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To analyze the challenges and intricacies involved in managing the physical and administrative aspects of central mosques, with a specific focus on the Kumasi Central Mosque in Ghana. Through this case study, the paper aims to provide insights into the unique estate management practices and design a conceptual framework that incorporates efficient estate management practices for Ghana’s central mosques.

The study adopted a mixed-methods approach. Structured questionnaires were used to gather the data administered to the head of the development office, maintenance workers and mosque users. The data were analyzed using descriptive methods.

The overall findings of the study indicate that the estate management practices of the mosque are having difficulties financing maintenance. Other difficulties include the type of maintenance regime, the period of planned maintenance, the lack of an asset register, the absence of insurance policies and the causes of emergency maintenance.

The research provides valuable insights into the management of central mosques in Ghana and offers scalable practical recommendations such as adopting a planned maintenance regime, insuring mosque facilities and implementing comprehensive recording-keeping guidelines guided by policies.

The paper will assist stakeholders in comprehending the most practical approach to mosque estate management practices.

The purpose of this study is to investigate the potential of using computer vision and deep learning (DL) techniques for improving safety on construction sites. It provides an overview of the current state of research in the field of construction site safety (CSS) management using these technologies. Specifically, the study focuses on identifying hazards and monitoring the usage of personal protective equipment (PPE) on construction sites. The findings highlight the potential of computer vision and DL to enhance safety management in the construction industry.

The study involves a scientometric analysis of the current direction for using computer vision and DL for CSS management. The analysis reviews relevant studies, their methods, results and limitations, providing insights into the state of research in this area.

The study finds that computer vision and DL techniques can be effective for enhancing safety management in the construction industry. The potential of these technologies is specifically highlighted for identifying hazards and monitoring PPE usage on construction sites. The findings suggest that the use of these technologies can significantly reduce accidents and injuries on construction sites.

This study provides valuable insights into the potential of computer vision and DL techniques for improving safety management in the construction industry. The findings can help construction companies adopt innovative technologies to reduce the number of accidents and injuries on construction sites. The study also identifies areas for future research in this field, highlighting the need for further investigation into the use of these technologies for CSS management.

Children’s finances are increasingly digitised through the emergence and development of a range of finance applications, or apps, for managing chores, saving and spending. This paper aims to offer a preliminary scoping study of these child finance apps in the nascent consumer research area of children’s FinTech.

This paper undertakes a qualitative analysis of the design features and marketing taglines of child finance apps to explore their role in the digitisation of children’s financial literacy, consumer socialisation and economic agency.

The present analysis reveals five key design functions of child finance apps: chore management; child savings; payment and spending systems; parental control features; and banking and finance features. Furthermore, three key child consumer themes emerge from the analysis of these child finance apps: gamification of child household labour; surveillance of children’s consumer participation; and datafication of children’s financial lives.

To date, there is little research into the increasingly popular use of child chore, consumption and financial management apps, and thus a research gap or problem is that we do not yet have sufficient understanding of how finance apps operate through their design and marketing to influence the financial conditions of contemporary childhoods. This study is significant in bringing theories of surveillance, gamification and datafication from digital platform studies to the fields of childhood studies, children’s consumer research and child FinTech studies. The findings suggest that child finance apps use gamification features to encourage children’s financial learning, surveillance features to enable parenting care in children’s financial development and datafication to exploit children’s financial data within the finance industry. This study is clearly limited to the app environment, and so future work should investigate the use and perceptions of these apps in more detail using more situated social research methods with families and children.

By examining types of literacies taught by public libraries and the modes through which these programs were offered, this study aims to explore how public libraries might integrate data literacy training for the general public into existing library educational programs.

This study examined programs offered in 30 US public libraries during 2019 and 2020 to better understand types of literacy education announced to the public through library website listings and Facebook Events pages.

While public libraries offered educational programs in literacy areas ranging from basic reading and writing to technology, vocational skills, health literacy and more, data literacy training was not widely offered. However, this study identified many already-existing programs highly compatible for integrating with data literacy training.

This study offered new insights into both the literacies taught in public library programs as well as ways for public libraries to integrate data literacy training into existing educational programming, in order to better provide data literacy education for the general public.

Since public and private spaces are generally considered to be the fundamental building blocks for residential settings, this study draws attention to the need to consider and detail threshold spaces as one of the key aspects for accomplishing sociocultural needs, restoration and well-being in the residential environment. Understanding the function and uses of these spaces allows us to appreciate their benefits which are often neglected. This research particularly focuses on the social dimension of one fundamental threshold pattern; the Cairene balcony.

A qualitative anthropological approach was adopted in this research where in-depth interviews with female residents (n = 46) were conducted in three local contexts in Cairo, Egypt in parallel with non-participatory observation. The present piece focuses on the results elicited from the female residents’ interviews.

Irrespective of the income group, sociocultural background and context, dominating factors influencing women’s perception of the role of the Cairene balcony were commonly present. These included issues of; well-being and restoration, the phenomenon of personalization and identity, functional and communicative purposes, safety and security and privacy and control. Overall, the majority of interviewees stressed the significance of the balcony as a prominent source of prospect and an impermissible part of the residential environment.

The fact that little research has been conducted to examine the everyday use of the balcony and the role it plays in Cairene homes makes this “dedicated” research piece a valuable addition.

With the rapid advancement of lifestyle and technology, human lives are becoming increasingly threatened. Accidents, exposure to dangerous substances and animal strikes are all possible threats. Human lives are increasingly being harmed as a result of attacks by wild animals. Further investigation into the cases reported revealed that such events can be detected early on. Techniques such as machine learning and deep learning will be used to solve this challenge. The upgraded VGG-16 model with deep learning-based detection is appropriate for such real-time applications because it overcomes the low accuracy and poor real-time performance of traditional detection methods and detects medium- and long-distance objects more accurately. Many organizations use various safety and security measures, particularly CCTV/video surveillance systems, to address physical security concerns. CCTV/video monitoring systems are quite good at visually detecting a range of attacks associated with suspicious behavior on the premises and in the workplace. Many have indeed begun to use automated systems such as video analytics solutions such as motion detection, object/perimeter detection, face recognition and artificial intelligence/machine learning, among others. Anomaly identification can be performed with the data collected from the CCTV cameras. The camera surveillance can generate enormous quantities of data, which is laborious and expensive to screen for the species of interest. Many cases have been recorded where wild animals enter public places, causing havoc and damaging lives and property. There are many cases where people have lost their lives to wild attacks. The conventional approach of sifting through images by eye can be expensive and risky. Therefore, an automated wild animal detection system is required to avoid these circumstances.

The proposed system consists of a wild animal detection module, a classifier and an alarm module, for which video frames are fed as input and the output is prediction results. Frames extracted from videos are pre-processed and then delivered to the neural network classifier as filtered frames. The classifier module categorizes the identified animal into one of the several categories. An email or WhatsApp notice is issued to the appropriate authorities or users based on the classifier outcome.

Evaluation metrics are used to assess the quality of a statistical or machine learning model. Any system will include a review of machine learning models or algorithms. A number of evaluation measures can be performed to put a model to the test. Among them are classification accuracy, logarithmic loss, confusion matrix and other metrics. The model must be evaluated using a range of evaluation metrics. This is because a model may perform well when one measurement from one evaluation metric is used but perform poorly when another measurement from another evaluation metric is used. We must utilize evaluation metrics to guarantee that the model is running correctly and optimally.

The output of conv5 3 will be of size 7*7*512 in the ImageNet VGG-16 in Figure 4, which operates on images of size 224*224*3. Therefore, the parameters of fc6 with a flattened input size of 7*7*512 and an output size of 4,096 are 4,096, 7*7*512. With reshaped parameters of dimensions 4,096*7*7*512, the comparable convolutional layer conv6 has a 7*7 kernel size and 4,096 output channels. The parameters of fc7 with an input size of 4,096 (i.e. the output size of fc6) and an output size of 4,096 are 4,096, 4,096. The input can be thought of as a one-of-a-kind image with 4,096 input channels. With reshaped parameters of dimensions 4,096*1*1*4,096, the comparable convolutional layer conv7 has a 1*1 kernel size and 4,096 output channels. It is clear that conv6 has 4,096 filters, each with dimensions 7*7*512, and conv7 has 4,096 filters, each with dimensions 1*1*4,096. These filters are numerous, large and computationally expensive. To remedy this, the authors opt to reduce both their number and the size of each filter by subsampling parameters from the converted convolutional layers. Conv6 will use 1,024 filters, each with dimensions 3*3*512. Therefore, the parameters are subsampled from 4,096*7*7*512 to 1,024*3*3*512. Conv7 will use 1,024 filters, each with dimensions 1*1*1,024. Therefore, the parameters are subsampled from 4,096*1*1*4,096 to 1,024*1*1*1,024.

Past research has shown that wellness culture projects identities that are predominantly middle-class, white, thin, able-bodied women. Wellness cultures are amplified through digital media, namely highly visual social media platforms such as Instagram and TikTok, that promote a feminine ideal that women can (and should) achieve through rigorous commitment and investment. However, discourses surrounding wellness culture are a cause for concern when consumption, choice, and responsibility are positioned as a mode to constantly improve oneself until an idealised appearance is achieved.

In this chapter, the author explores the experiences of five Asian-Australian women aged 18–35 living in Australia as they navigate ideals of femininity. The author draws on perspectives from feminist new materialism to understand the material-discursive practices that form norms and ideals of the female body. Findings are presented in the form of vignettes to help trace affective encounters with objects, digital media, discourses, and other bodies that produce different affective relations as they seek to understand Asian-Australian femininity. The author argues that digital media and wellness culture prompt individual understanding and practices to adhere to transnational ideals of the feminine body rather than dismantling social and cultural norms that limit individual choice, an issue that has thus far received limited scholarly attention for Asian-Australians. This chapter builds on previous studies that position wellness culture within an established white female neoliberal rhetoric.

This paper analyses whether the humanisation of smart home speakers can improve users' attitudes towards covert information collection. Additionally, it examines the direct and indirect impact of trust, social presence and user's perceived surveillance on attitude towards covert information collection.

A total of 679 American users of smart home speakers are surveyed, and their responses are analysed using structural equation modelling. Mediating effects are also examined.

Humanisation increases social presence, improves users' attitude towards covert information collection and has a U-shaped effect on trust. A negative effect of humanisation on perceived surveillance is demonstrated. Social presence reduces perceived surveillance levels and improves users' attitude towards covert information collection.

We examine attitude towards covert information collection as a new outcome variable. This study contributes to the growing body of research on humanisation by providing new evidence of how humanisation helps improve users' attitude towards covert information collection and generates trust in the service provider. This research indicates the important role of social presence.