Sampled Data Points Research Articles

Relationship between Hardness and Deformation during Cold Rolling Process of Complex Profiles

The hardening on surface of complex profiles such as thread and spline manufactured by cold rolling can effectively improve the mechanical properties and surface quality of rolled parts. The distribution of hardness in superficial layer is closely related to the deformation by rolling. To establish the suitable correlation model for describing the relationship between strain and hardness during cold rolling forming process of complex profiles is helpful to the optimization of rolling parameters and improvement of rolling process. In this study, a physical analog experiment reflecting the uneven deformation during complex-profile rolling process has been extracted and designed, and then the large date set (more than 400 data points) of training samples reflecting the local deformation characteristics of complex-profile rolling have been obtained. Several types of polynomials and power functions were adopted in regression analysis, and the regression correlation models of 45# steel were evaluated by the single-pass and multi-pass physical analog experiments and the complex-profile rolling experiment. The results indicated that the predicting accuracy of polynomial regression model is better in the strain range (i.e., varepsilon < 1.2) of training samples, and the correlation relationship between strain and hardness out strain range (i.e., varepsilon > 1.2) of training samples can be well described by power regression model; so the correlation relationship between strain and hardness during complex-profile rolling process of 45# steel can be characterized by a segmented function such as third-order polynomial in the range varepsilon < 1.2 and power function with a fitting constant in the range varepsilon > 1.2; and the predicting error of the regression model by segmented function is less than 10%.

Nutrient Loadings to Utah Lake from Precipitation-Related Atmospheric Deposition

Atmospheric deposition (AD) is a less understood and quantified source of nutrient loading to waterbodies. AD occurs via settling (large particulates), contact (smaller particulates and gaseous matter), and precipitation (rain, snow) transport pathways. Utah Lake is a shallow eutrophic freshwater lake located in central Utah, USA, with geophysical characteristics that make it particularly susceptible to AD-related nutrient loading. Studies have shown AD to be a significant contributor to the lake’s nutrient budget. This study analyzes nutrient samples from nine locations around the lake and four precipitation gauges over a 6-year study period using three different methods to estimate AD from the precipitation transport pathway. The methods used are simple averaging, Thiessen polygons, and inverse distance weighting, which we use to spatially interpolate point sample data to estimate nutrient lake loads. We hold that the inverse distance weighting method produces the most accurate results. We quantify, present, and compare nutrient loads and nutrient loading rates for total phosphorus (TP), total inorganic nitrogen (TIN), and ortho phosphate (OP) from precipitation events. We compute loading rates for the calendar year (Mg/yr) from each of the three analysis methods along with monthly loading rates where Mg is 106 g. Our estimated annual precipitation AD loads for TP, OP, and TIN are 120.96 Mg/yr (132.97 tons/yr), 60.87 Mg/yr (67.1 tons/yr), and 435 Mg/yr (479.5 tons/yr), respectively. We compare these results with published data on total AD nutrient loads and show that AD from precipitation is a significant nutrient source for Utah Lake, contributing between 25% and 40% of the total AD nutrient load to the lake.

Data-driven safe control via finite-time Koopman identifier

In this paper, a novel data-driven invariant-based safe control scheme for control of a nonlinear vehicle is developed. First, a novel incremental finite-time Koopman representation-based identifier is introduced to learn approximated lifted-states linear system model of system from a limited budget of incrementally sampled data points which not only minimises the instantaneous Koopman representation's identification errors but also the identification errors for a batch of past samples collected in a history stack. A great advantage of the presented identifier is that the uncertainty of the identified system is also quantified and updated over time on a slower time scale. Using set-theoretic tools, then, the quantified uncertainty will be leveraged in conjunction with the learned model in order to impose robust contractivity on the safe set, which guarantees its safety and stability. The proposed approach significantly reduces conservativeness through quantifying and updating the uncertainty level of the learned model over time.

Relationship of Momentary Volition to Occupational Experience and Life Perspective in Undergraduate Students.

Our lives are comprised of moment-to-moment activities and experiences. According to the Model of Human Occupation, our occupational experiences can be affected by volition, which consists of personal causation, values, and interests. This study investigated how momentary volition could affect activity satisfaction and mind-wandering while performing occupations. This study also examined the relationship of momentary volition with overall life perspectives on life satisfaction and life balance. Undergraduate students participated in this cross-sectional study. The experience sampling method (ESM), which repeatedly collects real-time data in everyday life, was applied in this study to measure students' momentary states such as activity, volition, activity satisfaction, and mind-wandering. After completing the ESM, participants' life satisfaction and life balance were measured using the Satisfaction With Life Scale (SWLS) and the Life Balance Inventory (LBI), respectively. Forty-two participants and 1092 sampling data points were included in the analysis. At the event level, regression analysis was performed to identify volition elements to contribute to activity satisfaction and mind-wandering. At the personal level, correlation analysis was used to determine the relationship of momentary volition to life satisfaction and life balance. Momentary personal causation, values, and interests contributed to activity satisfaction. Mind-wandering was predicted negatively by interests but positively by personal causation. Momentary interests were positively correlated with SWLS and LBI scores. This study demonstrated that momentary volition was associated with activity satisfaction and engagement, as well as life satisfaction and balance in undergraduate students. Momentary volition, especially interests, contributed to positive occupational experiences and life perspectives. This study suggests that occupational therapy practitioners need to consider momentary interests to provide occupation-centered interventions for undergraduate students.

Prediction of Blade Tip Timing Sensor Waveforms Based on Radial Basis Function Neural Network

As the existing Blade Tip Timing (BTT) vibration measurement methods have serious under-sampling problems, where the blade resonance frequency is usually higher than the sampling frequency of the data acquisition system of the BTT method, resulting in large errors in the identification of blade vibration parameters, new solutions are needed to extend the capability of BTT to nonlinear and multimodal vibration analysis. Therefore, it is the current research direction to pursue new and more accurate measurement and signal processing methods. By analyzing the waveform data from the BTT sensor and using it for vibration analysis, it significantly extends the BTT database. To avoid the current problems of under-sampling and low recognition accuracy, this paper conducts a study on the recognition of rotating blade vibration parameters based on the Radial Basis Function (RBF) model by establishing a RBF neural network prediction model to analyze the static calibration experimental data and predict the waveform of the BTT sensor, and comparing the prediction curves of various models. As the results show, for the RBF model, the prediction accuracy is closely related to the source data of the sampling point data, when the source data predicted by the RBF model is close to the center of the samples, the prediction accuracy is high, meanwhile, the prediction accuracy decreases as it is far away from the center of these data. At the same time, the number of samples is too small to affect the prediction ability of the RBF model. By using this method, more waveforms under the Blade Tip Clearance (BTC) can be predicted with the available sample point data, and the errors in the experimental measurement process can be corrected.

Exploring Older Adults' Perceived Affordability and Accessibility of the Healthcare System: Empirical Evidence from the Chinese Social Survey 2021.

The difficulties in accessibility and affordability of healthcare services have posed persistent challenges to the Chinese government ever since the 1990s. This research aimed to explore the perceived accessibility and affordability of the healthcare system, which is also referred to as the social problem of Kan Bing Nan, Kan Bing Gui among older Chinese adults. The research, based on 2169 sample data points from the Chinese Social Survey (CSS) 2021 database, explored the objective and subjective factors in constructing the public perceptions of accessibility and affordability of the healthcare system among both older adult users and older adult non-users of healthcare services, including healthcare expenditure variables, patient experience variables, financial protection variables, and social evaluation variables. The study utilized binary logistic regression analysis to investigate how four different sets of independent variables impact the perceived affordability and accessibility of the healthcare system by older adults. The research found that Kan Bing Nan, Kan Bing Gui was considered the most pressing issue among the older adults in China. Self-reported medical burdens, the cost of healthcare services, and the perceived fairness of public healthcare services were significantly associated with older adults' perceived affordability and accessibility of the healthcare system.

Correlation analysis and fault detection of current and speed signals for underwater thruster entanglement

AbstractThe fault detection with a single sensor cannot comprehensively use the multi‐sensor correlation information of underwater thruster. To solve the issue that in the case of entanglement, a kind of fault diagnosis method of underwater thruster based on the combination of current and rotational speed signal correlation analysis and support vector machine is proposed. Firstly, the collected current and speed signals of underwater thrusters under different states are normalized, the variable sampling data points is adopted to refine the time of fault occurrence; Secondly, the cross correlation and autocorrelation coefficients of normalized current and speed signals are calculated based the variable sampling data points number and the correlation matrix is formed at different sampling time based on the cross correlation and autocorrelation coefficients. Finally, the support vector machine was applied to diagnose whether or not the fault produces and the time of fault occurrence with the correlation coefficients sequence. To verify the effectiveness of the proposed method, the fault simulation platform and data acquisition software are designed, the fault data in the case of thruster with entanglement are collected in time to test the proposed method. The results show that compared with the fault diagnosis method using only one speed or current signal, the proposed analytical method based on the correlation coefficients can fully extract the correlation information of underwater thruster fault, and the time extraction of fault occurrence is more sufficient, effectively improving the accuracy of underwater thruster fault diagnosis.

Noise Reduction Method of Pipeline Infrasonic Leakage Signal Based on Improved Prony Algorithm and Difference Energy Model

To solve the difficult problem of low-frequency noise processing in pipeline infrasonic leakage detection signals, a pipeline infrasonic leakage signal denoising method based on improved Prony algorithm and differential energy model was proposed to reduce the low-frequency interference noise in the signal and to improve the signal denoising effect. First, the frequency window of the effective signal is obtained according to the spectrogram of the infrasound signal. Aiming at the problem that the Prony algorithm is affected by noise, a difference energy model is proposed. The difference energy model is used to filter out the part of the frequency domain signal with large energy fluctuation, and a relatively stable preprocessing signal is obtained. In view of the instability of the traditional Prony algorithm, a Hankel matrix is established in the operation process. The stability is improved by extracting the extremum and residue of the signal instead of directly solving the sampling data points, and the extremum and residue of the effective signal are selected by combining the frequency window of the active ingredient. Finally, the effective signal is reconstructed to obtain a relatively stable infrasound leakage noise reduction signal. Experimental results show that the noise reduction technology based on the improved Prony algorithm and differential energy model can effectively reduce the noise of pipeline leakage signals. Compared with the traditional Prony algorithm, the noise reduction effect of the proposed method is up to 38.01% higher. Compared with the empirical mode decomposition method, the noise reduction effect of this method is improved by 9.25% at least, which opens up a new idea for pipeline leakage signal noise reduction.

A dataset of Trollius chinensis distribution in Altay, Xinjiang during 1995–2018

Xinjiang is rich in wild medicinal resources, which are mainly distributed in Tianshan Mountains and Altai Mountains in Northern Xinjiang. Trollius chinensis is one of the plants with heat clearing and detoxifying effects. It has been listed as one of the recommended Chinese medicinal materials for poverty alleviation in China’s traditional Chinese medicine industry, with a good market prospect. This dataset is formed based on the field study on Trollius chinensis in Altay, Xinjiang. It consists of three types of multi-source data: 81 Trollius chinensis sample point data, 42 environmental variable data and one Trollius chinensis distribution data. Among them, the data of sample points were collected through field study in July, 2018 as well as June and July, 2019; The number of environmental variables includes 39 climatic data and 3 topographic data. The climatic data are obtained by interpolating the data of 6 meteorological stations in Altay region from 1995 to 2018, and the topographic data are obtained based on elevation data processing; the distribution data of Trollius chinensis are obtained through inputting the sample point data and environmental variable data into MaxEnt model for analysis. This dataset can provide a data basis for predicting the function evaluation of the potential suitable growth area of Altay Golden Lotus. Referring to the distribution area of Altay Golden Lotus, suitable growth areas can be selected for introduction and artificial cultivation. The dataset can support the resource development and species diversity protection of Altay wild Golden chinensis, and prop up the research of scientific researchers on such medicinal plants. Moreover, it also has certain value for the scientific research of other wild medicinal plants in this field.

Emission ratio determination from road vehicles using a range of remote emission sensing techniques

The development of remote emission sensing techniques such as plume chasing and point sampling has progressed significantly and is providing new insight into vehicle emissions behaviour. However, the analysis of remote emission sensing data can be highly challenging and there is currently no standardised method available. In this study we present a single data processing approach to quantify vehicle exhaust emissions measured using a range of remote emission sensing techniques. The method uses rolling regression calculated over short time intervals to derive the characteristics of diluting plumes. We apply the method to high time-resolution plume chasing and point sampling data to quantify gaseous exhaust emission ratios from individual vehicles. Data from a series of vehicle emission characterisation experiments conducted under controlled conditions is used to demonstrate the potential of this approach. First, the method is validated through comparison with on-board emission measurements. Second, the ability of this approach to detect changes in NOx / CO2 ratios associated with aftertreatment system tampering and different engine operating conditions is shown. Third, the flexibility of the approach is demonstrated by varying the pollutants used as regression variables and quantifying the NO2 / NOx ratios for different vehicle types. A higher proportion of total NOx is emitted as NO2 when the selective catalytic reduction system of the measured heavy duty truck is tampered. In addition, the applicability of this approach to urban environments is illustrated using mobile measurements conducted in Milan, Italy in 2021. Emissions from local combustion sources are distinguished from a complex urban background and the spatiotemporal variability in emissions is shown. The mean NOx / CO2 ratio of 1.61 ppb/ppm is considered representative of the local vehicle fleet. It is envisaged that this approach can be used to quantify emissions from a range of mobile and stationary fuel combustion sources, including non-road vehicles, ships, trains, boilers and incinerators.

Performance-Enhanced Static Modulated Fourier Transform Spectrometer with a Spectral Reconstruction

A static modulated Fourier transform spectrometer has been noted to be a compact and fast evaluation tool for spectroscopic inspection, and many novel structures have been reported to support its performance. However, it still suffers from poor spectral resolution due to the limited sampling data points, which marks its intrinsic drawback. In this paper, we outline the enhanced performance of a static modulated Fourier transform spectrometer with a spectral reconstruction method that can compensate for the insufficient data points. An enhanced spectrum can be reconstructed by applying a linear regression method to a measured interferogram. We obtain the transfer function of a spectrometer by analyzing what interferogram can be detected with different values of parameters such as focal length of the Fourier lens, mirror displacement, and wavenumber range, instead of direct measurement of the transfer function. Additionally, the optimal experimental conditions for the narrowest spectral width are investigated. Application of the spectral reconstruction method achieves an improved spectral resolution from 74 cm-1 when spectral reconstruction is not applied to 8.9 cm-1, and a narrowed spectral width from 414 cm-1 to 371 cm-1, which are close to the values of the spectral reference. In conclusion, the spectral reconstruction method in a compact static modulated Fourier transform spectrometer effectively enhances its performance without any additional optic in the structure.

Automatic Extraction of Flooding Control Knowledge from Rich Literature Texts Using Deep Learning

Flood control is a global problem; increasing number of flooding disasters occur annually induced by global climate change and extreme weather events. Flood studies are important knowledge sources for flood risk reduction and have been recorded in the academic literature. The main objective of this paper was to acquire flood control knowledge from long-tail data of the literature by using deep learning techniques. Screening was conducted to obtain 4742 flood-related academic documents from past two decades. Machine learning was conducted to parse the documents, and 347 sample data points from different years were collected for sentence segmentation (approximately 61,000 sentences) and manual annotation. Traditional machine learning (NB, LR, SVM, and RF) and artificial neural network-based deep learning algorithms (Bert, Bert-CNN, Bert-RNN, and ERNIE) were implemented for model training, and complete sentence-level knowledge extraction was conducted in batches. The results revealed that artificial neural network-based deep learning methods exhibit better performance than traditional machine learning methods in terms of accuracy, but their training time is much longer. Based on comprehensive feature extraction capability and computational efficiency, the performances of deep learning methods were ranked as: ERNIE &gt; Bert-CNN &gt; Bert &gt; Bert-RNN. When using Bert as the benchmark model, several deformation models showed applicable characteristics. Bert, Bert-CNN, and Bert-RNN were good at acquiring global features, local features, and processing variable-length inputs, respectively. ERNIE showed improved masking mechanism and corpus and therefore exhibited better performance. Finally, 124,196 usage method and 8935 quotation method sentences were obtained in batches. The proportions of method sentence in the literature showed increasing trends over the last 20 years. Thus, as literature with more method sentences accumulates, this study lays a foundation for knowledge extraction in the future.

Pengaruh Kepemilikan Pemerintah, Kecakapan Manajerial, Investment Opportunity Set, dan Capital Intensity Ratio terhadap Effective Tax Rate (Studi Empiris Perusahaan yang Terdaftar pada Indeks Saham IDX30 di Bursa Efek Indonesia Periode 2018-2020)

The purpose of this study is to determine the effective tax rate with government ownership, managerial skills, investment opportunity set, and capital intensity ratio. This study uses a quantitative method with the effective tax rate as the dependent variable. The company's effective tax rate calculation is used as a proxy in assessing the company's tax burden. This study uses secondary data in the form of financial statements of companies listed on the IDX30 stock index on the Indonesia Stock Exchange (IDX) in the period 2018 to 2020. The sampling technique used a purposive sampling technique and obtained as many as 31 company sample data points. The hypothesis was tested using multiple linear regression analysis with the Eviews program. The result shows that the variables GOV, KM, IOS, and CIR simultaneously affect on ETR. Based on the results of the t test, it can be cocluded that partially the GOV, IOS, and CIR variables have no significant effect on ETR., while the KM variables have a significant effect on ETR. The result of the calculation of the coefficient of determination show that the influence given by the independent variable to the dependent variable is 40,72% while the remaining 59,28% is influenced by other variables not included in this study.

Climate change and human activity impacts on future flood risk in the Pearl River Delta based on the MaxEnt model

In the background of global climate change and rapid urbanization, extreme climate events are frequent, and highly urbanized areas flooding problems are becoming increasingly prominent. It becomes important to develop qualitative scenario storylines to assess future flooding risk in a changing environment over the highly urbanized areas. In this study, the future (2030–2050) flood risk in the Pearl River Delta (PRD) region of China was assessed based on the MaxEnt model. We have developed four future scenarios under different emission conditions based on the representative concentration pathways (RCPs) and the shared socio-economic pathways (SSPs). The MaxEnt model was trained by using flood hazard sample point data and flood risk indicators, meanwhile, food risk prediction with high accuracy was obtained. We analyzed the influencing factors of flood risk and predicted the flood risk of the PRD under four future scenarios. According to the results, there is a significant increase in the size and proportion of high flood risk areas in most scenarios. Flood risk under scenario SSP5-RCP8.5 are expected to be the most serious in the future. Population density (POPD) and Gross domestic product density (GDPD) have the highest explanatory power for flood risk. This study predict the changes of flood risk under the combined influence of climate change and human activities, and hoped to provide a reference for future planning and disaster mitigation construction in PRD cities.

Study on Factors Influencing Public Participation in River and Lake Governance in the Context of the River Chief System—Based on the Integrated Model of TPB-NAM

Public participation in the context of the river chief system is not only beneficial for long-term river and lake governance (RLG), but it also is an important supplement to the existing governance mode led by the government. On the basis of the integrated model of TPB-NAM, this paper discusses the influencing factors and driving mechanisms of public participation in river and lake governance in the context of the river chief system from aspects of self-interest and altruism. Through the investigation of residents, 508 sample data points were obtained and analyzed by the structural equation model (SEM). The results were as follows: (1) the explanatory power of the TPB-NAM model (R2 = 60.7%) was superior to that of the extended TPB model (R2 = 60.0%) and NAM model (R2 = 50.0%). (2) From the perspective of individual rationality, the intention for public participation in RLG had a significant positive influence on behavior, and behavior attitudes and subjective norms could predict their intentions. However, the roles were different; from the perspective of social rationality, awareness of negative consequences could actively affect positive personal norms through the ascription of responsibility, and positive personal norms could promote public participation in RLG. (3) Government norms were another important factor driving public participating in RLG. The results are of great theoretical significance for further exploring the public intention and behavior related to participation in RLG.

STUDY OF SOIL TEXTURE AND MOISTURE CONTENT EFFECT ON SOIL COMPACTION FOR LONG-YEAR TILLED FARM LAND

Farm mechanization is the main indicator of modernizing the agriculture and use of farm machines that can take the place of human and animal power in agricultural processes. During field operation the weight of farm machinery compact the agricultural soil due to the contact with the tires or tracks of tractors. The aim of the article is to study the effects of soil texture and moisture content on soil compaction. During field experimental test 5 different depth (5, 10, 15, 20 and 25 cm) were selected for taking soil compaction data and soil samples at three depth range (0-10, 10-20 and 20-30 cm) from Kulumsa Agricultural research center farm land at two seasons. During compaction test 15 sample point data were taken from 0.6 ha farm land. The result of the study shows from average of all sample point the maximum and minimum value of soil compaction value where 3.73 Mpa and 3.032 Mpa during harvesting season and 1.37 Mpa and 1.19 Mpa during seeding season respectively. The laboratory result shows farm land soil is clay with 25.0% of sand, 51% of clay and 24 % silt during both experimental seasons. The maximum and minimum percentage of soil moisture value where 28.92% and 20.04% during harvesting season and 41.8% and 30.8% at 20 to 30 cm and 0 to 10 cm depth respectively.

Entrepreneurship education and entrepreneurial intention of Chinese college students: Evidence from a moderated multi-mediation model.

Entrepreneurship plays an active role in promoting economic and population integration and social mobility. To further promote economic and social development, the Chinese government and universities have launched entrepreneurship education courses and encouraged college students to participate in entrepreneurship competitions to enhance their entrepreneurial knowledge, entrepreneurial ability and entrepreneurial intention. However, the entrepreneurial intention of Chinese college students is still not high. Therefore, a question arises: How should entrepreneurial education be carried out? Can entrepreneurial competitions and entrepreneurial self-efficacy be an effective medium in augmenting entrepreneurial education on entrepreneurial intention? Is family income an effective moderator affecting college students' entrepreneurial intention? To answer these questions, this study used quantitative methods to collect 351 sample data points, and a theoretical model was constructed to explain the mechanism forming entrepreneurial education and entrepreneurial intention. The results show that entrepreneurial self-efficacy plays a partial mediating role between entrepreneurial education and entrepreneurial intention, entrepreneurial competition and entrepreneurial self-efficacy play a chain mediating role and family income positively moderates the relationship between entrepreneurial education and entrepreneurial intention. The contribution of this study is to reveal the black box of the formation mechanism in college students' entrepreneurial intentions, affirms the role of the Chinese government in promoting entrepreneurial competitions and provides empirical evidence for the effective development of entrepreneurial practise activities, as well as theoretical references for entrepreneurial policy makers.

Real-Time Infrared Spectroscopic Measurement of Natural Moisturizing Factor

Real-Time Infrared Spectroscopic Measurement of Natural Moisturizing Factor

Sequential One-step Estimator by Sub-sampling for Customer Churn Analysis with Massive Data sets

AbstractCustomer churn is one of the most important concerns for large companies. Currently, massive data are often encountered in customer churn analysis, which bring new challenges for model computation. To cope with these concerns, sub-sampling methods are often used to accomplish data analysis tasks of large scale. To cover more informative samples in one sampling round, classic sub-sampling methods need to compute sampling probabilities for all data points. However, this method creates a huge computational burden for data sets of large scale and therefore, is not applicable in practice. In this study, we propose a sequential one-step (SOS) estimation method based on repeated sub-sampling data sets. In the SOS method, data points need to be sampled only with probabilities, and the sampling step is conducted repeatedly. In each sampling step, a new estimate is computed via one-step updating based on the newly sampled data points. This leads to a sequence of estimates, of which the final SOS estimate is their average. We theoretically show that both the bias and the standard error of the SOS estimator can decrease with increasing sub-sampling sizes or sub-sampling times. The finite sample SOS performances are assessed through simulations. Finally, we apply this SOS method to analyse a real large-scale customer churn data set in a securities company. The results show that the SOS method has good interpretability and prediction power in this real application.

How to facilitate employees' green behavior? The joint role of green human resource management practice and green transformational leadership.

Given the severity of today’s environmental issues, companies are increasingly making green concepts a key component of their operational strategies. As an essential complement to corporate environmental strategy, employees’ green behavior has received attention from all sectors of society. Based on resource conservation theory, this study explores the formation mechanism of employees’ green behaviors in enterprises starting from two green management tools: green human resource management (HRM) practices and green transformational leadership. Through two-stage questionnaire research, 296 sample data points were obtained, and the research hypotheses were tested by using linear regression analysis. The results showed that green HRM practices in enterprises enhance employees’ green mindfulness and thus stimulate their green behaviors and that green transformational leadership and green self-efficacy play a positive moderating role in the above relationship. These results support the applicability of resource conservation theory in green management and suggest that green HRM practices and green transformational leadership can be used together in the process of green management.