While the attempts to deploy drones in the foodservice industry focus on the technical aspects, research studies from behavioral perspectives that support service substantially are still in their infancy. When new technology-based services are introduced, it is crucial to examine and understand consumers’ perceptions by identifying a set of actions that influence acceptance and fulfilling their target. Therefore, service providers of drone food delivery (DFD) services need to identify significant factors that influence potential consumers to use drone delivery. Although a few existing models are significant, these models lack a comprehensive basic theory that addresses factors which influence consumers’ intention and behavior. To overcome this limitation and propose a more comprehensive model, relevant research studies from the domain of drone delivery services and other emerging technology such as IoT, Autonomous Vehicles, and Mobile Banking are identified, reviewed, and analyzed, and ten potential factors are subsequently extracted. This study’s data were collected from 209 participants who regularly order food online for delivery and were analyzed using SPSS. Descriptive statistics, reliability, Pearson correlation, regression analysis, r-squared, and standardized beta coefficient analyses are carried out to present the study’s findings. The results show that there is a significant relationship between behavioral intention and the user behavior of DFD. Although the participants in this study are yet to experience drone technology in the foodservice domain, the identified factors explain around 32.9% of the variation in the use behavior of DFD services. In the early stage of adoption, it is highly recommended for stakeholders to conduct marketing campaigns through media channels such as television and different social media platforms to bring awareness of this technology.

In the present work, a thermal treatment technique is applied for the synthesis of Ce_xSn_1−xO₂ nanoparticles. Using this method has developed understanding of how lower and higher precursor values affect the morphology, structure, and optical properties of Ce_xSn_1−xO₂ nanoparticles. Ce_xSn_1−xO₂ nanoparticle synthesis involves a reaction between cerium and tin sources, namely, cerium nitrate hexahydrate and tin (II) chloride dihydrate, respectively, and the capping agent, polyvinylpyrrolidone (PVP). The findings indicate that lower x values yield smaller particle size with a higher energy band gap, while higher x values yield a larger particle size with a smaller energy band gap. Thus, products with lower x values may be suitable for antibacterial activity applications as smaller particles can diffuse through the cell wall faster, while products with higher x values may be suitable for solar cell energy applications as more electrons can be generated at larger particle sizes. The synthesized samples were profiled via a number of methods, such as scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray diffraction (XRD), and Fourier transform infrared spectroscopy (FT-IR). As revealed by the XRD pattern analysis, the Ce_xSn_1−xO₂ nanoparticles formed after calcination reflect the cubic fluorite structure and cassiterite-type tetragonal structure of Ce_xSn_1−xO₂ nanoparticles. Meanwhile, using FT-IR analysis, Ce-O and Sn-O were confirmed as the primary bonds of ready Ce_xSn_1−xO₂ nanoparticle samples, whilst TEM analysis highlighted that the average particle size was in the range 6−21 nm as the precursor concentration (Ce(NO₃)₃·6H₂O) increased from 0.00 to 1.00. Moreover, the diffuse UV-visible reflectance spectra used to determine the optical band gap based on the Kubelka–Munk equation showed that an increase in x value has caused a decrease in the energy band gap and vice versa.

Once an image has been processed by, for example, a robot machine, for the purpose of, for example, features extraction or meaningful information retrieval, has a secure scheme been applied to preserve security and privacy of such information before sending it to another processing party? A huge number of image-related Internet of Things (IoT) applications face such an issue. But what are applied and potentially being applied image processing techniques that have contributed to enhance the security and privacy of IoT applications? There are numerous IoT applications that utilize image processing techniques in this direction. This article aims to survey and review a number of recently published papers and research studies that encompass proposed methods in which image processing techniques are applied to enhance the security, privacy, and safety of IoT applications. It also aims to help interested researchers in related fields have insights on what the role of image processing in enhancing the security of IoT applications is and what those techniques applied to enhance the security of IoT applications are. A comprehensive framework has been graphically extracted to give readers in the field of IoT security a map with the suitable image processing techniques that serve better to enhancing IoT applications in terms of security and privacy.

The goal of this review paper is to highlight the image processing techniques’ role in the Internet of Things (IoT), aiming to attain an energy-efficient and secure IoT. IoT-dependent systems (IoTSs) cause heavy usage of energy. This is one of the biggest issues associated with IoTSs. Another issue is that the security of digital content is a big challenge and difficulty. Image processing has recently played an essential role in resolving these difficulties. Several researchers have made efforts to improve future IoTSs, which are summarized in this article. Day-by-day, proposed methods are developed, and thus IoT deployment has been plainly engaged in our everyday activities. Several efficient image-processing techniques that can be utilized by IoTSs to overcome such issues have been proposed. This review paper aims to highlight those proposed methods that can make contributions in this direction. Thus, this study aims to review numerous research studies on this subject. This study looks at 36 publications relevant to image-processing techniques utilized by several types of IoTSs. The innovative work of this review paper is to provide readers with a map of suitable image processing techniques to be used with certain types of IoT systems (i.e., scenarios). Both methodology and analysis have come out with a suggested mind map highlighting a number of proposed solutions (i.e., image processing techniques) that can be suitable to help design an energy-efficient, secure, and intelligent IoT system. We have made some conclusions and projections for future research work.

Numerous models are suggested in the extant literature to explain the adoption of technologies in which pressure considerations of the adoption are not discussed. Based on institutional theory, this study suggests a model for analyzing the three (3) pressures of normative, coercive, and mimetic factors within the scope of enterprise architecture (EA). The model is examined by 255 respondents using the survey questionnaires. The results indicate that normative, coercive, and mimetic pressures have a significant impact on the decision to adopt EA. The study leads an operational review in the context of EA at the organizational level. The results also provide valuable insights into the management of the EA process for decision-makers and EA initiators.