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Recognising the as-built state of construction elements is crucial for construction progress monitoring. Construction scholars have used computer vision-based algorithms to automate this process. Robust object recognition from indoor site images has been inhibited by technical challenges related to indoor objects, lighting conditions and camera positioning. Compared with traditional machine learning algorithms, one-stage detector deep learning (DL) algorithms can prioritise the inference speed, enable real-time accurate object detection and classification. This study aims to present a DL-based approach to facilitate the as-built state recognition of indoor construction works.

The one-stage DL-based approach was built upon YOLO version 4 (YOLOv4) algorithm using transfer learning with few hyperparameters customised and trained in the Google Colab virtual machine. The process of framing, insulation and drywall installation of indoor partitions was selected as the as-built scenario. For training, images were captured from two indoor sites with publicly available online images.

The DL model reported a best-trained weight with a mean average precision of 92% and an average loss of 0.83. Compared to previous studies, the automation level of this study is high due to the use of fixed time-lapse cameras for data collection and zero manual intervention from the pre-processing algorithms to enhance visual quality of indoor images.

This study extends the application of DL models for recognising as-built state of indoor construction works upon providing training images. Presenting a workflow on training DL models in a virtual machine platform by reducing the computational complexities associated with DL models is also materialised.

Incipient detection of pavement deterioration (such as crack identification) is critical to optimizing road maintenance because it enables preventative steps to be implemented to mitigate damage and possible failure. Traditional visual inspection has been largely superseded by semi-automatic/automatic procedures given significant advancements in image processing. Therefore, there is a need to develop automated tools to detect and classify cracks.

The literature review is employed to evaluate existing attempts to use Hough transform algorithm and highlight issues that should be improved. Then, developing a simple low-cost crack detection method based on the Hough transform algorithm for pavement crack detection and classification.

Analysis results reveal that model accuracy reaches 92.14% for vertical cracks, 93.03% for diagonal cracks and 95.61% for horizontal cracks. The time lapse for detecting the crack type for one image is circa 0.98 s for vertical cracks, 0.79 s for horizontal cracks and 0.83 s for diagonal cracks. Ensuing discourse serves to illustrate the inherent potential of a simple low-cost image processing method in automated pavement crack detection. Moreover, this method provides direct guidance for long-term pavement optimal maintenance decisions.

The outcome of this research can help highway agencies to detect and classify cracks accurately for a very long highway without a need for manual inspection, which can significantly minimize cost.

Hough transform algorithm was tested in terms of detect and classify a large dataset of highway images, and the accuracy reaches 92.14%, which can be considered as a very accurate percentage regarding automated cracks and distresses classification.

Doctoral degrees are generally the highest level of education provided by educational institutions in Western countries. Nevertheless, doctoral degree holders – i.e. Philosophiae Doctors (PhDs) – struggle to find a job that matches their knowledge and expertise. This article investigates the effects that PhDs' satisfaction with different attributes of educational services has on their ability to obtain employment either in academia or outside it.

Secondary data were accessed from a nationwide survey performed in Italy between February and July 2014. More than 16,000 people who achieved a doctoral degree between January 2008 and December 2010 were involved in the analysis. The four-years' time-span was justified by the need to avoid potential biases produced by a short time lapse between data collection and the awarding of the respondents' doctoral degree. A logistic regression model was designed to shed light on the relationship between doctoral degree holders' satisfaction and their ability to find employment.

This study results suggested that the attributes of educational services had varying effects on the doctoral degree holders' ability to obtain work. More specifically, the perceived quality of research and methodological courses delivered by educational institutions and the quality of the technologies and digital resources available at the host university were found to positively affect the ability of doctoral degree holders to get a job in academia. Conversely, the satisfaction with the quality of the teaching activities was positively related to the doctoral degree holders' employability outside academia.

The quality of educational services provided to students attending a doctoral degree course affects their ability to find work. Enhancing the quality of educational services may reduce the risk of unemployment amongst doctoral degree holders.

To the best of the authors' knowledge, few attempts have been made to investigate the interplay between the quality of educational services and doctoral degree holders' employability.

Construction material management plays a significant role in achieving successful project delivery of a construction project. However, ineffective material management is a critical issue in the construction industry, especially in developing economies, of which Sri Lanka is not an exception. Therefore, this study aims to focus on exploring the causes of ineffective material management practices in civil engineering construction projects in Sri Lanka and their impact on successful project delivery.

Furthermore, the literature findings were validated through the preliminary survey. Subsequently, a quantitative research approach was adopted to pursue the research aim. Questionnaire responses were obtained from 215 construction professionals in civil engineering projects who were selected using the judgemental and snowball sampling techniques. Collected data were analysed through Statistical Package for the Social Sciences (SPSS) V26 and Microsoft Excel 2016.

Moreover, the study revealed that material price fluctuation, shortage of material in the market, delay in material procurement, inadequate planning and delays in material delivery are the most frequent causes of ineffective material management in civil engineering projects. In addition, it was evidenced that most ineffective material management practices cause both time and cost overruns in civil engineering construction projects. Most respondents emphasized inadequate planning, inadequate qualified and experienced staff, lack of supervision and lack of leadership as the causes for both time and cost overruns.

The study was concluded by proposing strategies for effective material management. Education/training/enlightenment of staff in charge of materials management, use of software like Microsoft Project, Primavera and similar software to eliminate manual errors in material management, and providing clear specifications to suppliers were the most agreed strategies for effective material management in civil engineering construction projects.

The purpose of this paper is to systematically analyze the literature around regulatory compliance and market manipulation in capital markets through the use of bibliometrics and propose future research directions. Under the domain of capital markets, this theme is a niche area of research where greater academic investigations are required. Most of the research is fragmented and limited to a few conventional aspects only. To address this gap, this study engages in a large-scale systematic literature review approach to collect and analyze the research corpus in the post-2000 era.

The big data corpus comprising research articles has been extracted from the scientific Scopus database and analyzed using the VoSviewer application. The literature around the subject has been presented using bibliometrics to give useful insights on the most popular research work and articles, top contributing journals, authors, institutions and countries leading to identification of gaps and potential research areas.

Based on the review, this study concludes that, even in an era of global market integration and disruptive technological advancements, many important aspects of this subject remain significantly underexplored. Over the past two decades, research has lagged behind the evolution of capital market crime and market regulations. Finally, based on the findings, the study suggests important future research directions as well as a few research questions. This includes market manipulation, market regulations and new-age technologies, all of which could be very useful to researchers in this field and generate key inputs for stock market regulators.

The limitation of this research is that it is based on Scopus database so the possibility of omission of some literature cannot be completely ruled out. More advanced machine learning techniques could be applied to decode the finer aspects of the studies undertaken so far.

Increased integration among global markets, fast-paced technological disruptions and complexity of financial crimes in stock markets have put immense pressure on market regulators. As economies and equity markets evolve, good research investigations can aid in a better understanding of market manipulation and regulatory compliance. The proposed research directions will be very useful to researchers in this field as well as generate key inputs for stock market regulators to deal with market misbehavior.

This study has adopted a period-wise broad-based scientific approach to identify some of the most pertinent gaps in the subject and has proposed practical areas of study to strengthen the literature in the said field.

This paper aims to simulate vital corrective actions (CAs) affecting system availability through a system dynamics approach based on the results obtained by analyzing the causal relationships among failure modes and effects analysis elements.

A stock and flow diagram has been developed to simulate system behaviors during a timeframe. Some improvement scenarios regarding the most necessary CAs according to their strategic priority and the possibility of eliminating root causes of critical failure modes in a roller-transmission system have been simulated and analyzed to choose the most effective one(s) for the system availability. The proposed approach has been examined in a steel-manufacturing company.

Results indicated the most effective CAs to remove or diminish critical failure causes that led to the less reliability of the system. It illustrated the impacts of the selected CAs on eliminating or decreasing root causes of the critical failure modes, lessening the system’s failure rate and increasing the system availability more effectively.

Results allow managers and decision-makers to consider different maintenance scenarios without wasting time and more cost, choosing the most appropriate option according to system conditions.

This study innovation would be the dynamic analysis of interactions among failure modes, effects and causes over time to predict the system behavior and improve availability by choosing the most effective CAs through improvement scenario simulation via VENSIM software.

The main purpose of this paper is to establish whether trust plays a significant mediating role in the relationship between access to microcredit and survival of young women microenterprises in under-developed financial markets in sub-Saharan Africa. The main focus of this paper is to specifically test whether relational social capital built by young women from homogeneous and heterogeneous groups can be more effective in promoting economic exchange in under-developed financial markets since interpersonal trust has recently been found to harbor group collusion, especially among kins. Overall, the paper distinguishes trust among individuals based on their age, gender and ethnic diversity.

This study used structural equation model to test whether trust significantly mediates the relationship between access to microcredit and survival of young women microenterprises using Analysis of Moments Structures (AMOS) based on recommendations by Hair et al. (2022) and Baron and Kenny (1986).

The findings from this study revealed that trust significantly and positively mediate the relationship between access to microcredit and survival of young women microenterprises in under-developed financial markets in sub-Saharan Africa. Trust developed from relational social capital among young women from homogeneous and heterogeneous groups create a stronger basis for economic exchange in under-developed financial markets.

While this study generates a positive evidence on the impact of access to microcredit on survival of young women microenterprises, the results cannot be over emphasized and generalized because the data were collected from only a single developing country. Future research may extend the current study to include other developing countries to make a more justified comprehensive analysis.

The findings from this study highlights the importance of using a blend of social policy guided by norms combined with formal regulations as an informal contract enforcement mechanism to achieve efficient economic exchange in under-developed financial markets. Relational social capital formed on the basis of informal norms among groups from diverse population can supplement formal laws to enforce contractual obligations in microcredit access, especially among youthful microentrepreneurs, who seems to have stronger relational behaviors than adults. Financial institutions such as banks should use informal contract enforcement system to increase the scope of financial inclusion of young microentrepreneurs, especially in unbanked rural communities in sub-Saharan Africa, Uganda inclusive where formal laws are weak and sometimes not functional. The findings also show that younger people have a stronger relationship behavior than adults. Therefore, policy should create structures that can promote social activities among youth. Governments in sub-Saharan Africa, Uganda inclusive through their respective Ministry of Gender, Labour and Youth Affairs should create youth clubs that can increase interaction and relational social capital among the younger population to derive economic empowerment. sub-Saharan African governments, Uganda inclusive should rely more on social policy based on relational social capital as a missing link to promote and achieve economic development.

This paper provides an evidence on the unique role of age, gender and ethnicity in information sharing and exchange based on social policy in the financial market to limit group collusion. The authors indicate that diversity in relational social capital among young women microentrepreneurs prohibit strategic defaults, which promotes access to microcredit for survival of women micro small and medium enterprises (MSMEs) through socialization. High level of interaction among younger women microentrepreneurs from homogeneous and heterogeneous groups allow them to close the information gap to timely meet borrowing contractual obligations to derive economic benefits. The paper shows that younger women have more trust than older women while searching for economic value through socialization. In fact, social policy can wholly supplement formal policy to promote growth and survival of young women microenterprises, especially in sub-Saharan Africa, Uganda inclusive.

The purpose of this paper is to evaluate if, given personal, supply-related features, and labour demand-related variables, there is a difference in the share of women finding more stable jobs with respect to men, in an eight-year time span.

Fragmentation leads to a lower probability of transitioning into more certain, full-time work positions. The authors analyse a rich cohort of dependent workers in Emilia-Romagna to investigate whether part-time jobs lead to full-time jobs in a “stepping-stone” fashion and whether this happens with the same probability for men and women. The focus is on the cost of part-time jobs rather than the contrast between permanent and temporary jobs, as often observed in the literature. The authors also evaluate the transition between part-time job formulae and open-ended work arrangements to determine whether women's transition to full-fledged, stable work positions is slightly rarer than their male counterparts. Even if the authors allow for the fact that part-time contracts can be a choice and not an obligation, these contracts generate more flexibility in managing the equilibrium between private and work life and create more uncertainty than full-time contracts because of the fragmentation associated with these arrangements.

The authors find that women have a more fragmented working career than men, in that they hold more contracts than men in the same time span; moreover, the authors find that part-time jobs act more as bottlenecks for women than for men.

The authors use a large administrative dataset with over 600,000 workers observed in the 2008–2015 time span, in Emilia Romagna, Italy. The authors can disentangle the number of contracts per worker and observe individual, anonymise personal features, that the authors consider in the authors' propensity score estimate. The authors ran a robustness check of the PSM estimates through coarsened exact matching (CEM).

Accurate and rapid tracking and counting of building materials are crucial in managing on-site construction processes and evaluating their progress. Such processes are typically conducted by visual inspection, making them time-consuming and error prone. This paper aims to propose a video-based deep-learning approach to the automated detection and counting of building materials.

A framework for accurately counting building materials at indoor construction sites with low light levels was developed using state-of-the-art deep learning methods. An existing object-detection model, the You Only Look Once version 4 (YOLO v4) algorithm, was adapted to achieve rapid convergence and accurate detection of materials and site operatives. Then, DenseNet was deployed to recognise these objects. Finally, a material-counting module based on morphology operations and the Hough transform was applied to automatically count stacks of building materials.

The proposed approach was tested by counting site operatives and stacks of elevated floor tiles in video footage from a real indoor construction site. The proposed YOLO v4 object-detection system provided higher average accuracy within a shorter time than the traditional YOLO v4 approach.

The proposed framework makes it feasible to separately monitor stockpiled, installed and waste materials in low-light construction environments. The improved YOLO v4 detection method is superior to the current YOLO v4 approach and advances the existing object detection algorithm. This framework can potentially reduce the time required to track construction progress and count materials, thereby increasing the efficiency of work-in-progress evaluation. It also exhibits great potential for developing a more reliable system for monitoring construction materials and activities.

This paper aims to explore the impact of the data observation period on the computation of altmetric measures like velocity index (VI) and half-life. Furthermore, it also attempts to determine whether article-level computations are better than computations on the whole of the data for computing such measures.

The complete publication records for the year 2016 indexed in Web of Science and their altmetric data (original tweets) obtained from PlumX are obtained and analysed. The creation date of articles is taken from Crossref. Two time-dependent variables, namely, half-life and VI are computed. The altmetric measures are computed for all articles at different observation points, and by using whole group as well as article-level averaging.

The results show that use of longer observation period significantly changes the values of different altmetric measures computed. Furthermore, use of article-level delineation is advocated for computing different measures for a more accurate representation of the true values for the article distribution.

The analytical results show that using different observation periods change the measured values of the time-related altmetric measures. It is suggested that longer observation period should be used for appropriate measurement of altmetric measures. Furthermore, the use of article-level delineation for computing the measures is advocated as a more accurate method to capture the true values of such measures.

The research work suggests that altmetric mentions accrue for a longer period than the commonly believed short life span and therefore the altmetric measurements should not be limited to observation of early accrued data only.

The present study indicates that use of altmetric measures for research evaluation or other purposes should be based on data for a longer observation period and article-level delineation may be preferred. It contradicts the common belief that tweet accumulation about scholarly articles decay quickly.

Several studies have shown that altmetric data correlate well with citations and hence early altmetric counts can be used to predict future citations. Inspired by these findings, majority of such monitoring and measuring exercises have focused mainly on capturing immediate altmetric event data for articles just after the publication of the paper. This paper demonstrates the impact of the observation period and article-level aggregation on such computations and suggests to use a longer observation period and article-level delineation. To the best of the authors’ knowledge, this is the first such study of its kind and presents novel findings.