Abstract:This study investigates how risk assessment techniques are applied in Build-OperateTransfer (BOT) student housing projects and responds to the growing need for risk assessment frameworks tailored to concessionary educational infrastructure. This is against the background of the knowledge gap in risk assessment techniques in BOT student housing development. A systematic literature review was conducted using the PRISMA protocol. This review contributes to the discourse on PPP infrastructure risk management, showcasing limitations in applying risk assessment techniques to student housing. The review considered 543 peer-reviewed articles published between 2004 and 2024, sourced from Scopus, Web of Science, Google Scholar, and Dimensions. Following screening for relevance and methodological rigour, 45 studies were selected for qualitative synthesis. The analysis shows that while quantitative methods, such as Monte Carlo Simulation, Analytic Hierarchy Process (AHP), and Fuzzy Logic, are widely adopted in BOT risk assessment, contextual variables relevant to student housing projects, such as interruption of academic calendars, student tenant behaviour, student unrest, and shifting policy environments, were overlooked. A significant gap was identified in integrating stakeholder-driven qualitative insights with quantitative modelling approaches. By excluding non-English language sources and incongruous literature, the study may not fully capture region-specific practices, particularly from non-English-speaking developing economies. Future research should consider broader inclusion criteria to strengthen the generalizability of findings.
Abstract:Globally, healthcare systems encounter tremendous obstacles as a result of the progressive neurological disease called Alzheimer's. To effectively intervene and control Alzheimer's disease (AD), the earliest and most accurate diagnosis is necessary. However, traditional diagnostic methods are often expensive, take a long time, and do not provide the accuracy needed for early diagnosis. This study addresses these limitations by proposing a machine learning-based (ML-based) approach for predicting AD using advanced data classification methods and an explainable artificial intelligence (AI) approach. Three distinct methods were utilized to carry out the feature selection procedure: chi-square, mutual information, and analysis of variance (ANOVA). We identified the analysis's most relevant elements by utilizing each technique. We found the best algorithm for predicting the early signs of AD by testing seven different ML methods: logistic regression, AdaBoost, random forest, support vector machine, decision tree, XGBoost, and K-nearest neighbors. We employed the SMOTE method to rectify the data imbalance. To test the proposed method, we employed both a publicly available and a private dataset. We applied multiple cross-validation approaches to provide a strong performance evaluation. The results of the experiments illustrated that, out of all the models tested, the XGBoost classifier performed the best. Using the combined dataset, the XGBoost classifier had 97.32% accuracy, 96.56% precision, 97.00% specificity, 97.68% sensitivity, 98.43% AUC, and 97.12% F1-score. Using the public dataset, XGBoost achieved 97.23% accuracy, 96.14% precision, 96.52% specificity, 98.03% sensitivity, 98.30% AUC, and 97.07% F1-score. Furthermore, XGBoost did exceptionally well on the private dataset with 95.83% accuracy, 93.94% sensitivity, 96.88% precision, 97.44% specificity, 98.52% AUC, and 95.38% F1- score. Understanding the model's findings and decision-making process can be enhanced with the help of an explicable AI framework that was developed using SHAP methods. The proposed approach shows enormous potential as a healthcare solution that reduces healthcare costs and improves efficiency in AD’s diagnosis. Patients benefit from improved diagnostic tools for AD brought about by this study's combination of powerful ML models with explainable AI.
Abstract:Since the inception of space exploration from the 1950s, the selection of astronauts has been a critical component along with technological advancements in rocket and spacecraft development. As missions began to extend beyond six months in the 2010s, the management of daily schedules— particularly in terms of striking a balance between public duties and private time—became essential for sustainable productivity and well-being among astronauts. As space lacks natural day and night cycles, questions have emerged regarding daily routines, the structure of personal time and its potential impact on astronautical performance. Astronaut’s management of private time—sleep, rest and leisure—may significantly influence their mental health and the overall success of a mission. This study explores time management strategies for future deep space explorations, including lunar and Martian expeditions in the 2030s. While official schedules are meticulously structured—covering operations, scientific research, maintenance, training, exercise and meals—safeguards for private time remains in place, except during emergencies. The role of this private sphere extends beyond basic rest, thus contributing to human health under extreme environments. In addition to analysing the competitiveness of personal time, this paper argues a new hypothesis that non-pharmaceutical activities of yoga (physical movement) and meditation (mental sustainability) can enhance resilience and psychological vitality among the crews. These feasible practices, although outside the scope of traditional STEM frameworks, may mitigate key internal risks in the space condition of social isolation and confinement. Understanding and optimising the balance between private and public times could be imperative in maintaining the health, safety and team performance of astronauts on future interplanetary missions.
Abstract:Heart disease is still a major global health problem that needs early, accurate, and scalable prediction systems that can work in both clinical settings and remote healthcare delivery. This study suggests a better deep learning (DL) and IoT-based predictive method that can find heart diseases with high accuracy, explainability, and real-time use. A detailed comparison was made using ten different DL models, including ensemble DL, deep belief networks (DBN), tabular neural networks, XGBoost with neural network embedding, gradient-boosted neural networks (GBNN), multilayer perceptron (MLP) with feature engineering, long short-term memory (LSTM), residual neural network (ResNet), neural factorization machines (NFM), and recurrent neural networks (RNN). The classifiers were trained and tested on three sets of data: a public set with 685 samples, a private set with 355 samples, and a combined set with 1040 samples, using different cross-validation methods to confirm that the model works well. To improve prediction accuracy and speed up calculations, we used three statistical methods—ANOVA, Mutual Information (MI), and the chi-square test—to select the most important features. These methods helped isolate the most influential health indicators, reducing noise and dimensionality in the input space. Because medical datasets often have uneven class distributions, the SMOTE was used to create new samples from less common classes, helping to reduce the chance of biased learning. Among all models tested, the GBNN demonstrated superior performance across all datasets. Specifically, it achieved 96.35% accuracy, 94.83% sensitivity, 97.47% specificity, 96.49% precision, 95.65% F1 score, and 97.12% AUC on the public dataset; 97.18% accuracy, 100% sensitivity, 95.35% specificity, 93.33% precision, 96.55% F1 score, and 98.42% AUC on the private dataset; and 99.52% accuracy, 98.84% sensitivity, 100% specificity, 100% precision, 99.42% F1 score, and 99.33% AUC on the combined dataset. We used the SHAP method to make sure the system's predictions could be understood and trusted by clinicians. This explainable AI technique offers information about individual feature contributions for each prediction, helping clinicians and users understand the rationale behind classification outcomes and build confidence in the system’s decisions. As a final application layer, the top achievers integrated the DL into a mobile application that enables users to input how they feel and receive immediate forecasts of potential heart disease issues.
Abstract:This study examines the response and failure behavior of SUS304 stainless steel square tubes with varying outer side lengths (20–50 mm) under symmetric, curvature-controlled cyclic bending at bending directions of 0°, 22.5°, and 45°. The moment–curvature curves revealed stable hysteresis loops observed across all tested geometries and bending directions. Increasing the outer side length resulted in a higher peak bending moment, whereas increasing the bending direction led to a slight increase in the peak moment. For a given bending direction, the curves representing the variation of outer side length (i.e., the ratio of length change to the original length) as a function of curvature exhibited symmetric patterns, serrated features, and progressive growth with increasing cycle number, regardless of the initial side length. Moreover, increasing either the outer side length or the bending direction led to a greater variation in outer side length. As for the relationship between curvature and the number of cycles to failure, the data corresponding to the four outer side lengths formed four distinct straight lines in double-logarithmic coordinates for each bending direction. Based on the experimental observations, empirical equations were developed to describe these relationships. The predictions obtained using these empirical equations showed good agreement with the experimental results.
Abstract:Leaders adopt innovative and creative approaches to make sure their organizations stay relevant amid the challenges posed by rapid technological advancement and globalization. The study explores the ways in which innovative practices, creativity, and leadership behavior influence organizational growth focusing on church settings in Lagos, Nigeria. A cross-sectional survey design was used to collect information from 337 church leaders from various denominations. Statistical analysis revealed positive relationships between leadership behavior, creativity, and innovation, underscoring their impact on church growth. The effects of creativity and innovation on sustainable growth of church organization were found to be moderated by leadership behavior, highlighting the need for church leaders to foster innovative strategies. The study provides useful insights for enhancing leadership effectiveness and adds to the limited body of research on organizational growth within church settings. This research offers suggestions for promoting innovation and creativity in church organizations, such as utilizing technology, fostering an inclusive environment, and promoting creativity. Furthermore, the study has important implications for church leaders aiming to adapt to innovative dynamics while maintaining their organizational mission and values.
Abstract:Sustainable environment play a significant role in development of every country on the globe. However, the former has been undergoing various changes due various factors adopted to assist in enhancing countries developments. The current study aims to evaluate the impact of foreign and domestic investments, industrialization, population growth and GDP per capita on environmental degradation in South Africa. Auto-regressive Distributed Lag (ARDL) and error correction are the approaches employed to determine the relationship among variables. The study outcome endorsed the existence of a long-run impact of all explanatory variables on environment degradation. Additionally, the results revealed that industrialization, economic growth and foreign direct investment heighten CO2 emissions and environmental degradation, while employment and population growth reduce the level of CO2 emissions in long-run. The study recommends collaborations amid foreign investors, domestic industries, and research institutions to advance the establishment of cleaner technologies and sustainable environmental practices in South Africa. Similarly, government should encourage and invest in green energy production to improve economy without impeding the environment.
Abstract:Space science has been developed with a long-term perspective beyond missions on the Earth’s orbit, the International Space Station (ISS) and Shenzhou. While space tourism is a commercial project, NASA’s SpaceX is considering innovative missions for the moon station and Mars in the 2030s. The major hazards of radiation, microgravity, distance from Earth and hostile environments will be technologically addressed for the biological and physiological safety of human spaceflight. However, prolonged isolation in space was not the key issue of past missions but nevertheless critically affected psychological and behavioural changes. How can we enhance future astronauts’ mental well-being? What methodology would be efficient for outer space travel? Are space agencies’ gender policies still valuable? If not, how can they be reconsidered in risk management? Since insecurity illogically leads to the internal circumstances of solitude, fear, anxiety, low morale, negativity, unwillingness and longing for family, this paper explores the significance of spaceship community life in the context of gender equality. The physiological characteristics of future astronauts (around late twenties to early forties) are evaluated in relation to sexual nature for 2.5–3 years. The paper argues a feasible hypothesis as a competitive prerequisite of confinement and isolation that, in the secured system of STEM and medical environments, the application of ‘couple astronaut theory’ could be an alternative countermeasure for the resilience, safety and success of deep space exploration.
Abstract:Governments manage retirement reforms and legislation related to inadequate retirement savings and low preservation of retirement benefits among working individuals. Pre-retirement withdrawals are a contributing factor to a lack of retirement security, and retirement fund members with insufficient retirement benefits become a liability to the government. The South African government implemented the two-pot retirement system to improve retirement savings adequacy and fund preservation, and to assist fund members with income for emergencies. This pension reform is in its infancy, and researchers are investigating its impact on retirement fund members of different income groups and employment. The study aims to establish awareness, understanding, and perception of lowincome earners on the legislated two-pot retirement system. Using a quantitative research survey methodology and a non-probability sampling approach (convenience), primary data were collected from low-income retirement fund members employed in the Gauteng province through a self-administered questionnaire. The research discovered widespread awareness of the two-pot retirement system, achieved mostly through the media, workplaces, and fund administrators. However, participants demonstrated limited knowledge of structure and regulations. Financial difficulties, such as debt payments and basic requirements, were the primary cause for early withdrawals, despite being aware of the two-pot system. The study concludes that while there is high awareness of the two-pot retirement system, there is limited understanding of its structure and regulations.
Abstract:The P vs NP problem is one of the central unsolved questions in theoretical computer science. Traditionally formulated within the framework of Turing machines, “time” represents the number of discrete computation steps. This paper explores the speculative idea that reinterpreting time as a dynamic, controllable parameter—rather than a fixed measure—could suggest alternative models of computation that challenge or extend classical complexity boundaries. By drawing connections between causality, temporal dynamics, and computational processes, we outline potential for future research that unites concepts from theoretical physics and computation.