Decomposition Results Research Articles

The relationship between self-efficacy, health literacy, and quality of life in patients with chronic diseases: a cross-sectional study in China.

Self-efficacy and health literacy are closely related to the quality of life in patients with chronic diseases; however, it remains unclear whether their combined effects on the quality of life (QoL) in these patients operate through mediation, interaction, or a combination of both. The research occurred in China between July 10 and September 15, 2021. A multi-stage random sampling technique was utilized to gather information on self-efficacy, health literacy, and QoL among individuals with chronic diseases. Linear regression models investigated the relationships between these patients' self-efficacy, health literacy, and QoL. Additionally, the four-way decomposition method was used to decompose the overall effects of self-efficacy and health literacy on the QoL in patients with chronic diseases. Significant correlations were found between self-efficacy, health literacy, and QoL among individuals with chronic diseases (all p < 0.05). In the four-way decomposition results, the results of the European Quality of Life Five Dimension Five Level (EQ-5D-5L) displayed the interaction effects mediated by self-efficacy, and the reference interaction effects were not significant, with small effect sizes observed. The influence of health literacy levels on the QoL in these patients was primarily attributed to the controlled direct effect (CDE), accounting for approximately 86.12% [excess relative risk = 0.00415; 95% CI: 0.00326, 0.00504; p < 0.0001]. The proportion solely attributable to the pure indirect effect (PIE) of self-efficacy was 14.5% [excess relative risk = 0.0007; 95% CI: 0.00031, 0.00109; p < 0.0001]. In the EQ visual analog scale (EQ-VAS) results, the proportion of the controlled direct effect was 84.9% [excess relative risk = 0.62443; 95% CI: 0.52269, 0.72618; p < 0.0001], while the proportion solely attributable to the pure indirect effect of mediation was 14.8% [excess relative risk = 0.10876; 95% CI: 0.06409, 0.15344; p < 0.0001]. Self-efficacy and health literacy primarily influence QoL in patients with chronic diseases through controlled and pure indirect effects. Enhancing patients' health literacy and self-efficacy can contribute to improving their QoL.

Shanghai Transport Carbon Emission Forecasting Study Based on CEEMD-IWOA-KELM Model

In the light of the worsening of, and the adverse effects produced by, global warming, a study of Shanghai’s transport carbon emissions can provide an advanced model that can be replicated throughout other cities, thus assisting in the management and reduction of carbon emissions. Considering the volatility and nonlinearity of the carbon emission data series of the transport industry, a prediction model combining complementary ensemble empirical modal decomposition (CEEMD), the improved whale optimization algorithm (IWOA), and the Kernel Extreme Learning Machine (KELM) is proposed for a more accurate prediction of the forecasting of carbon emissions from Shanghai’s transport sector. First, nine indicators were screened as the influencing factors of Shanghai’s transport carbon emissions through the STIRPAT model, and the corresponding carbon emissions were calculated with data related to Shanghai’s transport carbon emissions from 1995 to 2019; Secondly, CEEMD was used to decompose the original data into multiple smooth series and one residual term, and KELM was applied to build a prediction model for each decomposition result, and IWOA was used to optimize the model parameters. The experimental results also demonstrate that CEEMD can effectively reduce model errors. Comparative experiments show that the IWOA algorithm can significantly enhance the stability of machine learning models. The outcomes of various experiments indicate that the CEEMD-IWOA-KELM model produces optimal results with the highest accuracy. Additionally, this model exhibits high stability, as it provides a wider range of methods for predicting carbon emissions and contributing to carbon reduction targets.

A VMD-LSTNet-Attention model for concentration prediction of mixed gases

Gases typically exist as mixed states, which normally contain more information and exhibit more intricate features than single gas. Hence, the task of predicting mixed gas concentrations poses a challenging endeavor in the field of gas detection. In this study, a Variational Mode Decomposition method was combined with Long and Short-term Time-series Network-Attention to build a VMD-LSTNet-Attention model, based on which the response signals of electronic nose (E-nose) is processed, achieving a high-precision concentrations prediction of carbon monoxide (CO) and ethylene mixed gases. Firstly, a dataset containing the raw response signals of CO and ethylene was collected using an gas data collection system. Then, VMD algorithm was used to extract and decompose the essential features from the E-nose signals into multiple components. Further, in pursuit of optimal decomposition results, Particle Swarm Optimization (PSO) algorithm was employed to optimize the penalty factor α and determine the most effective decomposition level K. Subsequently, the LSTNet-Attention model was employed to analyze the concentrations of mixed gases, and the predictive R-square values for CO and ethylene reach 0.993 and 0.975, respectively. Finally, the comparison with the traditional deep learning models and the popular models demonstrates the effectiveness of the proposed VMD-LSTNet-Attention model for concentration prediction of mixed gases.

Spectral decomposition for collective Thomson scattering based on an improved genetic algorithm.

Collective Thomson scattering (CTS) is a diagnostic technique that obtains ion temperature and ion composition of plasma by spectral decomposition from scattering spectra. Bayesian estimation and least squares fitting are usually applied in this spectral decomposition process. Nevertheless, these spectral decomposition methods strongly rely on measurements of other diagnostic systems, and the measurement errors of other systems would influence the spectral decomposition results. In this article, an improved genetic algorithm is applied to decompose the scattering spectra of CTS. By analyzing the sensitivity of plasma parameters, the width and slope of the scattering spectrum are found to be strongly associated with ion temperature. Based on this correlation relation, a new fitness function is designed to provide a more precise estimation of ion temperature. Meanwhile, adaptive crossover and mutation operators are introduced to solve the premature convergence problem. This improved genetic algorithm with the new fitness function can obtain a more precise ion temperature from scattering spectra of CTS and does not rely on the measurement of other diagnostic systems, which has an extensive application prospect in data processing of CTS.

Decoupling economic development from carbon emissions: Insights from Chinese provinces

Decoupling economic development from carbon emissions is crucial for achieving sustainable development, particularly at the regional level. It also guides coordinated socio-economic development between regions. However, fewer studies analyze the long-term and short-term factors affecting decoupling from a dynamic perspective. Using annual data from 30 Chinese provinces from 2000 to 2021, this study assesses the level of decoupling in each province at both temporal and spatial levels and explores the factors influencing decoupling in the long and short term. The empirical results show that in the long run, the growth of industrial structure, R&D investment intensity, urban-rural income gap, and foreign trade promote the decoupling of economic development from carbon emissions. Conversely, the growth of energy consumption structure and energy prices inhibit decoupling. Short-term factors affecting decoupling vary significantly from province to province. To ensure the accuracy of the empirical results, this study conducted multidimensional heterogeneity and robustness analyses. Combined with the impulse response and variance decomposition results, it provides rational suggestions for effective decoupling strategies. This study offers a scientific basis for formulating sustainable development policies in various regions and serves as a valuable reference for other developing countries aiming to achieve harmonious economic and environmental development.

Analysis of unsteady wake flow in centrifugal pump volute by using mode decomposition method

To analyze the coherent structure of wake flow in volute and its corresponding frequency information, dynamic modal decomposition (DMD), classic and spectral proper orthogonal decomposition (SPOD), were employed to decompose the transient flow field of the centrifugal pump volute. The snapshot set was constructed by means of the velocity field data at different moments in volute based on the large eddy simulation (LES) approach. The basic principles of the DMD, POD, and SPOD methods were compared in detail, and the decomposition results of the three methods on the wake flow structure in volute were compared and analyzed. The analysis results show that DMD can decompose the wake flow into coherent structures with different frequencies, including the basic steady-state structure, the dynamic modal flow field structure characterizing rotor–stator interaction (the first three modes with frequencies of 145.81, 291.61, and 437.43 Hz, respectively), and dissipative modal flow field structure characterizing fragmentized vortex (the fourth mode with a frequency of 486.03 Hz) in the volute. The POD can decompose the wake flow into flow structures with different energy levels. The first four modal energies account for more than 66% of total energy, which represents the large-scale structure with higher energy, and its dominant frequencies correspond to the blade passing frequency (145 Hz) and its frequency multiplication (290 Hz). The SPOD can not only decompose the complex wake flow into structural features of different energy levels but also has single-frequency characteristics of its modal structures. Compared with DMD and POD methods, the SPOD has the advantages of both, and can reflect the evolution characteristics of the wake flow in the volute.

RV-FELM: Futures commodity price forecasting based on RIME-VMD algorithm coupled with FA-ELM

Commodity futures are an important hedging tool in material trade, and by accurately predicting prices, countries and firms are able to make informed production and consumption decisions. This paper introduces a novel machine learning ensemble method that combines decomposition algorithms and physical optimization algorithms to predict commodity futures prices. First, the VMD(Variational mode decomposition) is optimized by the RIME algorithm (Rime optimization algorithm) to obtain the optimal modal decomposition results, and the trend and seasonal terms are predicted using the ELM (Extreme Learning Machines) and FA (Fourier Attention) models, respectively, and the results are finally synthesized. The results show that the MAPE(mean absolute percentage error) of one-step, three-step, and six-step methods for predicting crude oil prices are 0.48%, 0.66%, and 0.75%, respectively, and the MAPE of soybean prediction results are 0.22%, 0.27%, and 0.37%, respectively. The empirical results and ablation experiments show that it outperforms other benchmark models in terms of both horizontal and directional accuracy. Notably, it outperforms in predicting soybean futures prices, which demonstrates the ability of our model to better capture the characteristics of both the time and frequency domains of the series, to take sufficient consideration of the series characteristics, and to ensure robustness.

Kinetic study on the thermal decomposition of iron (II) sulfate using a global optimization approach

The thermal decomposition of sulfates is a major intermediate reaction in oxidizing roasting processes associated with metal sulfides. The optimization of iron (II) sulfate could contribute to lowering processes’ temperatures in complex systems. The present work presents the experimental and modeling results of the thermal decomposition of iron (II) sulfate heptahydrate. The investigation was first based on thermodynamic simulations followed by non-isothermal thermogravimetric analysis. The modeling technique consisted of differential equations formulated to describe each decomposition step individually, with the kinetic parameters estimated using particle swarm optimization (PSO). Using this modeling methodology over a graphical method is advantageous as it can simulate reactions that may occur simultaneously. The modeling succeeded in dehydration and desulfation steps, with an overall R2 value of 0.99. The activation energies and apparent reaction order associated with the dehydration steps were 53.3kJ.mol−1/n=1.65, 88.8kJ.mol−1/n=1.12, and 124kJ.mol−1/n=2.0 for FeSO4.7H2O, FeSO4.4H2O, and FeSO4.H2O, respectively. The desulfation steps were associated with higher activation energy values. In that case, 184kJ.mol−1 and 264kJ.mol−1 for FeSO4 and Fe2(SO4)3, respectively. Regarding the apparent reaction order, both desulfation steps showed to be first-order reactions.

Pengaruh Kebijakan Moneter dan Fiskal terhadap Pengangguran di Indonesia

The purpose of this study is to analyze the impact of monetary policy as seen from the BI Rate and the money supply (M2, and fiscal policy as seen from government spending and tax revenue in influencing the unemployment rate in Indonesia. The approach used in this research is quantitative. The data used are the BI Rate, the money supply (M2), government spending, tax revenue and unemployment in the form of time series data in an annual form from 1995 to 2019. The method used in this study is the Vector Auto analysis model. Regression (VAR). The stages used in this research test are a stationarity test, optimum lag test, VAR stability test, impulse response test, and variance decomposition test. The results of the impulse response indicate that the unemployment variable responds most to the shock of the interest rate variable (monetary policy) compared to other variables. The results of variance decomposition indicate that the contribution given by the BI Rate to the unemployment rate is the most significant relative to the contribution given by the variable money supply (M2), government spending, and tax revenue.

An Eco-Friendly Approach for the Synthesis of Ni/NiO/g-C3N4 Nanocomposite Using Cleome Gynandra Leaf Extract for Biomedical and Photocatalytic Applications

The (Ni/NiO)[Formula: see text]/g-C3N4 nanocomposite was green synthesized using Cleome gynandra via sol–gel method. The photocatalytic dye decomposition, antioxidant and antidiabetic activities of the nanocomposite were studied and compared with those of (Ni/NiO)[Formula: see text] and g-C3N4. The nanocomposite exhibited enhanced photocatalytic efficiency of 97% compared to (Ni/NiO)[Formula: see text] (63%) and g-C3N4 (80%) due to the synergistic effect between both the composite partners. The nanocomposite, (Ni/NiO)[Formula: see text]/g-C3N4 showed notable antioxidant activity in DPPH (IC[Formula: see text] of [Formula: see text][Formula: see text][Formula: see text]g/mL with 87.06% of inhibition at 500[Formula: see text][Formula: see text]g/mL) and ABTS (IC[Formula: see text] of [Formula: see text][Formula: see text][Formula: see text]g/mL with 88.40 % of inhibition at 500[Formula: see text][Formula: see text]g/mL) radical scavenging assays. The green synthesized nanocomposite also exhibited significant antidiabetic activity in [Formula: see text]-amylase and [Formula: see text]-glucosidase enzyme inhibition assays with the IC[Formula: see text] of [Formula: see text][Formula: see text][Formula: see text]g/mL and [Formula: see text][Formula: see text][Formula: see text]g/mL, respectively. The XRD and SEM analyses confirmed the nanocomposite formation of (Ni/NiO)[Formula: see text]/g-C3N4. The results showed that the green synthesized (Ni/NiO)[Formula: see text]/g-C3N4 has desirable dye degradation, antidiabetic and antioxidant properties. This paper presents the results of the remarkable photocatalytic dye decomposition, antidiabetic and antioxidant properties of the nanocomposite (Ni/NiO)[Formula: see text]/g-C3N4 synthesized via an eco-friendly greeny way using Cleome gynandra leaf extract which may be the first report of this kind utilizing this plant.

Pelatihan Pemanfaatan Limbah Kelapa Sawit Menjadi Pupuk Semi Organik Bagi Siswa Jurusan Agribisnis Tanaman Perkebunan SMKN 1 Kuok

In 2023, Riau's oil palm plantation area with cultivation status will reach 3.5 million hectares with palm oil production reaching 9.06 million tons (Ministry of Agriculture, 2023). This figure makes Riau Province the largest palm oil center province in Indonesia. As the area of oil palm plantations increases, palm oil waste will be produced consisting of empty bunches, fronds, shells and fiber. This palm oil waste can pollute the environment in water and soil and can also threaten biodiversity. One use of palm oil waste is compost. Organic fertilizer (compost) is the final and/or intermediate result of changes or decomposition of plant and animal parts and remains, for example cake, guano, bone meal, livestock waste and so on (Ege et al., 2018). The objectives of this PKM are given to students of SMKN 1 Kuok, namely 1) providing early knowledge about the use of palm oil waste, 2) understanding how to make compost from palm oil waste, and 3) socializing the facilities and infrastructure available at Kampar Polytechnic . Large land and facilities for making compost fertilizer are not available in the ATP department of SMKN 1 Kuok. So with this training in making compost fertilizer, we can overcome the problem of places and practicum areas that are not available at SMKN 1 Kuok.

Pulsations of Three Rapidly Oscillating Ap Stars TIC 96315731, TIC 72392575, and TIC 318007796

We analyze the frequencies of three known roAp stars, TIC 96315731, TIC 72392575, and TIC 318007796, using the light curves from the Transiting Exoplanet Survey Satellite. For TIC 96315731, the rotational and pulsational frequencies are 0.1498360 day−1 and 165.2609 day−1, respectively. In the case of TIC 72392575, the rotational frequency is 0.25551 day−1. We detect a quintuplet of pulsation frequencies with a center frequency of 135.9233 day−1, along with two signals within the second pair of rotational sidelobes of the quintuplet separated by the rotation frequency. These two signals may correspond to the frequencies of a dipole mode. In TIC 318007796, the rotational and pulsational frequencies are 0.2475021 day−1, 192.73995 day−1, and 196.33065 day−1, respectively. Based on the oblique pulsator model, we calculate the rotation inclination i and magnetic obliquity angle β for the stars, which provide the geometry of the pulsation modes. Combining the phases of the frequency quintuplets, the pulsation amplitude and phase modulation curves, and the results of spherical harmonic decomposition, we conclude that the pulsation modes of frequency quintuplets in TIC 96315731, TIC 72392575, and TIC 318007796 correspond to distorted dipole mode, distorted quadrupole mode, and distorted dipole mode, respectively.

Unveiling the fungal diversity and associated secondary metabolism on black apples.

Black apples are the result of late-stage microbial decomposition after falling to the ground. This phenomenon is highly comparable from year to year, with the filamentous fungus Monilinia fructigena most commonly being the first invader, followed by Penicillium expansum. Motivated by the fact that only little chemistry has been reported from apple microbiomes, we set out to investigate the chemical diversity and potential ecological roles of secondary metabolites (SMs) in a total of 38 black apples. Metabolomics analyses were conducted on either whole apples or small excisions of fungal biomass derived from black apples. Annotation of fungal SMs in black apple extracts was aided by the cultivation of 15 recently isolated fungal strains on 9 different substrates in a One Strain Many Compounds (OSMAC) approach, leading to the identification of 3,319 unique chemical features. Only 6.4% were attributable to known compounds based on analysis of high-performance liquid chromatography-high-resolution mass spectrometry (HPLC-HRMS/MS) data using spectral library matching tools. Of the 1,606 features detected in the black apple extracts, 32% could be assigned as fungal-derived, due to their presence in the OSMAC-based training data set. Notably, the detection of several antifungal compounds indicates the importance of such compounds for the invasion of and control of other microbial competitors on apples. In conclusion, the diversity and abundance of microbial SMs on black apples were found to be much higher than that typically observed for other environmental microbiomes. Detection of SMs known to be produced by the six fungal species tested also highlights a succession of fungal growth following the initial invader M. fructigena.IMPORTANCEMicrobial secondary metabolites constitute a significant reservoir of biologically potent and clinically valuable chemical scaffolds. However, their usefulness is hampered by rapidly developing resistance, resulting in reduced profitability of such research endeavors. Hence, the ecological role of such microbial secondary metabolites must be considered to understand how best to utilize such compounds as chemotherapeutics. Here, we explore an under-investigated environmental microbiome in the case of black apples; a veritable "low-hanging fruit," with relatively high abundances and diversity of microbially produced secondary metabolites. Using both a targeted and untargeted metabolomics approach, the interplay between metabolites, other microbes, and the apple host itself was investigated. This study highlights the surprisingly low incidence of known secondary metabolites in such a system, highlighting the need to study the functionality of secondary metabolites in microbial interactions and complex microbiomes.

Adaptive cyclic content ratiogram: a new signal decomposition method for bearing concurrent fault diagnosis

Fast kurtogram (FK) has been proven to be an effective tool for resonance frequency band detection, which is widely used in bearing fault diagnosis. However, FK is not robust to impulsive noise, and its frequency band segmentation rule is fixed, which leads to over-decomposition or under-decomposition of the fault resonance frequency band in its signal decomposition results. Therefore, an adaptive cyclic content ratiogram is proposed in this paper. Firstly, based on the energy distribution of vibration signals on different frequency components, the frequency spectral segmentation is performed adaptively, and multiple sub-signals containing different frequency components are obtained. Secondly, the ratio of cyclic content (RCC), which cannot only more accurately characterize the cyclostationarity of bearing fault impacts but also be insensitive to impulsive noise, is applied to evaluate the fault feature information contained in each sub-signal separately. In the meanwhile, considering that fault characteristic frequency information is required in the process of RCC evaluation, the proposed method performs adaptive fault characteristic frequency detection for each sub-signal based on the envelope spectrum. The RCC maximization is used to locate the fault resonance frequency band. Also, combined with the estimated fault characteristic frequencies, the proposed method can achieve the extraction of concurrent fault features. Simulation and experimental data verify the effectiveness of the proposed method.

Efficient Bayesian inference using physics-informed invertible neural networks for inverse problems

This paper presents an innovative approach to tackle Bayesian inverse problems using physics-informed invertible neural networks (PI-INN). Serving as a neural operator model, PI-INN employs an invertible neural network (INN) to elucidate the relationship between the parameter field and the solution function in latent variable spaces. Specifically, the INN decomposes the latent variable of the parameter field into two distinct components: the expansion coefficients that represent the solution to the forward problem, and the noise that captures the inherent uncertainty associated with the inverse problem. Through precise estimation of the forward mapping and preservation of statistical independence between expansion coefficients and latent noise, PI-INN offers an accurate and efficient generative model for resolving Bayesian inverse problems, even in the absence of labeled data. For a given solution function, PI-INN can provide tractable and accurate estimates of the posterior distribution of the underlying parameter field. Moreover, capitalizing on the INN’s characteristics, we propose a novel independent loss function to effectively ensure the independence of the INN’s decomposition results. The efficacy and precision of the proposed PI-INN are demonstrated through a series of numerical experiments.

Energy fluctuation pattern recognition coupled with decomposition-integration: A novel ocean tidal energy forecasting system

Ocean tidal energy is a new energy source which is recognized and utilized earlier by human beings. For the complexity of tidal generation and the singleness of tidal prediction, this paper proposes a multi-factor forecasting system for ocean tidal energy based on energy fluctuation pattern recognition with confluent ReliefF-density-based spatial clustering of applications with noise (ReliefF-DBSCAN), improved symplectic geometric mode decomposition (ISGMD) and convolutional neural network−bidirectional long short-term memory (CNN-BiLSTM) with temporal pattern attention (TPA), named ReliefF-DBSCAN-ISGMD-TPA-CNN-BiLSTM. Precisely, ReliefF-DBSCAN can extract effective features and fluctuation patterns, and reduce the forecasting difficulty. ISGMD significantly improves the endpoint effect and false components of the decomposition results, and heightens the stability and correlation of each component. TPA-CNN-BiLSTM method have strong feature extraction ability, memory ability and recognition ability. In the evaluation stage, this study introduces some indicators such as root mean square error (RMSE) and Diebold Mariano (DM) test to evaluate the forecasting system comprehensively. To highlight the effect of the developed system, use actual data collected from Port SanLuis and Port Chicago as experimental data and compare with five contrastive models. The experimental result shows that the predicted value for the developed system is the closest to the real value, and the RMSE, mean absolute error (MAE) and goodness of fit (R2) of Port Chicago are 0.3152, 0.2900 and 0.9964, respectively, indicating that the prediction ability is better than all contrastive models. In addition, the result proves that the decomposition-integration system is beneficial to improve forecasting accuracy. This study can make a meaningful improvement and innovation for the forecasting of ocean tidal energy.

Impact of international trade on the income gaps between formal and informal sectors: Evidence from China

AbstractUsing national longitudinal survey data, this study examines the impact of international trade on income gaps between formal and heterogeneous informal workers in China. The results demonstrate that the positive effect of trade on income is larger for informal employees and smaller for self‐employed workers compared to formal employees. The decomposition results indicate that the disproportionate geographic distribution in tradable regions widens the income gaps between employment sectors, while the difference in international trade income premiums widens the income gap between formal and informal employees and narrows the income gap between formal employees and self‐employed workers.

Homotopy Theoretic Properties Of Open Books

ABSTRACT We study the homotopy groups of open books in terms of those of their pages and bindings. Under homotopy theoretic conditions on the monodromy we prove an integral decomposition result for the based loop space on an open book, and under more relaxed conditions we prove a rational loop space decomposition. The latter case allows for a rational dichotomy theorem for open books, as an extension of the classical dichotomy in rational homotopy theory. As a direct application, we show that for Milnor’s open book decomposition of an odd sphere with monodromy of finite order the induced action of the monodromy on the homology groups of its page cannot be nilpotent.

Molybdenum oxide catalyst for wastewater treatment studied by faradaic efficiency

The measurements of gas volumes evolved during electrochemical wastewater treatment provide the information on the process faradaic efficiency. This work demonstrates how it enables identification of the water oxidation pathway (2 or 4 electron) in flow electrolysis process through the application of the formulae suggested in the study. This way, the real wastewater oxidation strength, i.e. production of hydrogen peroxide can be determined based on calculations. These provide more accurate results than direct measurements of accumulated hydrogen peroxide concentration after the electrolysis process, as its instability may lead to its immediate decomposition and false results. As an example provided in the paper, the application of MoO3 as a catalyst in the carbon composite electrode indicates almost no hydrogen peroxide detected after the process, whereas its oxidative properties owing to production of hydroxyradicals demonstrate complete decomposition of wastewater analogue (methylene blue). The calculations point to high production of H2O2. Using pure carbon material without the catalyst in the same experiment, decomposition occurs at much lower rate. The possible mechanisms behind the processes are discussed. The approach presented in the work can aid to efficient development of catalysts towards two-electron water oxidation.

Determination of ballistic resistance model of finite thickness material based on Artificial Neural Network

Ballistic resistance behavior of finite thickness material is affected by factors of target thickness, projectile nose shape, and impact angle. The widely used ballistic limit velocity (BLV) model can only reflect the ballistic resistance behavior of a target in a single given impact scenario. The collected BLV from different impact scenarios can only partially reflect the effect of those factors on the ballistic resistance. A general model of BLV considering these factors simultaneously is desired.In this study, numerical impact models are first verified by a significant number of experiments. A guided random sampling method is proposed to collect numerical BLV data for training/validating usage. BLV data from experiments and simulations are then assembled by this method and subsequently trained by Artificial Neural Network (ANN). k-Folder Cross Validation (K-CV) technique designed for evaluating small sample training network is introduced to validate and test the network. The ANN-predicted results are decomposed by proper singular value decomposition methods. It is found that the Rank-1 decomposition results (with highest singular value) can fully reveal the decoupled relationship between BLV and its independent factors (MAPE < 6 %), with which analytical ballistic models are established. The results show that our proposed model can quantitatively describe the fully decoupled effect of target thickness, impact angle and projectile nose shape on the BLV with high accuracy.