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Total risk score (TRS) is considered one of the main indicators for risk evaluation. Several studies attempted to employ different types of risk indices for the evaluation of cost overrun causes. Hence, this study aims at performing a comparative analysis to evaluate the efficiency of three different approaches for TRS calculation.

Thirty-eight unique causes of cost overrun in urban-related construction projects were identified and a survey was conducted among construction professionals in Iran. The TRS for each cost overrun cause is calculated using single-attribute (SA), double-attribute (DA), and multiple-attribute (MA) approaches, and eventually, causes were ranked. Furthermore, principal component analysis (PCA), logistic regression analysis (LRA), and K-means clustering are utilized to compare the differences in the generated TRS using different approaches.

The results revealed that the TRS generated through the MA approach demonstrated the highest efficiency in terms of generating correlation between causes and their identified latent constructs, prediction capability, and classification of the influential causes in the same group.

The originality of this study primarily stems from the adoption of statistical approaches in the evaluation of the recently introduced TRS calculation approach in comparison to traditional ones. Additionally, this study proposed a modified application of the relative importance index (RII) for risk prioritization. The results from this study are expected to fulfill the gap in previous literature toward exploring the most efficient TRS calculation approach for those researchers and practitioners who seek to utilize them as a measure to identify the influential cost overrun causes.

Cerebral palsy is one of the leading causes of chronic disability in children. The current pilot study investigated (1) whether an exoskeleton system enables physiological gait patterns and (2) whether the system is user-friendly enough to envision its use in a clinical setting.

Participants included a convenience sample of six children with cerebral palsy. Following informed consent, study volunteers underwent baseline assessments, participated in eight sessions during which they used the exoskeleton system with the objective of achieving proficiency in use of the system, and underwent an end-of-study assessment of walking. Satisfaction and usability questionnaires were given to the family/caregiver.

All participants achieved a more regular gait pattern and improved their 6-Minute Walk Test scores. Overall satisfaction and usability were rated as good.

The exoskeleton system enabled physiological gait patterns, and the system was user-friendly enough to envision its use in a clinical setting.

There is potential for guiding treatment plans for individuals with cerebral palsy.

This study investigates the impact of ESG performance on the duration of dividend sustainability, introducing survival analysis as a novel methodological approach in this context and highlighting its differences from commonly used regression analyses such as OLS and logistic regression.

Survival analysis methods, including Kaplan–Meier estimates and Cox proportional hazards time-dependent regression, were employed to examine data from publicly listed companies in Taiwan between 2016 and 2023. Additionally, logistic regression was tested to compare results with those from the survival analysis.

While overall ESG performance did not show a significant impact on the duration of dividend sustainability, a detailed analysis of the individual ESG components revealed that the environmental performance component can extend the duration of dividend sustainability.

The findings based on companies in Taiwan may not generalize to other contexts. However, this study primarily highlights the application of survival analysis in ESG-related literature. Future research could explore similar analyses in different international settings to better understand the broader applicability of these results.

The results suggest that the impact of ESG performance on dividend amounts and the duration of dividend sustainability are distinct issues. Investors and stakeholders should consider these differences when assessing corporate performance and making investment decisions.

The study highlights the importance of environmental sustainability in corporate dividend policies, indicating that companies with better environmental performance provide more stable returns.

This study introduces survival analysis to the study of ESG performance and the duration of dividend sustainability, addressing a gap in the literature by focusing on the duration of dividends rather than their amount.

This research delves into the transformative potential of Generative Artificial Intelligence (AI), particularly ChatGPT, in enhancing higher education. It aims to explore how these advanced AI tools can be integrated into different educational settings to improve interactive learning experiences and student engagement, addressing the current challenges and opportunities in academic and administrative applications. Adopting a qualitative approach, the research utilizes the case vignette method to simulate realistic scenarios in various academic disciplines. It examines the potential applications and outcomes of AI in higher education, structured around key areas like intelligent tutoring systems, assessment, personalization and student profiling. This study employs the 4Cs framework (Critical Thinking, Creativity, Collaboration and Communication) to evaluate the effectiveness of AI integration in improving educational outcomes. The study reveals that ChatGPT can significantly enhance learning experiences by providing personalized tutoring, efficient assessment, tailored content and predictive insights into student performance. However, challenges such as ensuring content accuracy, ethical concerns and balancing AI with human interaction are also identified. Best practices for effectively integrating ChatGPT in higher education are proposed, emphasizing the complementarity of AI and human elements in education. This research contributes to the growing body of knowledge on AI in education by providing a nuanced understanding of generative AI's potential and challenges in higher education. It offers valuable insights and practical recommendations for educators and institutions, guiding the effective integration of AI technologies to enhance teaching and learning.

Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations.

This “flickering” behavior often results from CV algorithms treating successive observations as independent, which ignores the dependence inherent in most videos. Bayesian neural networks are a potential remedy to this issue using Bayesian priors. This research compares a traditional video classification neural network to its Bayesian equivalent based on performance and capabilities. Additionally, this work introduces the concept of smoothing to reduce the opportunities for “flickering.”

The augmentation of Bayesian layers to CNNs matched with an exponentially decaying weighted average for classifications demonstrates promising benefits in reducing flickering. In the best case the proposed Bayesian CNN model reduces flickering by 67% while maintaining both overall accuracy and class level accuracy.

The training of the Bayesian CNN is more computationally demanding and the requirement to classify frames multiple times reduces resulting framerate. However, for some high surety mission applications this is a tradeoff the decision analyst may be willing to make.

Our research expands on previous efforts by first using a variable number of frames to produce the moving average as well as by using an exponentially decaying moving average in conjunction with Bayesian augmentation.

A “state-driven” approach suggests that colonists use census categories to rule. However, a “society-driven” approach suggests that this state-driven perspective confers too much power upon states. A third approach views census-taking and official categorization as a product of state–society interaction that depends upon: (a) the population's lay categories, (b) information intellectuals' ability to take up and transform these lay categories, and (c) the balance of power between social and state actors. We evaluate the above positions by analyzing official records, key texts, travelogues, and statistical memoirs from three key periods in India: Indus Valley civilization through classical Gupta rule (ca. 3300 BCE–700 CE), the “medieval” period (ca. 700–1700 CE), and East India Company (EIC) rule (1757–1857 CE), using historical narrative. We show that information gathering early in the first period was society driven; however, over time, a strong interactive pattern emerged. Scribes (information intellectuals) increased their social status and power (thus, shifting the balance of power) by drawing on caste categories (lay categories) and incorporating them into official information gathering. This intensification of interactive information gathering allowed the Mughals, the EIC, and finally British direct rule officials to collect large quantities of information. Our evidence thus suggests that the intensification of state–society interactions over time laid the groundwork for the success of the direct rule British censuses. It also suggests that any transformative effect of these censuses lay in this interactive pattern, not in the strength of the British colonial state.

This study aims to examine the relationship between the chemical composition of juices obtained from fruits of autochthonous wild pomegranate (Punica granatum L.) grown in Montenegro and their cytotoxic effects on cancer cells.

To explore the potential value of wild pomegranate fruits, in vitro biological assays were carried out with juices whose composition was analyzed in detail for sugars, organic acids, vitamin C and phenolic compounds. The effect of juices on survival was determined in human lung A549, cervical HeLa and breast MCF-7 carcinoma cells by MTT assay. As a control, the cytotoxicity against normal fetal lung fibroblasts (MRC-5) was monitored.

Among cancer cell lines, considering the IC50 related to total phenolics, the lowest value – 13 µg/mL was found for the A549. The strongest effect on lung cells was assumed due to the favorable contribution of ellagitannins to total phenolics in juice as well as the given combination of anthocyanins and their synergistic action. For HeLa cells, the lowest IC50 value was obtained at 88 µg/mL, and the cytotoxicity could be matched with the effects of anthocyanins and catechin. For MCF-7 cells, the lowest IC50 was 504 µg/mL, and the elevated levels of vitamin C and ellagic acid derivatives should have a noticeable effect on these cells.

This study provides an important contribution to the knowledge on the effect of phytochemicals from wild pomegranate juice on lung, cervical and breast cancer cells, in vitro. The present observations suggest that the juice of wild pomegranate has the potential in the fight against cancer.

Pakistan ranks third globally in terms of newborn deaths occuring within the first 24 hours of life. With a neonatal mortality rate of 42.0%, it carries the highest burden compared to neighboring countries such as Bangladesh (17%), India (22.7%) and Afghanistan (37%). While there has been a decline in neonatal mortality rates in Pakistan, the pace of this decline is slower than that of other countries in the region. Hence, it is crucial to conduct a comprehensive examination of the risk factors contributing to neonatal mortality in Pakistan over an extended period. This study aims to analyze the trends and determinants of neonatal mortality in Pakistan over three decades, providing valuable insights into this persistent issue.

The study focused on neonatal mortality as the response variable, which is defined as the death of a live-born child within 28 days of birth. Neonates who passed away during this period were categorized as “cases,” while those who survived beyond a specific timeframe were referred to as “noncases.” To conduct a pooled analysis of neonatal mortality, birth records of 39,976 children born in the five years preceding the survey were extracted from four waves (1990–2018) of the Pakistan Demographic and Household Survey. The relationship between risk factors and the response variable was examined using the Cox Proportional Hazard Model. Neonatal mortality rates were calculated through the direct method using the “syncmrates” package in Stata 15.

During the extended period in Pakistan, several critical protective factors against neonatal mortality were identified, including a large family size, improved toilet facilities, middle-aged and educated mothers, female children, singleton live births, large size at birth and longer birth intervals. These factors were found to reduce the risk of neonatal mortality significantly.

This study makes the first attempt to analyze the trends and patterns of potential risk factors associated with neonatal mortality in Pakistan. By examining a large dataset spanning several years, the study provides valuable insights into the factors influencing neonatal mortality.

The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-09-2022-0604