In recent years, machine learning, especially deep neural networks, has made substantial progress, consistently surpassing conventional time-series forecasting methods across various domains. This paper introduces a novel hybrid approach that combines the Lorenz system and the echo state network (ESN) to tackle and reduce the "butterfly effect" in chaos forecasting. The core contribution lies in harnessing the Lorenz system's unique properties, where initially converging trajectories gradually diverge, to train the ESN—a neural network celebrated for its non-linear computational capabilities, echo state property, and input forgetting capability. The primary aim is to establish a more robust and precise framework for predicting chaotic systems, given their sensitivity to initial conditions. This research endeavors to provide a versatile tool with wide-ranging applications, particularly in areas like stock price prediction, where accurately forecasting chaotic behavior holds paramount importance. The Lorenz system initiates with nearly identical initial states, differing by a mere 10^-3 in the x-coordinate at t=0. Initially, these trajectories seem to overlap, but after t=1000, they significantly diverge. In this proposed approach, data from t=0 to t=1000 serves as the training input for the ESN. Once the training phase concludes, the ESN's formidable non-linear computational capabilities, echo state property, and input forgetting capability render it exceptionally well-suited for stepwise predictions and tasks sensitive to initial conditions. The simulation results demonstrate that over the subsequent 360 prediction steps conducted by the ESN, the "butterfly effect" stemming from the slightly varying initial states provided to the Lorenz System is effectively minimized. Notably, the simulation results underscore the superior performance of our hybrid approach, revealing a minimal root mean square error (RMSE) of less than 1.0. In contrast, a prior study introduced the MrESN (Multiple Reservoir Echo State Network) approach, which is a specific type of Echo State Network (ESN) used for forecasting multivariate chaotic time series. It employs multiple internal reservoirs within the network architecture to handle the complex dynamics of chaotic data but achieved lower accuracy with a larger RMSE of 43.70. Another preceding algorithm, BFA-DRESN, aimed at enhancing forecasting accuracy but yielded an RMSE value of 18.83. This research advances ESN-based predictability, offering a promising solution for addressing the challenges posed by chaos.

The main duty of engineers is to guarantee that structures are both erect and adhere to codes, which proves their outstanding functionality and economic viability. In today's elastic materials, the von Mises stress values have to be verified when examining fatigue or failure. In the domains of heavy lifting, robotics, mechanical and offshore engineering, oil and gas engineering, and civil engineering, the Von Mises criteria are among the most often used benchmarks for assessing productivity conditions. In this study, seven I-beams models will be built, the first model without holes and the other six models with holes in various shapes (square, triangular, circular, hexagonal, and rectangular). The ANSYS program will be used to solve it using the finite element method. For the upper surface of these models, equal loads will be applied. The findings demonstrate that the shear stress values for the seven models were less than the shear stress values of the metal, which came to (370MPa), in line with the theory of maximum shear stress. With a value of (62.7MPa), the second-best model was the best. One of the most important conclusions when comparing the values of von Mess stresses with the von Mess theory of stress is that the third model (with rectangular openings) performed better than the other models when compared to the first model because its value was the same in both models (370MPa). The seventh model (hexagonal holes) had the lowest maximum value of stress intensity at 261MPa, per the results. being aware that this model weighs (70Kg) less than the first.

India is widely recognized for its wealthy heritage, subculture and myriad Indian cuisines. Indian Cuisines are famous around the globe for their taste and flavors. Indian Cuisines detection using computer vision-based methods has been limited till now because of the absence of a standard dataset needed to inspect the deep learning-based object detection models for detecting Indian Food Cuisine using electronic devices. Measuring food quantities in each item are very challenging tasks for a person. In this study the dataset IndianFoodNet has been introduced, containing more than 5500 high-quality images and 5000+ annotations spreading across thirty classes of Indian food items. A comparative study of various state-of-the-art object detection models- YOLO5, YOLO7 and YOLO8 has been provided in the study. Further, the model performance has been inspected and evaluated (As in training summary of YOLO at 5 epochs YOLO8 precision is 0.775 higher than precision of YOLO7 and YOLO5.Recall value of YOLO7 is least in comparison with YOLO5 having value 0.671 and YOLO8 having recall value 0.719) by qualitatively analyzing the prognostic made on the images of the dataset which are segregate for testing.

Many studies have indicated that only a non-uniform magnetic field can interact with flame, and a small laminar diffusion flame is more affected than a premixed or partially premixed flame. Additionally, the mechanism for magnet–flame interaction is due to the magnetic para-magnetism of oxygen in the air, which is diffused into the flame. However, the combustion characteristics of the flame subject to the influence of magnetic field are not fully understood yet. This paper describes a numerical study of influence of magnetic field on premixed flame on the counter burner. Laminar premixed flames for different LPG gas flow rates propagating in counter burner of a different magnetic field intensities 1000 to 5000 gauss have been numerically investigated. An influence of the changing a distance between magnetic poles and magnetic force on the flame behavior, combustion velocity and flame temperature has been analyzed. The simulation was carried out using ANSYS Fluent software version 17.0, with premixed flame-let model and the dynamics of premixed flame through counter vertical burner under influencing of magnetic field. CFD results were appeared in the area of counter flame. Flame disc diameter in the counter burner is decreased gradually with increase magnetic field intensity and it affects positively on the combustion velocity of fuel/ air mixtures, and this behavior due to probably caused by effect magnetic force on oxygen zone. While, the results CFD results were shown decrease in the combustion velocity with increasing the distance between magnetic poles. The results have been demonstrated by an increase in the distance between magnetic poles on the combustion for LPG mixtures with air at 150, 180 to 220 mm leads to a significant decrease in both flame temperature with 3.7% and 4.7%. So, there was slight effect on the flame temperature in the middle of the anti-flame disc with effect magnetic field.

In vapor compression refrigeration systems, refrigerants are among the most significant parameters additional to the compressor, condenser, throttling device, and the evaporator. Non-condensable gases during refrigerant manufacturing affect chest freezer performance. The temperature of the refrigerant in the condenser and evaporator is influenced by the quality of the refrigerant and its concentration. To study this effect this work is carried out on a chest freezer working with R-134a, which has a capacity of 145 liters. A high percentage of non-condensable gases in samples 3 and 6 increases the temperature of the refrigerant condenser, increases the electricity consumption, and decreases the temperature of the refrigerant flow in the evaporator. This blocks the circulation of refrigerant throughout the system and for a long time the compressor may be damaged. Samples 2,4,5 which contain low non-condensable gases work similarly to standard sample 1 with a low effect on power and refrigerant circulation, so cooling capacity is not affected.

The quenching of a pre-mixed counter flame was studied experimentally, as described in this paper. Experimental research has been done on flames spreading in methane/air mixes in counter burners with various burner diameters. It has been determined how the counter burner diameter changes, the methane/air mixing ratio affects the flame burning velocity, and the quenching diameter. In this study, the quenching diameter was examined in relation to altering burner diameter (9, 12, 16, 19, and 23 mm) using a digital image processing technique. In counter flame, significant results were attained. The geometry of the burner edges and the air and fuel velocity have an impact on the quenching diameter in the counter flow. The top and bottom flame disc quenching diameters are nearly equal for both lean and rich combinations and grow with the burner diameter. The values of the quenching distance were smaller than the quenching diameter at the wide range of the equivalence ratio (0.46 < φ < 1.57) for mixtures, and this behavior was likely caused by the dead space.

Heat and mass transfer in ternary nanofluid flows over diverse geometries is particularly significant for thermal management in electronic devices, precipitation, and filtration. Chemical reactions are vital processes that occur in a variety of natural and industrial systems. With this initiation, this research explores the impacts of chemical reaction and heat source/sink over MHD ternary nano fluid flow. In addition to this model, we assessed joule heating, viscous dissipation, and activation energy for the study. The ODEs are obtained by using appropriate similarities and the altered non-linear governing equations are solved numerically utilizing RKF-45 and shooting technique. The influence of vital variables on common profiles (flow velocity, thermal gradient, and mass transmission rate) is explored and deliberated graphically in three distinct scenarios. When compared to the other scenarios, the mass transfer for the case of fluid flow across a plate lowers as the activation energy parameter goes up.