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This manuscript is related to compute $N$-kink soliton solutions for conformable Fisher–Kolmogorov equation (CFKE) by using the generalized extended direct algebraic method (EDAM). The considered problem has important applications in mathematical biology and reaction diffusion processes. Also, the mentioned problem has significant applications in population dynamics. The fractional order conformable derivative has many features as compared to the other fractional order differential operators. For instance, the chain, product and quotient procedures do not satisfy by other fractional differential operators, but conformable operators obey the mentioned rules. Hence, we compute the soliton solutions for the mentioned problem and present its various dynamical behaviours graphically.

The generalized EDAM is used in this article to examine the calculation of N-kink soliton solutions for the CFKE. In mathematical biology and reaction-diffusion processes, the topic under consideration holds great significance, especially when considering population dynamics.

The results highlight the benefits of utilising conformable derivatives in mathematical modelling and further our understanding of fractional differential equations and their applications.

The work focuses primarily on N-kink soliton solutions, which may limit the examination of alternative types of solutions (e.g., multi-soliton or periodic solutions) that might give new insights into the dynamics of the CFKE.

The generated N N-kink soliton solutions can enhance mathematical models in biological contexts, notably in modelling population dynamics, disease propagation and ecological interactions, leading to better forecasts and interventions.

Public health initiatives can benefit from the understanding of disease transmission and intervention efficacy that comes from modelling population dynamics and reaction-diffusion processes.

The use of the generalized EDAM to obtain solutions for N-kink soliton problems is an innovative method for solving the conformable Fisher–Kolmogorov equation, demonstrating the power of this mathematical tool.

In this paper, we address the need to study automatic propaganda detection to establish a course of action when faced with such a complex task. Although many isolated tasks have been proposed, a roadmap on how to best approach a new task from the perspective of text formality or the leverage of existing resources has not been explored yet.

We present a comprehensive study using several datasets on textual propaganda and different techniques to tackle it. We explore diverse collections with varied characteristics and analyze methodologies, from classic machine learning algorithms, to multi-task learning to utilize the available data in such models.

Our results show that transformer-based approaches are the best option with high-quality collections, and emotionally enriched inputs improve the results for Twitter content. Additionally, MTL achieves the best results in two of the five scenarios we analyzed. Notably, in one of the scenarios, the model achieves an F1 score of 0.78, significantly surpassing the transformer baseline model’s F1 score of 0.68.

After finding a positive impact when leveraging propaganda’s emotional content, we propose further research into exploiting other complex dimensions, such as moral issues or logical reasoning.

Based on our findings, we provide a roadmap for tackling propaganda-related tasks, depending on the types of training data available and the task to solve. This includes the application of MTL, which has yet to be fully exploited in propaganda detection.

In smart cities striving for innovation, development, and prosperity, hydrogen offers a promising path for decarbonization. However, its effective integration into the evolving energy landscape requires understanding regional intricacies and identifying areas for improvement. This chapter examines hydrogen transport from production to utilization, evaluating technologies’ pros, cons, and process equations and using Analytic Hierarchy Process (AHP) as a Multi-Criteria Decision-Making (MCDM) tool to assess these technologies based on multiple criteria. It also explores barriers and opportunities in hydrogen transport within the 21st-century energy transition, providing insights for overcoming challenges. Evaluation criteria for hydrogen transport technologies were ranked by relative importance, with energy efficiency topping the list, followed by energy density, infrastructure requirements, cost, range, and flexibility. Safety, technological maturity, scalability, and compatibility with existing infrastructure received lower weights. Hydrogen transport technologies were categorized into three performance levels: low, medium, and high. Hydrogen tube trailers ranked lowest, while chemical hydrides, hydrail, liquid organic hydrogen carriers, hydrogen pipelines, and hydrogen blending exhibited moderate performance. Compressed hydrogen gas, liquid hydrogen, ammonia carriers, and hydrogen fueling stations demonstrated the highest performance. The proposed framework is crucial for next-gen smart cities, cutting emissions, boosting growth, and speeding up development with a strong hydrogen infrastructure. This makes the region a sustainable tech leader, improving air quality and well-being. Aligned with Gulf Region goals, it is key for smart cities. Policymakers, industries, and researchers can use these insights to overcome barriers and seize hydrogen transport tech opportunities.

This study aims to investigate the complexity factors associated with BIM-enabled projects. BIM has been widely promoted as a potential solution to numerous challenges that hinder productivity in construction projects, owing to its numerous advantages. Nevertheless, it is crucial to acknowledge the heightened complexity it introduces to project workflows, stakeholder coordination and information management.

This study employs the Delphi method to identify and extract complexity factors specific to BIM-enabled projects. A panel of industry and academic experts is engaged to discern and prioritise these factors based on their expertise and knowledge.

The study reveals a comprehensive list of 34 complexity factors that significantly impact BIM-enabled projects. Among the most influential factors are laws and regulations, variety of procurement methods, technical capabilities of teams, project manager competence, information transfer capacity, range of project deliverables and diversity of project locations. The findings highlight the importance of these factors and emphasise the need for proactive and adaptive management to navigate their impact and achieve positive project outcomes.

This study introduces the DEBACCS framework, a metric-based model designed to understand and evaluate complexity within BIM-enabled projects. DEBACCS stands for seven key dimensions: diversity, emergence, belonging, autonomy, connectivity, context and size. These dimensions represent essential aspects for gauging project complexity. By applying the concept of complexity from project management to BIM, the study offers valuable insights for practitioners and researchers. It provides a unique perspective on the challenges and considerations associated with implementing and managing BIM in construction projects. The findings have practical value for practitioners, enabling them to better understand and address the implications of complexity in BIM-enabled projects, ultimately leading to improved project outcomes.

This paper proposes a two-level supply chain including suppliers and manufacturers. The purpose of this paper is to design a resilient fuzzy risk-averse supply portfolio selection approach with lead-time sensitive manufacturers under partial and complete supply facility disruption in addition to the operational risk of imprecise demand to minimize the mean-risk costs. This problem is analyzed for a risk-averse decision maker, and the authors use the conditional value-at-risk (CVaR) as a risk measure, which has particular applications in financial engineering.

The methodology of the current research includes two phases of conceptual model and mathematical model. In the conceptual model phase, a new supply portfolio selection problem is presented under disruption and operational risks for lead-time sensitive manufacturers and considers resilience strategies for risk-averse decision makers. In the mathematical model phase, the stages of risk-averse two-stage fuzzy-stochastic programming model are formulated according to the above conceptual model, which minimizes the mean-CVaR costs.

In this paper, several computational experiments were conducted with sensitivity analysis by GAMS (General algebraic modeling system) software to determine the efficiency and significance of the developed model. Results show that the sensitivity of manufacturers to the lead time as well as the occurrence of disruption and operational risks, significantly affect the structure of the supply portfolio selection; hence, manufacturers should be taken into account in the design of this problem.

The study proposes a new two-stage fuzzy-stochastic scenario-based mathematical programming model for the resilient supply portfolio selection for risk-averse decision-makers under disruption and operational risks. This model assumes that the manufacturers are sensitive to lead time, so the demand of manufacturers depends on the suppliers who provide them with services. To manage risks, this model also considers proactive (supplier fortification, pre-positioned emergency inventory) and reactive (revision of allocation decisions) resilience strategies.

Nowadays, the topic of supply chain network design has gained significant attention as a critical area within the field of supply chain management. Researchers have dedicated their studies to exploring market competitiveness and pursuing greater benefits. This paper presents a multi-objective mathematical model aimed at designing a competitive and sustainable closed-loop supply chain network specifically for perishable products. The proposed model takes into account the competition between two supply chains, considering various factors such as environmental impacts, social performance and resilience.

To address the competitive nature of this model, this study introduces a two-stage approach. In the initial stage, this study uses a game theory approach to determine equilibrium values for competitive decisions. This allows establishing a fair and balanced playing field between the two supply chains. Subsequently, due to the complexity of the proposed model, this study uses a multi-objective metaheuristic algorithm based on the Non-dominated Sorting Genetic Algorithm. This algorithm serves as a robust computational tool to effectively solve the network design problem and optimize the desired objectives.

To evaluate the effectiveness of the proposed model and solution approach, this study provides a numerical example. Through graphical and statistical analysis, this study assesses the performance of the model and its ability to achieve the desired objectives. The results and charts obtained demonstrate the strong performance and efficacy of the proposed model in effectively addressing the challenges associated with designing a competitive and sustainable closed-loop supply chain network for perishable products.

By considering the various factors at play and incorporating them into the model, this study aims to contribute to the existing body of knowledge in supply chain management. The findings provide valuable insights into designing supply chain networks that not only enhance competitiveness but also promote sustainability.

This study represents one of the initial investigations into the evaluation of competitive supply chains, taking into account crucial factors such as environmental, social and resilience. A supply chain that considers these factors can attain greater durability and competitiveness compared to other supply chains.

Nucleic acid testing is an effective method of accurate prevention and control and a key measure to block the spread of the epidemic. However, the fraud in nucleic acid testing occurred frequently during epidemics. This paper aims to provide a viable scheme for the government to strengthen the supervision of nucleic acid testing and to provide a new condition for the punishment for the negative act of the government and the upper limit of the reward for nucleic acid testing institution of no data fraud.

This paper formulates an evolutionary game model between the government and nucleic acid testing institution under four different mechanisms of reward and punishment to solve the issue of nucleic acid testing supervision. The authors discuss the stability of equilibrium points under the four distinct strategies and conduct simulation experiments.

The authors find that the strategy of dynamic reward and static penalty outperforms the strategies of static reward and static penalty, dynamic reward and static penalty, static reward and dynamic penalty, dynamic reward and dynamic penalty. The results reveal the appropriate punishment for the negative act of the government can enhance the positivity of the government's supervision in the strategy of dynamic reward and static penalty, while the upper limit of the reward for nucleic acid testing institution of no data fraud cannot be too high. Otherwise, it will backfire. Another interesting and counterintuitive result is that in the strategy of dynamic reward and dynamic penalty, the upper limit of the penalty for data fraud of nucleic acid testing institution cannot be augmented recklessly. Otherwise, it will diminish the government's positivity for supervision.

Most of the existing evolutionary game researches related to the reward and punishment mechanism and data fraud merely highlight that increasing the intensity of reward and punishment can help improve the government's supervision initiative and can minimize data fraud of nucleic acid institution, but they fall short of the boundary conditions for the punishment and reward mechanism. Previous literature only study the supervision of nucleic acid testing qualitatively and lacks quantitative research. Moreover, they do not depict the problem scenario of testing data fraud of nucleic acid institution regulated by the government via the evolutionary game model. Thus, this study effectively bridges these gaps. This research is universal and can be extended to other industries.

Family startups are essential to a nation’s development because they foster creativity, economic expansion, and job creation. They introduce novel concepts, game-changing technologies, and innovative business approaches to the market. Family startups also help a country be more competitive abroad by luring capital, encouraging entrepreneurship, and diversifying the economy beyond its core industries. Additionally, family startups operate as agents of social change by addressing societal problems and providing answers that benefit communities. Therefore, fostering a thriving start-up ecosystem is crucial for a nation’s long-term sustainable development and prosperity. Against this background, this book chapter examines the potential and difficulties that family startups in Oman’s developing entrepreneurial environment confront. The chapter explores the challenges faced by entrepreneurs as well as the opportunities for growth and innovation in this dynamic market to illuminate the distinctive dynamics of the Omani start-up scene.

There is an urgent call for transitioning towards a circular economy (CE) in housing. Pivotal to this transition is implementing business models aligned with CE principles, such as the ones informed by the product-service system (PSS). However, incorporating the PSS into housing to realize a CE faces significant challenges within an industry characterized by systemic rigidity and institutional inertia. This study investigates the barriers faced in deploying the PSS and its CE potential in housing.

The authors conducted 15 semi-structured interviews with stakeholders experienced in the deployment of PSS and CE in housing projects. Analysis used deductive coding, guided by institutional theory’s regulative, normative and cultural-cognitive pillars, followed by inductive coding development.

Twelve key barriers emerged across three pillars, underlying the significance of not only regulative but also normative and cultural-cognitive barriers. The findings indicate that the current institutional environment impedes the establishment of legitimacy for the deployment of PSS and its CE potential in housing.

Following the findings, a diversified institutional support system enabled by the collaborative effort of the government, managing and financing actors and industry associations is required to overcome deployment barriers.

This study advances knowledge at the intersection of housing and circular business model innovation. It connects theory to practice by applying institutional theory to real-world barriers in deploying the PSS for a CE in housing and lays the groundwork for practical changes.

The appropriate physical layout of hospital services can help resolve management problems by streamlining the work of medical teams, improving the flow of patients between specific areas and the medical support environment. Nevertheless, the academic literature lacks structured research into how the physical layout of hospitals might be improved. Our study aims to fill this research gap, providing information for researchers and professionals who intend to guide the hospital facility layout planning (HFLP) from the steps and prescribed approaches found in the literature.

This study analyzes the current literature status and concerning approaches that support HFLP and identifies their strengths and weaknesses. The literature was classified using the following criteria: approaches for layout generation, approaches for layout evaluation and healthcare facility layout outcomes.

The hospital facility layout outcomes achieved for each phase served as a basis for identifying a list of strengths and weaknesses for the hospital layout facility generation and evaluation approaches. Readers can refer to this paper to identify the approach that best fits the desired goal and the HFLP step.

This is a contribution to current studies into HFLP, and it provides guidelines for selecting the approach to be utilized based on the desired outcome.

The paper describes how to conduct an HFLP and lists the strengths and weaknesses of each approach. The research may be used as a strategy for determining which tool is most suited based on the practitioner's target purpose.