Final Result Research Articles

The Use of Big Data Analysis in Digital Empowerment of Sustainable Technological Innovation Management of Ecological Enterprises.

With the social economy's rapid progress and the popularization of environmental awareness, ecological enterprises have gradually become a crucial trend in the development of modern enterprises. This work intends to promote the development of ecological enterprises to a higher level. This work first analyzes the management mode of ecological enterprises in the context of big data in China. Then, it establishes various indicators to analyze the role of sustainable technological innovation in enterprise development and the impact of digital empowerment on enterprise development. Finally, this work takes China's manufacturing industry and ecological enterprises in Hubei Province as examples to summarize the digital empowerment of sustainable technological innovation management of ecological enterprises under the background of big data. The final result indicates that sustainable technological innovation significantly reduces ecological enterprises' resource consumption and waste emissions. Additionally, it has a significant positive effect on improving enterprise output value and economic benefits. The digital empowerment of enterprises has a significant driving effect on sustainable technological innovation, with a digital driving coefficient of 26. This work provides a feasible scheme for the specific application of big data analysis in the technology innovation management of ecological enterprises, including market demand analysis, environmental monitoring and governance, technology assessment and risk management. This work expounds the role of big data analysis technology in improving decision-making efficiency, optimizing resource allocation and enhancing the competitiveness of enterprises in the digital empowerment of ecological enterprises.

Streamlining BIM models for GIS integration: an advanced approach to building envelope extraction

ABSTRACT Building Information Modelling (BIM) models are crucial for urban building modeling in three-dimensional (3D) Geographic Information Systems (GIS); however, their intricate nature requires lightweight design for seamless GIS integration. Envelope extraction is a primary method for achieving lightweight BIM; nevertheless, it encounters challenges related to robustness and simplification. To tackle this issue, this study proposes a more effective envelope extraction method. Initially, BIM elements are categorized into three types based on their semantics: space-occupying, portal, and other facility elements. Subsequently, for space-occupying elements, a 3D alpha wrapping algorithm is employed to obtain a tightly enveloped triangular mesh and then simplified into a lightweight polygonal mesh. Finally, portal elements are replaced with simplified proxies and embedded into a polygonal mesh through extrusion operations to refine the model. The refined model is combined with other facility elements to achieve the final simplification result. Experiments were conducted using publicly available datasets and manually created BIM models of various types and complexities. The experimental results demonstrate that our method significantly reduces the data volume of BIM models while maintaining their accuracy. Furthermore, this approach does not necessitate strict geometric requirements for BIM building elements and allows for gaps between them, thereby exhibiting higher robustness.

The Influence Of Problem-Based Learning Models On Critical Thinking Skills In Early Childhood

The purpose of this study is to determine the effect of the problem-based learning model on the critical thinking abilities of children aged 5-6 years. This type of research uses quantitative with quasi- experimental research methods. Sampling in this study used purposive sampling with the number of samples in this study totaling 20 children and the data collection techniques used were observation and documentation. The data analysis technique used is the Wilcoxon signed rank test. This results of the research showed that the treatment scores using the problem-based learning model and the posttest were increased by 45% for children Developing According to Expectations (DAE) compared to before using the problem-based learning model. The results of the pretest for critical thinking abilities of children who were Beginning to Develop (BD) were 35%. then the final result of the total number of children's pretest and posttest is at the Very Well Developing (VWD) stage of 80%. This shows that there is an influence from using the problem-based learning model on the critical thinking abilities of children aged 5-6 years .

Pelatihan Fotografi dan Konten Produk Pada Siswa Kelas X SMK 1 Semarang Guna Meningkatkan Pemasaran Produk

Digital marketing is becoming increasingly important in today's digital age. One of the key aspects of digital marketing is engaging visual content, which often involves quality photography. However, a lack of skills in photography and content creation is often a barrier for businesses and marketers. The students are expected to help the process of becoming entrepreneurs who are able to compete in various sectors today as they are trained in entrepreneurship. With the title Visual Strategy of Photography and Content Training for Digital Marketing for Students of SMK N 1 Semarang, this activity was carried out offline with a total of 64 students of SMK N 1 Semarang. Various ways of delivering material, discussion, and training were used during this seminar. The materials were visual photography strategies and marketing content. Evaluation of this activity was carried out with a questionnaire and product photography training, which aims to measure how much the seminar participants understand this training activity. The final result of this community service activity is that the students have a better ability and understanding of photography strategies and digital marketing content.

LOCAL DERIVATION OF REAL OPERATOR ALGEBRAS

Local derivations of real W*-algebras are considered. Since in the self-adjoint part of the real W*-algebra a local derivation is studied in the same way as in the complex case, here we consider the skew-adjoint part of the real W*-algebra. In the case of factors, the final result has been obtained, namely, it has been proven that any local derivation of real (complex) factors is an inner derivation.

Anisotropic tomography of eastern Tibet and its uncertainty from hypocentral errors

SUMMARY The mechanism responsible for the lateral expansion and uplift of the eastern Tibetan Plateau remains a topic of ongoing debate, partly due to discrepancies in the results of seismic velocity and anisotropy. In local earthquake tomography, hypocentral uncertainties can cause significant errors in the tomographic model. However, this issue has received limited attention in previous studies. In this work, we employ the weighted least-squares (WLS) method to solve the tomographic inversion problem. A power exponent coefficient, which is called weighting level, is introduced into the weighting matrix to control the relative contribution of the data with different hypocentral errors to the final tomographic result. Our data set contains high-quality Pg, Pn and Sg arrival times of local earthquakes recorded by the dense Chinese seismic network in eastern Tibet during 2008–2022. We comprehensively analyse the inversion results derived from the WLS inversions with different weighting levels to evaluate the robustness of isotropic velocity anomalies and azimuthal anisotropy. The most robust feature of our results is a striking low-velocity (low-Vp) zone surrounded by high-velocity (high-Vp) anomalies and fault parallel fast-velocity directions (FVDs) of azimuthal anisotropy in the lower crust beneath the western side of the Longmenshan fault zone. Taking into account many previous results of the region, we deem that the low-Vp zone reflects hot and wet upwelling flow from the deep asthenosphere, which ascends to the lower crust along the fault zone. At the NE margin of the Tibetan Plateau, significant low-Vp anomalies exist in the lower crust and the FVDs are consistent with the motion direction of the Tibetan block revealed by GPS (Global Positioning System) observations. We think that lower crustal flow exists beneath NE Tibet, which controls the plateau expansion toward the northeast. A low-Vp anomaly appears at 30 km depth beneath the Sichuan Basin. However, as the weighting level increases, the amplitude of this low-Vp anomaly decreases by more than 6 per cent, suggesting that this low-Vp anomaly has a lower accuracy than the other features.

Analysis of the Book "Arab Economics" at the Faculty of Islamic Economics and Business on Ibn Khaldun's Theory

The teaching of Arabic in Islamic universities faces many problems, including the lack of special educational materials and used by each teacher differently, the existing class is few than he had to perform for four skills, the lack of vocabulary to practice the language skill, and in addition to the lack of enthusiasm in learning Arabic only from the Faculty of Arabic Language Teaching only. The aim of this study is to know the objectives, material, method and evaluation of the book "Arab Economic" on the theory of Ibn Khaldun, and this research of the type of qualitative descriptive study, the researcher observed the Arabic book of economics, and the evidence and its sources of objectives, material, method and evaluation in the Arab book of economics, collected books and documents in the style of analysis of the doctrine of Mehlis and Hubermin. It is concluded that Ibn Khaldun's theory towards the goals, material, method and evaluation in this book Arabic economics is: the goal aspect includes improving thinking, improving society and improving spirituality suitable for improving thinking for the community of the Faculty of Economics with good Arabic language and the Holy Quran. In terms of the subject, it includes conversation, vocabulary, grammar and exercises to understand the language like the queen at the Faculty of Economics. In terms of method, it is appropriate to gradually or repetition, discussion and exemplary in the process of teaching economic Arabic writers. In terms of the calendar, it has exercises, exercises and songs to support the acquisition of the four language skills. The final result of this research is that the Arabic book of economics has helped students at the Faculty of Economics and Business to learn the fun Arabic language and train the four skills according to the practice of objectives, material, method and evaluation according to Ibn Khaldun.

The Modified Superomedial Pedicle Breast Reduction Technique for Cases with SN-N Distance Exceeding 33 cm.

Symptomatic breast hypertrophy affects the quality of life of a large number of women globally. Many reduction mammoplasty techniques have been described for patients with breast hypertrophy. The aim of this study was to provide our clinic's experience in utilizing the modified superomedial pedicle breast reduction technique in specific patients suffering from breast hypertrophy, with sternal notch-to-nipple distance of more than 33 cm. Our study included twenty patients who underwent, from January 2022 to December 2023, the modified superomedial pedicle breast reduction technique due to symptomatic breast hypertrophy with sternal notch-to-nipple distance of more than 33 cm in the Plastic and Reconstructive Surgery Department at Nicosia General Hospital in Cyprus. Patient demographics, comorbidities, pre- and postoperative breast anthropometric measurements and surgical complications were recorded and analyzed. The average age at the time of the reduction was 48 years. The mean preoperative body mass index was 28.52 kg/m2. Patients' comorbidities included one (5%) patient with diabetes, seven (35%) with obesity and three (15%) with hypertension. The mean preoperative sternal notch-to-nipple distance was 35.25 cm for the right breast and 34.90 cm for the left breast, while the mean postoperative was 20.65 cm for both breasts. The total mean resection weight of both breasts was 1643.45 g. Surgical complications were minor including two (10%) cases of local hematoma and one (5%) case of T-Junction wound breakdown. All patients were relieved from their preoperative symptoms and were satisfied with the final result. Our modified superomedial pedicle technique is a safe, effective and versatile pedicle to be used with many advantages, in specific patients suffering from breast hypertrophy with sternal notch-to-nipple distance of more than 33 cm, including its shape and rotational abilities, viability of the nipple and excellent outcome of glandular plication and breast reshaping. This journal requires that authors assign a level of evidence to each article. For a full description of these Evidence-Based Medicine ratings, please refer to the Table of Contents or the online Instructions to Authors www.springer.com/00266 .

Association between Albumin-Corrected Anion Gap and Mortality in Patients with Cardiogenic Shock.

Cardiogenic shock (CS) is a critical illness with a high mortality rate in clinical practice. Although some biomarkers have been found to be associated with mortality in patients suffering from CS in previous studies. The albumin-corrected anion gap (ACAG) has not been studied in depth. Our study aimed to explore the relationship between ACAG and mortality in patients with CS. All baseline data was extracted from Medical Information Mart for Intensive Care-IV version: 2.0 (MIMIC-IV). According to the prognosis at 30 days of follow-up, they were divided into survivors and non-survivors groups. The survival curves between the two groups were drawn using the Kaplan-Meier method and the log-rank test. Valid factors were selected using the least absolute shrinkage and selection operator (LASSO) logistic analysis model. Analysis was performed to investigate the relationship between mortality and all enrolled patients using restricted cubic spline (RCS) and Cox proportional hazards models. Receiver operating characteristic (ROC) curves were used to assess the predictive ability of ACAG. Evaluation of final result stability using sensitivity analysis. 839 cases were selected to meet the inclusion criteria and categorized into survivors and non-survivors groups in the final analysis. The ACAG value measured for the first time at the time of admission was selected as the research object. Kaplan-Meier (K-M) survival curves showed that cumulative 30- and 90-day survival decreased progressively with elevated ACAG (p 0.001), and multifactorial Cox regression analyses showed ACAG to be an independent risk factor for increased 30- and 90-day mortality in patients suffering from CS (p 0.05). RCS curves revealed that all-cause mortality in this group of patients increased with increasing ACAG ( 2 = 5.830, p = 0.120). The ROC curve showed that the best cutoff value for ACAG for predicting 30-day mortality in patients with CS was 22.625, with a sensitivity of 44.0% and a specificity of 74.7%. The relationship between ACAG and CS short-term mortality remained stable in all sensitivity analyses (All p 0.05). The ACAG is an independent risk factor for 30- and 90-day mortality in CS patients and predicts poor clinical outcomes in CS patients. According to our study, elevated ACAG at admission, especially when ACAG 20 mmol/L, was an independent predictor of all-cause mortality in CS.

A GA based SVM-Bayesian technique and its application in process monitoring for mixed distributed data

Traditional data-driven fault diagnosis methods, such as principal component analysis (PCA) and independent component analysis (ICA), have significantly improved condition monitoring and fault diagnosis of industrial process by analyzing process data. However, their accuracy is limited due to the assumption that the data follows Gaussian or non-Gaussian distribution, respectively. In reality, process data is often a mixture of both Gaussian and non-Gaussian distributions, leading to reduced accuracy of the training model and fault diagnosis results. To address this issue, a genetic algorithm-based SVM-Bayesian (GSB) scheme is proposed in this paper for feature combination and model integration. The scheme first employs ICA and PCA to extract the independent/principal components of the original data, and then constructs the corresponding fault features for SVM training to obtain basic models. The extraction of fault features are optimized using genetic algorithms based on defined accuracy and diversity functions. During online monitoring, the final diagnosis result is obtained by combining the results of the basic models through Bayesian inference. The effectiveness of the proposed scheme is evaluated using a numerical example and a case study of the Tennessee Eastman process. The results show that the accuracy of fault classification is improved by about 5% compared with traditional methods.

Improving consensus in social network group decision-making: Emphasizing overlapping subgroups and interactive behaviors

The multilevel nature of social networks and the multiple identities of decision makers (DMs) have led to the co-existence of overlapping and non-overlapping subgroups in social network group decision-making. This paper focuses on three issues in an intuitionistic fuzzy environment in this situation. Firstly, some unknown trust relationships exist between DMs belonging to different subgroups. Secondly, interactions between DMs and subgroups affect the final consensus result. Thirdly, opinions of DMs belonging to multiple subgroups simultaneously (i.e., key DMs) affect the opinions of these overlapping subgroups to varying degrees. To address the first issue, a trust propagation operator considering trust influence and trust decay is proposed to estimate the trust values between DMs belonging to different subgroups. To solve the second issue, we describe interactions within and between subgroups by factors such as trust value and overlapping level. To manage the third issue, we introduce the Choquet integral to gather opinions and construct an interactive behavior-driven hierarchical consensus model to obtain the group opinion for the non-additive interactions between DMs and subgroups. Finally, the effectiveness and applicability of the method are verified through illustrative examples and comparisons.

Cultural adaptation of a self-review tool for health promoting universities in Bulgaria.

The Health Promoting University initiative is unknown in Bulgaria, and the health promotion potential of Bulgarian universities has not been studied. In order to examine it, a suitable instrument is needed. The UK Healthy Universities Network provides an accessible Self-Review-Tool (SRT). Aim: To present the process of cultural adaptation of the SRT in Bulgarian language. The standardized WHO methodology for cultural adaptation of instruments was followed in four stages: (1) Two language translations of the instrument into Bulgarian were made; (2) An expert Delphi discussion reached a consensus on specific health promoting (HP) terms, followed by a backward translation; (3) Pilot testing of the tool among university community representatives was conducted through a survey among a small sample, with independent responses to the SRT questionnaire followed by cognitive interviews; (4) Final revision of the instrument. Ten public health experts reached a consensus on the name of the initiative and various HP terms. Ten other respondents pre-tested the tool. Difficulties in responding the SRT concerned the meaning of some HP terms, complex words, the system of answers, limited applicability of some statements. Changes were made to 61 of the total 68 elements in the SRT. All stages of the cultural adaptation were important for the final result. The adapted Bulgarian version of the SRT would be useful to Bulgarian universities that want to make a clear commitment to improving the health of their university community and the wider society.

Metric perturbations in noncommutative gravity

We use the framework of Hopf algebra and noncommutative differential geometry to build a noncommutative (NC) theory of gravity in a bottom-up approach. Noncommutativity is introduced via deformed Hopf algebra of diffeomorphisms by means of a Drinfeld twist. The final result of the construction is a general formalism for obtaining NC corrections to the classical theory of gravity for a wide class of deformations and a general background. This also includes a novel proposal for noncommutative Einstein manifold. Moreover, the general construction is applied to the case of a linearized gravitational perturbation theory to describe a NC deformation of the metric perturbations. We specifically present an example for the Schwarzschild background and axial perturbations, which gives rise to a generalization of the work by Regge and Wheeler. All calculations are performed up to first order in perturbation of the metric and noncommutativity parameter. The main result is the noncommutative Regge-Wheeler potential. Finally, we comment on some differences in properties between the Regge-Wheeler potential and its noncommutative counterpart.

Patching-based deep-learning model for the inpainting of Bragg coherent diffraction patterns affected by detector gaps.

A deep-learning algorithm is proposed for the inpainting of Bragg coherent diffraction imaging (BCDI) patterns affected by detector gaps. These regions of missing intensity can compromise the accuracy of reconstruction algorithms, inducing artefacts in the final result. It is thus desirable to restore the intensity in these regions in order to ensure more reliable reconstructions. The key aspect of the method lies in the choice of training the neural network with cropped sections of diffraction data and subsequently patching the predictions generated by the model along the gap, thus completing the full diffraction peak. This approach enables access to a greater amount of experimental data for training and offers the ability to average overlapping sections during patching. As a result, it produces robust and dependable predictions for experimental data arrays of any size. It is shown that the method is able to remove gap-induced artefacts on the reconstructed objects for both simulated and experimental data, which becomes essential in the case of high-resolution BCDI experiments.

Sustainable construction of green school building using energy simulation analysis and modeling

The aim of this research is to manage energy efficiency in the design of green school buildings in order to create an optimal educational environment and sustainable architecture. The research method is quantitative and a simulation calculation. For this purpose, Ecotect 2021 software is used for modeling and Revit 2021 and DesignBuilder software for analyzing the results. The case study of this research is the schools built by the School Renovation Organization of East Azarbaijan Province, Tabriz City, based on the cold climate type plan. On this basis, a school designed and implemented by the School Renovation Organization of East Azarbaijan Province was selected based on a non-random purposive sample. The model is implemented in Revit simulation software and the energy analysis is then performed with DesignBuilder software using sustainable materials and renewable energy. The case study building includes appropriate materials in the walls and windows, standard thermal insulation, solar panels on the ceiling and walls, standard ventilation and wind turbines for lighting. The model is analyzed with the DesignBuilder simulator software and then the design is created with the most suitable placement angle based on the optimal energy consumption. The simulation results show that the optimal orientation of the building with the optimal overlap of the shadow finder band and the diagram obtained is 10° to the southeast. The final result of the analysis of the optimal energy consumption of the building at a cooling set point temperature of 24°–26° based on the window to wall ratio from October to February shows a value of 84900 kw/h to 88800 kw/h. According to the optimal design of the building and the results of the energy analysis, the cost of building a school based on the cost of services and infrastructure per gross internal floor area is £959,815, which is the optimal cost of implementing a green school. The results have shown that all selected components in the green schools in the cold regions of Iran to achieve the main goal of using as much solar energy as possible, compensating for unfavorable climatic conditions and creating comfortable conditions for students through the implementation of sustainable architecture.

The usefulness of narrow-band Imaging (NBI) in nasopharyngeal lesions-Validation of the Ni NBI classification dedicated for vascular pattern in the nasopharynx.

This study aims to explore the applicability of narrow-band imaging (NBI) involving the Ni classification for the diagnosis of nasopharyngeal mucosal lesions in order to distinguish malignant tumours (NPT) from non-malignant lesions. Each patient (n = 53) with a suspected nasopharyngeal lesion underwent a trans-nasal flexible video endoscopy with an optical filter for NBI. We assessed the suspected area using white light imaging (WLI) in terms of location and morphology as well as the vascular pattern (using Ni classification of nasopharyngeal microvessels) and surrounding tissue by using NBI. Based on the results of the NBI and WLI, patients were classified into "positive" or "negative" groups. All lesions of the nasopharynx were biopsied and submitted for final histological evaluation. NBI showed higher sensitivity, specificity, and accuracy than WLI. There was a significant correlation between the final histological result and the NBI pattern of the NPT: Chi2(1) = 31.34; p = 0.000001 and the WLI assessment of the NPT: Chi2(1) = 14.78; p = 0.00012. The assessment of the NPT in NBI using Ni NBI classification proved valuable in suspected mucosa assessment. NBI not only confirms the suspicious areas in WLI, but it also shows microlesions beyond the scope of WLI and allows for proper sampling.

FA-VTON: A Feature Alignment-Based Model for Virtual Try-On

The virtual try-on technology based on 2D images aims to seamlessly transfer provided garments onto target person images. Prior methods mainly concentrated on warping garments and generating images, overlooking the influence of feature alignment on the try-on results. In this study, we initially analyze the distortions present by existing methods and elucidate the critical role of feature alignment in the extraction stage. Building on this, we propose a novel feature alignment-based model (FA-VTON). Specifically, FA-VTON aligns the upsampled higher-level features from both person and garment images to acquire precise boundary information, which serves as guidance for subsequent garment warping. Concurrently, the Efficient Channel Attention mechanism (ECA) is introduced to generate the final result in the try-on generation module. This mechanism enables adaptive adjustment of channel feature weights to extract important features and reduce artifact generation. Furthermore, to make the student network focus on salient regions of each channel, we utilize channel-wise distillation (CWD) to minimize the Kullback–Leibler (KL) divergence between the channel probability maps of the two networks. The experiments show that our model achieves better results in both qualitative and quantitative analyses compared to current methods on the popular virtual try-on datasets.

The Approach for Identifying Opportunities in Product Innovation Design through Requirement Conflict Analysis

This study introduces a method to explore and broaden potential design avenues for a product by identifying opportunities for innovative design through requirement conflicts. The process involves clarifying user-expected demands using the Ideal Final Result approach and identifying potential conflicts by assessing product performance constraints and user expectations. Utilizing standardized representations of requirement conflicts, a knowledge retrieval technique based on expanding co-referential relationships is applied to analyze these conflicts. A judgment matrix is created to pinpoint clues for recognizing innovative design opportunities initially. Furthermore, the nine-windows method is utilized to expand and identify innovation opportunities along the time and system axis, leading to the identification of two types of innovation opportunities and their expanded outcomes. Ultimately, the method’s feasibility and practicality are validated by identifying innovative design opportunities within the context of the alpine tunnel construction system.

Classification of Diabetes Using Ensemble Machine Learning Techniques

Diabetes is a widespread chronic condition that impacts people all over the globe and requires a clear and timely diagnosis. Untreated diabetes leads to retinopathy, nephropathy, and damage to the nervous system. In this context, Machine Learning (ML) might be used to detect health problems early, diagnose them, and track their progress. Ensemble techniques are a promising approach that combines many classifiers to improve forecast accuracy and resilience. This study investigates the categorization of diabetes using an ensemble machine learning technique known as a voting classifier. Using a variety of classifiers, including Light Gradient Boosting Machine (LightGBM), Gradient Boost classifier (GBC), and Random Forest (RF). The predictions are aggregated using voting methods to get a final classification result. The research is carried out using two benchmarking datasets: the Pima Indian Diabetes Dataset (PIDD) and the German Dataset. The Boruta technique is used to choose the best attributes from the datasets, while the Random Over Sampling approach balances the range of classes and eliminates abnormal data using the interquartile range approach. The findings showed that the combination of the Boruta feature selection algorithm and ensemble Voting Classifier performed better for both PIDD and German datasets with an accuracy of 93% and 90% respectively. These algorithms are evaluated and the maximum accuracy is produced using the combination of the Boruta feature selection algorithm and ensemble Voting Classifier. This research helps medical professionals in the early prediction of diabetes, reducing physician’s time.

Shear-wave velocity prediction of tight reservoirs based on poroelasticity theory: A comparative study of deep neural network and rock physics model

The absence of shear-wave (S-wave) velocity in well log data limits the effective seismic characterization of lithology, petrophysical properties, and fluid distribution of tight reservoirs in the Chang 7 shale oil formations of Ordos Basin, west China. However, conventional rock physics models (RPMs), e.g., the Xu-White model, struggle to accurately obtain the key parameters such as pore aspect ratio in the process of S-wave velocity prediction. This work performs a comparative study of S-wave velocity prediction based on deep neural network (DNN) and RPM for such reservoirs. By employing the poroelasticity equations which are capable of describing elastic wave propagation in the complex reservoirs saturated with viscous fluid, the relation between elastic and petrophysical properties is established with a DNN-based method. This approach quantifies the connection between P-/S-wave velocities and rock properties. On the other hand, we reformulate the traditional Xu-White model by incorporating the decoupled Kuster-Toksöz (K-T) model and the poroelasticity equations with viscoelastic fluid in the modeling process. The variation of pore aspect ratio with depth is also considered to characterize the complex pore structures, which is appropriate for improving the accuracy of prediction. Log data from the three wells are considered for verification, of which results show that the S-wave velocity prediction methods help improve the final result. Aided by the DNN-based method, the prediction exhibits a better performance if the training data is sufficient. In particular, provided a small amount of measured S-wave data (even ten data points), the trained DNN model can also provide reasonable predictions.