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This study aimed to optimize the dyeing scheduling process with uncertain job completion time to reduce resource consumption and wastewater generation, and while reconciling the conflicting objectives of minimizing the makespan and the need to limit the production on specific machines to minimize rework.

We employed a UNISON framework that integrates fuzzy decision tree (FDT) to optimize dyeing machine scheduling by minimizing the makespan and water consumption, in which the critical attributes such as machine capacity and processing time can be incorporated into the scheduling model for smart production.

An empirical study of a high-tech textile company has shown the validity and effectiveness of the proposed approach in reducing the makespan and water consumption by over 8% while high product quality and efficiency being maintained.

High-tech textile industry is facing the challenges in reducing the environmental impact of the dyeing process while maintaining product quality and efficiency for smart production. Conventional scheduling approaches have not addressed the relationship between machine groups and reworking, resulting in difficulty in controlling the makespan and water consumption and increasing costs and environmental issues. The proposed approach has addressed uncertain job completion via integrating FDT into the scheduling process to effectively reduce makespan and wastewater. The results have shown practical viability of the developed solution in real settings.

Nanofluids are used in technology, engineering processes and thermal exchanges. In thermal transfer processing, these are used for the smooth transportation of heat and mass through various mechanisms. In the current investigation, we have examined multiple effects like activation energy thermal radiation, magnetic field, external heat source and especially slippery effects on a bioconvective Casson nanofluid flow through a stretching cylinder.

Several studies used non-Newtonian fluid models to study blood flow in the cardiovascular system. In our research, Lewis numbers for bioconvection and the influence of important parameters, such as Brownian diffusion and thermophoresis effects, are also considered. This system is developed as a partial differential equation for the mathematical treatment. Well-defined similarity transformations convert partial differential equation systems into ordinary differential equations. The resultant system is then numerically solved using the bvp4c built-in function of MATLAB.

After utilizing the numerical approach to the system of ordinary differential equations (ODEs), the results are generated in the form of graphs and tables. These generated results show a suitable accuracy rate compared to the previous results. The consequence of various parameters under the assumed boundary conditions on the temperature, motile microorganisms, concentration and velocity profiles are discussed in detail. The velocity profile decreases as the Magnetic and Reynolds number increases. The temperature profile exhibits increasing behavior for the Brownian motion and thermal radiation count augmentation. The concentration profile decreased on greater inputs of the Schmidt number and magnetic effect. The density of motile microorganisms decreases for the increased value of the bio-convective Lewis number.

The numerical analysis of the flow problem is addressed using graphical results and tabular data; our reported results are refined and novel based on available literature. This method is useful for addressing such fluidic flow efficiently.

This research focuses on the controlling irreversibilities in a radiative, chemically reactive electromagnetohydrodynamics (EMHD) flow of a nanofluid toward a stagnation point. Key considerations include the presence of Ohmic dissipation, linear thermal radiation, second-order chemical reaction with the multiple slips. With these factors, this study aims to provide insights for practical applications where thermal management and energy efficiency are paramount.

Lie group transformation is used to revert the leading partial differential equations into nonlinear ODE form. Hence, the solutions are attained analytically through differential transformation method-Padé and numerically using the Runge–Kutta–Fehlberg method with shooting procedure, to ensure the precise and reliable determination of the solution. This dual approach highlights the robustness and versatility of the methods.

The system’s entropy generation is enhanced by incrementing the magnetic field parameter (M), while the electric field (E) and velocity slip parameters (ξ) control its growth. Mass transportation irreversibility and the Bejan number (Be) are significantly increased by the chemical reaction rate (C_r). In addition, there is a boost in the rate of heat transportation by 3.66% while 0.05⩽ξ⩽0.2; meanwhile for 0.2⩽ξ⩽1.1, the rate of mass transportation gets enhanced by 12.87%.

This paper presents a novel approach to analyzing the entropy optimization in a radiative, chemically reactive EMHD nanofluid flow near a stagnation point. Moreover, this research represents a significant advancement in the application of analytical techniques, complemented by numerical approaches to study boundary layer equations.

Recent advancements in technology have led to the exploration of solar-based thermal radiation and nanotechnology in the field of fluid dynamics. Solar energy is captured through sunlight absorption, acting as the primary source of heat. Various solar technologies, such as solar water heating and photovoltaic cells, rely on solar energy for heat generation. This study focuses on investigating heat transfer mechanisms by utilizing a hybrid nanofluid within a parabolic trough solar collector (PTSC) to advance research in solar ship technology. The model incorporates multiple effects that are detailed in the formulation.

The mathematical model is transformed using suitable similarity transformations into a system of higher-order nonlinear differential equations. The model was solved by implementing a numerical procedure based on the Wavelets and Chebyshev wavelet method for simulating the outcome.

The velocity profile is reduced by Deborah's number and velocity slip parameter. The Ag-EG nanoparticles mixture demonstrates less smooth fluid flow compared to the significantly smoother fluid flow of the Ag-Fe3O4/EG hybrid nanofluids (HNFs). Additionally, the Ag-Ethylene Glycol nanofluids (NFs) exhibit higher radiative performance compared to the Ag-Fe3O4/Ethylene Glycol hybrid nanofluids (HNFs).

Additionally, the Oldroyd-B hybrid nanofluid demonstrates improved thermal conductivity compared to traditional fluids, making it suitable for use in cooling systems and energy applications in the maritime industry.

The originality of the study lies in the exploration of the thermal transport enhancement in sun-powered energy ships through the incorporation of silver-magnetite hybrid nanoparticles within the heat transfer fluid circulating in parabolic trough solar collectors. This particular aspect has not been thoroughly researched previously. The findings have been validated and provide a highly positive comparison with the research papers.

This paper aims to analyze the current state of technological advancements research in addressing the diverse risk factors involved in earthmoving equipment operations through Rasmussen's (1997) risk management framework. It examines how existing technologies research capture, manage and disseminate risk information across various levels of safety management by defining their core functionalities. The research highlights gaps in current technological solutions research regarding the flow of information in the risk management framework. It emphasizes the need for an integrated approach in technological advancements to enhance the holistic safety management approach capable of capturing various risks across different levels of risk management.

This research employs a multistep approach. Initially, earthmoving equipment risk factors and functionalities of technological solutions were identified through a systematic review of current scholarly works. Subsequently, social network analysis (SNA) and Pareto analysis were applied to evaluate and determine the importance of risk factors and functionalities of technologies for improving them.

The findings highlight the importance of multilevel approaches that expand technological functionalities to address risk factors across all levels of Rasmussen's (1997) risk management framework. The current combination of technological advancements focuses primarily on on-site monitoring, congested work sites, site layout/path planning, utility problems, safety training, and blind spot and visibility. Site monitoring and warning systems, supported by sensors and computer vision (CV), are pivotal for identifying risks and enabling data-driven safety management. However, workforce-level cognitive factors (W1-W6), which influence safety behavior, remain underexplored for enhancing their functionality to anticipation and response during the operation. Prevention is the core function of current technological solutions, emphasizing the need to address human and equipment risk factors such as sources of hazards in earthmoving operations. Learning: AI as a data-driven approach and IoT systems are key for future development, and when grounded in ontology-based knowledge of earthwork, they gain a structured vision of earthmoving equipment types, their interactions and the earthwork activities. It enhances the capabilities of these technologies to capture and manage complex interactions between hazard sources (human and equipment), supporting comprehensive risk factors across all levels of the risk management framework.

This paper elucidates that technological solutions for safety management in earthmoving equipment operations require a more holistic approach—grounded in an understanding of functionalities of technologies—to effectively capture risks across various levels of Rasmussen (1997) risk management. It emphasizes that technological solutions should not only address isolated hazards but also ensure the continuous flow of information on multiple risk factors across the risk management framework.

This study aims to present the consequences of activation energy and the chemical reactions on the unsteady MHD squeezing flow of an incompressible ternary hybrid nanofluid (THN) comprising magnetite (FE₃O₄), multiwalled carbon nano-tubes (MWCNT) and copper (Cu) along with water (H2O) as the base fluid. This investigation is performed within the framework of two moving parallel plates under the influence of magnetic field and viscous dissipation.

Due to the complementary benefits of nanoparticles, THN is used to augment the heat transmit fluid’s efficacy. The flow situation is expressed as a system of dimensionless, nonlinear partial differential equations, which are reduced to a set of nonlinear ordinary differential equations (ODEs) by suitable similarity substitutions. These transformed ODEs are then solved through a semianalytical technique called differential transform method (DTM). The effects of several changing physical parameters on the flow, temperature, concentration and the substantial measures of interest have been deliberated through graphs. This study verifies the reliability of the results by performing a comparison analysis with prior researches.

The enhanced activation energy results in improved concentration distribution and declined Sherwood number. Enhancement in chemical reaction parameter causes disparities in concentration of the ternary nanofluid. When the Hartmann number is zero, value of skin friction is high, but Nusselt and Sherwood numbers values are small. Rising nanoparticles concentrations correspond to a boost in overall thermal conductivity, causing reduced temperature profile.

Due to its firm and simple nature, its implications are in various fields like chemical industry and medical industry for designing practical problems into mathematical models and experimental analysis.

Deployment of the squeezed flow of ternary nanofluid with activation energy has significant consideration in nuclear reactors, vehicles, manufacturing facilities and engineering environments.

This study would be contributing significantly in the field of medical technology for treating cancer through hyperthermia treatment, and in industrial processes like water desalination and purification.

In this problem, a semianalytical approach called DTM is adopted to explore the consequences of activation energy and chemical reactions on the squeezing flow of ternary nanofluid.

This study identifies key challenges to adopting smart real estate (SRE) technologies and offers insights and recommendations to enhance decision-making for stakeholders, including buyers and property investors.

To achieve the aim of the study, a rigorous research approach was employed, conducting an in-depth analysis of 41 academic papers utilising PRISMA guidelines and checklists. The chosen methodology also applies a PEST (Political, Economic, Social and Technological) framework to identify factors influencing technology adoption in the real estate sector.

The study uncovers critical challenges to adopting smart real estate technologies, such as regulatory ambiguity, high implementation costs, and societal resistance. PEST analysis reveals that unclear standards and guidelines, coupled with the high financial burden of technology implementation, are significant obstacles. Socially, resistance to change and difficulties in integrating new technologies are prevalent. The study also underscores the potential of artificial intelligence (AI) for predictive analytics and blockchain for secure transactions and records, though their adoption is currently hindered by inadequate infrastructure and regulatory challenges. These findings underscore the need for strategic interventions to address these challenges and facilitate the effective integration of advanced technologies in the real estate sector, thereby enhancing industry innovation and competitiveness.

The study offers insights for real estate stakeholders to embrace technology effectively, with a conceptual framework contributing to industry advancements.

The study’s key contribution is offering real estate stakeholders execution tactics and recommendations to navigate challenges and utilise technology, thereby driving industry innovation and enhancing competitiveness.

The main objective of this study is to investigate the factors that influence the adoption intention of cloud computing services among individual users using the extended theory of planned behavior.

A purposive sampling technique was used to collect a total of 339 data points, which were analyzed using SmartPLS to derive variance-based structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA).

The results obtained from PLS-SEM indicate that attitude towards cloud computing, subjective norms, perceived behavioral control, perceived security, cost-effectiveness, and performance expectancy all have a positive and significant impact on the adoption intention of cloud computing services among individual users. On the other hand, the findings from fsQCA provide a clear interpretation and deeper insights into the adoption intention of individual users of cloud computing services by revealing the complex relationships between multiple combinations of antecedents. This helps to understand the reasons for individual users' adoption intention in emerging countries.

This study offers valuable insights to cloud service providers and cyber entrepreneurs on how to promote cloud computing services to individual users in developing countries. It helps these organizations understand their priorities for encouraging cloud computing adoption among individual users from emerging countries. Additionally, policymakers can also understand their role in creating a comfortable and flexible cloud computing access environment for individual users.

This study has contributed to the increasingly growing empirical literature on cloud computing adoption and demonstrates the effectiveness of the proposed theoretical framework in identifying the potential reasons for the slow growth of cloud computing services adoption in the developing world.

This study delves into the influence of contractual frameworks on infrastructure project timelines and evaluates the role of long-term warranty agreements in maintenance efficacy. It underscores the correlation between construction contract structures and prevalent project delays, advocating for a revision in the allocation of responsibilities to mitigate such delays effectively.

While previous research has explored individual aspects of construction management, such as contractor incentives and risk allocation, our study uniquely integrates these elements to develop a comprehensive model that includes the effects of long-term warranty agreements and penalty clauses.

The findings advocate for contract revisions that entail clearly articulated responsibilities and thorough impact assessments, aimed at enhancing the efficiency of project execution and optimizing infrastructure investment returns. Concrete examples are provided from large-scale infrastructure and public works maintenance projects, illustrating the benefits of well-defined penalty clauses in curbing delays and ensuring sustained quality through long-term warranties. Our results demonstrate that optimized contractual structures can significantly reduce project delays and enhance maintenance effectiveness.

This study addresses significant gaps in understanding construction contract management dynamics, especially in transportation infrastructure. It rigorously analyzes how penalty clauses and long-term warranties impact contractor behavior and project outcomes. Key findings show that the benefits of long-term warranties, including social advantages, depend heavily on the strictness of penalty clauses. Innovatively, it employs a First-Price Sealed-Bid Auction framework with empirical data from various case studies, enhancing contract structure optimization for better stakeholder alignment and infrastructure integrity. These insights notably advance construction contract management methodologies.

The main purpose of this study is to analyze the heat transfer phenomena in a dynamically bulging enclosure filled with Cu-water nanofluid. This study examines the convective heat transfer process induced by a bulging area considered a heat source, with the enclosure's side walls having a low temperature and top and bottom walls being treated as adiabatic. Various factors, such as the Rayleigh number (Ra), nanoparticle volume fraction, Darcy effects, Hartmann number (Ha) and effects of magnetic inclination, are analyzed for their impact on the flow behavior and temperature distribution.

The finite element method (FEM) is employed for simulating variations in flow and temperature after validating the results. Solving the non-linear partial differential equations while incorporating the modified Darcy number (10⁻³ ≤ Da ≤ 10⁻¹), Ra (10³ ≤ Ra ≤ 10⁵) and Ha (0 ≤ Ha ≤ 100) as the dimensionless operational parameters.

This study demonstrates that in enclosures with dynamically positioned bulges filled with Cu-water nanofluid, heat transfer is significantly influenced by the bulge location and nanoparticle volume fraction, which alter flow and heat patterns. The varying impact of magnetic fields on heat transfer depends on the Rayleigh and Has.

The geometry configurations employed in this research have broad applications in various engineering disciplines, including heat exchangers, energy storage, biomedical systems and food processing.

This research provides insights into how different shapes of the heated bulging area impact the hydromagnetic convection of Cu-water nanofluid flow in a dynamically bulging-shaped porous system, encompassing curved surfaces and various multi-physical conditions.