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The future construction site will be pervasive, context aware and embedded with intelligence. The purpose of this paper is to explore and define the concept of the digital skin of the future smart construction site.

The paper provides a systematic and hierarchical classification of 114 articles from both industry and academia on the digital skin concept and evaluates them. The hierarchical classification is based on application areas relevant to construction, such as augmented reality, building information model-based visualisation, labour tracking, supply chain tracking, safety management, mobile equipment tracking and schedule and progress monitoring. Evaluations of the research papers were conducted based on three pillars: validation of technological feasibility, onsite application and user acceptance testing.

Technologies learned about in the literature review enabled the envisaging of the pervasive construction site of the future. The paper presents scenarios for the future context-aware construction site, including the construction worker, construction procurement management and future real-time safety management systems.

Based on the gaps identified by the review in the body of knowledge and on a broader analysis of technology diffusion, the paper highlights the research challenges to be overcome in the advent of digital skin. The paper recommends that researchers follow a coherent process for smart technology design, development and implementation in order to achieve this vision for the construction industry.

Credit default swaps (CDSs) are among the most widely used credit derivatives since their innovation and designed to hedge the credit risk of reference entities. They were exposed after the global financial crisis of 2007–08, and were blamed for its occurrence. This paper aims to describe the fundamental mechanism of CDSs, demonstrating how a CDSs contract works. Further, this study explores the growth of the global and Indian CDS market by taking a holistic perspective.

An objective-driven descriptive research design is adopted to achieve a rigorous and accurate analysis of the study. Therefore, research papers from high-impact journals have been carefully reviewed to achieve the aim of the study.

The study shows that CDSs are still in their infancy in India. Banks are the primary market makers and users in the Indian CDSs market; therefore, regulatory authorities must assist them to boost the market. For banks to become more confident, they should gain experience and knowledge from other active CDSs markets around the world.

This study attempts to provide insights into the current state of the global as well as the Indian CDS market. Further, this study suggests approaches for the Indian banking sector to play an active role in the Indian CDSs market.

Optimization algorithms can differ in performance for a specific problem. Hybrid approaches, using this difference, might give a higher performance in many cases. This paper presents a hybrid approach of Genetic Algorithm (GA) and Ant Colony Optimization (ACO) specifically for the Integrated Process Planning and Scheduling (IPPS) problems. GA and ACO have given different performances in different cases of IPPS problems. In some cases, GA has outperformed, and so do ACO in other cases. This hybrid method can be constructed as (I) GA to improve ACO results or (II) ACO to improve GA results. Based on the performances of the algorithm pairs on the given problem scale. This proposed hybrid GA-ACO approach (hAG) runs both GA and ACO simultaneously, and the better performing one is selected as the primary algorithm in the hybrid approach. hAG also avoids convergence by resetting parameters which cause algorithms to converge local optimum points. Moreover, the algorithm can obtain more accurate solutions with avoidance strategy. The new hybrid optimization technique (hAG) merges a GA with a local search strategy based on the interior point method. The efficiency of hAG is demonstrated by solving a constrained multi-objective mathematical test-case. The benchmarking results of the experimental studies with AIS (Artificial Immune System), GA, and ACO indicate that the proposed model has outperformed other non-hybrid algorithms in different scenarios.

Mucorales has been described to be widely distributed during the most recent COVID-19 pandemic, with a greater frequency of disease in India, particularly among those with immune deficiencies. This study aims to use computational tools to develop a vaccine.

The authors investigated at Mucorales proteins that had previously been associated to virulence factors. Recent research suggests that a vaccine based on high-level cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL) and B-cell lymphocyte (BCL) epitopes from diverse proteins might be developed. Furthermore, the vaccine assembly contains the targeted epitopes as well as PADRE peptides to induce an immune response. Computational approaches were used to analyze the immunological parameters used to build the suggested vaccine and validate its TLR-3 binding.

These studies show that the vaccination is capable of triggering a particular immune response. The authors offer a technique for developing and evaluating candidate vaccines using computational tools. To the best of their knowledge, this is the first immunoinformatic research of a prospective mucormycosis vaccine.

During this audit, a successful attempt was made to create a subunit MEV against black fungus. In the current study, MEV has been proposed as a suitable neutralizer candidate since it is immunogenic, secure, stable and interacts with human receptors. A stream study, on the other hand, is produced via a mixed vaccinosis approach. Following that, vaccinologists may perform more exploratory testing to evaluate whether the vaccine is effective.

Ant Colony Optimization and Particle Swarm Optimization represent two widely used Swarm Intelligence (SI) optimization techniques. Information processing using Multiple-Valued Logic (MVL) is carried out using more than two discrete logic levels. In this paper, we compare two the SI-based algorithms in synthesizing MVL functions. A benchmark consisting of 50,000 randomly generated 2-variable 4-valued functions is used for assessing the performance of the algorithms using the benchmark. Simulation results show that the PSO outperforms the ACO technique in terms of the average number of product terms (PTs) needed. We also compare the results obtained using both ACO-MVL and PSO-MVL with those obtained using Espresso-MV logic minimizer. It is shown that on average, both of the SI-based techniques produced better results compared to those produced by Espresso-MV. We show that the SI-based techniques outperform the conventional direct-cover (DC) techniques in terms of the average number of product terms required.

This research presents machine learning models for predicting international tourist arrivals in Indonesia during the COVID-19 pandemic using multisource Internet data.

To develop the prediction models, this research utilizes multisource Internet data from TripAdvisor travel forum and Google Trends. Temporal factors, posts and comments, search queries index and previous tourist arrivals records are set as predictors. Four sets of predictors and three distinct data compositions were utilized for training the machine learning models, namely artificial neural networks (ANNs), support vector regression (SVR) and random forest (RF). To evaluate the models, this research uses three accuracy metrics, namely root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE).

Prediction models trained using multisource Internet data predictors have better accuracy than those trained using single-source Internet data or other predictors. In addition, using more training sets that cover the phenomenon of interest, such as COVID-19, will enhance the prediction model's learning process and accuracy. The experiments show that the RF models have better prediction accuracy than the ANN and SVR models.

First, this study pioneers the practice of a multisource Internet data approach in predicting tourist arrivals amid the unprecedented COVID-19 pandemic. Second, the use of multisource Internet data to improve prediction performance is validated with real empirical data. Finally, this is one of the few papers to provide perspectives on the current dynamics of Indonesia's tourism demand.

This study aims to examine the association between board gender diversity (BGD) and workplace diversity and the relative importance of various board and firm characteristics in predicting diversity.

With a novel machine learning (ML) approach, this study models the association between three workplace diversity variables and BGD using a social media data set of approximately 250,000 employee reviews. Using the tools of explainable artificial intelligence, the authors interpret the results of the ML model.

The results show that BGD has a strong positive association with the gender equality and inclusiveness dimensions of corporate diversity culture. However, BGD is found to have a weak negative association with age diversity in a company. Furthermore, the authors find that workplace diversity is an important predictor of firm value, indicating a possible channel on how BGD affects firm performance.

The effects of BGD on workplace diversity below management levels are mainly omitted in the current corporate governance literature. Furthermore, existing research has not considered different dimensions of this diversity and has mainly focused on its gender aspects. In this study, the authors address this research problem and examine how BGD affects different dimensions of diversity at the overall company level. This study reveals important associations and identifies key variables that should be included as a part of theoretical causal models in future research.

The paper aims to look into the implications of urban informality in Chris Abani's Graceland as represented in slum life and urban poverty as products of over urbanization and globalization, seeking to unravel multi-layers of the human side of the slum.

The paper examines slum life from a descriptive approach to highlight how people survive under poverty. The study of the culture of slums entails an analysis of the survival techniques and everyday practices of slum dwellers, the relations and patterns of behavior and the outcomes of the interplay between place, culture and power relations in such communities.

The urban slum dwellers utilize everyday forms of resistance which comprise a number of “low-profile techniques” to subvert state-imposed power structures and break the cycle of poverty.

Despite the relevance of a post-colonial approach to the texts, this paper is limited to the study of the impact of urban poverty on individuals.

The margin, represented in the urban poor, is brought into focus and perceived in a new light of empowerment which challenges alienating discourses.

The multidimensional vision of Nigeria in Abani's text highlights the cultural and economic impacts of multiculturalism, neocolonialism and globalization on the urban poor.

The paper formulates a framework for understanding the culture of the slum as a space of a peculiar nature, seeking to deconstruct a fixed view of slum life and poverty culture.

This study aims to examine the impact of external debt on economic growth in Bangladesh within a broader macroeconomic scenario.

In the process of doing so, it assesses the empirical cointegration, long-run and short-run dynamics of the concerned variables for the period of 1980–2017 applying the autoregressive distributed lag (ARDL) bounds testing approach to cointegration. First, debt-gross domestic product linkage explores the impact of external debt impact on economic growth using a set of macro and country risk variables, and then this linkage is also analyzed along with a newly formed macroeconomic policy (MEP) variable using principal component analysis.

The study results reveal the negative impact of external debt on GDP growth, but the larger positive impact of MEP index indicates that this adverse effect of debt can be mitigated or even nullified by sound MEP and appropriate human resource policy.

The dynamic effects of different shocks (external debt and macro policy variable) on economic growth by vector autoregression impulse response function also confirm our ARDL findings.

To interrogate the grand narrative of “entrepreneurship for development” that dominates international development circles, by applying a feminist critical discourse analysis that prioritizes women's situated experiences as local stories.

Two existing frameworks for analysing women's entrepreneurship, namely the 5M (Brush et al., 2009) and the 8M (Abuhussein and Koburtay, 2021) frameworks, are used to examine the local stories of women in rural Ethiopia to provide a counter-narrative to the grand narrative of “entrepreneurship for development”. The local stories are derived from 16 focus group discussions and 32 interviews.

The findings provide a counter-narrative to the grand narrative of “entrepreneurship for development”, evident in Ethiopia and in international development generally, while demonstrating larger structural issues at play. They challenge entrepreneurship's solely positive effects. While women recognize the benefits of having a business, particularly in terms of financial gains, empowerment and social recognition, they also highlight negative consequences, including uncertainty, concerns for their own personal safety, criticism, stress, limited social life and fear of indebtedness and poverty.

Policymakers, scholars and development professionals are urged to reflect on the limitations of “entrepreneurship for development” and to consider the negative effects that promoting an acritical grand narrative of entrepreneurship could have on women's lives.

The article advances an innovative partnership between feminist analysis and established women's entrepreneurship frameworks to contest dominant assumptions in the fields of entrepreneurship and international development studies. It adds to the limited empirical evidence on women's entrepreneurial activity in Ethiopia, tests the adequacy of the 5M and 8M frameworks in the rural low-income context of Ethiopia, and proposes a 7+M framework as an alternative to study rural women's entrepreneurship in low and middle income countries.