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The central argument of this article is that the underlying theme of the five reports entitled Cities & Names in Italo Calvino's Invisible Cities is the fundamental inadequacy of names to signify cities. By challenging the taken-for-granted, common-sense idea that a name of a city corresponds to a well-defined urban entity, Calvino implicitly suggests that different cities cohabitate under the same name and that fundamentally names of cities are semiotically incorrect. The article is divided into two parts. The first expands on ideas about the correctness of proper names that since being presented in Plato's dialogue have prevailed in western thought. The second part consists of five commentaries on the deceptive conflation between a city and its name that runs through the five reports included in

The context of recognizing handwritten city names, this research addresses the challenges posed by the manual inscription of Bangladeshi city names in the Bangla script. In today’s technology-driven era, where precise tools for reading handwritten text are essential, this study focuses on leveraging deep learning to understand the intricacies of Bangla handwriting. The existing dearth of dedicated datasets has impeded the progress of Bangla handwritten city name recognition systems, particularly in critical areas such as postal automation and document processing. Notably, no prior research has specifically targeted the unique needs of Bangla handwritten city name recognition. To bridge this gap, the study collects real-world images from diverse sources to construct a comprehensive dataset for Bangla Hand Written City name recognition. The emphasis on practical data for system training enhances accuracy. The research further conducts a comparative analysis, pitting state-of-the-art (SOTA) deep learning models, including EfficientNetB0, VGG16, ResNet50, DenseNet201, InceptionV3, and Xception, against a custom Convolutional Neural Networks (CNN) model named “Our CNN.” The results showcase the superior performance of “Our CNN,” with a test accuracy of 99.97% and an outstanding F1 score of 99.95%. These metrics underscore its potential for automating city name recognition, particularly in postal services. The study concludes by highlighting the significance of meticulous dataset curation and the promising outlook for custom CNN architectures. It encourages future research avenues, including dataset expansion, algorithm refinement, exploration of recurrent neural networks and attention mechanisms, real-world deployment of models, and extension to other regional languages and scripts. These recommendations offer exciting possibilities for advancing the field of handwritten recognition technology and hold practical implications for enhancing global postal services.

Feature selection plays a key role in reducing the dimensionality of a feature vector by discarding redundant and irrelevant ones. In this paper, a Genetic Algorithm-based hierarchical feature selection (HFS) model has been designed to optimize the local and global features extracted from each of the handwritten word images under consideration. In this context, two recently developed feature descriptors based on shape and texture of the word images have been taken into account. Experimentation is conducted on an in-house dataset of 12,000 handwritten word samples written in Bangla script. This database comprises names of 80 popular cities of West Bengal, a state of India. Proposed model not only reduces the feature dimension by nearly 28%, but also enhances the performance of the handwritten word recognition (HWR) technique by 1.28% over the recognition performance obtained with unreduced feature set. Moreover, the proposed HFS-based HWR system performs better in comparison with some recently developed methods on the present dataset.

It is important to recognize the destination city name correctly for a postal document to reach its desired address. In India people often mix up scripts while writing the address. Often the script of the destination city name is different from the other part of the postal document. This is common in India due to the multilingual and multi script nature of the country. In this paper, a Convolutional Neural Network (CNN) based approach towards the recognition of handwritten multilingual multiscript Indian city names is presented. Experiments were performed not only in a single script scenario but also in multi script, considering English, Bangla and Devanagari scripts. An accuracy of 91.72% was obtained on 106 city names in mixed script scenario from the proposed scheme and the data set will be made available to the researcher on request. Further experiments were also performed with different script combinations and obtained results up to 98.01%. The system also produced a mean performance difference of approximately ± 1% for successive changes in the data set size, thereby pointing to the robustness of the proposed architecture.

Postal documents are often used for official communication, online shopping, etc. At times, the delivery gets delayed due to multiple scripts leading to the need for postal sorting facilities. Understanding the destination city name plays a major part in solving automatic sorting problems as the same becomes more challenging due to the presence of handwritten documents. In order to develop an autonomous system to solve the problem, a Deep Learning-based system is proposed to recognize handwritten city names written in 6 major scripts namely Tamil, Roman, Devanagari, Bangla, Gurumukhi, and Arabic. Experiments were performed in both script-dependent (bi-stage) and independent approaches. In the bi-stage framework, we have obtained an average accuracy of 97.58 % along with a back-end script recognition rate of 99.07 % while in the script-independent approach, an accuracy of 97.03 % was obtained on a dataset consisting of 807 classes.

The rising amount of waste generated worldwide is inducing issues of pollution, waste management, and recycling, calling for new strategies to improve the waste ecosystem, such as the use of artificial intelligence. Here, we review the application of artificial intelligence in waste-to-energy, smart bins, waste-sorting robots, waste generation models, waste monitoring and tracking, plastic pyrolysis, distinguishing fossil and modern materials, logistics, disposal, illegal dumping, resource recovery, smart cities, process efficiency, cost savings, and improving public health. Using artificial intelligence in waste logistics can reduce transportation distance by up to 36.8%, cost savings by up to 13.35%, and time savings by up to 28.22%. Artificial intelligence allows for identifying and sorting waste with an accuracy ranging from 72.8 to 99.95%. Artificial intelligence combined with chemical analysis improves waste pyrolysis, carbon emission estimation, and energy conversion. We also explain how efficiency can be increased and costs can be reduced by artificial intelligence in waste management systems for smart cities.

Cities operate in a competitive social environment requiring local authorities to adopt marketing strategies with significant economic ratings. City marketing that is related to the meaning of a city’s name encourages activities in the city or region. The present study adopted a quantitative survey on a sample of 152 employees in companies to explore how important marketing is perceived for a city’s development. The research was done in Trikala, a city in Greece. The key conclusion was that the more critical the participants consider the interventions in the city’s natural environment, the more they believe that the city can benefit from corporate sponsorships. Subsequently, it appeared that the more they support the interventions in the structured environment of the city, the less they consider that corporate sponsorships can benefit it. It was explained that structured interventions usually involve very high investments that require funding from the central government, as sponsorships are not enough. The most substantial positive relationship was found between the importance of interventions in employment, entrepreneurship and tourism, and the importance of business sponsorships.

In this article a planning approach is proposed to accommodate different paces of urbanisation. Instead of responding to a single problem with a Pavlov-type of response, analysis shows that the transformational tempi of different urban landscapes require multiple deployment strategies to develop urban environments that are sustainable and resilient. The application of nature-based solutions, enhancing both human and natural health in cities, is used as the foundation for the design of deployment strategies that respond to different paces of urban change. The results show that urban characteristics, such as population density and built space is, partly, dependent on the underlying landscape characteristics, therefore show specific development pathways. To create liveable and sustainable urban areas that can deal holistically with a range of intertwined problems, specific deployment strategies should be used in each specific urban context. This benefits the city-precinct as a whole and at the local scale. Even small nature-based solutions, applied as the right deployment strategy in the right context, have profound impact as the starting point of a far-reaching urban transformation. The case-study for Oimachi in Japan illustrates how this planning approach can be applied, how the different urban rhythms are identified, and to which results this leads.