Ignorance of laws and policies creates barriers to the social inclusion of persons with disabilities (PWDs), hindering their full participation in communal life and opportunities. The current study aims to analyze the social inclusion of PWDs in the context of ignorance of laws and policies and how it influences their overall social inclusion. To achieve the study objectives, data were collected from a sample of 488 PWDs, comprising 284 males and 204 females, in the selected six Union Councils (sub-administrative units) of District Malakand, Pakistan. Respondents were chosen through multistage stratified random sampling. In the univariate and multivariate level analyses, the chi-square test and Kendall’s Tau-b test statistics were used to test the relationship between ignorance of laws and policies and the social inclusion of PWDs. Gender and level of disability were used as control variables at the multivariate level. The results of Kendal Tb and chi-square significance values depicted a spurious relation among ignorance of laws and policies and social inclusion of PWDs while controlling respondent’s gender. The results highlighted that ignorance of laws and policies reduced social inclusion in male to a higher extent than female. Additionally, the social inclusion of PWDs with moderate disabilities is more significantly hampered by ignorance of laws and polices than those with severe disabilities.

The aim of this article is to investigate the impediments to creativity perceived by managers, the levels of creativity, its indicators, and personal characteristics conducive to creativity, as well as to elucidate the correlations among them. An experimental study was conducted involving 300 participants. Methods employed include surveying, testing, and mathematical statistical analysis. As the level of creativity increases, participants tend to assess their opportunities more favorably. The expression of creativity depends on the interconnection among the barriers to creativity, indicators of creativity, and personal qualities of creativity. A high level of creativity is manifested when there are fewer barriers and personal qualities such as Imagination and a propensity for Risk-taking. Conversely, the level of expression of creativity is low when there is an interconnection between Creativity and Complexity, Imagination, and creativity barriers such as lack of confidence and conformity to majority opinion.

This research conducts a comparative urban analysis of two coastal cities with analogous tourism models situated in distinct geographical regions: Balneário Camboriú in Brazil and Benidorm in Spain. The study delves into two critical urban phenomena impacting the sustainability of tourist cities, utilising social network data to gather insights into economic and urban activities (Google Places) and spatio-temporal patterns of citizen presence (Twitter). The spatial analysis explores the municipal and, to a more detailed extent, the coastal strip extending 500 m inland from the coastline, spanning the entire length of each city to their municipal boundaries. The analysis uncovers both similarities and differences between the two destinations, offering insights that could inform future development strategies aimed at fostering sustainable urban environments in these well-established coastal tourist areas.

Outsourcing logistics operations is a common trend as businesses prioritize core activities. Establishing a sustainable partnership between businesses and logistics service providers requires a systematic approach. This study is needed to develop a more effective and adaptive framework for logistics service provider selection by integrating diverse criteria and decision-making methodologies, ultimately enhancing the precision and sustainability of procurement processes. This study advocate for leveraging industry-based knowledge in procurement, emphasizing the need to define decision-making elements. The research analyzes nearly 300 logistics procurement projects, using a neural network-based methodology to propose a model that aids businesses in identifying optimal criteria for evaluating logistics service providers based on extensive industry knowledge. The goal of this study is to develop and test a practical model that would support businesses in choosing most suitable criteria for selection of logistics service providers based on cumulative market patterns. The results of this study are as follows. It introduces novel elements by gathering and systematizing unique market data using developed data processing methodology. It innovatively classifies decision-making elements, allocating them into distinct groups for use as features in a neural network. The study further contributes by developing and training a predictive model based on a prepared dataset, addressing pre-defined goals, expectations related to green logistics, and specific requirements in the tendering process for selecting logistics service providers. Study is concluded by summarizing suggestions for future research in area of adopting neural networks for selection of logistics service providers.

This paper provides a disaster resilience-based approach. For the definition of the approach, a three-step method (definition of components, analysis of the resilience pillars and definitions of resilience-based actions) has been followed. To validate the approach, an application scenario for mitigating the COVID-19 pandemic is provided in the paper. The proposed approach contributes to stimulating the co-responsibility quadruple helix of actors in the implementation of actions for disaster management. Moreover, the approach is adaptable and flexible, as it can be used to manage different kinds of disasters, adjusting or changing itself to meet specific needs.

In view of the fact that the convolution neural network segmentation method lacks to capture the global dependency of infected areas in COVID-19 images, which is not conducive to the complete segmentation of scattered lesion areas, this paper proposes a COVID-19 lesion segmentation method UniUNet based on UniFormer with its strong ability to capture global dependency. Firstly, a U-shaped encoder-decoder structure based on UniFormer is designed, which can enhance the cooperation ability of local and global relations. Secondly, Swin spatial pyramid pooling module is introduced to compensate the influence of spatial resolution reduction in the encoder process and generate multi-scale representation. Multi-scale attention gate is introduced at the skip connection to suppress redundant features and enhance important features. Experiment results show that, compared with the other four methods, the proposed model achieves better results in Dice, loU and Recall on COVID-19-CT-Seg and CC-CCIII dataset, and achieves a more complete segmentation of the lesion area.