Efficient Prediction Research Articles

A Lincomycin-Specific Antibody Was Developed Using Hapten Prediction, and an Immunoassay Was Established to Detect Lincomycin in Pork and Milk.

Prolonged consumption of animal-derived foods containing high levels of lincomycin (LIN) residues can adversely impact human health. Therefore, it is essential to develop specific antibodies and immunoassay methods for LIN. This study utilized computational chemistry to predict the efficacy of LIN haptens prior to chemical synthesis, with subsequent confirmation obtained through an immunization experiment. A hybridoma cell line named LIN/1B11 was established, which is specific to LIN. The optimized indirect competitive enzyme-linked immunosorbent assay (ic-ELISA) method exhibited high specificity for detecting LIN residues, with an IC50 value of 0.57 ± 0.03 µg/kg. The method effectively detected LIN residues in pork and milk samples, achieving a limit of detection (LOD) ranging from 0.81 to 1.20 µg/kg and a limit of quantification (LOQ) ranging from 2.09 to 2.29 µg/kg, with recovery rates between 81.9% and 108.8%. This study offers a valuable tool for identifying LIN residues in animal-derived food products. Furthermore, the efficient hapten prediction method presented herein improves antibody preparation efficiency and provides a simple method for researchers in screening haptens.

Combining 2.5D deep learning and conventional features in a joint model for the early detection of sICH expansion.

The study aims to investigate the potential of training efficient deep learning models by using 2.5D (2.5-Dimension) masks of sICH. Furthermore, it intends to evaluate and compare the predictive performance of a joint model incorporating four types of features with standalone 2.5D deep learning, radiomics, radiology, and clinical models for early expansion in sICH. A total of 254 sICH patients were enrolled retrospectively and divided into two groups according to whether the hematoma was enlarged or not. The 2.5D mask of sICH is constructed with the maximum axial, coronal and sagittal planes of the hematoma, which is used to train the deep learning model and extract deep learning features. Predictive models were built on clinic, radiology, radiomics and deep learning features separately and four type features jointly. The diagnostic performance of each model was measured using the receiver operating characteristic curve (AUC), Accuracy, Recall, F1 and decision curve analysis (DCA). The AUCs of the clinic model, radiology model, radiomics model, deep learning model, joint model, and nomogram model on the train set (training and Cross-validation) were 0.639, 0.682, 0.859, 0.807, 0.939, and 0.942, respectively, while the AUCs on the test set (external validation) were 0.680, 0.758, 0.802, 0.857, 0.929, and 0.926. Decision curve analysis showed that the joint model was superior to the other models and demonstrated good consistency between the predicted probability of early hematoma expansion and the actual occurrence probability. Our study demonstrates that the joint model is a more efficient and robust prediction model, as verified by multicenter data. This finding highlights the potential clinical utility of a multifactorial prediction model that integrates various data sources for prognostication in patients with intracerebral hemorrhage. The Critical Relevance Statement: Combining 2.5D deep learning features with clinic features, radiology markers, and radiomics signatures to establish a joint model enabling physicians to conduct better-individualized assessments the risk of early expansion of sICH.

A quantum-corrected Peng‒Robinson equation of state for helium-4 from 3 K to 50 K considering quantum swelling effects through the Feynman‒Hibbs correction of the EXP-6 potential

Accurate and efficient prediction of the properties of helium-4 (He-4) via an equation of state (EOS) is a prerequisite for evaluating liquid helium-4 (LHe-4) storage technology. However, the performance of the Peng–Robinson (PR) EOS in predicting the density of LHe-4 and vapour helium-4 (VHe-4) deteriorates within the thermodynamic ranges of the LHe-4 tank: 3–50 K and 60–600 kPa. To modify the PR EOS, we establish first-order and second-order Feynman–Hibbs (FH)-corrected EXP-6 potentials and propose a reduced effective particle diameter (REPD) correlation with four parameters to consider the quantum swelling effects of LHe-4. On the basis of the rational function form of the REPD correlation, we introduce a quantum-corrected covolume term to develop a regression model for the FH-corrected Peng–Robinson (FH-PR) EOS. Moreover, to improve the effectiveness of regression near the saturation curve, we propose a hypothetical boundary consisting of the saturation curve from 3 K to the critical temperature and a virtual saturation curve from the critical pressure to 600 kPa. The results indicate that the FH − PR EOS shows satisfactory engineering application performance in predicting the density of He-4 within the studied range. Under verification conditions, the average absolute relative deviation (AARD) of the density determined via the FH − PR EOS ranges from 0.72 % to 1.77 %, and the maximum relative deviation (MRD) ranges from 2.17 % to 5.62 %. Under test conditions, the AARD of the density ranged from 1.06 % to 1.71 %, and the MRD ranged from 3.77 % to 7.38 %.

Development of a robust Machine learning model for Ames test outcome prediction

The mutagenicity is an essential parameter for evaluating the safety of pharmaceuticals, chemicals, consumer products, environmentally related compounds and the Ames assay is a significant test for predicting the mutagenicity of chemical compounds. In the data-driven era, developing robust models for efficient mutagenicity prediction before synthesizing and testing in vitro has gained increasing attention. In this study, a machine learning model that could predict Ames mutagenicity based on 2D molecular descriptors was developed. A multistep filtering process that adequately helps in identifying significant descriptors was adopted in this study. Three different sets of descriptors, namely, RDKit, Mordred and CDK were used to train three machine learning algorithms, viz., random forest, xgboost and catboost. The datasets were collected from different resources to develop a robust machine learning model. The robustness of this model was further validated by comparing different available ML and DL models for Ames genotoxicity. Specifically, 12 models, including our xgboost model, were used to validate an external dataset, and our model exhibited excellent performance, with an impressive AUC of 0.97. The codes to predict the genotoxicity of a molecule is available at https://github.com/Naga270588/Genotoxicity.

Remaining useful life prognostics of bearings based on convolution attention networks and enhanced transformer

Rolling bearings are critical components of industrial equipment, and predicting their remaining useful life (RUL) is a challenging task. This article proposes a new end-to-end model (MDSCT) to achieve efficient and accurate prediction of bearing RUL. MDSCT uses the raw vibration signals collected by sensors for prediction, without relying on a large amount of prior knowledge. The feature extraction backbone of this model is composed of an MDSC attention module that integrates multiple parallel depth-wise separable convolutions and an efficient attention mechanism, combined with a tansformer encoder (PPSformer) optimized using patch embedding and probesparse self attention techniques. They are respectively used to capture local subtle features and global dependent features in degraded signals. This article also proposes an improved adaptive activation function AdaptH_Swish to enhance the model's ability to model nonlinear relationships. To comprehensively verify the comprehensive performance of the model, this paper conducted detailed ablation and comparative experiments using two standard datasets, PHM2012 and XJTU-SY. The experimental results not only confirm the rationality and efficiency of the model structure design, but also demonstrate significant advantages in three evaluation indicators compared to various existing methods, fully demonstrating the high generalization and strong robustness of the MDSCT model in bearing RUL prediction tasks.

Prediction and optimization of wet bulb efficiency in solar energy-based novel evaporative cooling system: Response Surface Method Approach

Abstract The wet bulb efficiency is an essential parameter in performance of Indirect evaporative cooling systems, as it impacts efficiently cool air while minimizing energy usage. This paper focuses on model and optimize multi tubular type wet channel for indirect evaporative cooler system. The optimized wet bulb efficiency based on Box-Behnken experimental design model and response surface method. The methodology included predicting and optimizing input process and working flow parameters of indirect evaporative cooling system to maximize wet bulb efficiency. A quadratic model of response surface method was used to evaluate influence of each factor on wet bulb efficiency, which was validated through analysis of variance results. The study found a maximum WBE of 89.65% under specific conditions, with a relative error of 3.25% between predicted and experimental result. This optimization process, governed by RSM, determined essential parameters for enhancing efficiency of system. These parameters include inlet process temperature of 28°C, working air velocity of 3.5 m/s, relative humidity of 45%, and inlet working air temperature of 24°C. These results demonstrate practical significance of current research, providing useful insights for optimal design and optimization of indirect evaporative cooling systems to fulfil cooling requirements while minimising energy consumption.

Efficient damage prediction and sensitivity analysis in rectangular welded plates subjected to repeated blast loads utilizing deep learning networks

Efficient damage prediction and sensitivity analysis in rectangular welded plates subjected to repeated blast loads utilizing deep learning networks

The Key Descriptors for Predicting the Exciton Binding Energy of Organic Photovoltaic Materials

Exciton binding energy (Eb) is a key parameter to determine the mechanism and performance of organic optoelectronic devices. Small Eb benefits to reduce the interfacial energy offset and the energy loss of organic solar cells. However, quantum‐chemical calculations of the Eb in solid state with considering electronic polarization effects are extremely time‐consuming. Furthermore, current studies lack critical descriptors. Here, we use data‐driven machine learning (ML) to accelerate the computation and identify the key descriptors most relevant to the solid‐state Eb. The results verify two key descriptors associated with molecular and aggregation‐state properties for efficient prediction of the solid‐state Eb. Moreover, a very high accuracy is achieved by using the extreme gradient boosting algorithm, with the Pearson’s correlation coefficient of 0.92. Finally, we use this ML model to predict the Eb of thin films, which is difficult to achieve using the current quantum‐chemical calculations due to the large structural disorder. Remarkably, the predicted thin‐film Eb values are fully consistent with the results of temperature‐dependent photoluminescence spectra. Therefore, our work provides an accurate and efficient approach to predict the solid‐state Eb and would be helpful to accelerate the exploitation of novel promising organic photovoltaic materials.

The Key Descriptors for Predicting the Exciton Binding Energy of Organic Photovoltaic Materials.

Exciton binding energy (Eb) is a key parameter to determine the mechanism and performance of organic optoelectronic devices. Small Eb benefits to reduce the interfacial energy offset and the energy loss of organic solar cells. However, quantum-chemical calculations of the Eb in solid state with considering electronic polarization effects are extremely time-consuming. Furthermore, current studies lack critical descriptors. Here, we use data-driven machine learning (ML) to accelerate the computation and identify the key descriptors most relevant to the solid-state Eb. The results verify two key descriptors associated with molecular and aggregation-state properties for efficient prediction of the solid-state Eb. Moreover, a very high accuracy is achieved by using the extreme gradient boosting algorithm, with the Pearson's correlation coefficient of 0.92. Finally, we use this ML model to predict the Eb of thin films, which is difficult to achieve using the current quantum-chemical calculations due to the large structural disorder. Remarkably, the predicted thin-film Eb values are fully consistent with the results of temperature-dependent photoluminescence spectra. Therefore, our work provides an accurate and efficient approach to predict the solid-state Eb and would be helpful to accelerate the exploitation of novel promising organic photovoltaic materials.

Hybrid Class Balancing Approach for Chemical Compound Toxicity Prediction.

Computational methods are crucial for efficient and cost-effective drug toxicity prediction. Unfortunately, the data used for prediction is often imbalanced, resulting in biased models that favor the majority class. This paper proposes an approach to apply a hybrid class balancing technique and evaluate its performance on computational models for toxicity prediction in Tox21 datasets. The process begins by converting chemical compound data structures (SMILES strings) from various bioassay datasets into molecular descriptors that can be processed by algorithms. Subsequently, Undersampling and Oversampling techniques are applied in two different schemes on the training data. In the first scheme (Individual), only one balancing technique (Oversampling or Undersampling) is used. In the second scheme (Hybrid), the training data is divided according to a ratio (e.g., 90-10), applying a different balancing technique to each proportion. We considered eight resampling techniques (four Oversampling and four Undersampling), six molecular descriptors (based on MACCS, ECFP, and Mordred), and five classification models (KNN, MLP, RF, XGB and SVM) over 10 bioassay datasets to determine the configurations that yield the best performance. We defined three testing scenarios: without balancing techniques (baseline), Individual, and Hybrid. We found that using the ENN technique in the MACCS-MLP combination resulted in a 10.01% improvement in performance. The increase for ECFP6-2048 was 16.47% after incorporating a combination of the SMOTE (10%) and RUS (90%) techniques. Meanwhile, using the same combination of techniques, MORDRED-XGB showed the most significant increase in performance, achieving a 22.62% improvement. Integrating any of the class balancing schemes resulted in a minimum of 10.01% improvement in prediction performance compared to the best baseline configuration. In this study, Undersampling techniques were more appropriate due to the significant overlap among samples. By eliminating specific samples from the predominant class that are close to the minority class, this overlap is greatly reduced.

YOLOv5-POS: research on cabbage pose prediction method based on multi-task perception technology.

Accurate and rapid identification of cabbage posture is crucial for minimizing damage to cabbage heads during mechanical harvesting. However, due to the structural complexity of cabbages, current methods encounter challenges in detecting and segmenting the heads and roots. Therefore, exploring efficient cabbage posture prediction methods is of great significance. This study introduces YOLOv5-POS, an innovative cabbage posture prediction approach. Building on the YOLOv5s backbone, this method enhances detection and segmentation capabilities for cabbage heads and roots by incorporating C-RepGFPN to replace the traditional Neck layer, optimizing feature extraction and upsampling strategies, and refining the C-Seg segmentation head. Additionally, a cabbage root growth prediction model based on Bézier curves is proposed, using the geometric moment method for key point identification and the anti-gravity stem-seeking principle to determine root-head junctions. It performs precision root growth curve fitting and prediction, effectively overcoming the challenge posed by the outer leaves completely enclosing the cabbage root stem. YOLOv5-POS was tested on a multi-variety cabbage dataset, achieving an F1 score of 98.8% for head and root detection, with an instance segmentation accuracy of 93.5%. The posture recognition model demonstrated an average absolute error of 1.38° and an average relative error of 2.32%, while the root growth prediction model reached an accuracy of 98%. Cabbage posture recognition was completed within 28 milliseconds, enabling real-time harvesting. The enhanced model effectively addresses the challenges of cabbage segmentation and posture prediction, providing a highly accurate and efficient solution for automated harvesting, minimizing crop damage, and improving operational efficiency.

Multi-Cover Persistence (MCP)-based machine learning for polymer property prediction.

Accurate and efficient prediction of polymers properties is crucial for polymer design. Recently, data-driven artificial intelligence (AI) models have demonstrated great promise in polymers property analysis. Even with the great progresses, a pivotal challenge in all the AI-driven models remains to be the effective representation of molecules. Here we introduce Multi-Cover Persistence (MCP)-based molecular representation and featurization for the first time. Our MCP-based polymer descriptors are combined with machine learning models, in particular, Gradient Boosting Tree (GBT) models, for polymers property prediction. Different from all previous molecular representation, polymer molecular structure and interactions are represented as MCP, which utilizes Delaunay slices at different dimensions and Rhomboid tiling to characterize the complicated geometric and topological information within the data. Statistic features from the generated persistent barcodes are used as polymer descriptors, and further combined with GBT model. Our model has been extensively validated on polymer benchmark datasets. It has been found that our models can outperform traditional fingerprint-based models and has similar accuracy with geometric deep learning models. In particular, our model tends to be more effective on large-sized monomer structures, demonstrating the great potential of MCP in characterizing more complicated polymer data. This work underscores the potential of MCP in polymer informatics, presenting a novel perspective on molecular representation and its application in polymer science.

A sensitivity study on the starting simulations of turbojet engines

Abstract Gas turbine engine starting models require a lot of calibration to represent reality with acceptable accuracy due to the lack of high-quality component rig data in the sub-idle region. A detailed sensitivity study is presented in this paper to guide such calibration efforts. A thermodynamic component-matching type transient model of a single-spool turbojet engine with shaft and heat-soakage dynamics is employed for this purpose. Turbomachinery component maps are extended to sub-idle using an in-house map smoothing tool and the strategies presented by Kurzke recently. These extension strategies make use of the correlations hidden in the already available regions of the maps and ensure physical consistency. However, they contain some uncertainty, even when an experimentally obtained zero-speed line is available. Combustor sub-idle efficiency, stability limits, and delay are taken from the literature. Due to the chaotic nature of a combustor in the sub-idle region, a precise prediction of the combustor efficiency seems impossible. Effects of uncertainties related to sub-idle turbomachinery map extensions, burner efficiency, and heat soakage are investigated in this paper. Two popular fuel control strategies are employed and compared to see how controls deal with these uncertainties. It is concluded that turbomachinery torque characteristics and turbine capacities are the most important parameters when calibrating a starting model with a control based on rotational acceleration while burner efficiency and heat soakage are added on top of these with a control based on fuel flow rate.

Data-Driven Materials Research and Development for Functional Coatings.

Functional coatings, including organic and inorganic coatings, play a vital role in various industries by providing a protective layer and introducing unique functionalities. However, its design often involves time-consuming experimentation with multiple materials and processing parameters. To overcome these limitations, data-driven approaches are gaining traction in materials science. In this paper, recent advances in data-driven materials research and development (R&D) for functional coatings, highlighting the importance, data sources, working processes, and applications of this paradigm are summarized. It is begun by discussing the challenges of traditional methods, then introduce typical data-driven processes. It is demonstrated how data-driven approaches enable the identification of correlations between input parameters and coating performance, thus allowing for efficient prediction and design. Furthermore, carefully selected case studies are presented across diverse industries that exemplify the effectiveness of data-driven methods in accelerating the discovery of new functional coatings with tailored properties. Finally, the emerging research directions, involving integrating advanced techniques and data from different sources, are addressed. Overall, this review provides an overview of data-driven materials R&D for functional coatings, shedding light on its potential and future developments.

Systematization of sunflower genotypes based on seed phenotypic characteristics using neural networks

In recent years, various datasets related to the phenotyping of sunflower genotypes have become increasingly accessible. However, one of the key challenges remains the efficient and accurate prediction of phenotypes based on genotypes in the context of climate change. Analyzing phenotypes at different levels of organization and detecting connections between phenotypes and genotypes require the integration and processing of large, diverse, and often noisy datasets. Machine learning offers a broad arsenal of methods and approaches for identifying predictive patterns in such data. Therefore, the research aimed to develop a methodology for the systematization of sunflower genotypes based on seed phenotypic characteristics using the data vector quantization method and neural networks. The study revealed the phenotypic characteristics of sunflower seeds from various genotypes selected by the Institute of Oilseed Crops of NAAS, grown in the southern Steppe of Ukraine, including seed length, width, thickness, seed mass, kernel mass, and seed coat cracking force. For this purpose, appropriate laboratory equipment was developed, including two modules for determining the morphological and rheological properties of seeds. The developed methodology for the systematization of sunflower genotypes based on seed phenotypic characteristics includes the following steps: measuring the characteristics of sunflower seeds from various samples (parental components); studying the mutual correlation of characteristics; conducting hierarchical cluster analysis of the data using the Ward's method; determining the optimal number of groups; performing k-means clustering using the vector quantization method; determining the correspondence of ranges of characteristics to the group; training a neural network to group the data by samples and created groups; verifying the adequacy of the neural network on test data. The developed methodology was tested, and the MLP 30-15-3 neural network for grouping data by samples and created groups of sunflower seeds was developed in the Statistica software package. The network's training efficiency was 99.4%, and such of testing and validation was 95.6% and 96.7%, respectively.

Optimizing modified asphalt binder performance at high and intermediate temperatures using experimental and machine learning approaches

Asphalt pavements are highly susceptible to temperature-induced stresses, which can lead to significant deterioration such as rutting at high temperatures and cracking at low temperatures. These challenges are exacerbated by global warming, which raises pavement temperatures and potentially accelerates material degradation. Addressing these issues, this study utilizes two base asphalt binders, PG 64–22 and PG 76–22, and the use of phase change materials (PCMs) like styrene-butadiene-styrene, lignin, and ground tire rubber to enhance the resilience of asphalt binders. Through an extensive experimental investigation, which includes aging processes such as rolling thin film oven and pressure aging vessel, alongside a series of both traditional and advanced tests (including dynamic shear rheometer, rotational viscometer, Fourier transform infrared spectroscopy, among others), a comprehensive set of 51 samples were analysed. By incorporating these binders and PCMs into asphalt, the experimental work demonstrated a significant improvement in the thermal and mechanical properties, effectively increasing resistance to high-temperature rutting and fatigue cracking. Complementing the experimental approach, machine learning models were employed to predict complex asphalt binder properties, such as combustion value and failure temperature, based on the experimental data. This dual approach fills a significant gap in the literature by offering a more efficient method and prediction model for optimizing pavement materials amidst climate change, while also promising to significantly advance pavement technology by enhancing material design and performance.

SLAM: Structure-aware lysine β-hydroxybutyrylation prediction with protein language model

Post-translational modifications (PTMs) diversify protein functions by adding/removing chemical groups to certain amino acid. As a newly-reported PTM, lysine β-hydroxybutyrylation (Kbhb) presents a new avenue to functional proteomics. Therefore, accurate and efficient prediction of Kbhb sites is imperative. However, the current experimental methods for identifying PTM sites are often expensive and time-consuming. Up to now, there is no computational method proposed for Kbhb sites detection. To this end, we present the first deep learning-based method, termed SLAM, to in silico identify lysine β-hydroxybutyrylation sites. The performance of SLAM is evaluated on both 5-fold cross-validation and independent test, achieving 0.890, 0.899, 0.907 and 0.923 in terms of AUROC values, on the general and species-specific independent test sets, respectively. As one example, we predicted the potential Kbhb sites in human S-adenosyl-L-homocysteine hydrolase, which is in agreement with experimentally-verified Kbhb sites. In summary, our method could enable accurate and efficient characterization of novel Kbhb sites that are crucial for the function and stability of proteins and could be applied in the structure-guided identification of other important PTM sites. The SLAM online service and source code is available at https://ai4bio.online/SLAM and https://github.com/Gabriel-QIN/SLAM, respectively.

PSVI-6 Genomic determinants of the rumen microbiome and metabolome in Angus steers

Abstract The rumen microbiome provides approximately 70% of energy required for the ruminant host through fermentation byproducts. Due to microbial impacts on host health and productivity, several studies have associated microbiome and metabolome variation with important sustainability phenotypes, such as feed efficiency and methane emissions. Recent studies have indicated the microbial communities present in rumen fluid are under low-to-moderate host genetic control. However, microorganisms present on the rumen wall have been underrepresented in research, negating their unique role in host-microbiome interactions. Current studies investigating heritability of the rumen planktonic microorganisms are plagued by small sample sizes, high management and breed heterogeneity, and low resolution for classifying microbial communities. To determine the heritability of the rumen fluid and epithelium-associated microbiome and metabolome, Angus steers (n = 350) were enrolled in a 70-d feed efficiency trial, where real-time dry matter intake was measured. Body weights were collected throughout the trial to calculate average daily gain and residual feed intake. Mid-trial, a novel surgery method was used to swab the rumen wall to capture epithelium-associated microorganisms. On d 70, 50 mL of rumen fluid was obtained from steers using orogastric tubing for collection of planktonic microbial communities. Metagenomic DNA extracted from both rumen fluid and epimural samples was used for reduced representation sequencing to predict microbial features such as diversity, composition, and putative function. Genomic DNA isolated from whole blood was used to perform low-pass whole genome sequencing for use in downstream heritability estimates and variant association analyses. Many core microbial abundances are under low-to-moderate host genetic control (h2 > 0.15; P < 0.05); however, many microorganisms had low heritability estimates (h2 < 0.10). Preliminary genome-wide associations studies are vastly underpowered to detect host genetic variants associated with microbial features, but data generated using the total number of animals will better refine these polymorphisms. Several key heritable metabolites were identified in amino acid metabolism pathways in rumen fluid (P < 0.05). The rumen microbiome appears to act as a complex polygenic trait and incorporating microbial features into prediction models may improve feed efficiency estimations. Understanding host genetic control of the rumen microbiome and metabolome may empower downstream applications to enhance genomic and phenotypic predictions for feed efficiency and other sustainability traits in beef cattle.

Prediction of outflow temperature of reservoir based on theory-guided machine learning models and optimization of operation for improving outflow temperature

The proportion of hydropower within the energy-resource structure is gradually increasing. However, the construction of reservoirs inevitably leads to ecological and environmental issues, especially the release of low-temperature water in summer and its detrimental effects on fish spawning and reproduction. These issues can be alleviated by optimizing the reservoir operation scheme, but the large amount of time required for predicting the reservoir outflow temperature emerges as the primary constraint. In this study, we considered Pubugou (PBG) Reservoir and proposed an efficient and high-precision prediction method for reservoir outflow temperature based on theory-guided machine learning (TGML) algorithms. This method was applied to the multi-objective optimization of the reservoir operations, and an optimization operation plan was proposed to improve the outflow temperature. The research results indicate that the LightGBM machine learning model shows good predictive performance, with a maximum deviation of no more than 1 °C in predicting the outflow temperature. There is a significant competitive relationship between reservoir power generation and the outflow temperature. Compared with the current operational scheme, the maximum power operation plan results in a 4.4% increase in total power generation but causes the average outflow temperature during the fish spawning period to decrease by 0.1 °C. The total power generation of the improved outflow temperature operation scheme was 0.8% lower than that of the actual scheme, but the average outflow temperature during fish spawning increased by 0.2 °C. Properly increasing the discharge flow from the reservoir and lowering the water level can thus help alleviate the issue of outflow temperature.

A study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation

This paper is a study on 5-cycle fuel economy prediction model of electric vehicles using numerical simulation. It aims to develop a prediction model for the energy efficiency of electric vehicles and compare it with the results of chassis dynamometer tests. Driving tests were conducted on a chassis dynamometer, which included various test cycles of the 5-cycle test. Using MATLAB Simulink, a vehicle dynamics analysis model for electric vehicles was constructed, and analytical research was conducted focusing on light-duty electric vehicles. By comparing and analyzing the experimental data obtained from the chassis dynamometer with the data calculated through simulation, it is possible to verify the accuracy of the electric vehicle energy efficiency prediction model. And the reliability of the simulation model was secured by comparing the experimental data obtained through chassis dynamometer test with the simulation results of the developed electric vehicle energy efficiency prediction model. There is good agreement between the experimental and simulation results. In most areas, the error rate remains below 3 %, indicating that the simulation model closely follows the experimental results from the chassis dynamometer. Based on these results, it is expected that the developed simulation model can be utilized for measuring the energy efficiency of electric vehicles.