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 deleterious environmental impacts of crude oil, notably significant pollution and escalated greenhouse gas emissions, necessitate alternative fuels. In this context, biodiesel, particularly when blended with diesel, emerges as a viable substitute. This study investigates the emissions and performance characteristics of diesel-biodiesel blends, utilizing microalgae oil-based biodiesel. Variations in the catalyst (potassium hydroxide, KOH), reaction duration (30-110 minutes), and temperature (30-70^oC) were explored to determine their influence on biodiesel yield. The biodiesel produced was characterized using Fourier-transform infrared spectroscopy (FTIR), revealing distinct absorption bands indicative of various functional groups present. Furthermore, emission testing was conducted on a TecQuipment TD202 diesel engine, a naturally aspirated, single-cylinder, four-stroke, direct-injection, air-cooled model. Optimization studies revealed that the optimal biodiesel yield was achieved using 2g of KOH, at a temperature of 60^oC, and within a reaction time of 90 minutes. Emission testing demonstrated a decrease in exhaust gas temperature (EGT) with reduced biodiesel blend ratios and an increase with engine speed across all blends. Carbon monoxide (CO) emissions diminished with lower biodiesel concentrations, whereas carbon dioxide (CO₂) and nitrogen oxides (NO_x) emissions escalated. Total hydrocarbons (THCs) emissions increased with reduced biodiesel content, and smoke opacity escalated with lower biodiesel blend ratios. This investigation methodically examines the emissions from various biodiesel blends, underscoring their potential as a cleaner, more sustainable option for the transportation sector.

This research examines the possibility of food and beverage (F&B)-processing multinational corporations serving as a viable conduit for the international diffusion of technology. It utilizes existing literature to analyse three potential avenues through which technology transfer occurs from these corporations to host sectors: contract farming, domestic collaboration for innovation, and spillover effects of Foreign Direct Investment (FDI). In specific instances, these firms might provide support to local innovators through financial assistance or complementary resources. Additionally, they may actively facilitate technology transfers to particular types of local partners and they may generate demonstration effects. Nevertheless, the prevailing evidence consistently indicates that the impact of FDI on the host sector is generally limited or selective. The findings of this study cast doubt on the overly optimistic views held by international organizations and host governments regarding FDI in the food sector as a major source of cutting-edge technology for host countries. The incentives offered to food and beverage multinationals should be carefully calibrated to strike a balance between acknowledging potential benefits to the sector's innovation system and maintaining a realistic perspective on the actual outcomes. This study combines and analyses three separate empirical lines of research in parallel to offer factual elements for a policy debate. By integrating these different research approaches, the study aims to contribute to a well-informed discussion on relevant policy matters.

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.

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.

This study rigorously assesses the environmental impact of Zambia’s cement industry, utilizing the methodology of Life Cycle Assessment (LCA) and the application of SimaPro software. The focus is primarily laid on the stages of raw material extraction and transportation, pivotal in the cement production process. The analysis, grounded in the use of the eco-invent database, renowned for its reliability, encompasses a comprehensive evaluation of resource depletion, energy usage, and greenhouse gas (GHG) emissions, with a particular emphasis on the latter. Findings reveal that raw material extraction and transportation collectively contribute to 80% of the environmental footprint associated with the production of 1000 tonnes of cement as a functional unit. Specifically, raw material extraction is responsible for 44%, transportation 36%, and coal consumption for limestone decomposition 19% of the total impact. The assessment critically examines environmental impact categories such as climate change, freshwater eutrophication, terrestrial acidification, fossil depletion, and human toxicity. These categories are selected due to their direct relevance to the overarching goal of the study. A noteworthy aspect of the analysis is the cement industry's dependency on hydroelectricity. The role of renewable energy sources, particularly hydroelectricity, in mitigating ecological impacts is underscored. The systematic approach of SimaPro, enhanced through the incorporation of industry-specific and region-specific data, adds a layer of reliability to the study. This research, conforming to industry standards and evaluated by experts, delves deeply into aspects such as energy consumption, GHG emissions, water utilization, and land use. To augment the robustness of the findings, a sensitivity analysis is also conducted. The study underlines that the processes of raw material extraction and transportation are key contributors to the environmental footprint of the cement industry in Zambia. Recommendations are made for ethical sourcing, exploration of alternative transportation methods, and optimization of logistics. The study acknowledges the vital interplay between corporations, governments, and academic institutions in shaping tailored sustainability policies. Proposals for the adoption of alternative fuels and the optimization of transportation logistics are put forward, highlighting that ethical raw material extraction is imperative for transitioning towards a more sustainable cement industry.

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.

Based on the financial panel data of Chinese A-share listed companies from 2015 to 2022, this paper takes the “Fee-to-Tax” reform in environmental protection in 2018 as a quasi-natural experiment and employs the Propensity Score Matching-Difference-in-Differences (PSM-DID) model to explore the impact of the environmental protection tax on the valuation of heavily polluting enterprises. The research results show that the environmental protection tax can significantly promote the valuation increase of heavily polluting enterprises and has passed a series of robustness tests. In addition, the analysis of regional heterogeneity and enterprise ownership heterogeneity further reveals the differences between regions and enterprise ownership. The environmental protection tax has a significant positive impact on the valuation of heavily polluting enterprises in Eastern China and non-state-owned heavily polluting enterprises. It is also further found that the environmental protection tax policy improves the valuation of heavily polluting enterprises through the paths of environmental information disclosure, green technology innovation, and intelligent transformation. In light of these findings, this paper proposes relevant policy recommendations to provide reference for accelerating enterprise transformation and upgrading and promoting steady economic growth.

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.

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.

Considering the recent adverse developments, studies have focused on the environmental degradation of countries. In this context, various indicators for the environment as well as explanatory variables have been used. In line with the increasing geopolitical risk (GPR) in recent times, the study focuses on investigating the impact of GPR on the environment for G7 countries, which are the leading economies in the world. In doing so, the study considers carbon dioxide (CO₂) emissions and ecological footprint (EF) as the environmental indicators; and performs quantile-on-quantile regression (QQ) as the fundamental model, which investigates the relationship between two variables across quantiles (i.e., levels); applies quantile regression (QR) for robustness; and uses using monthly data between 1985 and 2022. The study proves that (i) GPR generally decreases CO₂ emissions at higher quantiles, whereas it causes an increasing impact at lower quantiles; (ii) GPR mainly curbs EF at higher levels, whereas it causes a stimulating impact at lower levels; (iii) the power of the impact of GPR differentiates a bit according to quantiles, indicators, and countries; (iv) the alternative method mostly validates the robustness of the results. Thus, the study implies that GPR has a stimulating impact on environmental degradation at the beginning (i.e., lower quantiles) by causing much more consumption and short-term-based decisions, whereas it causes a decline at the last (i.e., higher quantiles) through making consumption more responsible and decisions with more long-term-based perspective. So, GPR is an important predictor of the environment and has a critical impact on environmental degradation. Accordingly, policymakers have to consider the quantile, country, and environmental indicator-based differentiation impact of GPR on the environment in designing environmental policies. In this way, it is possible for the countries to achieve sustainable development goals by ensuring environmental degradation.

The escalating costs, transmission losses, and environmental ramifications associated with fossil fuel utilization have catalyzed a paradigm shift towards Renewable Energy Sources (RES) in electricity generation. Smart Grid (SG) technologies, which are inherently reliant on a RES-exclusive electricity framework, facilitate efficient energy consumption and the distribution of decentralized energy resources. This investigation underscores the integration of RES within SG infrastructure and the potential for Jordan’s transition towards an SG-enabled future. Situated in a locale characterized by abundant solar irradiance and significant wind velocities, Ma’an city presents an optimal case study for RES deployment. An amalgamated RES system, comprising wind and photovoltaic (PV) modules with an aggregate capacity of 180 MW, has been meticulously sized and designed to cater to the electrical demand of Ma'an. The load requirements for Ma'an were determined through an analysis of the city's average annual energy consumption, adjusted for population growth projections. To bolster the system's reliability and cater to emergency load demands, a storage solution has been integrated. The performance of the proposed design was substantiated and assessed via mathematical modeling and simulation analysis, utilizing the MATLAB Simulink platform. The simulations were conducted accounting for factors impinging upon each system's production capacity, inclusive of transmission line losses. Moreover, a Proportional-Integral-Derivative (PID) controller was incorporated and evaluated under simulated fault conditions, ensuring system disconnection within a five-second window subsequent to fault detection. The simulation outcomes exhibited congruence with the mathematical model predictions. Economically, the installation of the proposed systems is justifiable, with projected savings of approximately 80 million Jordanian Dinars (JD) annually and a favorable payback period of 14 months. The levelized cost of electricity is competitively priced at 14.41 JD/MWh. The findings advocate for the expansion of RES integration across Jordan, suggesting the feasibility of a nationwide RES-based SG implementation.

This study investigates the influence of magnetic fields on the behavior of Liquefied Petroleum Gas (LPG)/air mixtures, with a particular focus on the stability limits and flame temperature. The primary objective is to elucidate the impact of magnetic fields on the modification of premixed and diffusion laminar combustion within a vertical counter-flow burner. An integrated experimental setup, encompassing a counter-flow burner, an optical image system, an electromagnetic induction charger, and a digital image processing technique, was employed. This apparatus array enabled the capture of flame images across varying intensities of magnetic field and air/fuel ratios, thereby providing comprehensive data on both diffusion and premixed flames. A sophisticated image processing technique was utilized to delineate details concerning the counter flame front’s geometry, including shape, area, and diameter. Acquired flame images were subsequently subjected to analysis using MATLAB software. Findings indicated a slight increase in flame temperature concurrent with the intensification of the magnetic field for both premixed and diffusion combustion. Notably, the presence of a magnetic field significantly enhanced flame stability across both flame categories. Furthermore, the flame disk operating area demonstrated a proportional expansion with the magnetic field intensity, with a more pronounced effect observed at 5000 gausses in the diffusion flame as compared to its premixed counterpart. In conclusion, this investigation underscores the pivotal role of magnetic fields in augmenting flame stability, offering valuable insights towards optimizing combustion processes.

Addressing pressing global challenges—such as energy crises, population growth, food scarcity, resource depletion, and global warming—requires innovative and sustainable solutions. Biomass-derived wood pellets present a promising eco-friendly energy alternative. This study investigates the conversion of agricultural residues into wood pellets, utilizing two distinct biomass compositions. Composition A comprises equal parts young coconut fiber, empty palm fruit bunches, and sawdust (1:1:1 ratio), while Composition B uses a 1:1:0.5 ratio of the same materials. Laboratory analyses were conducted in accordance with Indonesian National Standard (SNI) SNI 8021:2014 to determine the physical and chemical properties of the resulting wood pellets. It was found that the moisture content of Composition A ranged from 3.52% to 4.59% in Composition B, while ash content was significantly higher in Composition A at 10.09%, compared to 4.25% for Composition B. The energy content was measured to be approximately 4102 Kcal/Kg (17,173 MJ/Kg) for Composition A and 4613 Kcal/Kg (19,313 MJ/Kg) for Composition B. The results indicate that the moisture content and calorific value of the wood pellets are in compliance with several international standards, including SNI 8021:2014 (Indonesia), ONORM M7135 (Austria), and DIN 51731 (Germany). However, the ash content of Composition A exceeds these standards. The findings suggest that optimal composition ratios can yield biomass pellets that contribute to sustainable energy solutions in line with Indonesia's renewable energy goals and the broader Sustainable Development Goals (SDGs).

In this investigation, the performance and emission profiles of a diesel engine, fueled by biodiesel derived from waste cooking oil (WCO), were evaluated. The biodiesel was incorporated into diesel fuel in various concentrations, and the potential enhancement of these mixtures with butanol was also explored. Experimental trials were conducted at a consistent engine speed of 2250 rpm across five distinct engine loads (4, 5.5, 7, 8.5, and 10 kW) to scrutinize engine performance and quantify exhaust emissions. An air-cooled, single-cylinder diesel engine served as the experimental apparatus. Pure Iraqi diesel (D) was used as a baseline, prior to the assessment of several fuel blends, including D80B20 (20% biodiesel, 80% diesel), D80B10BU10 (10% biodiesel, 10% butanol, 80% diesel), and D70B15BU15 (15% biodiesel, 15% butanol, 70% diesel). The results indicated a decline in engine performance across all fuel types, with the most pronounced deterioration observed at lower loads. The brake specific fuel consumption escalated by 13.37%, 16.98%, and 3.92% for the tested blends, relative to diesel. Concurrently, exhaust gas temperatures decreased by 12.5%, 23.5%, and 2.9%, respectively. Furthermore, CO emissions diminished by 22.00%, 46.0%, and 14.4%, while CO2 emissions rose by 16.67%, 41.36%, and 11.73%, respectively, when compared to diesel. HC concentrations were curtailed by 42.55%, 69.11%, and 10.64%, respectively. NOx emissions exhibited a reduction of 3.8% and 24.9% for D80B10BU10 and D70B15BU15, while a 3.5% increase was observed with D80B20. The findings suggest that ternary mixtures were associated with less favorable outcomes compared to their binary counterparts.