Fog computing (FC) has been presented as a modern distributed technology that will overcome the different issues that Cloud computing faces and provide many services. It brings computation and data storage closer to data resources such as sensors, cameras, and mobile devices. The fog computing paradigm is instrumental in scenarios where low latency, real-time processing, and high bandwidth are critical, such as in smart cities, industrial IoT, and autonomous vehicles. However, the distributed nature of fog computing introduces complexities in managing and predicting the execution time of tasks across heterogeneous devices with varying computational capabilities. Neural network models have demonstrated exceptional capability in prediction tasks because of their capacity to extract insightful patterns from data. Neural networks can capture non-linear interactions and provide precise predictions in various fields by using numerous layers of linked nodes. In addition, choosing the right inputs is essential to forecasting the correct value since neural network models rely on the data fed into the network to make predictions. The scheduler may choose the appropriate resource and schedule for practical resource usage and decreased make-span based on the expected value. In this paper, we suggest a model Neural Network model for fog computing task time execution prediction and an input assessment of the Interpretive Structural Modeling (ISM) technique. The proposed model showed a 23.9% reduction in MRE compared to other methods in the state-of-arts.

This study evaluated the development and validation of an integrated operational model for the Underground Logistics System (ULS) in South Korea’s metropolitan area, aiming to address challenges in urban logistics and freight transportation by highlighting the potential of innovative logistics systems that utilize underground spaces. This study used conceptual modeling to define the core concepts of ULS and explored the system architecture, including cargo handling, transportation, operations and control systems, as well as the roles of cargo crews and train drivers. The ULS operational scenarios were verified through model simulation, incorporating both logical and temporal analyses. The simulation outcomes affirm the model’s logical coherence and precision, emphasizing ULS’s pivotal role in boosting logistics efficiency. Thus, ULS systems in Korea offer prospects for elevating national competitiveness and spurring urban growth, underscoring the merits of ULS in navigating contemporary urban challenges and championing sustainability.

The efficiencies and performance of gas turbine cycles are highly dependent on parameters such as the turbine inlet temperature (TIT), compressor inlet temperature (T1), and pressure ratio (Rc). This study analyzed the effects of these parameters on the energy efficiency, exergy efficiency, and specific fuel consumption (SFC) of a simple gas turbine cycle. The analysis found that increasing the TIT leads to higher efficiencies and lower SFC, while increasing the To or Rc results in lower efficiencies and higher SFC. For a TIT of 1400 ℃, T1 of 20 ℃, and Rc of 8, the energy and exergy efficiencies were 32.75% and 30.9%, respectively, with an SFC of 187.9 g/kWh. However, for a TIT of 900 ℃, T1 of 30 ℃, and Rc of 30, the energy and exergy efficiencies dropped to 13.18% and 12.44%, respectively, while the SFC increased to 570.3 g/kWh. The results show that there are optimal combinations of TIT, To, and Rc that maximize performance for a given application. Designers must consider trade-offs between efficiency, emissions, cost, and other factors to optimize gas turbine cycles. Overall, this study provides data and insights to improve the design and operation of simple gas turbine cycles.

Scientists have harnessed the diverse capabilities of nanofluids to solve a variety of engineering and scientific problems due to high-temperature predictions. The contribution of nanoparticles is often discussed in thermal devices, chemical reactions, automobile engines, fusion processes, energy results, and many industrial systems based on unique heat transfer results. Examining bioconvection in non-Newtonian nanofluids reveals diverse applications in advanced fields such as biotechnology, biomechanics, microbiology, computational biology, and medicine. This study investigates the enhancement of heat transfer with the impact of magnetic forces on a linearly stretched surface, examining the two-dimensional Darcy-Forchheimer flow of nanofluids based on blood. The research explores the influence of velocity, temperature, concentration, and microorganism profile on fluid flow assumptions. This investigation utilizes blood as the primary fluid for nanofluids, introducing nanoparticles like zinc oxide  and titanium dioxide (. The study aims to explore their interactions and potential applications in the field of biomedicine. In order to streamline the complex scheme of partial differential equations (PDEs), boundary layer assumptions are employed. Through appropriate transformations, the governing partial differential equations (PDEs) and their associated boundary conditions are transformed into a dimensionless representation. By employing a local non-similarity technique with a second-degree truncation and utilizing MATLAB’s built-in finite difference code (bvp4c), the modified model’s outcomes are obtained. Once the calculated results and published results are satisfactorily aligned, graphical representations are used to illustrate and analyze how changing variables affect the fluid flow characteristics problems under consideration. In order to visualize the numerical variations of the drag coefficient and the Nusselt number, tables have been specially designed. Velocity profile of -blood and -blood decreases for increasing values of  and , while temperature profile increases for increasing values of  and . Concentration profile decreases for increasing values of , and microorganism profile increases for increasing values of . For rising values of  and  the drag coefficient increases and the Nusselt number decreases for rising values of  and  The model introduces a novel approach by conducting a non-similar analysis of the Darchy-Forchheimer bioconvection flow of a two-dimensional blood-based nanofluid in the presence of a magnetic field.

Cucumber (Cucumis sativus L.) is a tropical vegetable and a source of vitamins such as K, C, and B. It is commonly grown and sold for daily consumption, but picking the right fruit size is more profitable. Therefore, a method for estimating the fruit weight is highly recommended. This paper aimed to determine the dimensions of cucumber fruit based on its usual harvesting size and to establish a model to show the relationship between fruit weight, fruit length, and fruit diameter. Cucumber was planted in the experimental field belonging to the Faculty of Agricultural Biosystems Engineering, Royal University of Agriculture, Phnom Penh, Cambodia, from January to June 2022. In the study, 48 market-size fruits were randomly selected from the plots to measure their weight, length, and diameter. The result shows that fruit length and fruit diameter had a positive relationship (P < 0.001; R = 0.70). Fruit weight was 3.38 fruit length × fruit diameter (P <0.001; R = 0.95). Nevertheless, L/D ratio negatively affected fruit weight, when it exceeded 3:1. Fruit weight was greater than 100 g when fruit diameter was over 4 cm and fruit length was over 10 cm. Therefore, when picking cucumber fruits, one must consider fruit length and diameter to be profitable. Further studies will focus on measuring cucumber fruit already available on the market to understand more about actual consumer preferences.