Search

Encouraging older and newer mobility alternatives to standard privately owned cars, such as cycling and autonomous vehicles, is necessary to reduce pollution, enhance safety, increase transportation efficiency, and create a more sustainable urban environment. Implementing mobility plans that identify the use of different transport modes in their confidence intervals can lead to the development of smarter and more efficient cities, where all citizens can benefit from safe and environmentally friendly streets. This research aims to provide insights into designing urban streets that seamlessly integrate autonomous vehicles and cyclists, promoting sustainable mobility while ensuring urban transport efficiency. With this aim, the research identifies and prioritizes the factors that are relevant to street design as well as the appropriate strategies to address them. Our methodology combines Multi-Criteria Decision-Making (MCDM) with Game theory to identify and realize the most convenient conditions for this integration. Initially, the basic factors were identified using the value-interval fuzzy Delphi method. Following this, the factors were weighted with the interval-fuzzy Analytic Network Process (ANP), and the cause-and-effect variables were evaluated using the interval-fuzzy Decision-Making Trial and Evaluation Laboratory ANP (DANP). Finally, Game theory was employed to determine the optimal model for addressing these challenges. The results indicate that safety emerged as the most significant factor and two optimal strategies were identified; the integration of green infrastructure and smart technology.

The optimization of sustainable smart cities is an essential endeavor in modern urban development, aiming to enhance the quality of life for citizens while minimizing environmental impacts. Big data plays a critical role in achieving these goals by enabling the collection, analysis, and utilization of vast amounts of information to make informed decisions. However, implementing big data in smart cities faces significant barriers, including data-sharing challenges, technical limitations, and organizational non-cooperation. Addressing these barriers is crucial for the successful deployment of smart city initiatives. We propose a novel approach to tackle these challenges using the Improved Zero-Sum Grey Game (IZSGG) theory and the Grey Best-Worst Method (G-BWM). This method comprehensively analyzes the risks and uncertainties associated with big data implementation in smart cities. By modeling the interactions between different stakeholders and their competing interests, IZSGG theory provides a framework to identify optimal strategies for data management. The G-BWM further refines these strategies by evaluating and prioritizing the various factors influencing big data utilization. Our findings reveal that the worst-case scenario for a smart city involves the simultaneous occurrence of several risks, all of which have positive values, indicating their potential to significantly disrupt smart city operations. The specific risks identified include: the sharing of data and information, the collection and recording of data, technical limitations and challenges associated with technology, the non-cooperation of organizations, and issues related to the interpretation of complex information. The technical barrier is the most significant with a weight of w(T)=0.6152, indicating its critical role compared to other barriers. Within this category, the sub-barrier of technical and technological constraints is particularly critical, with a weight of 0.39267375.

Food adulteration is a serious concern faced by the importers of various food products across the globe. In this study, a simple, sensitive and robust method for detecting pork in processed/complex food samples using next-generation DNA sequencing (NGS) technology is described. The experimentation involves a generalized library preparation kit for performing shotgun sequencing of the genomic DNA irrespective of its intactness. The method was applied on different complex food samples containing pork along with other species (up to twelve) as well as without pork to test the specificity of the method. The DNA sequences were mapped with the online NCBI nucleotide database for their identification followed by a calculation of the relative abundance of the reads. The adulteration of pork was correctly identified in the analyzed samples. Although the relative abundance of pork DNA reads could not make a precise quantitative relevance with the contributed amount of the tissue sample, yet this method has the potential to determine extremely low as well as high contents of adulterating/contaminating species in complex food products.