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This paper aims to identify aspects of how work-life interaction has changed in the post-pandemic situations and propose strategies of the security concept for living-working patterns in the post-pandemic interior as future disease prevention.

We conducted a systematic literature search and review to select previous research systematically and relate concepts by coding the data and synthesising the data critically. The systematic literature search and review considered 90 papers (35 were studied).

The findings identify three strategies: hybrid activity patterns, new layout for hybrid and changing behaviour and culture. Each strategy demonstrates the connection between the hybrid living-working interior spaces in the post-pandemic period and security-pandemic variables. The results on security design factors focused on interior control, detection and deterrence; connection to nature creates a safer environment to prevent further variables; and hybrid activity requires more elements to govern users' behaviour and culture.

Limitations of this study are as follows: excluded papers that are not written in English/Bahasa or do not have gold/green open access; some aspects were not discussed (such as social distancing); the articles included in this review are up to April 2023 (and there is the possibility of recent papers). Future studies can be developed to update building certification for post-pandemic interiors or research with psychological, social equity or family vitality issues.

The study offers strategies and the holistic relationship between the post-pandemic concept and security-pandemic design variables within the built environment, especially in the users' culture and behaviour context.

Recent research suggests positive links between adolescents' participation in sports and the quality of their relationships with their fathers. It is unclear, however, the degree to which the gendered nature of sports, and in particular the ways sports have been used to define and express masculinity, mean that these important links between sports participation and parent–child relationships might or might not extend to relationships with mothers. Recent scholarship connects joint recreational activities to mother–child relationships, but here we extend these ideas in connection to adolescents' formal sports participation. We use the National Longitudinal Study of Adolescent to Adult Health from the United States to evaluate potential connections between adolescent sports participation and maternal relationships, focusing on the adolescent's perspective on joint activities with mother, closeness to and warmth and communication with mother, and satisfaction with the relationship with mother. We also examine whether these associations differ for boys and girls. We found that adolescents' sports participation was good for relationships with their mothers, but also that there were gender differences in how sport paid off for boys and girls. We discuss our findings in terms of contemporary shifts in gendered norms and conclude that gender may remain salient to how sports participation can promote mother–child relationships.

This study examined the relationship between adult respondents' COVID-19 risk perception and its impact on changes in eating habits, physical activity, alcohol consumption, and smoking.

A cross-sectional online questionnaire was used to collect data during the first wave of the pandemic, in the European spring and summer of 2020. Kruskal–Wallis and Pearson chi-squared tests were used to determine the associations between the consumers' COVID-19 risk perception and the respondents' country of origin. The respondents were primarily university employees and students from four European countries (the Czech Republic, France, the United Kingdom, and Italy).

The study showed significant changes in lifestyle behaviours of the respondents during the COVID-19 pandemic compared with the pre-outbreak period. Approximately half of all respondents reported a decrease in alcohol consumption, smoking, and physical activity, while eating habits among European consumers showed trends towards both healthier and less healthy dietary patterns. The most significant changes were recorded in reducing alcohol consumption, smoking, and physical activity among almost half of the respondents. Positive COVID-19 test experience, age, and country of the respondents had a negative influence, whereas healthy food consumption and alcohol consumption had a positive influence on the COVID-19 related risk perception.

The fear of the COVID-19 outbreak together with the restrictions imposed by national governments in response to the pandemic fundamentally affected the respondents' lifestyles. Understanding these changes can help establish interventions to alleviate the adoption of negative lifestyles and attitudes in subsequent waves of the COVID-19 pandemic or other similar situations.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

The research intends to investigate the different typological and morphological characteristics of the neighbourhood spaces produced by different urban dynamics since the late nineteenth century in Turkey.

The main problem that drives the research is that a significant increase in the construction of gated communities has started to be seen in the last five years as a solution to safety issues in Turkey. However, these gated communities as safe-spaces have resulted in the danger of destroying neighbourhood life's physical and social dynamics by changing their spatial configuration. The study offers an analytical framework structured from defensible space theory and other safe-space theories in the literature. It has analysed the effect of physical characteristics of urban fabric on the production of safe-space in neighbourhoods through mappings and site observations. The case studies are conducted in three different morphological periods in Turkey, which are the Seyrancik neighbourhood in Bolu (the Ottoman Period), the Subayevleri neighbourhood in Ankara (the Republican Period) and the Karaman neighbourhood in Sakarya (the Liberalisation Period).

The analysis concludes that any type does not come to the fore in the production of defensible space in terms of its spatial features, but each has various strengths and weaknesses. As a result, this study emphasises the role of design in providing defensible neighbourhoods in the case of Turkey's cities and reveals the aspects that will guide the design of an urban and housing form in terms of the safety–environment relationship in Turkey.

The significance of the study is the comparative analytical approach to studying the housing development and demonstrates a method for analysing safety issues in the transformation process of neighbourhood structures in Turkey.

The purpose of this paper is to explore the prospect of using neurophenomenology to understand, design and test phygital consumer experiences. It aims to clarify interpretivist approaches to consumer neuroscience, wherein theoretical models of individual phenomenology can be combined with modern neuroimaging techniques to detect and interpret the first-person accounts of phygital experiences.

The argument is conceptual in nature, building its position through synthesizing insights from phenomenology, phygital marketing, theoretical neuroscience and other related fields.

Ultimately, the paper presents the argument that interpretivist neuroscience in general, and neurophenomenology specifically, provides a valuable new perspective on phygital marketing experiences. In particular, we argue that the approach to studying first-personal experiences within the phygital domain can be significantly refined by adopting this perspective.

One of the primary goals of this paper is to stimulate a novel approach to interpretivist phygital research, and in doing so, provide a foundation by which the impact of phygital interventions can be empirically tested through neuroscience, and through which future research into this topic can be developed. As such, the success of such an approach is yet untested.

Phygital marketing is distinguished by its focus on the quality of subjective first-personal consumer experiences, but few papers to date have explored how neuroscience can be used as a tool for exploring these inner landscapes. This paper addresses this lacuna by providing a novel perspective on “interpretivist neuroscience” and proposes ways that current neuroscientific models can be used as a practical methodology for addressing these questions.

Course evaluations are formative and are used to evaluate learnings of the students for a course. Anomalies in the evaluation process can lead to a faulty educational outcome. Learning analytics and educational data mining provide a set of techniques that can be conveniently applied to extensive data collected as part of the evaluation process to ensure remedial actions. This study aims to conduct an experimental research to detect anomalies in the evaluation methods.

Experimental research is conducted with scientific approach and design. The researchers categorized anomaly into three categories, namely, an anomaly in criteria assessment, subject anomaly and anomaly in subject marks allocation. The different anomaly detection algorithms are used to educate data through the software R, and the results are summarized in the tables.

The data points occurring in all algorithms are finally detected as an anomaly. The anomaly identifies the data points that deviate from the data set’s normal behavior. The subject which is consistently identified as anomalous by the different techniques is marked as an anomaly in evaluation. After identification, one can drill down to more details into the title of anomalies in the evaluation criteria.

This paper proposes an analytical model for the course evaluation process and demonstrates the use of actionable analytics to detect anomalies in the evaluation process.

The paper aims to provide a comprehensive analysis of artificial intelligence’s (AI) transformative impact on the real estate industry. By examining various AI applications, from property recommendations to compliance automation, this study highlights potential benefits such as increased accuracy and efficiency. At the same time, this study critically discusses potential drawbacks, like privacy concerns and job displacement. The paper's goal is to offer valuable insights to industry professionals and policy makers, aiding strategic decision-making as AI continues to reshape the landscape of the real estate sector.

This paper employs an extensive literature review, combined with a qualitative analysis of case studies. Various AI applications in the real estate industry are examined, including machine learning for property recommendations and valuation, VR/AR property tours, AI automation for contract and regulatory compliance, and chatbots for customer service. The study also delves into the optimisation potential of AI in building management, lead generation, and risk assessment, whilst critically discussing potential challenges such as data privacy, algorithmic bias, and job displacement. The outcomes aim to inform strategic decisions for industry professionals and policy makers.

The study finds that AI has significant potential to revolutionise the real estate industry through enhanced accuracy in property valuation, efficient automation and immersive AR/VR experiences. AI-driven chatbots and optimisation in building management also hold promise. However, this study also uncovers potential challenges, including data privacy issues, algorithmic biases, and possible job displacement due to increased automation. The insights gleaned from this study underscore the importance of strategic decision-making in harnessing the benefits of AI while mitigating potential drawbacks in the real estate sector.

The paper's practical implications extend to industry professionals, policy makers, and technology developers. Professionals gain insights into how AI can enhance efficiency and accuracy in the real estate sector, guiding strategic decision-making. For policy makers, understanding potential challenges like data privacy and job displacement informs regulatory measures. Technology developers can also benefit from understanding the sector-specific applications and concerns raised. Additionally, highlighting the need for addressing algorithmic bias and privacy concerns in AI systems may foster better design practices. Therefore, the paper's findings could significantly shape the future trajectory of AI integration in real estate.

The paper provides original value by offering a comprehensive analysis of the transformative impact of AI in the real estate industry. Its multi-faceted examination of AI applications, coupled with a critical discussion on potential challenges, provides a balanced perspective. The paper's focus on informing strategic decisions for professionals and policy makers makes it a valuable resource. Moreover, by considering both benefits and drawbacks, this study contributes to the discourse on AI's broader societal implications. In the context of rapid technological change, such comprehensive studies are rare, adding to the paper's originality.

Female adults diagnosed with myalgia encephalomyelitis (ME) and chronic fatigue syndrome (CFS) often are marginalised because their condition is not fully recognised by medical and health-care systems. The purpose of this small-scale study was to explore the lived experiences of adult females with ME/CFS in England in relation to contributing factors that impact their occupational participation.

A qualitative study design using semi-structured interviews was used with nine female adult participants who were selected using a purposive sampling method. A Thematic Networks tool was used to analyse data.

Four organising themes were identified: impairment-, person-, environment- and society-related factors. Two global themes, invisibility and diagnosis stigma, were identified as the overarching issues that female adults with ME/CFS face in occupational participation.

Many of the issues that contribute to lack of participation by this population are associated with environmental factors which are secondary to their illness.