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The purpose of the paper is to conduct a numerical and experimental investigation into the properties of nanofluids containing spherical nanoparticles of random sizes flowing through a porous medium. The study aims to understand how the thermophysical properties of the nanofluid are affected by factors such as nanoparticle volume fraction, permeability of the porous medium, and pore size. The paper provides insights into the behavior of nanofluids in complex environments and explores the impact of varying conditions on key properties such as thermal conductivity, density, viscosity, and specific heat. Ultimately, the research contributes to the broader understanding of nanofluid dynamics and has potential implications for engineering and industrial applications in porous media.

This paper investigates nanofluids with spherical nanoparticles in a porous medium, exploring thermal conductivity, density, specific heat, and dynamic viscosity. Studying three compositions, the analysis employs the classical Maxwell model and Koo and Kleinstreuer’s approach for thermal conductivity, considering particle shape and temperature effects. Density and specific heat are defined based on mass and volume ratios. Dynamic viscosity models, including Brinkman’s and Gherasim et al.'s, are discussed. Numerical simulations, implemented in Python using the Langevin model, yield results processed in Origin Pro. This research enhances understanding of nanofluid behavior, contributing valuable insights to porous media applications.

This study involves a numerical examination of nanofluid properties, featuring spherical nanoparticles of varying sizes suspended in a base fluid with known density, flowing through a porous medium. Experimental findings reveal a notable increase in thermal conductivity, density, and viscosity as the volume fraction of particles rises. Conversely, specific heat experiences a decrease with higher particle volume concentration.xD; xA; The influence of permeability and pore size on particle volume fraction variation is a key focus. Interestingly, while the permeability of the medium has a significant effect, it is observed that it increases with permeability. This underscores the role of the medium’s nature in altering the thermophysical properties of nanofluids.

This paper presents a novel numerical study on nanofluids with randomly sized spherical nanoparticles flowing in a porous medium. It explores the impact of porous medium properties on nanofluid thermophysical characteristics, emphasizing the significance of permeability and pore size. The inclusion of random nanoparticle sizes adds practical relevance. Contrasting trends are observed, where thermal conductivity, density, and viscosity increase with particle volume fraction, while specific heat decreases. These findings offer valuable insights for engineering applications, providing a deeper understanding of nanofluid behavior in porous environments and guiding the design of efficient systems in various industrial contexts.

The paper aims to explore the value generated by a specific configuration of a smart city's infrastructure by proposing a comparison between a silos configuration versus a crowd configuration at the data storage and processing level.

A system dynamics simulation is adopted to determine and compare the value created by the two configurations of smart city's infrastructure. The simulation outlines the flow of data and their positive and negative feedback that reinforce and hinder the smart city value generation.

The results demonstrate the huge impact of the availability of data for App developers when crowdsourcing configuration is adopted. Furthermore, results unveil the potential in value generation of a crowdsourcing smart city platform configuration compared to a silos architecture.

The authors have proposed a comparison between two alternative smart city digital platform configurations. The paper seeks to test the magnitude of the pros and cons of a crowdsourcing approach in setting up a smart city digital platform. The paper provides new guidelines for improving the data management of smart cities.

This paper aims to study the feasibility of proposed method to focus the electroporation ablation by mean of multi-output multi-electrode system.

The proposed method has been developed based on a previously designed electroporation system, which has the capabilities to modify the electric field distribution in real time, and to estimate the impedance distribution. Taking into consideration the features of the system and biological tissues, the problem has been addressed in three phases: modeling, control system design and simulation testing. In the first phase, a finite element analysis model has been proposed to reproduce the electric field distribution within the hepatic tissue, based on the characteristics of the electroporation system. Then, a control strategy has been proposed with the goal of ensuring complete ablation while minimizing the affected volume of healthy tissue. Finally, to check the feasibility of the proposal, several representative cases have been simulated, and the results have been compared with those obtained by a traditional system.

The proposed method achieves the proposed goal, as part of a complex electroporation system designed to improve the targeting, effectiveness and control of electroporation treatments and serve to demonstrate the feasibility of developing new electroporation systems capable of adapting to changes in the preplanning of the treatment in real-time.

The work presents a thorough study of control method to multi-output multi-electrode electroporation system by mean of a rigorous numerical simulation.

This study aims to identify and prioritize the challenges to adopting blockchain in supply chain management and to make its taxonomic model. Moreover, validate whether these challenging factors exist in the real world and, if they exist, then in what percentage.

This research adopted the fuzzy best-worst method (F-BWM), which integrates fuzzy set theory with the best-worst method to identify and prioritize the prominent challenges of the blockchain-based supply chain by developing a weighted multi-criteria model.

A total of 20 challenges (CH's) were identified. Lack of storage capacity/scalability and lack of data privacy challenges were found as key challenges. The findings of this study will provide a robust framework of the challenges that will assist academic researchers and industry practitioners in considering the most significant category concerning their working area.

Blockchain provides the best solution for tracing and tracking where RFID has not succeeded. It can improve quality management in a supply chain network by improving standards and speeding up operations. For inventory management, blockchain provides transparency of documentation for both parties within no time.

To the best of the authors' knowledge, no previous research has adopted the fuzzy best-worst method to prioritize the identified challenges of blockchain implementation in the supply chain. Moreover, no study provides a taxonomic model for the challenges of implementing a blockchain-based supply chain.

In this paper, a mixture of exponential and Rayleigh distributions in the proportions α and 1 − α and all the parameters in the mixture distribution are estimated based on fuzzy data.

The methods such as maximum likelihood estimation (MLE) and method of moments (MOM) are applied for estimation. Fuzzy data of triangular fuzzy numbers and Gaussian fuzzy numbers for different sample sizes are considered to illustrate the resulting estimation and to compare these methods. In addition to this, the obtained results are compared with existing results for crisp data in the literature.

The application of fuzziness in the data will be very useful to obtain precise results in the presence of vagueness in data. Mean square errors (MSEs) of the resulting estimators are computed using crisp data and fuzzy data. On comparison, in terms of MSEs, it is observed that maximum likelihood estimators perform better than moment estimators.

Classical methods of obtaining estimators of unknown parameters fail to give realistic estimators since these methods assume the data collected to be crisp or exact. Normally, such case of precise data is not always feasible and realistic in practice. Most of them will be incomplete and sometimes expressed in linguistic variables. Such data can be handled by generalizing the classical inference methods using fuzzy set theory.

To explore how the blockchain affects the pricing and financing decisions in a low-carbon platform supply chain.

Considering the dual roles of the e-commerce platform as a seller and an initiator, a typical game-theoretical method is applied to analyze the behavior of supply chain decision-makers and the impact of key variables on equilibriums.

When loan interest rates are symmetric, whether blockchain is used or not, the e-commerce platform financing mode will always produce higher wholesale price and unit carbon emission reduction, while the retail price is the opposite. Higher unit additional income brought by the blockchain can bring higher economic and environmental performances, and the e-commerce platform financing mode is superior to bank financing mode. The application of blockchain may cause the manufacturer to change his/her financing choice. For bank financing, with the increase of loan interest rates, the advantages brought by blockchain will gradually disappear, but this situation will not occur under e-commerce platform financing.

Blockchain is known for its information transparency properties and its ability to enhance user trust. However, the impacts of applying blockchain in a low-carbon platform supply chain with different financing options are not clear. The authors examine the manufacturer's strategic choices for platform financing and bank financing, whether to adopt blockchain, and the impact of these decisions on carbon emissions reduction, consumer surplus and social welfare. The research conclusion can provide reference for the operation and financing decisions of platform supply chain under the carbon reduction target in the digital economy era.

The COVID-19 pandemic has showcased the lack of resilience found in the global value chains (GVCs) of multinational enterprises (MNEs). Existing evidence shows that MNEs have only recently and slowly started recovering and attempting to rebuild the resilience of their GVCs. This paper analyzes the challenges/inhibitors faced by MNEs in building their resilience through their GVCs.

A four-stage hybrid model was used to identify the interrelationship among the identified inhibitors and to distinguish the most critical ones by ranking them. In the first stage, we employed a modified total interpretive structural modeling (m-TISM) approach to determine the inter-relationship among the inhibitors. Additionally, we identified the inhibitors' driving power and dependency by performing a matrix multiplication applied to classification (MICMAC) analysis. In the second stage, we employed the Pythagorean fuzzy analytic hierarchy process (PF-AHP) method to determine the weight of the criteria. The next stage followed, in which we used the Pythagorean fuzzy combined compromise solution (PF-CoCoSo) method to rank the inhibitors. Finally, we performed a sensitivity analysis to determine the robustness of the framework we had built based on the criteria and inhibitors.

We find business sustainability to have the highest importance and managerial governance as the most critical inhibitor hindering the path to resilience. Based on these insights, we derive four research propositions aimed at strengthening the resilience of such GVCs, followed by their implications for theory and practice.

Our findings contribute to the extant literature by uncovering key inhibitors that act as barriers to MNEs. We link out our findings with a number of propositions that we derive, which may be considered for implementation by MNEs and could help them endow their GVCs with resilience.

Using a combination of the geographical information system (GIS) and the Canadian water quality index (WQI), the current study sought to provide a long-term general assessment of the water quality of the Shatt Al-Arab River (SAAR), focusing on its suitability for living organisms. Likewise, SPSS statistics was used to develop a nonlinear WQI regression model for the study area.

The study required four decades of data collection on some environmental characteristics of river water. After that, calculate the WQI and conduct the spatial analysis. Eight variables in total, including water temperature, dissolved oxygen, potential hydrogen ions, electrical conductivity (EC), biological oxygen demand, turbidity, nitrate and phosphate, were chosen to calculate the WQI.

Throughout the study periods, the WQI values varied from 55.2 to 79.83, falling into the categories of four (marginal) and three (fair), with the sixth period (2007–2008) showing the most decline. The present research demonstrated that the high concentration of phosphates, the high EC values, and minor changes in the other environmental factors are the major causes of the decline in water quality. The variations in ecological variables' overlap are a senior contributor to changes in water quality in general. Notably, using GIS in conjunction with the WQI has shown to be very effective in reducing the time and effort spent on investigating water quality while obtaining precise findings and information at the lowest possible expense. Calibration and validation of the developed model showed that this model had a perfect estimate of the WQI value. Due to its flexibility and impartiality, this study recommends using the proposed model to estimate and predict the WQI in the study area.

Even though the water quality of the SAAR has been the subject of numerous studies, this is the only long-term investigation that has been done to evaluate and predict its water quality.

Jubail Industrial City is one of the largest industrial centers in the Middle East, offering potential opportunities for renewable energy generation. This research paper presents a comprehensive analysis of the wind resources in Jubail Industrial City and proposes the design of a smart grid-connected wind farm for this strategic location.

The study used wind data collected at three different heights above ground level – 10, 50 and 90 m – over four years from 2017 to 2020. Key parameters, such as average wind speeds (WS), predominant wind direction, Weibull shape, scale parameters and wind power density (WPD), were analyzed. The study used Windographer, an exclusive software program designed to evaluate wind resources.

The average WS at the respective heights were 3.07, 4.29 and 4.58 m/s. The predominant wind direction was from the north-west. The Weibull shape parameter (k) at the three heights was 1.77, 2.15 and 2.01, while the scale parameter (c) was 3.36, 4.88 and 5.33 m/s. The WPD values at different heights were 17.9, 48.8 and 59.3 W/m², respectively.

The findings suggest that Jubail Industrial City possesses favorable wind resources for wind energy generation. The proposed smart grid-connected wind farm design demonstrates the feasibility of harnessing wind power in the region, contributing to sustainable energy production and economic benefits.