Mathematical Modeling Approach Research Articles

Quantitative Systems Pharmacology & Machine Learning - A match made in heaven or hell?

As pharmaceutical development moves from early stage in vitro experimentation to later in vivo and subsequent clinical trials, data and knowledge are acquired across multiple time and length scales, from the subcellular to whole patient cohort scale. Realising the potential of this data for informing decision making in pharmaceutical development requires the individual and combined application of machine learning (ML) and mechanistic multiscale mathematical modelling approaches. Here we outline how these two approaches, both individually and in tandem, can be applied at different stages of the drug discovery and development pipeline to inform decision making compound development. The importance of discerning between knowledge and data is highlighted in informing the initial use of ML or mechanistic Quantitative Systems Pharmacology (QSP) models. We discuss the application of sensitivity and structural identifiability analyses of QSP models in informing future experimental studies, to which ML may be applied, as well as how ML approaches can be used to inform mechanistic model development. Relevant literature studies are highlighted and we close by discussing caveats regarding the application of each approach in an age of constant data acquisition. Significance Statement We consider when best to apply Machine Learning (ML) and mechanistic Quantitative Systems Pharmacology (QSP) approaches in the context of the drug discovery and development pipeline. We discuss the importance of prior knowledge and data available for the system of interest and how this informs the individual and combined application of ML and QSP approaches at each stage of the pipeline.

Enhancing gas pipeline network efficiency through VIKOR method

The optimization of gas pipeline networks is critical for efficient and cost-effective transportation of natural gas. This study develops a mathematical model capable of analyzing different network configurations, including branched and branched-cyclic topologies, to explore the optimization of gas pipeline network conditions. The research provides valuable insights into the gas pipeline network optimization process, empowering industry stakeholders to make informed decisions and enhance performance in terms of efficiency, reliability, and cost-effectiveness. To attain these objectives, this study utilizes advanced simulation tools, state-of-the-art optimization algorithms, and sophisticated mathematical models that accurately represent the network's behavior. The optimization process aims to minimize the network's power requirements while simultaneously maximizing gas flow rate and optimizing line pack, ensuring optimal utilization of the pipeline infrastructure. The VIKOR (VIekriterijumsko KOmpromisno Rangiranje) method is identifying the most optimal network configuration and operating conditions. Our analysis applies this approach to three case studies, demonstrating its effectiveness in identifying the best network configurations. Additionally, the calculations of total cost and fuel consumption coincide with relative closeness, which confirms the accuracy of our proposed method, whereas optimal scenarios of the three cases have the minimum total cost among all scenarios. In conclusion, this research successfully develops a mathematical model and optimization approach to tackle the complexities of gas pipeline network optimization. The application of The VIKOR method and the analysis of case studies offer substantial evidence of its effectiveness.

Emerging trends and developments in green supplier management: A bibliometric review and analysis

Green supplier management (GSM) gained significant importance in addressing environmental concerns, promoting resource efficiency, and enhancing eco-efficiency within the green supply chain system. This study presents a systematic review to provide insights into the current research status and prospects in GSM literature. Results indicate that the research about GSM is gaining consistently growing attention over the past decades. However, there exists a regional imbalance in academic research, with a substantial portion of the authors originating from developing countries in China and India. The topics of green supplier selection and evaluation have received considerable attention in academia. In addition, the multi-attribute decision-making methods, such as TOPSIS, VIKOR, and AHP, and some mathematical modeling approaches have played a crucial role in the methodology employed for GSM research. As a fundamental algorithm in the artificial intelligence area, fuzzy sets theory has also been extensively employed in supplier selection and evaluation studies, whereas other big data analysis approaches have received little attention. Considering the inherent risks and uncertainties in the business strategy environment and developing more big data and artificial intelligence techniques represent promising avenues for future research in the field.

The potential cost-effectiveness of next generation influenza vaccines in England and Wales: A modelling analysis

Next generation influenza vaccines are in development and have the potential for widespread health and economic benefits. Determining the potential health and economic impact for these vaccines is needed to drive investment in bringing these vaccines to the market, and to inform which groups public health policies on influenza vaccination should target.We used a mathematical modelling approach to estimate the epidemiological impact and cost-effectiveness of next generation influenza vaccines in England and Wales. We used data from an existing fitted model, and evaluated new vaccines with different characteristics ranging from improved vaccines with increased efficacy duration and breadth of protection, to universal vaccines, defined in line with the World Health Organisation (WHO) Preferred Product Characteristics (PPC). We calculated the cost effectiveness of new vaccines in comparison to the current seasonal vaccination programme. We calculated and compared the Incremental Cost-Effectiveness Ratio and Incremental Net Monetary Benefit for each new vaccine type. All analysis was conducted in R.We show that next generation influenza vaccines may result in a 21% to 77% reduction in influenza infections, dependent on vaccine characteristics. Our economic modelling shows that using any of these next generation vaccines at 2019 coverage levels would be highly cost-effective at a willingness to pay threshold of £20,000 for a range of vaccine prices. The vaccine threshold price for the best next generation vaccines in £-2019 is £230 (95%CrI £192 - £269) per dose, but even minimally-improved influenza vaccines could be priced at £18 (95%CrI £16 - £21) per dose and still remain cost-effective.This evaluation demonstrates the promise of next generation influenza vaccines for impact on influenza epidemics, and likely cost-effectiveness profiles. We have provided evidence towards a full value of vaccines assessment which bolsters the investment case for development and roll-out of next-generation influenza vaccines.

Bat teeth illuminate the diversification of mammalian tooth classes

Tooth classes are an innovation that has contributed to the evolutionary success of mammals. However, our understanding of the mechanisms by which tooth classes diversified remain limited. We use the evolutionary radiation of noctilionoid bats to show how the tooth developmental program evolved during the adaptation to new diet types. Combining morphological, developmental and mathematical modeling approaches, we demonstrate that tooth classes develop through independent developmental cascades that deviate from classical models. We show that the diversification of tooth number and size is driven by jaw growth rate modulation, explaining the rapid gain/loss of teeth in this clade. Finally, we mathematically model the successive appearance of tooth buds, supporting the hypothesis that growth acts as a key driver of the evolution of tooth number and size. Our work reveal how growth, by tinkering with reaction/diffusion processes, drives the diversification of tooth classes and other repeated structure during adaptive radiations.

Analyzing the influence of differential equations in mathematical modeling approaches for Covid-19: A bibliometric perspective

Objective. This study aims to provide a comprehensive analysis of global publications on differential equations in mathematical modeling approaches for understanding and combating the COVID-19 pandemic. Design/Methodology/Approach. The study analyses a dataset of 964 documents from 353 sources from 2020 – 2023. Various parameters, such as publication growth rate, citation impact, collaboration patterns, document types, and distribution of citations, are examined. The analysis utilizes tables and figures to present the findings effectively. Results/Discussion. The analysis reveals a decline in publication output over the years, indicated by a negative annual growth rate. However, the dataset remains comprehensive and contributes valuable insights to the field. The publications have made significant contributions, evidenced by the average citations per document and the extensive reference list. Collaboration among authors is prevalent, with a substantial portion of co-authorships being international. The study identifies prominent papers with high citation counts, emphasizing their influence and recognition within the academic community. Conclusion. The findings highlight the need for continued research efforts and advancements in differential equations in mathematical modeling approaches for COVID-19. The study emphasizes the importance of maintaining a robust scientific impact and contributing effectively to the ongoing fight against the pandemic. It underscores the significance of collaboration and highlights countries and institutions with notable productivity and impact in the field. Originality/Value. This study provides a comprehensive analysis of global publications on the role of differential equations in mathematical modeling for COVID-19. It presents novel insights into publication trends, citation impact, collaboration patterns, and distribution of citations. The findings contribute to understanding the research landscape and offer valuable information for researchers and practitioners seeking to advance the field and combat the COVID-19 pandemic effectively.

Mathematical models of cystic fibrosis as a systemic disease.

Cystic fibrosis (CF) is widely known as a disease of the lung, even though it is in truth a systemic disease, whose symptoms typically manifest in gastrointestinal dysfunction first. CF ultimately impairs not only the pancreas and intestine but also the lungs, gonads, liver, kidneys, bones, and the cardiovascular system. It is caused by one of several mutations in the gene of the epithelial ion channel protein CFTR. Intense research and improved antimicrobial treatments during the past eight decades have steadily increased the predicted life expectancy of a person with CF (pwCF) from a few weeks to over 50 years. Moreover, several drugs ameliorating the sequelae of the disease have become available in recent years, and notable treatments of the root cause of the disease have recently generated substantial improvements in health for some but not all pwCF. Yet, numerous fundamental questions remain unanswered. Complicating CF, for instance in the lung, is the fact that the associated insufficient chloride secretion typically perturbs the electrochemical balance across epithelia and, in the airways, leads to the accumulation of thick, viscous mucus and mucus plaques that cannot be cleared effectively and provide a rich breeding ground for a spectrum of bacterial and fungal communities. The subsequent infections often become chronic and respond poorly to antibiotic treatments, with outcomes sometimes only weakly correlated with the drug susceptibility of the target pathogen. Furthermore, in contrast to rapidly resolved acute infections with a single target pathogen, chronic infections commonly involve multi-species bacterial communities, called "infection microbiomes," that develop their own ecological and evolutionary dynamics. It is presently impossible to devise mathematical models of CF in its entirety, but it is feasible to design models for many of the distinct drivers of the disease. Building upon these growing yet isolated modeling efforts, we discuss in the following the feasibility of a multi-scale modeling framework, known as template-and-anchor modeling, that allows the gradual integration of refined sub-models with different granularity. The article first reviews the most important biomedical aspects of CF and subsequently describes mathematical modeling approaches that already exist or have the potential to deepen our understanding of the multitude aspects of the disease and their interrelationships. The conceptual ideas behind the approaches proposed here do not only pertain to CF but are translatable to other systemic diseases. This article is categorized under: Congenital Diseases > Computational Models.

Design of Reconfigurable Periodic Structures Based on Machine Learning

The design of periodic structures is a very time-consuming process because it requires many cycles of full-wave simulations to obtain the desired electromagnetic (EM) properties. This challenge is amplified when designing reconfigurable periodic structures. This article proposes an intelligent method for the reconfigurable periodic structure design (RPSD) based on machine learning. First, an improved physical and mathematical modeling approach is proposed for reconfigurable periodic structures, which enables the application of machine learning in RPSD. Thereafter, we set up distributed predictors for different reconfigurable states to avoid the one-to-many mapping problem. In addition, the networks of these predictors are trained using variational autoencoders (VAEs) to reduce the associated learning difficulties. These enable the obtention of accurate predictors with small sample data. Moreover, the training of these predictors can be easily accelerated by parallel computation because of their independence. Finally, reconfigurable periodic structures can be intelligently designed by the combination of the trained predictors and a stochastic optimization technique. Some examples are provided to verify the practicality and accuracy of the proposed method.

Cross-Efficiency Evaluation Framework for Assessing the Sustainability of Agriculture Farms with Biogas Plants: Mathematical Modeling and Solution Approach

Cross-Efficiency Evaluation Framework for Assessing the Sustainability of Agriculture Farms with Biogas Plants: Mathematical Modeling and Solution Approach

A Bilevel Model for Robust Network Design and Biomass Pricing Under Farmers’ Risk Attitudes and Supply Uncertainty

This paper addresses an integrated biomass pricing and logistics network design problem. A bilevel design and pricing model is proposed to capture the dynamic decision process between a biofuel producer as a Stackelberg leader and farmers as Stackelberg followers. The bilevel optimization model is transformed into a tractable single-level formulation by using optimality constraints. Other unique characteristics of our problem at hand include the incorporation of the harvesting time and frequency decisions in the biomass supply chain network design problem for the first time and consideration of the uncertainty in switchgrass yield in a robust optimization setting to take into account the risk-averse behavior of the farmers (suppliers). To efficiently solve the model, we propose a Benders decomposition algorithm enhanced by surrogate constraints, strengthened Benders cuts, and in-out cut loop stabilization. The numerical experiments show that the proposed algorithm is significantly superior to the branch-and-cut approach of CPLEX in terms of run times and gaps. We conduct a case study with data from Texas to validate the capabilities of our mathematical model and solution approach. Based on extensive experiments, the benefits of modeling are analyzed, and significant insights are explored. Funding: This research was partially supported by the National Natural Science Foundation of China [Grants 71771135, 72171129]; and the scholarship from China Scholarship Council (CSC) [Grant CSC 201906210092]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2021.0357 .

Industry Linked Curriculum Enrichment System Towards the Improvement of Employability

Abstract: Enrichment of curriculum is most significant aspect for any academic program. It is essential to design curriculum on the basis of requirement towards particular aim of study. In this article the concept of curriculum enrichment targeted towards engineering study is elaborated. A computational systemic approach is designed which is capable of calculating the amount of curriculum/syllabus modification for any program. The amount of regulation wise curriculum/syllabus modification obtained in percentage manner. Mainly, the application study is depicted on engineering case applying the proposed methodology. In addition, this paper proposed one mathematical model approach for indication towards employability generation in our society. Considering engineering education this modeling approach is presented. Overall, curriculum enrichment system focusing on employability generation is specific contribution of this research. Keywords: Engineering Education, Curriculum Enrichment, Modification Percentage Calculation, Curriculum Employability Mapping

Robust & nonlinear control of an ultra-supercritical coal fired once-through boiler-turbine unit in order to optimize the uncertain problem

The ultra-supercritical once-through boiler (OTB) unit is an advanced power generation technology with high plant efficiency and low emissions. However, it is difficult to realize a coordinate control for the ultra-supercritical OTB unit to achieve the fast and stable dynamic response during the load tracking and grid frequency disturbances and in the presence of unavoidable uncertainties. In this paper, an accurate grey box multivariable coupled nonlinear model of an ultra-supercritical boiler-turbine unit is considered. Steam pressure at throttle valve, specific enthalpy in separator and active power are adjusted at desired values by manipulation of the fuel rate command, feedwater rate command and throttle valve opening. In addition to the mentioned accurate model, limitations of operational parameters and uncertainty sources are considered in the controller design. This makes this research unique from previous works. According to affine nonlinear control theory, a nonlinear robust sliding mode controller is developed. Then using MATLAB μ - DK iteration syntax, a linear H-infinity (H∞) robust control is synthesized with selecting proper weight functions. This method is suitable for all types of power plants with a similar mathematical modeling approach. Simulations in MATLAB Simulink toolbox reveals that both designed controllers achieve appropriate performance in the presence of uncertainties and parameters’ limitations, but nonlinear controller has better performance. Finally, deficiencies of a linear controller with respect to the proposed designed robust controllers are denoted.

OPTIMIZATION OF THE DYNAMIC PARAMETERS OF AN OBJECT IN A MATHEMATICAL MODEL OF SEISMO-ACOUSTIC MONITORING OF NATURAL AND ENGINEERING OBJECTS

The article is devoted to the mathematical modeling approach of seismo-acoustic monitoring of objects and structures of sizes whose own frequencies and emission signals are from seismic and the bottom part of acoustic ranges. The structural analysis and identification of dynamic parameters of such structures are extremely important in their monitoring to predict essential changes in dynamic characteristics. Moreover, the method of dynamic identification provides an opportunity to investigate the dynamic behavior of the given structure or source of signal origin by researching investigated objects' to study informative characteristics. The article enters methodology for identifying the main structural parameters, such as main intrinsic frequencies, and the decrement in these frequencies. Particular interest represents seismo-acoustic monitoring of objects with sources of emission signals whose parameters are subject to definition and are characteristic of structure or signal origin. Emissions can carry both irregular and regular character. The latter case can be modeled as a flow with the likelihood characteristics and subject definitions. The result of the analysis of the data received in seismo-acustic monitoring of such signals is reduced to an estimation of parameters of an emission signal, which fluctuates from a signal to a signal. A practical method for analyzing natural and man-made objects, whose natural frequencies lie in the seismic and lower part of the acoustic frequency ranges, based on monitoring their dynamics, is proposed. A new approach is proposed for identifying the state of such objects. A non-traditional model of the natural background of the monitored object is proposed in the form of a superposition of Berlage impulses. Such a model makes it possible to estimate such an essential parameter in the description of an object as its quality factor, the dynamics of which can give an idea of its structural changes. To predict the behavior of natural and engineering objects to prevent undesirable consequences of the behavior of the object under study, seismo-acoustic monitoring systems are used. The mathematical model and algorithm proposed by the authors can be integrated into the system of seismo-acoustic monitoring of natural and man-made objects.

A Multi-Layer blood supply chain configuration and optimization under uncertainty in COVID-19 pandemic

Blood is a rare and perishable substance, and a lack of inventory would cause the loss of lives. However, excessive donation or inappropriate management will cause blood waste and raise costs. In order to properly manage this valuable substance and prevent the shortage, it is necessary to integrate the various considerations into the supply chain and evaluate all the layers simultaneously. Moreover, uncertain conditions might exist in multiple components of a blood supply chain, such as blood donation and demand, as well as under human catastrophes, where the blood demand rises. In this paper, a multi-level blood supply chain network was designed under uncertain conditions, and disruption in COVID-19 was also considered. The proposed model tackled humanitarian factors such as selecting the most suitable locations and minimizing the amount of spoiled blood in the chain to cover all patients under COVID-19. The model was solved by elastic boundary objectives and modified weighted Chebyshev. After evaluating the suggested solution techniques, the TOPSIS algorithm was utilized to select the best approach. Then, the mathematical model and solution approach were validated in a real case study of the COVID-19 pandemic. Finally, several sensitivity analyses were performed to investigate the reaction of objective functions to changes in model parameters, and relevant insights were derived. The results showed that adding mobile blood facilities would mitigate the delivery time.

Astrocytic clearance and fragmentation of toxic proteins in Alzheimer’s disease on large-scale brain networks

The human brain is the most complicated biological structure on the planet. A major challenge of brain network modelling lies in its multi-scale spatio-temporal nature, covering scales from synapses to the whole brain. The coupled multiphysics and biochemical activities which spread through such a complex system shape brain capacity inside a structure-function relationship that requires a particular mathematical framework. Next-generation coupled-based mathematical modelling approaches to brain networks and the analysis of data-driven dynamical systems are needed to advance state-of-the-art therapeutic strategies for treating neurodegenerative diseases (NDDs) that affect millions of people worldwide, such as Alzheimer’s disease (AD) and Parkinson’s disease (PD). Importantly, AD is marked by the presence of amyloid-beta (Aβ) plaques and tau (τ) proteins. Some disease-specific misfolded proteins can interact with healthy proteins to form long chains and aggregates of different sizes that have different transport properties and toxicity. An improved large-scale brain network model is proposed here to understand the pathogenesis of AD, especially the role of astrocytes in the presence of misfolded proteins (Aβ and τ). The idea involves astrocytic clearance, which assists in eliminating toxic Aβ via fragmentation. We use the general Smoluchowski theory of nucleation, aggregation, and fragmentation to predict the development and propagation of aggregates of misfolded proteins in the brain. It has been shown that the developed model leads to different size distributions and propagation along the network. We predicted that astrocytic clearance varies with the aggregate size, which is key to slowing down AD progression. The clearance and fragmentation of toxic proteins span several spatial and temporal scales, and this research will potentially yield new insight into the associated processes and brain networks in health and disease. Detailed multi-scale brain modelling provides a promising approach for consolidating, organizing, and bridging the data sets of data-driven brain network models.

Can insecticide resistance increase malaria transmission? A genetics-epidemiology mathematical modeling approach.

The great successes recorded in the fight against malaria over the last two decades, resulting from the wide scale implementation of insecticide-based interventions in malaria-endemic areas, has prompted a renewed global effort to eradicate malaria. The widespread emergence of insecticide resistance in the population of adult female malaria mosquitoes is considered to pose a potential challenge to such effort. In this study, we address one of the key questions in malaria ecology, namely whether or not insecticide resistance increase malaria transmission. We developed a genetics-epidemiology modeling framework that incorporates a detailed genotype structure of the gene that confers insecticide resistance in mosquitoes, malaria epidemiology in mosquitoes and humans (stratified based on whether or not they are protected by Long-lasting insecticide-treated nets (LLINs) indoors), genotype-specific mosquito repellance property of LLINs and mosquito biting behavior (indoor and outdoor bites). Conditions for the existence and local asymptotic stability of the various disease-free equilibria (by genotype) of the resulting genetic-epidemiology model are derived. This study identifies four parameters of the model that play a crucial role on quantifying the impact of insecticide resistance on malaria transmission, namely the parameters related to the level of the dominance of the resistant allele in heterozygous mosquitoes, the coverage of long-lasting insecticidal nets in the community, the probability of endophilic mosquitoes to successfully take a bloodmeal indoors and the proportion of new adult mosquitoes that are endophilic. We showed that, depending on the values of these four identified parameters, insecticide resistance can increase, decrease, or have no effect on malaria transmission. Our simulations show that malaria eradication can indeed be achieved using the currently-available chemical insecticides, even in the wake of the prevailing widespread insecticide resistance in malaria-endemic areas, if the insecticide-based interventions implemented can result in the attainment of the optimal values of the four identified parameters in malaria-endemic areas.

Mathematical analysis of monkeypox infection with optimal control analysis: A case study with a new outbreak in the United States

Monkeypox infection is a serious illness to human health, and its outbreak has been reported in many non‐endemic regions. Various approaches have been implemented to explore the epidemiology, transmission patterns, and effective control of monkeypox. In this paper, we focus on the implementation of a mathematical modeling approach to study the dynamics of monkeypox infection with a case study of the United States, the most impacted country in 2022. The proposed mathematical model is initially constructed using a nonlinear system of differential equations with constant control measures. The human population is divided into four subgroups while the animal (non‐human) population is divided into three subclasses. An extensive theoretical analysis of the proposed monkeypox model is presented including the stability of equilibria. Further, to make the present study more visible to the literature, a real data set of the cumulative confirmed cases of monkeypox in the United States from May to October 2022 is used to estimate the proposed model parameters. Some of the demographic parameters are estimated from the population of the United States. The most important biological parameter of the problem is investigated theoretically as well as numerically based on the reported cases. The impact of various model parameters on the dynamics of state variables and is shown graphically. Furthermore, we analyzed the impact of various model parameters on the basic reproduction number to explore the most influential intervention strategy to curb the infection. The most crucial components of the proposed system are identified and health officials are advised. Finally, using optimization theory, the most effective optimized preventive strategies are suggested to curtail the infection in a community. We believe that the present investigation will be helpful in understanding the dynamics and prevention of monkeypox infection.

Mathematical and Computer Modeling as a Novel Approach for the Accelerated Development of New Inhalation and Intranasal Drug Delivery Systems

This paper presents modern methods of mathematical modeling, which are widely used in the development of new inhalation and intranasal drugs, including those necessary for the treatment of socially significant diseases, which include: tuberculosis, bronchial asthma, and mental and behavioral disorders. Based on the conducted studies, it was revealed that the methods of mathematical modeling used in the development of drugs are fragmented, and there is no single approach that would combine the existing methods. The results presented in the work should contribute to the development of a unified multiscale model as a new approach in mathematical modeling that contributes to the accelerated development and introduction to the market of new drugs with high bioavailability and the required therapeutic efficacy.

Optimisation of the operating parameters of a thermal plasma system for the conversion of waste containing polychlorinated biphenyls by thermodynamic modelling

This paper proposes a mathematical modelling approach to determine the thermodynamic equilibrium of chemical systems in order to optimise the operating parameters of the plasma chemical processes in a low-temperature thermal plasma pilot reactor with a direct current electric arc for the treatment of hazardous waste containing polychlorinated biphenyls. The model is primarily based on the principles of mass action and minimum Gibbs free energy. The obtained results can serve as a basis for multi-criteria optimisation based on ecological aspects, reduction of energy consumption and the possibility of using the produced synthetic gas for energy recovery in the process. The analysis carried out clearly showed the advantages of using water vapour as a plasma working medium and the appropriate mixing of the organic waste containing polychlorinated biphenyls with used engine oil for enriched syngas production. The presented analysis determines the optimal reactor temperature for an ecologically efficient treatment and shows possibilities for energy recovery in the process. The proposed model can also be used for other chemical reactors and for the treatment of other chemical substances.

Examining the Influence of Sustainable Construction Supply Chain Drivers on Sustainable Building Projects Using Mathematical Structural Equation Modeling Approach

This study focuses on the results of examining the impact of Sustainable Construction Supply Chains (SCSC) on sustainable success (OSS) throughout the life of a project in developing countries. While previous research has explored the challenges of implementing SCSC in these regions, limited attention has been given to the overall impact on sustainable success. To address this gap, a conceptual model was developed based on an extensive literature review, and data were collected through a survey involving 70 building professionals in Egypt. The findings indicate that the adoption of SCSC drivers has a significant influence on OSS during the construction phase, ranging from moderate to high. These results provide valuable insights for policymaking in developing nations, as they highlight the importance of overcoming barriers to SCSC adoption and promoting these drivers to ensure successful project completion. Ultimately, implementing SCSC approaches will contribute to improved project outcomes in the construction industry.