Selected Sampling Points Research Articles

Entropy-informed multi-stage sampling design for soil pollution mapping in heavy metal contaminated areas

The accuracy of soil heavy metal pollution mapping is heavily reliant on the sampling strategies utilized in both the preliminary and detailed survey stages of site investigations. This study introduces an entropy-informed multi-stage sampling design (EIMSD) method that leverages preliminary survey data as background information and utilizes relative entropy to progressively select sampling points in detailed surveys. Results indicate that the EIMSD method outperforms the grid sampling design (GSD) and conventional sampling design (CSD) methods across both hypothetical and real-world study areas. This superiority is evidenced by a notable rise in R2 values, ranging from 6.4% to 60.4% and a decrease in RMSE values from 16.7% to 54.0% relative to GSD, and a similar trend with an increase in R2 values from 6.7% to 44.1% and a reduction in RMSE values from 16.5% to 39.7% when compared to CSD. This study also investigates the optimal configurations for EIMSD, focusing on the number of detailed sampling points per stage (Nadd) and the ratio of preliminary-to-detailed survey sample sizes (Np/Nd) when the sum of Np and Nd is held constant. Our findings highlight that adding one detailed sampling point per stage (Nadd = 1) is the most effective. For areas with strong spatial variability, a larger Np/Nd value of approximately 3/2 is recommended, whereas a ratio close to 1 is apt for areas with moderate variability. Conversely, for areas with weak variability, a smaller Np/Nd value of about 2/3 is advised. EIMSD provides a more detailed and accurate map of soil heavy metal contamination, facilitating more targeted and effective remediation strategies.

The Morphology, Standing Stock and Habitat Distribution of Several Bamboo Species in the Reok Sub-District, Manggarai Regency, East Nusa Tenggara Province, Indonesia

Bamboo plays a vital role in preventing soil erosion and conserving water. In East Nusa Tenggara Province, many communities rely on bamboo species, making its cultivation a priority due to the region’s favorable biophysical conditions and available support from Non-Governmental Orgabization as facilitators. Approximately 387 villages have undergone assessment and socialization processes aimed at developing bamboo villages, including the Reok sub-district. One of the next steps in this process is conducting an inventory of bamboo standing stock in the area.This study aims to assess the species diversity and standing stock of bamboo in Reok sub-district. Research was carried out at 11 randomly selected sampling points across six villages, using the single circular plot method (radius of 17.9 m), with a total of 55 plots. Each plot was surveyed to identify and quantify clumps and culms, categorized by age (young, medium, and old). Four bamboo species were identified: Bambusa spinosa, Bambusa vulgaris, Gigantochloa atter, and Dendrocalamus asper. Gigantochloa atter was the most prevalent species, found at nearly all survey points, and had the highest number of clumps and culms. The estimated standing stock of bamboo in the Reok sub-district is 57,496 clumps, with a total of 3,692,890 culms. Gigantochloa atter accounted for the majority, with 35,526 clumps and 1,815,593 culms. This standing stock holds significant potential for further development, as bamboo is a key non-timber forest product (NTFP) in the region, with the potential to enhance the local economy.

Integrating multi-spectral imaging and Raman spectroscopy for in vivo endoscopic assessment of rat intestinal tract

An integrated system for in vivo multi-spectral imaging (MSI) and Raman spectroscopy was developed to understand the external morphology and internal molecular information of biological tissues. The achieved MSI images were reconstructed by eighteen separated images from 400 nm to 760 nm, whose illumination bands were selected with six tri-channel band filters. Based on the spectral analysis algorithms, the spatial distribution patterns of blood volume, blood oxygen content and tissue scatterer volume fraction were visualized. In vivo Raman spectral measurements were executed by inserting specially designed optical probe into instrumental channel of endoscope. By this way, the molecular composition at selected sampling points could be identified with its fingerprint spectral information under the guidance of molecular imaging modality. Therefore, both structural and compositional features of intestinal membrane could be addressed without labeling and continuously. The achieved results testified that our presented methodology reveals insights not easily extracted from either MSI or Raman spectroscopy individually, which brings the enrichment of biological and chemical meanings for future in vivo studies.

Estimating state of charge of cylindrical lithium-ion cells using multiple random convolutional kernel transform and low-frequency stress waves

Lithium-ion batteries (LIBs) represent an energy storage technology increasingly utilized across various fields. To maintain the safety and reliability of a LIB, monitoring the LIB state of charge (SoC) is essential. Currently, there are limited studies on SoC estimation of cylindrical LIBs using the low-frequency stress wave technique due to its limited sensitivity to internal changes of LIBs during the charging/discharging process. Here, we present the first demonstration of SoC estimation of cylindrical LIBs using low-frequency stress wave (5-50kHz). We adopt Pearson correlation coefficients (PCCs) to select sampling points relevant (>0.5) to the SoC from the response stress wave signals and then apply the multiple random convolutional kernel transform (Multi-Rocket) model to extract four types of features for SoC estimation. The SoC estimation performance of the developed method achieves similar accuracy in both original and down-sampled (low-resolution) signals, with estimation root mean square error of about 0.7%, 1.4%, 3.5%, and 5.3%, respectively in four scenarios. This new method based on low-frequency stress waves enables a drastic cost reduction compared to expensive high-frequency ultrasound methods, paving the way for low-cost, real-time SoC monitoring with excellent accuracy.

Optimization of Reservoir Water Quality Parameters Retrieval and Treatment Using Remote Sensing and Artificial Neural Networks

Inland water bodies are critical ecosystems that serve several functions including the provision of freshwater, regulation of climate and hydrological flows, and pollution control. Therefore, effective monitoring and management of these water resources is critical for sustainable water supply systems. This study evaluated the possible use of satellite data to estimate water quality parameters (WQPs) in an inland water body. The study also used artificial neural network (ANN) models in addition to the satellite data to determine the optimum coagulant dose for water treatment. The use of earth observations and machine learning methods has not been done extensively in developing countries, specifically, in water quality monitoring and management. The study utilized empirical multivariate regression modelling (EMRM) of the spectral reflectances from satellite data for the retrieval of Chla-a, Turbidity, and total suspended solids (TSS) concentrations in an inland water body. Using MLP-ANN modelling, the extracted spectral reflectance values from the selected sampling points in the reservoir were used as model inputs for the prediction of treated WQPs. A second MLP-ANN model was developed to predict the optimum coagulant dose required for raw water treatment. The R2 values achieved with AN model 1 were 0.81, 0.76, and 0.81 respectively for TSS, turbidity, and Chl-a, and 0.99 for the optimum coagulant dose. The study concluded that spectral reflectance from medium resolution satellite data products can be used to estimate WQPs from inland water bodies. Further, the ANN models demonstrate that extracted water quality data from satellite images can be used to inform ANN models for water quality predictions, and for the optimization of water treatment plant operations.

Application of Sampling Methods for Constrained Space in Hull Form Optimization

Use of approximation models instead of direct application of CFD tools plays a crucial role in hull form optimization to enhance efficiency. The selection of sample points directly impacts the accuracy and cost of approximation models, and the effectiveness of hull form optimization. This paper presents a sampling method based on constrained space. The distribution pattern of the constrained space is initially analyzed, and its boundary is subsequently extracted by the Support Vector Machine (SVM), providing guidance for the subsequent sample selection. To ensure effective sampling within the constrained space, the maximum minimum distance criterion is employed. The proposed methodology is validated via a case study involving a 13,000DWT inland twin-screw bulk carrier. The Kriging approximation model is constructed to optimize the hull form while adhering to specific constraint conditions, thereby demonstrating the feasibility and efficacy of the proposed approach.

Adaptive sampling physics-informed neural network method for high-order rogue waves and parameters discovery of the (2 + 1)-dimensional CHKP equation.

Rogue waves are important physical phenomena, which have wide applications in nonlinear optics, hydrodynamics, Bose-Einstein condensates, and oceanic and atmospheric dynamics. We find that when using the original PINNs to study rogue waves of high dimensional PDEs, the prediction performance will become very poor, especially for high-order rogue waves due to that the randomness of selection of sample points makes insufficient use of the physical information describing the local sharp regions of rogue waves. In this paper, we propose an adaptive sampling physics-informed neural network method (ASPINN), which renders the points in local sharp regions to be selected sufficiently by a new adaptive search algorithm to lead to a prefect prediction performance. To valid the performance of our method, the (2+1)-dimensional CHKP equation is taken as an illustrative example. Experimental results reveal that the original PINNs can hardly be able to predict dynamical behaviors of the high-order rogue waves for the CHKP equation, but the ASPINN method can not only predict dynamical behaviors of these high-order rogue waves, but also greatly improve the prediction efficiency and accuracy to four orders of magnitude. Then, the data-driven inverse problem for the CHKP equation with different levels of corrupted noise is studied to show that the ASPINN method has good robustness. Moreover, some main factors affecting the neural network performance are discussed in detail, including the size of training data, the number of layers of the neural network, and the number of neurons per layer.

An active learning Kriging model with approximating parallel strategy for structural reliability analysis

With the ever-increasing complexity of engineering problems, active learning functions fused with Kriging models are receiving significant attention and are extensively applied in various reliability analysis methods. It is argued unfavorably that the existing fusion-type methods usually obtain only a single optimal sample point during model updating process. Meanwhile, the information of the used sample points is not sufficiently utilized. To these gaps, this study proposes an adaptive Kriging reliability analysis method based on an approximate parallel computing strategy, namely AP-AK for short. To start with, a new Kriging model update strategy is proposed, where the first selected sample points help to introduce the intermediate model and further search for optimal points closer to the limit state surface. An improved learning function is then proposed to limit the locations of points based on the information of used sample points. The new function is characterized by setting the acceptance and rejection domains of the sample points, which prevents computational waste incurred by local sample point aggregation. Superior performance of the AP-AK method is demonstrated against other Kriging-based methods through five numerical cases and one solid attitude orbit control engine case. The comparison results show that the proposed method exhibits high efficiency and robustness for solving complex reliability analysis problems.

Exploring the Key Issues and Practical Paths of Modernizing the Governance of Vocational Education for Deep Learning

Abstract Deep learning algorithms are widely used in various fields due to the increasing popularity of education modernization, and the Ministry of Education has expressed a requirement to apply these algorithms to the governance of education in vocational schools in order to strengthen their teaching management. This paper constructs a student portrait model based on an improved K-means algorithm to monitor and analyze students’ daily behaviors. Firstly, we collect and integrate data from various sources. The dataset was preprocessed using data preprocessing methods, including data cleaning, data transformation, and data normalization. The Canopy algorithm was used to determine the number of clusters, and the number of clusters and cluster centers obtained by the Canopy algorithm were used as input parameters for the K-mean algorithm. The Maximum Minimum Distance algorithm was used to select sample points as far as possible for the K-means algorithm. Finally, we verify the effectiveness of the improved clustering algorithm and analyze the two dimensions of students using it. The findings show that students of type I in the learning behavior-oriented clustering visited the library an average of 22.54 times a month. There are a small number of students who spend more time online, averaging 48.45 hours per month. The majority of students’ categorical data and real-life learning behaviors coincide. This provides a basis for vocational school educators to optimize decision-making and teaching methods, indicating that the model in this paper is applicable to modern vocational education governance.

IMPROVING ACCURACY AND COMPUTATIONAL EFFICIENCY OF OPTIMAL DESIGN OF EXPERIMENTS VIA GREEDY BACKWARD APPROACH

Nonintrusive least-squares-based polynomial chaos expansion (PCE) techniques have attracted increasing attention among researchers for simple yet efficient surrogate constructions. Different sampling approaches, including optimal design of experiments (DoEs), have been developed to facilitate the least-squares-based PCE construction by reducing the number of required training samples. DoEs mainly include a random selection of the initial sample point and searching a pool of (coherence-optimal) candidate samples to iteratively select the next points based on some optimality criteria. Here, we propose a different way from the common practice to select sample points based on DoEs' optimality criteria, namely backward greedy. The proposed approach starts from a pool of coherence-optimal samples and iteratively removes the most uninfluential sample candidate among a small and randomly selected subset of the pool, instead of the whole pool. Several numerical examples are provided to demonstrate the promises of the proposed approach in improving the accuracy, robustness, and computational efficiency of DoEs. Specifically, it is observed that the proposed backward greedy approach not only improves the computational time for selecting the optimal design but also results in higher approximation accuracy. Most importantly, using the proposed approach, the choice of optimality becomes significantly less critical as different criteria yield similar accuracy when they are used in a backward procedure to select the design points.

Magnetic Characterization of Fine Sediment in the Solo Basin Indonesia

The magnetic characteristics of fine sediment samples from the Solo Basin are reported in this paper. Magnetic fine sediment was identified by magnetic susceptibility mapping based on sampling of 182 points. Then, a depth analysis (on 7 selected sampling points) was performed using X-ray fluorescence spectrometry, which showed an iron oxide content of up to 55.42%, while X-ray diffractometry confirmed magnetite minerals with crystallite size ≤100 nm. Further, the vibrating sample magnetometry results verified the magnetic characteristics under a single-domain configuration. The characteristic magnetic susceptibility map showed that there is a lithogenic effect on sediment in the Solo Basin. In addition, anthropogenic activities seem to play a pivotal role in distributing magnetic materials.

An improved adaptive Kriging model for importance sampling reliability and reliability global sensitivity analysis

An improved adaptive Kriging model for importance sampling (IS) reliability and reliability global sensitivity analysis is proposed by introducing the IS density function into learning function. Considering the variance information of Kriging prediction, the formula of traditional IS method is extended to the form considering the uncertainty of symbol function. The estimated variance of failure probability caused by the prediction uncertainty of Kriging model is obtained, and the corresponding coefficient of variation (COV) is defined. Based on the standard deviation information of failure probability, a novel learning function considering the characteristic of IS density function is proposed, which are used to minimize the prediction uncertainty of Kriging. The corresponding stopping criterion is defined based on the COV information. In order to increase the likelihood that the selected sample points fall around the limit state boundary, the penalty function method is introduced to improve the learning function. Once the failure probability is obtained, the variable global sensitivity index is calculated through the failed sample set and Bayes theorem. The results show that: By introducing IS density function and penalty function into learning function, the sample points which contribute more to the failure probability can be obtained more effectively in IS method. The proposed method has high accuracy and efficiency compared with traditional Kriging-based IS method.

Second-Order Level-Crossing Sampling Analog to Digital Converter for Electrocardiogram Delineation and Premature Ventricular Contraction Detection.

This article presents an electrocardiogram (ECG) delineation and arrhythmia heartbeat detection system using a novel second-order level-crossing sampling analog to digital converter (ADC) for real-time data compression and feature extraction. The proposed system consists of the front-end integrated circuit of the data converter, the delineation algorithm, and the arrhythmia detection algorithm. Compared with conventional level-sampling ADCs, the proposed circuit updates tracking thresholds using linear extrapolation, which forms a second-order level-crossing sampling ADC that has sloped sampling levels. The computing is done digitally and is implemented by modifying the digital control logic of a conventional Successive-approximation-register (SAR) ADC. The system separates the sampling and quantization processes and only selects the turning points in the input waveform for quantization. The output of the proposed data converter consists of both the digital value of the selected sampling points and the timestamp between the selected sampling points. The main advantages are data savings for the data converter and the following digital signal processing or communication circuits, which are ideal for low-power sensors. The test chip was fabricated using a 180 nm CMOS process. When sensing sparse signals such as ECG signals the proposed ADC achieves a compression factor of 8.33. The delineation algorithm uses a triangle filter method to locate the fiducial points and measures the intervals, slopes, and morphology of the QRS complex and the P/T waves. Those extracted features are then used in the arrhythmia heartbeat detection algorithm to identify Premature Ventricular Contraction (PVC). The overall performance of the system is evaluated using the MIT-BIH database and the QT database, which is also compared with the recently reported systems. The accuracy, sensitivity, specificity, PPV, and F1 score are 97.3%, 89.6%, 97.8%, 73.3%, and 0.81 for detecting PVC.

Density Function-Based Trust Region Algorithm for Approximating Pareto Front of Black-Box Multiobjective Optimization Problems

In this paper, we consider a black-box multiobjective optimization problem, whose objective functions are computationally expensive. We propose a density function-based trust region algorithm for approximating the Pareto front of this problem. At every iteration, we determine a trust region and then in this trust region, select several sample points, at which are evaluated objective function values. In order to obtain non-dominated solutions in the trust region, we convert given objective functions into one function: scalarization. Then, we construct quadratic models of this function and the objective functions. In current trust region, we find optimal solutions of all single-objective optimization problems with these models as objectives. After that, we remove dominated points from the set of obtained solutions. In order to estimate the distribution of non-dominated solutions, we introduce a density function. By using this density function, we obtain the most “isolated” point among the non-dominated points. Then, we construct a new trust region around this point and repeat the algorithm. We prove convergence of proposed algorithm under the several assumptions. Numerical results show that even in case of tri-objective optimization problems, the points generated by proposed algorithm are uniformly distributed over the Pareto front.

Temporal and spatial variation of microplastics in the urban rivers of Harbin

This study was to investigate temporal and spatial variation of microplastics in surface water and sediment in the urban rivers of Harbin during dry and wet season. Water samples (n = 25) in Xinyi River (n = 13) and Ashe River (n = 12) were collected from the selected sampling points. Microplastics in urban rivers in Harbin included polyethylene (PE), polypropylene (PP), polystyrene (PS), polyamide (PA), polyvinyl chloride (PVC) and polyethylene terephthalate (PET). The results show that urban rivers in Harbin had relatively mild microplastic abundance with most fragments in shape and colorless in color. PP and PE were the major polymers in surface water samples, while PVC and PET were the major polymers in sediment, which were dominated by large-size and granulate shape microplastics. Source apportionment demonstrate that the main sources of microplastics in Xinyi River and Ashe River during dry season were domestic wastewater and effluent from rainfall, while the main sources of microplastics in Xinyi River and Ashe River during wet season were wastewater, atmospheric sedimentation, and agricultural source. The morphology of microplastics in surface water and sediment in urban rivers of Harbin was negatively correlated with water velocity and positively correlated with the concentration of suspended matter, dissolved oxygen, and conductivity. Riparian vegetation on the sides of Xinyi and Ashe River decreased migration process of microplastics by vegetal purification and then resulted in low abundance of microplastics. In conclusion, this study highlighted the occurrence characteristics, source apportionment and environmental influencing factors of microplastics in urban rivers of Harbin, which may develop new insights into the reduction of abundance of microplastics in the urban rivers.

Adaptive Gaussian process regression for efficient building of surrogate models in inverse problems

In a task where many similar inverse problems must be solved, evaluating costly simulations is impractical. Therefore, replacing the model y with a surrogate model ys that can be evaluated quickly leads to a significant speedup. The approximation quality of the surrogate model depends strongly on the number, position, and accuracy of the sample points. With an additional finite computational budget, this leads to a problem of (computer) experimental design. In contrast to the selection of sample points, the trade-off between accuracy and effort has hardly been studied systematically. We therefore propose an adaptive algorithm to find an optimal design in terms of position and accuracy. Pursuing a sequential design by incrementally appending the computational budget leads to a convex and constrained optimization problem. As a surrogate, we construct a Gaussian process regression model. We measure the global approximation error in terms of its impact on the accuracy of the identified parameter and aim for a uniform absolute tolerance, assuming that ys is computed by finite element calculations. A priori error estimates and a coarse estimate of computational effort relate the expected improvement of the surrogate model error to computational effort, resulting in the most efficient combination of sample point and evaluation tolerance. We also allow for improving the accuracy of already existing sample points by continuing previously truncated finite element solution procedures.

A novel sampling method for adaptive gradient-enhanced Kriging

This paper presents a novel infill-sampling strategy for adaptive gradient-enhanced Kriging (AGEK) that delivers superior results on a limited budget. The primary innovation of this method is the adaptive use of gradient information, blurring the line between Kriging and gradient-enhanced Kriging. To construct a flexible AGEK model that automatically determines whether to incorporate gradients, our proposed method unfolds in three stages: (1) primary infill-sampling, (2) secondary infill-sampling, and (3) modeling time stages. In the first stage, the primary infill-sampling technique identifies potential sample point sites. In the second stage, the secondary infill-sampling process decides whether to obtain only the response or both the response and gradient at the selected sample point. During this stage, a newly defined pseudo expected improvement reduction, pseudo integrated uncertainty reduction, and weight functions are incorporated into the secondary infill-sampling criteria. In the third stage, we propose a strategy to manage instances where training time becomes overly demanding. Benchmark test results validate the excellent performance of the proposed method. Finally, in application to an engineering problem, our method outperforms conventional approaches by producing more accurate results within a limited computational budget.

Kajian Sifat Fisik Tanah Ustifluvents Sekotong Terkait Kandungan Merkuri (Hg) Dalam Tanah

People's gold mining in Sekotong, also known as Unlicensed Gold Mining (PETI), causes environmental pollution problems because located at the surrounding community processes gold using the amlagamation technique with the help of mercury (Hg) in binding gold. Mercury waste discharged directly into the environment could have adversely affects public health and degrades soil and water quality. The presence of mercury in the soil is strongly influenced by soil characteristics, one of which is soil physical properties. The purpose of this study was to determine the relationship between the physical characteristic ustifluvents Entisol soil of Sekotong soil and mercury content at 0-20 cm soil depth. This research was conducted using descriptive method with survey technique. Soil sampling was conducted at 3 location points near the logs and tailling at a depth of 0 - 10 cm and 10-20 cm, each location point was replicated 3 times. The selection of soil sample points was based on contour differences that affect the proportion of mercury content at each soil depth. The results showed that the highest mercury content was at point 3 with an average value of 0.3089 ppm and there was a strong to very strong relationship between soil physical properties and Hg content.

Early Molecular Immune Responses of Turbot (Scophthalmus maximus L.) Following Infection with Aeromonas salmonicida subsp. salmonicida.

Turbot aquaculture production is an important economic activity in several countries around the world; nonetheless, the incidence of diseases, such furunculosis, caused by the etiological agent A. salmonicida subsp. salmonicida, is responsible for important losses to this industry worldwide. Given this perspective, this study aimed to evaluate early immune responses in turbot (S. maximus L.) following infection with A. salmonicida subsp. salmonicida. For this, 72 fish were individually weighed and randomly distributed into 6 tanks in a circulating seawater system. For the bacterial challenge, half of the individuals (3 tanks with 36 individuals) were infected using a peritoneal injection with the bacterial suspension, while the other half of individuals were injected with PBS and kept as a control group. Several factors linked to the innate immune response were studied, including not only haematological (white blood cells, red blood cells, haematocrit, haemoglobin, mean corpuscular volume, mean cell haemoglobin, mean corpuscular haemoglobin concentration, neutrophils, monocytes, lymphocytes, thrombocytes) and oxidative stress parameters, but also the analyses of the expression of 13 key immune-related genes (tnf-α, il-1β, il-8, pparα-1, acox1, tgf-β1, nf-kB p65, srebp-1, il-10, c3, cpt1a, pcna, il-22). No significant differences were recorded in blood or innate humoral parameters (lysozyme, anti-protease, peroxidase) at the selected sampling points. There was neither any evidence of significant changes in the activity levels of the oxidative stress indicators (catalase, glutathione S-transferase, lipid peroxidation, superoxide dismutase). In contrast, pro-inflammatory (tnf-α, il-1β), anti-inflammatory (il-10), and innate immune-related genes (c3) were up-regulated, while another gene linked with the lipid metabolism (acox1) was down-regulated. The results showed new insights about early responses of turbot following infection with A. salmonicida subsp. salmonicida.

MODELADO DE METALES PESADOS EN EL FLUJO DE AGUA Y LODOS DEL ECOSISTEMA BENTÓNICO DEL RÍO BOGOTÁ – COLOMBIA

Industrial processes have created environmental problems because of the discharge of pollutants into the environment. Especially the water bodies have been contaminated by HM heavy metals. The Bogotá river has registered a considerable increase in these HM as it advances in its channel towards the Magdalena river without its effects on the benthic ecosystem have been established. The present study used the Bogotá river as a study source. This is born in the municipality of Villapinzón-Colombia and empties into the Magdalena River. The selected sampling points correspond to sectors where anthropogenic activities are carried out. The results showed the relation of its concentration for various metals, developing for each one the calibration curves in the water and the sediments. Although high concentrations were obtained in some sectors, they were within the parameters established by Colombian legislation.