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In the current paper, a CFD simulation study on a pent-roof, spark ignition engine was performed. The scope was to analyze the basic parameters that influence the performances of the simulation: mesh configuration, mesh refinement, turbulence model, and flame wrinkling model parameters. It was used as the OpenFOAM solver for spark ignition engines. The solver was modified by adding the layer addition/removal mechanism for the mesh. Further, a Zeldovich extended NOx model was implemented. Four different mesh configurations were tested with different refinement levels. A set of different turbulence models and initial parameters and different flame wrinkling model parameters were tested. It was observed how all these parameters influence the rate of heat release, cylinder pressure, temperature, the level of turbulent kinetic energy, the flame wrinkling factor, and finally the resulting concentration of nitride monoxide.

Android malware detection using artificial intelligence today is a mandatory tool to prevent cyber attacks. To address this problem in this paper the proposed methodology consists of the application of genetic programming symbolic classifier (GPSC) to obtain symbolic expressions (SEs) that can detect if the android is malware or not. To find the optimal combination of GPSC hyperparameter values the random hyperparameter values search method (RHVS) method and the GPSC were trained using 5-fold cross-validation (5FCV). It should be noted that the initial dataset is highly imbalanced (publicly available dataset). This problem was addressed by applying various preprocessing and oversampling techniques thus creating a huge number of balanced dataset variations and on each dataset variation the GPSC was trained. Since the dataset has many input variables three different approaches were considered: the initial investigation with all input variables, input variables with high feature importance, application of principal component analysis. After the SEs with the highest classification performance were obtained they were used in threshold-based voting ensembles and the threshold values were adjusted to improve classification performance. Multi-TBVE has been developed and using them the robust system for Android malware detection was achieved with the highest accuracy of 0.98 was obtained. [ABSTRACT FROM AUTHOR]

In response to the issue of short-term fluctuations in photovoltaic (PV) output due to Cloud movement, this paper proposes a method for forecasting short-term PV output based on a Depthwise Separable Convolution Visual Geometry Group (DSCVGG) and a Deep Gate Recurrent Neural Network (DGN). Initially, a cloud motion prediction model is constructed using a DSCVGG, which achieves edge recognition and motion prediction of clouds by replacing the previous convolution layer of the pooling layer in VGG with a depthwise separable convolution. Subsequently, the output results of the DSCVGG network, along with historical PV output data, are introduced into a Deep Gate Recurrent Unit Network (DGN) to establish a PV output prediction model, thereby achieving precise prediction of PV output. Through experiments on actual data, the Mean Absolute Error (MAE) and Mean Squared Error (MSE) of our model are only 2.18% and 5.32 x 10-5, respectively, which validates the effectiveness, accuracy, and superiority of the proposed method. This provides new insights and methods for improving the stability of PV power generation. [ABSTRACT FROM AUTHOR]

In this paper, a stochastic techno-economic optimization framework is proposed for three different hybrid energy systems that encompass photovoltaic (PV), wind turbine (WT), and hydrokinetic (HKT) energy sources, battery storage, combined heat and power generation, and thermal energy storage (Case I: PV–BA–CHP–TES, Case II: WT–BA–CHP–TES, and Case III: HKT–BA–CHP–TES), with the inclusion of electric and thermal storage using the 2m + 1 point estimate method (2m + 1 PEM) utilizing real data obtained from the city of Espoo, Finland. The objective function is defined as planning cost minimization. A new meta-heuristic optimization algorithm named improved fire hawk optimization (IFHO) based on the golden sine strategy is applied to find the optimal decision variables. The framework aims to determine the best configuration of the hybrid system, focusing on achieving the optimal size for resources and storage units to ensure efficient electricity and heat supply simultaneously with the lowest planning cost in different cases. Also, the impacts of the stochastic model incorporating the generation and load uncertainties using the 2m + 1 PEM are evaluated for different case results compared with the deterministic model without uncertainty. The results demonstrated that Case III obtained the best system configuration with the lowest planning cost in deterministic and stochastic models and. This case is capable of simply meeting the electrical and thermal load with the contribution of the energy resources, as well as the CHP and TESs. Also, the IFHO superiority is proved compared with the conventional FHO, and particle swarm optimization (PSO) achieves the lowest planning cost in all cases. Moreover, incorporating the stochastic optimization model, the planning costs of cases I–III are increased by 4.28%, 3.75%, and 3.57%, respectively, compared with the deterministic model. Therefore, the stochastic model is a reliable model due to its incorporating the existence of uncertainties in comparison with the deterministic model, which is based on uncertain data. [ABSTRACT FROM AUTHOR]

Introduction: This paper introduces a deep learning approach based on Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory Networks (BiLSTM), and attention mechanism for stock market prediction and investment decision making in financial management. These methods leverage the advantages of deep learning to capture complex patterns and dependencies in financial time series data. Stock market prediction and investment decisionmaking have always been important issues in financial management. Methods: Traditional statistical models often struggle to handle nonlinear relationships and complex temporal dependencies, thus necessitating the use of deep learning methods to improve prediction accuracy and decision effectiveness. This paper adopts a hybrid deep learning model incorporating CNN, BiLSTM, and attention mechanism. CNN can extract meaningful features from historical price or trading volume data, while BiLSTM can capture dependencies between past and future sequences. The attention mechanism allows the model to focus on the most relevant parts of the data. These methods are integrated to create a comprehensive stock market prediction model. We validate the effectiveness of the proposed methods through experiments on real stock market data. Compared to traditional models, the deep learning model utilizing CNN, BiLSTM, and attention mechanism demonstrates superior performance in stock market prediction and investment decision-making. Results and Discussion: Through ablation experiments on the dataset, our deep learning model achieves the best performance across all metrics. For example, the Mean Absolute Error (MAE) is 15.20, the Mean Absolute Percentage Error (MAPE) is 4.12%, the Root Mean Square Error (RMSE) is 2.13, and the Mean Squared Error (MSE) is 4.56. This indicates that these methods can predict stock market trends and price fluctuations more accurately, providing financial managers with more reliable decision guidance. This research holds significant implications for the field of financial management. It offers investors and financial institutions an innovative approach to better understand and predict stock market behavior, enabling them to make wiser investment decisions. [ABSTRACT FROM AUTHOR]

The research on the recovery of low-grade thermal energy carried away by boiler flue gas is significant for sugar mills. This paper designs a waste heat recovery system based on sugar plant flue gas, integrating absorption refrigeration cycle and the organic Rankine cycle, and the effects of nine working fluids on the system are investigated. The aim is to realize the multi-form conversion of energy. The performance of the system is evaluated in terms of energy, exergy, and economic metrics. Multi-objective optimization is performed with the method of the NSGA-II genetic algorithm. The results show that Butane is the most suitable working fluid for ORC. The exergy efficiency of the system is 32.125% before optimisation, with an increased space cooling capacity of 15820.56 MW per year for the sugar mill. The exergy destruction analysis of the system reveals that the generator accounts for the highest proportion of exergy destruction (50.8%). The entire system shows the LCOE is as low as 0.0406$/kWh under the optimized condition. The optimized system can obtain an estimated annual electricity sales revenue of $136,300, and the sugar mill can save $308,600 in cooling costs. In addition, the payback period can be shortened to 5.79years. [ABSTRACT FROM AUTHOR]

This paper presents thermodynamic (energy and exergy) analysis and comparison of two diff erent marine propulsion steam turbines based on their operating parameters from exploitation. The fi rst turbine did not possess steam reheating and had only two cylinders (high-pressure and low-pressure cylinders), while the second turbine possesses steam reheating and has one additional cylinder (intermediate-pressure cylinder). In the literature at the moment, there cannot be found a direct and exact comparison of these two marine steam turbines and their cylinders based on real exploitation conditions. Along with energy and exergy analyses, the research it is investigated the sensitivity of exergy parameters to the ambient temperature change for both turbines and each cylinder. It is also presented the infl uence of the steam reheating process on the energy and exergy effi ciency of the entire power plant. For both observed turbines and their cylinders it is valid that relative losses and effi ciencies (both energy and exergy) are reverse proportional. The operation of an intermediate pressure cylinder from a steam turbine with reheating is the closest to optimal. Due to the diff erent origins of losses considered in energy and exergy analyses, each analysis detects diff erent turbine cylinders as the most problematic ones. The steam reheating process decreases losses and increases effi ciencies (both energy of each turbine cylinder and the whole turbine. The whole turbine with reheating has an energy effi ciency equal to 81.46% and an exergy effi ciency equal to 86.48%, while the whole turbine without reheating has energy and exergy effi ciencies equal to 76.47% and 80.94%, respectively. Exergy parameters of a steam turbine without reheating as well as its cylinders are much more infl uenced by the ambient temperature change in comparison to the steam turbine with reheating and its cylinders. The steam reheating process will increase the effi ciency of the whole power plant in real exploitation conditions between 10% and 12%. [ABSTRACT FROM AUTHOR]

The commingling of the two types of heavy fuel oils (fuel remaining in the tanks and fuel loaded) is determined by the company's guidance and safety management system. Remained heavy fuel oil must be distributed in the heavy fuel oil tanks before bunkering efficiently, in order to obtain maximum bunker loading. This paper proposes the use of the bisection method to calculate the distribution of the remaining fuel in heavy fuel oil tanks on the liquefied natural gas carrier. The use of this method is illustrated by three examples. The first and second examples show that the iterations converge quickly to the solution. It is found that maximum tank filling with fuel commingling is achievable if the bunker intervals satisfy the company policy of 10% as a lower limit of tank filling, and 90% to 100% as an upper limit. The third example illustrates a situation when the proposed mathematical model does not allow maximization of the total tank capacity without risk assessment. The mathematical spectrum of the possible tank filling solution is presented. Despite its limitations, the bisection method is suitable for onboard applications because it is simple and fast, and can be easily programmed. [ABSTRACT FROM AUTHOR]

In this research is performed an exergy analysis of supercritical CO2 system which uses various waste heat flows from marine diesel engine to produce additional mechanical power. The performed exergy analysis contains whole system as well as each system component individually. The observed system produces useful mechanical power equal to 2299.47 kW which is transferred to the main propulsion propeller shaft. Additionally produced mechanical power by using waste heat only will reduce marine diesel engine fuel consumption and exhaust gas emissions. Main cooler has the highest exergy destruction of all system components and simultaneously the lowest exergy efficiency in the observed system, equal to 32.10% only. One of the possibilities how main cooler exergy efficiency can be increased is by decreasing water mass flow rate through the main cooler and simultaneously by increasing water temperature at the main cooler outlet. Observed system has five heat exchangers which are involved in the CO2 heating process, and it is interesting that the last CO2 heater (exhaust gas waste heat exchanger) increases the CO2 temperature more than all previous four heat exchangers. Whole analyzed waste heat recovery supercritical CO2 system has exergy destruction equal to 2161.68 kW and exergy efficiency of 51.54%. In comparison to a similar CO2 system which uses waste heat from marine gas turbine, system analyzed in this paper has approximately 12% lower exergy efficiency due to much lower waste heat temperature levels (from marine diesel engine) in comparison to temperature levels which occur at the marine gas turbine exhaust. [ABSTRACT FROM AUTHOR]