumption, processing time, and path smoothness as optimisation objectives is developed, and an improved ant colony algorithm is used to solve this problem. Two measures are proposed to prevent the algorithm from being trapped in a local optimum, thereby improving the global search ability of the algorithm. The standard test results indicate that the improved algorithm performs better than the ant colony algorithm and the max–min ant system. The numerical simulation results reveal that compared with the point cloud slicing technique, the proposed method can obtain a more efficient path. The laser ablation de-rusting experiment results specify the utility of the proposed approach.

Green areas or parks are the best way to encourage people to take part in physical exercise. Traditional techniques of researching the attractiveness of green parks, such as surveys and questionnaires, are naturally time consuming and expensive, with less transferable outcomes and only site-specific findings. This research provides a factfinding study by means of location-based social network (LBSN) data to gather spatial and temporal patterns of green park visits in the city center of Shanghai, China. During the period from July 2014 to June 2017, we examined the spatiotemporal behavior of visitors in 71 green parks in Shanghai. We conducted an empirical investigation through kernel density estimation (KDE) and relative difference methods on the effects of green spaces on public behavior in Shanghai, and our main categories of findings are as follows: (i) check-in distribution of visitors in different green spaces, (ii) users’ transition based on the hours of a day, (iii) famous parks in the study area based upon the number of check-ins, and (iv) gender difference among green park visitors. Furthermore, the purpose of obtaining these outcomes can be utilized in urban planning of a smart city for green environment according to the preferences of visitors.