Walking is a sustainable mode of transport and a healthy way of doing physical activity. Walkability is a concept that has gained enormous popularity in recent years due to its potential to promote more sustainable urban environments and healthy lifestyles. This paper provides a literature review to analyze the influence of built environment attributes on walkability. The Scopus and Web of Science databases were chosen to survey the peer-reviewed documents published up to June 2020. A total of 132 documents were selected by the search. The review of these 132 documents showed that various built environment attributes were differently analyzed and assessed. More specifically, the search identified 32 built environment attributes that were assessed by using 63 measures. Intersection density, residential density and land use mix were the most used attributes for assessing walkability, namely by using objective methods, such as ratios and spatial score tools. In turn, attributes related to streetscape design and security were much less adopted in walkability assessments. This paper provides additional insights into how built environment attributes influence walkability and identifies gaps and issues that should be analyzed in-depth in the future. The review could be helpful for researchers and urban planners in developing walkability studies and in defining policies to improve walkability.

Walking is undoubtedly a sustainable and healthy mode of transport. However, the decision to walk is influenced by many built environment and streetscape attributes. Specifically, the term walkability is used to describe the extent to which the urban environment is pedestrian-friendly, usually by quantifying multiple built environment attributes at the neighbourhood scale. The present study adopts a qualitative approach to evaluate perceived walkability. Based on a questionnaire (n = 1438) administered in the cities of Bologna and Porto, this paper analyses how respondents perceived and evaluated 19 built environment and streetscape attributes. An Exploratory Factor Analysis was carried out to examine the correlations between the various attributes and to identify the underlying walkability determinants. The analysis indicated that 13 attributes were highly correlated, resulting in four determinants: (i) urban ambiance, which includes land use and street design attributes, such as land use mix, enclosure, transparency, and architectural and landscape diversity; (ii) pedestrian infrastructure, which is related to sidewalk conditions; (iii) street connectivity and proximity to community facilities; and iv) access to other modes of transport. In turn, traffic safety and security were not correlated with perceived walkability in both cities. These findings suggest that specific urban design and pedestrian infrastructure attributes should be highly considered when formulating policies aiming to create more pedestrian-friendly cities, as well as in walkability studies and when developing walkability scores and indexes.

The recent advancements in electronics and computer networks, as well as the clear need for interoperability and connectivity makes communication technologies and algorithms crucial for the success of Intelligent Transportation Systems (ITS) applications. This survey paper provides a classification of communication technologies concerning their use in ITS and an assessment of their effectiveness. As a result, opportunities, gaps, and issues are identified in ITS and their associated Vehicular Ad-hoc Networks (VANETs). Even though several communication technology types are extensively used in ITS, there are challenges and issues regarding their operational effectiveness and reliability, which are thoroughly discussed in this paper. These challenges include the distance factor, bandwidth and medium access control, time/emergency situation, routing protocols, security, and privacy. In conclusion, this paper provides suggestions for improvement and directions for successfully utilizing communication technology in the area of ITS.

Amidst the fluidity of today's post‐COVID business environment, this paper aims to fortify the discourse on strategic agility, by presenting a comprehensive best practice framework for its development and sustenance. This is achieved by carrying out an empirical investigation on four best practice multinational corporations. Despite abundant scholarly attention, a critical gap persists in the empirical examination of specific functional practices and their role for fostering strategic agility. This research fills this void, leveraging in‐depth interviews with strategic‐level managers and analyzing archival data, revealing that strategic agility necessitates a systemic approach and congruent efforts across organizational functions. Key findings underscore the role knowledge management as the linchpin, with corresponding emphasis on dynamic talent management, strategic partnerships, open innovation, digitalization, and sustainability. As a result, this paper contributes to the strategic management literature by offering practical insights for managers of global businesses in navigating the complexities of a rapidly evolving competitive landscape towards organizational excellence

Recently, deep convolutional neural networks (DCNNs) have been widely adopted in scene classification for remote sensing images, yielding impressive results. One important assumption for training these models is that the image labels are free from errors. However, the mislabeling of certain images can severely degrade the performance of DCNN models. While existing methods offer some degree of mitigation, their effectiveness diminishes significantly in the presence of highly contaminated labels. This paper seeks to address this issue by proposing a robust scene classification model designed to learn from noisy labels. Firstly, a semi-supervised approach is proposed, involving bootstrapping a model that uses a cleaned training set through coarse screening of potentially noisy labels. In particular, the model is trained using consistency regularization to enhance its robustness. Secondly, the remaining images with uncertain labels are further used to increase the number of images available for training. Here, dynamic thresholding is applied to detect and correct label errors. Lastly, the images and their labels from the two steps above are jointly used for final training. The proposed method is evaluated on three widely used remote sensing datasets (AID, WHU-RS19 and UCMerced). The results demonstrate that the proposed approach outperforms competing methods, highlighting its efficacy in addressing label noise in remote sensing datasets.

In response to the transformative impact of digital technology on education, this study introduces a novel performance management framework for virtual learning environments suitable for the metaverse era. Based on the Structural Equation Modeling (SEM) approach, this paper proposes a comprehensive evaluative model, anchored on the integration of the Theory of Planned Behavior (TPB), the Unified Theory of Acceptance and Use of Technology (UTAUT), and the Community of Inquiry Framework (CoI). The model synthesizes five Key Performance Indicators (KPIs)—content delivery, student engagement, metaverse tool utilization, student performance, and adaptability—to intricately assess academic avatar performances in virtual educational settings. This theoretical approach marks a significant stride in understanding and enhancing avatar efficacy in the metaverse environment. It enriches the discourse on performance management in digital education and sets a foundation for future empirical studies. As virtual online environments gain prominence in education and training, this research study establishes the basic principles and highlights the key points for further empirical research in the new era of the metaverse educational environment.