The target date for achieving the 2030 UN Agenda [Sustainable Development Goals (SDGs)] is fast approaching. The construction sector is critical to achieving many SDGs, including Goal 5. Studies regarding achieving Goal 5 (Gender Equality) in the construction industry, especially women’s consultancy participation in developing countries, are scarce and complexly interrelated. Societal problems and divergence may have contributed to this. Therefore, this study explores issues hindering gender equality and suggests measures to promote more women construction consultants through policy to improve achieving Goal 5 in Nigeria. The research employed face-to-face data collection via a qualitative mechanism to achieve this. The study covered Abuja and Lagos. It accomplished saturation at the 20th participant. The research utilised a thematic method to analyse the collected data from knowledgeable participants. The perceived hindrances facing Nigerian construction consultants’ gender equality were clustered into culture/religion-related, profession-related, and government-related encumbrances. Achieving Goal 5 will be a mirage if these issues are not addressed. Thus, the study recommended measures to motivate women to study construction-related programmes and employment opportunities, including consultancy services slots through programmes and policy mechanisms to achieve Goal 5. As part of the implications, the study suggests that Nigerian construction consultants and other stakeholders need to make feasible improvements to achieve gender equality (Goal 5).

Falling is one of the most critical outcomes of loss of consciousness during triage in emergency department (ED). It is an important sign requires an immediate medical intervention. This paper presents a computer vision-based fall detection model in ED. In this study, we hypothesis that the proposed vision-based triage fall detection model provides accuracy equal to traditional triage system (TTS) conducted by the nursing team. Thus, to build the proposed model, we use MoveNet, a pose estimation model that can identify joints related to falls, consisting of 17 key points. To test the hypothesis, we conducted two experiments: In the deep learning (DL) model we used the complete feature consisting of 17 keypoints which was passed to the triage fall detection model and was built using Artificial Neural Network (ANN). In the second model we use dimensionality reduction Feature-Reduction for Fall model (FRF), Random Forest (RF) feature selection analysis to filter the key points triage fall classifier. We tested the performance of the two models using a dataset consisting of many images for real-world scenarios classified into two classes: Fall and Not fall. We split the dataset into 80% for training and 20% for validation. The models in these experiments were trained to obtain the results and compare them with the reference model. To test the effectiveness of the model, a t-test was performed to evaluate the null hypothesis for both experiments. The results show FRF outperforms DL model, and FRF has same accuracy of TTS.

This study aims to develop a robust prioritization model for municipal projects in the Holy Metropolitan Municipality (Makkah) to address the challenges of aligning short-term and long-term objectives. The research explores How multi-criteria decision-making (MCDM) techniques can prioritize municipal projects effectively while ensuring alignment with strategic goals and local needs. The methodology employs the analytic hierarchy process (AHP) and exploratory factor analysis (EFA) to ensure methodological rigor and data adequacy. Data were collected from key stakeholders, including municipal planners and community representatives, to enhance transparency and reliability. The model’s validity was assessed through latent factor analysis, confirming the relevance of identified criteria and factors. Results indicate that flood prevention projects are the highest priority (0.4246), followed by road projects (0.3532), park construction (0.1026), utility projects (0.0776), and digital transformation (0.0416). The study highlights that certain factors are critical for evaluating and prioritizing municipal projects. “Capacity and Demand” emerged as the most influential factor (0.5643), followed by “Strategic Alignment” (0.2013), “Project Interdependence” (0.1088), “Increasing Investment” (0.0950), and “Risk” (0.0306). These findings are significant as they offer a structured, data-driven approach to decision-making aligned with Saudi Vision 2030. The proposed model optimizes resource allocation and project selection, representing a pioneering effort to develop the first prioritization framework specifically tailored to Makkah’s unique municipal needs. Notably, this is the first study to establish a prioritization method specifically for Makkah’s municipal projects, providing valuable contributions to the field.

This paper delves into the intricate dynamics of suburban transportation transformation within the Jakarta Metropolitan Area, with a specific focus on the evolution of the Commuter Line and Bus Rapid Transit (BRT) systems. Utilizing spatial analysis, qualitative descriptions, and stakeholder insights, the paper unveils self-organizing dynamics. It critically examines the role of transportation infrastructure in shaping the broader landscape of urban development. Unlike a traditional approach, the paper seeks to unravel the self-organization processes embedded in transportation planning, unveiling adaptive strategies crafted to tackle the distinct challenges of suburban transportation. By using autonomy, flexibility, adaptability, and collaboration frameworks, the paper contributes to a nuanced understanding of suburban transportation dynamics, with implications for policymakers, planners, and researchers grappling with similar challenges in diverse metropolitan regions.

Current studies in disaster sociology, which were initiated and developed mostly in the USA upon the request of the army, are far from meeting the needs today. Today, more than ever, new theoretical and methodological approaches that are not human-centered are needed. The research, a part of which is presented here, aims to render invisible the damages and losses suffered by those who are marginalized by the powerful, in disasters in general and earthquakes in particular. The main question of this research is how to address the damages suffered by soil plants and animals, including immigrants in Turkey, due to the disaster on 6February 2023.(Based on this, the main question of the study is how to address the damages of the natural environment, including plants, animals and soils, as well as Syrian immigrants in Turkey, who were affected by the earthquakes centered in Kahramanmaraş on 6February 2023, which we experienced most recently, will be addressed with an antipositivist approach.) For this purpose, unlike classical sociological approaches, based on relational sociology, how immigrants, plants, animals and soil are affected together during the uncertainty and complexity in daily life has been analyzed based on available written and visual documents. The findings were discussed with a holistic view, based on the ‘One World’ terminology suggested by relational sociologist Bruno Latour. It has been revealed that due to the earthquake turning into a major disaster, the resident population has become openly or secretly immigrants, and they have been marginalized like other creatures, especially international immigrants, most of whom are Syrians, have been blamed, excluded and rendered invisible. While the research results reveal the inadequacy of classical essentialist sociological approaches based on the basic duality of nature and society, they also show that ‘differences’ and ‘uncertainties’ come to the fore in daily life instead of linear determinations. In addition, while the importance and contributions of interdisciplinary and transdisciplinary studies with concepts such as ‘liminality’ and ‘turning point’ are exhibited, on the other hand, some suggestions are made based on Bruno Latour’s ‘One World’ approach.