Selection Of Sample Data Research Articles

Analysis of Sri-Kehati Stock Performance in The Period Before and During Covid-19 Using The Sharpe, Treynor, and Jensen Methods

This research aims to compare the performance of the SRI-KEHATI index stocks before and during the COVID-19 period using the Sharpe Ratio, Treynor Ratio, and Jensen's Alpha. A quantitative method with a descriptive approach was used in this study, utilizing secondary data. Sample data selection employed a non-probability method with a purposive sampling technique from a single population. Statistical tests were then conducted using paired sample t-test to examine whether there is a difference in portfolio performance among the stocks in the SRI-KEHATI index between the periods before and during the COVID-19 pandemic using the Sharpe, Treynor, and Jensen methods. The results of this research indicate that there was a notable contrast between the periods before and during the COVID-19 pandemic when utilizing the Sharpe Ratio and Treynor Ratio measurement methods. However, this discrepancy was not observed when employing the Jensen Method, as there was no significant difference detected between the two periods.

Sample data selection based on the feature subspace for photometric correction of the Chang'E-4 in situ observations

The lunar surface exhibits noticeable variations in photometric properties across different regions, influenced by their specific compositional and physical characteristics. These variations are particularly pronounced at the millimeter to centimeter scale. In this study, we propose a feature subspace-based sample selection method to identify Chang'E-4 Visible Near-infrared Imaging Spectrometer (CE-4 VNIS) in situ observations that share similar photometric properties at the centimeter scale. By constructing a feature subspace through principal component transformation, the CE-4 VNIS observation sites with comparable surface photometric properties are able to be selected from a series of observations. The selection results demonstrate that the majority of observation sites are covered by the lunar regolith, with a few exceptions consisting of rocks, soil bulk, shadows, and rough surfaces. These observations should be excluded from the photometric correction process during phase function fitting. Analyzing the photometric performance of the VNIS data with lunar regolith reveals that reflectance decreases as the phase angle increases below 80°, while above 80°, reflectance increases with increasing phase angle. This phenomenon is likely attributed to stronger forward scattering. Thus, a second-order polynomial is employed to fit the phase function for VNIS data. Consequently, a photometric model is developed for lunar regolith observed by CE-4 VNIS, incorporating the fitted phase function. Experimental results demonstrate reduced differences among observations with varying phase angles, and the corrected VNIS spectra exhibit a phase-reddening effect. These findings validate the effectiveness of the proposed model. A comparison between the corrected data with and without sample selection reveals a 33 % decrease in mean standard deviation for VIS/NIR bands and a 19 % decrease for SWIR bands in the corrected spectra after sample selection. These results indicate the potential applicability of our method for future VNIS observations, enabling the acquisition of photometrically consistent data.

INNOVATION ADOPTION PROCESS IN CREATING CUSTOMER VALUE PT.PLN

The consumer adoption process is a process in which consumers assimilate information, use it, and verify what has been done. The many innovations implemented in the PT. PLN company will certainly have an impact that has value in front of customers. In carrying out the process of consumer adoption of schemes or innovations imposed by PT. PLN. With the innovation carried out by PLN, namely the use of the PLN mobile application, will there be a problem between the previous habit becoming a new habit, of course PLN customers must get used to adapting to this habit. This research certainly wants knowledge between the researchers' assumptions namely product quality, product design and product benefits in the adoption process of the PLN influencing customer assessment. Correlational Research Methods where researchers change qualitative data into quantitative data by using primary data and using the slovin method as a sample data selection. The analysis technique using structural equation modeling rather obtains accurate hypothesis results and uses validity and reliability tests using variance extract and construct reliability. As well as this research confirms the model suitability test by using the Goodness of fit test and finally the researcher gets the results of the regression weight for the results of determining the hypothesis. The results obtained by the researchers in this study were that product quality had a significant and positive effect on customer ratings, while product design had no significant relationship with customer ratings and product benefits/usages also had no significant relationship at all with PLN product ratings.

A LightGBM-based landslide susceptibility model considering the uncertainty of non-landslide samples

The quality of samples is crucial in constructing a data-driven landslide susceptibility model. This article aims to construct a data-driven landslide susceptibility model that takes into account the selection of non-landslide samples. First, 21 conditioning factors are selected, including four types of topography and landform, geological conditions, environmental conditions, and human activities. Grid units with 30 m resolution are established by combining 942 historical landslide events in study area. Second, non-landslide samples are selected using both the traditional method and the information quantity method. Two landslide susceptibility models are established using the Bayesian optimization-LightGBM model. The accuracy of the model is evaluated by significance test and the area under curve (AUC). Finally, the SHAP algorithm is used to analyse the internal mechanism of the model’s decision-making. Based on the information quantity method, the LightGBM model identifies very high-high susceptibility areas that account for 77.92% of the total number of landslides. Additionally, the AUC of test set and the AUC of training set are 23.2% and 17.1% higher, respectively, compared to the traditional model. The selection of different sample data, whether landslide or non-landslide, impacts the factor rank, model accuracy, and the interal decision-making mechanism of the model. This finding provides valuable for the selection of sample data in the binary classification model.

Energy method of geophysical logging lithology based on K-means dynamic clustering analysis

Lithology identification is an important part of reservoir evaluation and reservoir description when processing and interpreting geophysical record data. Clustering analysis refers to the analysis process of grouping a collection of physical or abstract objects into several classes composed of similar objects. K-means clustering algorithm is an iterative clustering analysis algorithm. In this paper, seven mechanical property parameters of 49 rock samples are selected as experimental data in an engineering survey, and the geophysical logging method of K-means dynamic clustering analysis is adopted. The rock samples are divided into three categories, and the classification results are matched by mechanical property parameter method. By changing the order of data grouping, the misjudgment rates were 0.021, 0.021 and 0.102, respectively. Therefore, it is feasible and effective to use k-means dynamic clustering analysis to classify lithology. The number of samples decreased to 15, and the misjudgment rate increased to 0.267 The results of K-means dynamic clustering analysis may be different from the actual situation of rock sample data selection.

Sample data selection method for improving the prediction accuracy of the heating energy consumption

Back propagation neural network (BPNN) models and multiple linear regression (MLR) models are widely used to predict heating energy consumption. To improve the prediction accuracies for the BPNN and MLR models, we propose a novel sample data selection method (SDSM) combining the similar days selection with the virtual samples generation. First, a grey correlation method integrated with an entropy weight method is given to optimize the similar days selection. Then virtual samples are generated by Gaussian distribution function based on the similar samples. Finally, a new sample set (including similar and virtual samples) is obtained, and then, it is regarded as the input variable for the BPNN and MLR models. The results show that training errors and prediction errors are obviously reduced in the developed BPNN model. Although the prediction accuracy of the developed MLR model is improved by different degrees, the coefficient of determination obtained in the regression fitting process is poor. It is proved that the novel SDSM is applicable only for the BPNN model, but not for the MLR model.

An improved QSPR method based on support vector machine applying rational sample data selection and genetic algorithm-controlled training parameters optimization

An improved QSPR method based on support vector machine (SVM) applying rational sample data selection and genetic algorithm (GA)-controlled training parameters optimization was developed to study the standard formation Gibbs free energy of 78 kinds of acyclic alkanes. The SVMs were trained applying the standard regression algorithm based on quadratic programming theory, and the Gaussian radial basis kernel function (RBKF) was employed in the training process. Meanwhile, eight well-known topological indices were used as structural descriptors for each alkane molecule, and they were also considered to be the potential input variables for the proposed QSPR models. Subsequently, by optimizing the ε parameter in insensitive loss function, the penal factor C, the σ parameter in RBKF and the input variable representations simultaneously via GA, a novel QSPR approach based on the combination of GA and SVM was proposed to improve the prediction results of the independent external test samples. For independent external test samples selected randomly prior to QSPR model development, an improved predictive modeling method based on SVM was achieved by rationally selecting the training and the internal test data set with sphere exclusion algorithm and optimizing the SVM training parameters by the proposed GA method.For comparing purpose, partial least square (PLS) regression method was also used as another QSPR modeling tool for the experimental data set. Moreover, to verify the improved modeling method in a more general way, two mathematically simulated QSPR data sets were built to confirm its validity.

The Assessment of Machine Learning Model Performance for Predicting Alluvial Deposits Distribution

This paper discusses the development and evaluation of distribution models for predicting alluvial mineral potential mapping. A number of existing models includes Weight of Evidence, Knowledge-driven Fuzzy, Data-driven Fuzzy, Neural-Network, Bayesian Classifier and Geostatistical Kriging. We offer classification models developed in our laboratory, where point pattern analysis was used to identify presence or absence of a known secondary alluvial (cassiterite) deposits in the Nigerian Younger Granite Region (NYGR) and the model performance assessed. We focused on the training and testing data split using longitudinal spatial data splitting (strips and halves) to ensure predictive attribute's independence. The spatial data split runs counter to the traditional random sample data selection as a procedure for checking overfitting of models mainly due to spatial data autocorrelation. Specifically, we used classification algorithms such as; Naive Bayes, Support Vector Machine, K-Nearest Neighbour, Decision Tree Bagging and Discriminant Analysis algorithms for training and testing. We analysed the model's performance results using model predictive accuracy and ROC curve values in two different approaches that improve spatial data independence among predictive attributes to give a meaningful model performance.

The Arc Sound Characteristic on MIG Weld Penetration

Arc sound signal in MIG welding contains plenty of welding information, which is an important signal source of weld penetration. Aiming at on-line monitoring of weld penetration, the relationship between arc sound signal and penetration is analyzed in time-frequency domain through a reasonable selection of sample data. The results show that the spectrum of arc sound signal and penetration status are highly correlative. The changes of penetration can accurately detected by variation of signal energy from 1500 Hz to 4500 Hz in welding process. The above experiments verify the feasibility of monitoring the penetration through arc sound signal and provides theory and application foundation for penetration adaptive control.

Evaluating Temporal Variability in Bacterial Indicator Samples for an Urban Watershed

Temporal variability in water quality sampling can lead to under or overestimation of pollution. More frequent sampling during times of expected high variability can reduce temporal bias. However, bacterial indicator measurements require manual sampling and time-intensive and expensive laboratory analysis. Thus, methods to assess the effect of temporal variability would be useful. In this work, fecal coliform and E. coli samples were taken weekly during the recreational season at 25 sites in an urban watershed. The data were categorized into wet versus dry days, and upper versus lower watershed, and the geometric means and geometric standard deviations of various five-sample data sets were analyzed to determine if sample data selection would result in different stream classifications. Results indicate if the bacterial indicator samples are near the regulated limits for water contact recreational use, temporal bias could sway impairment classification decisions. To reduce the temporal bias, sampling data s...

Simultaneous high throughput and quantitative analysis of MTBE and BTEX by P&T-GCMS using a Precept® autosampler

AbstractFuel-oil contamination of groundwater and waterways is common throughout the world. The fuel components methyl-tert-butyl ether (MTBE) and benzene, toluene, ethylbenzene and o-, p- and m-xylenes (BTEX) compounds are most likely to reach a borehole after a contamination event, and also those that pose the greatest risk.Various gas chromatography-mass spectrometry (GCMS) methods are available for the analysis of volatile organic compounds (VOCs) but many of these are not suitable for rapid and combined analysis of MTBE and BTEX. Here we describe and validate a simple and reliable method that fulfils these requirements. The method is quick: 48 samples can be analysed in a 24 hour period using the Precept II® autosampler and therefore is suitable for high throughput work. Validation of our experiments is presented in the form of linearity, precision and intra-day repeatability of standards as well as limits of detection and limits of quantification for the range 1—20 μg/l.This MTBE/BTEX analytical method developed was used to study the optimization of a modular post-abstraction remediation system incorporating air-stripping and granular activated carbon (GAC) technologies (Hall, 2004), from which a selection of sample data is presented.

Approaches to Assessing Sustainable Agriculture

ABSTRACT This paper brings together several case studies in which different methodological approaches and techniques mobilizing single or composite indicators were applied to assess agricultural sustainability at four hierarchical levels, including the land management system, the cropping system, the farming system, and the agricultural sector system. The first case study (Iran) focuses on the land management system at the level of individual soil units, using statistical quality control charting to assess the soil fertility status and its effect on agricultural sustainability. Statistical limits are adequate to monitor the behavior of a data population over time, but must be replaced by acceptance/sufficiency standards for sustainability assessment. A relevant limitation of control charting is that large data sets are needed to allow for random sample data selection from the whole population and check for normal distribution of the data. The second case (Kenya) concentrates on the cropping system at parcel level, using yield gap analysis to evaluate sustainable crop productivity. Although yield gap analysis does not indicate by itself what yield level is sustainable, it points at levels of crop productivity higher than farmers' yields, which could be achievable with additional inputs and improved management practices. If the farmer can raise the yield to a higher level, his/her farming activity will become more profitable and, therefore, economically more sustainable. The third study (Iran) refers to the farming system at the production unit level, using the energy balance analysis to compare the sustainability of traditional and modern agricultural systems. Energy balance analysis has the advantage of expressing all input and output parameters in the same unit. The approach allows us to establish input/output ratios and compare different farming systems in quantitative terms for assessing their sustainability, but it must be combined with complementary techniques to cover the many facets of the sustainability concept. The last case study (Venezuela) addresses the agricultural sector as a whole, using an aggregated index to monitor the sustainability of the farming activity at regional/national level. Component indicators must be chosen according to data availability, data sensitivity to temporal changes, and the capacity of the data to describe quantitatively the behavior of the agricultural activity. The index needs refinement by integrating additional indicators and by allocating differential weights to the indicators to properly reflect their relevance and dynamics. In general, much effort is still needed to integrate the scale-specific methodological approaches in one coherent framework allowing to navigate through the hierarchical levels of the agricultural macro-system and to take into account the many requirements involved in a holistic model of sustainability.