Dynamic Sampling Technique Research Articles

Carbon emission warning method for machine tool manufacturing process based on dynamic adaptive EWMA control chart.

Machine tools constitute the backbone of the industrial sector, representing the largest global inventory of equipment. The carbon emissions resulting from the production of each machine tool merit attention. Effective management of carbon emissions in the machine tool manufacturing process is crucial. This paper introduces a novel method for early carbon emission warnings in the machine tool manufacturing process, utilizing an adaptive dynamic exponentially weighted moving average (EWMA) approach. This method addresses the challenges in identifying and monitoring abnormal carbon emissions, emerging from uncertainties and dynamic correlations. Utilizing dynamic sampling techniques and adaptive principles, this method constructs an adaptive dynamic EWMA control chart. The EWMA control chart incorporates a multi-objective optimization design model, concentrating on carbon emissions in the machine tool manufacturing process, and incorporates statistical, economic, and environmental objectives. To mitigate slow convergence rates and enhance optimization accuracy in complex control chart multi-objective optimization algorithms, this study proposes an enhanced Harris hawks optimization (HHO) algorithm as the solving algorithm. Finally, the application of this method is demonstrated through the monitoring of carbon emissions in the manufacturing process of a five-axis machine tool (EOC), as a case study. The results validate the method's rapid responsiveness to abnormal carbon emissions, providing alerts. This further confirms the efficacy and feasibility of the proposed approach. Ultimately, this approach offers a viable strategy for fostering environmentally conscious and high-quality growth in the machine tool industry.

Large Seasonal Variation in Nitrogen Isotopic Abundances of Ammonia Volatilized from a Cropland Ecosystem and Implications for Regional NH3 Source Partitioning.

Ammonia (NH3) volatilization from agricultural lands is a main source of atmospheric reduced nitrogen species (NHx). Accurately quantifying its contribution to regional atmospheric NHx deposition is critical for controlling regional air nitrogen pollution. The stable nitrogen isotope composition (expressed by δ15N) is a promising indicator to trace atmospheric NHx sources, presupposing a reliable nitrogen isotopic signature of NH3 emission sources. To obtain more specific seasonal δ15N values of soil NH3 volatilization for reliable regional seasonal NH3 source partitioning, we utilized an active dynamic sampling technique to measure the δ15N-NH3 values volatilized from maize cropping land in northeast China. These values varied from -38.0 to -0.2‰, with a significantly lower rate-weighted value observed in the early period (May-June, -30.5 ± 6.7‰) as compared with the late period (July-October, -8.5 ± 4.3‰). Seasonal δ15N-NH3 variations were related to the main NH3 production pathway, degree of soil ammonium consumption, and soil environment. Bayesian isotope mixing model analysis revealed that without considering the seasonal δ15N variation in soil-volatilized NH3 could result in an overestimate by up to absolute 38% for agricultural volatile NH3 to regional atmospheric bulk ammonium deposition during July-October, further demonstrating that it is essential to distinguish seasonal δ15N profile of agricultural volatile NH3 in regional source apportionment.

Estimating COVID-19 Prevalence in Sri Lanka: A Dynamic Sampling Model Approach

Over history, human has had to face various crises and diseases, and among them, the COVID 19 virus stands out as one of the most deathful diseases ever. It has brought numerous challenges to all the fields worldwide. Despite efforts to control its spread, the virus persists globally with varying intensity. Addressing this challenge requires an effective and precise control measure. The progression of the virus in different sub-regions is influenced by factors such as population density, public mobility, and healthcare infrastructure. Consequently, the prevalence of the virus varies across sub-regions. This study proposes an adaptive sampling design that modifies the stratified sampling technique to capture the changing prevalence of COVID-19, considering the dynamic nature of infected populations. This adaptation is essential as the increase of infected cases boosts the virus spread, and the standard sampling techniques do not address such dynamic population conditions in determining the sample size. The study aims to narrow the gap between reported and actual daily infections, providing more accurate estimates of virus distribution. The weighted allocation method incorporates the skewed pattern of coronavirus progression, with weights determined based on the first derivative of reported infected cases. This derivative information is based on the recent dynamics of the infected cases. Thereby larger weights were assigned when the virus progression increased, and smaller weights were assigned when the virus progression decreased. The resulting sample sizes for each sub-region are calculated using the modified stratified sampling method. Further, to illustrate the accuracy of the sampling design, simulated data from different epidemic scenarios, such as community spread, cluster spread, and border spread was used. This simulation allowed us to test the robustness of the techniques for the different states of the virus progression based on the infected cases. The sample size obtained through this dynamic sampling technique exhibits a direct correlation with the fluctuations in the number of infected cases, increasing as the infection cases rise and decreasing as they decline. In conclusion, the study introduces a novel sampling technique that accommodates the dynamic nature of population sizes, and it can be straightforwardly applied for the real-world data as well. Thus, this modified stratified sampling method emerges as a precise approach for capturing the actual prevalence of COVID-19.

Profiles of Volatiles Emitted from Orange Fruits Infested by Ceratitis capitata (Diptera: Tephritidae).

The profiles of volatile organic compounds (VOCs) emitted from orange fruits infested by Ceratitis capitata were examined. VOCs were collected from the headspace of oranges immediately, 24 h, 5 days, 8 days, and 13 days after oviposition and when larvae exit the fruit by applying either static or dynamic sampling techniques. A total of 32 and 47 compounds were detected in infested orange fruits when using static and dynamic techniques, respectively. Differences in the volatile profile of oranges were observed. Classification models were employed, showing that infested fruits emitted a chemical profile distinct from that of non-infested ones. Limonene was associated with cell disruption. (E)-β-Ocimene, (E)-4,8-dimethyl-1,3,7-nonatriene, hexyl butanoate, butyl hexanoate, and hexyl hexanoate were the VOCs that were correlated with the infestation of sweet oranges by C. capitata.

Understanding Current Methods for Sampling of Aflatoxins in Corn and to Generate a Best Practice Framework.

Aflatoxin contamination in corn is a significant issue, posing substantial health threats to humans and animals. Aflatoxin testing protects consumer health, ensures the safe global trade of corn, and verifies compliance with legislation; however, effective sampling procedures are essential to ensure reliable results. While many sampling procedures exist, there is no evidence to indicate which is the best approach to ensure accurate detection. Using scientific and gray literature sources, this review analyzed sampling procedures to determine an optimum approach to guide the development of standard practices. Results revealed that sampling is the major source of error in the accurate assessment of aflatoxin levels in food and crucial for obtaining reliable results. To guarantee low variability and sample bias-increased sample size and sampling frequency, the use of automatic dynamic sampling techniques, adequate storage, and homogenization of aggregate samples for analysis are advised to ensure a representative sample. However, there is a lack of evidence to support this or indicate the current utilization of the reviewed procedures. Inadequate data prevented the recommendation of sample sizes or frequency for optimum practice, and thus, further research is required. There is an urgent need to make sampling procedures fit-for-purpose to obtain accurate and reliable aflatoxin measurements.

Fall Detection using Deep Learning Algorithms and Analysis of Wearable Sensor Data by Presenting a New Sampling Method

Fall is one of the most critical health challenges in the community, which can cause severe injuries and even death. The primary purpose of this study is to develop a deep neural network using wearable sensor data to detect falls. Most datasets in this field suffer from the problem of data imbalance so that the instances belonging to the Fall classes are significantly less than the data of the normal class. This study offers a dynamic sampling technique for increasing the balance rate between the samples belonging to fall and normal classes to improve the accuracy of the learning algorithms. The Sisfall dataset was used in which human activity is divided into three categories: normal activity (BKG), moments before the fall (Alert), and role on the ground (Fall). Three deep learning models, CNN, LSTM, and a hybrid model called Conv-LSTM, were implemented on this dataset, and their performance was evaluated. Accordingly, the Conv-LSTM hybrid model presents 96.23%, 98.59%, and 99.38% in the Sensitivity parameter for the BKG, Alert, and Fall classes, respectively. For the accuracy parameter, we have managed to reach 97.12%. In addition, by using noise smoothing and removal techniques, we can hit a 97.83% accuracy rate. The results indicate the proposed model's superiority compared to other similar studies.

A Convolutional Neural Network-Based Intelligent Medical System with Sensors for Assistive Diagnosis and Decision-Making in Non-Small Cell Lung Cancer.

In many regions of the world, early diagnosis of non-small cell lung cancer (NSCLC) is a major challenge due to the large population and lack of medical resources, which is difficult toeffectively address via limited physician manpower alone. Therefore, we developed a convolutional neural network (CNN)-based assisted diagnosis and decision-making intelligent medical system with sensors. This system analyzes NSCLC patients’ medical records using sensors to assist staging a diagnosis and provides recommended treatment plans to physicians. To address the problem of unbalanced case samples across pathological stages, we used transfer learning and dynamic sampling techniques to reconstruct and iteratively train the model to improve the accuracy of the prediction system. In this paper, all data for training and testing the system were obtained from the medical records of 2,789,675 patients with NSCLC, which were recorded in three hospitals in China over a five-year period. When the number of case samples reached 8000, the system achieved an accuracy rate of 0.84, which is already close to that of the doctors (accuracy: 0.86). The experimental results proved that the system can quickly and accurately analyze patient data and provide decision information support for physicians.

Trends and applications in plant volatile sampling and analysis.

Volatile organic compounds (VOCs) released by plants serve as information and defense chemicals in mutualistic and antagonistic interactions and mitigate effects of abiotic stress. Passive and dynamic sampling techniques combined with gas chromatography-mass spectrometry analysis have become routine tools to measure emissions of VOCs and determine their various functions. More recently, knowledge of the roles of plant VOCs in the aboveground environment has led to the exploration of similar functions in the soil and rhizosphere. Moreover, VOC patterns have been recognized as sensitive and time-dependent markers of biotic and abiotic stress. This focused review addresses these developments by presenting recent progress in VOC sampling and analysis. We show advances in the use of small, inexpensive sampling devices and describe methods to monitor plant VOC emissions in the belowground environment. We further address latest trends in real-time measurements of volatilomes in plant phenotyping and most recent developments of small portable devices and VOC sensors for non-invasive VOC fingerprinting of plant disease. These technologies allow for innovative approaches to study plant VOC biology and application in agriculture.

Emission Characteristics of Biogenic Volatile Compounds (BVOCs) from Common Greening Tree Species in Northern China and Their Correlations with Photosynthetic Parameters

In order to understand the emission characteristics of common greening trees in Beijing and analyze their correlations with photosynthetic parameters, including the net photosynthetic rate (Pn), stomatal conductance (gs), intercellular CO2 concentration (ci), and transpiration rate (Tr), we collected samples of biogenic volatile organic compounds (BVOCs) using a dynamic sampling technique from 14 species of deciduous trees. The results showed that there were significant differences in isoprene and total BVOC emissions between family or genus levels (P<0.01). With the exception of Lonicera maackii Maxim and Ulmus pumila L., all species were found to emit isoprene, monoterpenes, and sesquiterpenes, of which, species from the Salicaceae (e.g., Populus deltoides cv. '55/56'× P.deltoides cv. 'Imperial', P. euramericana cv. '74/76', Populus simonii Carr, and Salix babylonica) and Legume (Sophora japonica, Robinia pseudoacacia, and S. japonica Linn. var. japonica f. pendula Hort) families were the higher isoprene emitters, with emission rates that ranged from (30.1±4.3) μg·(g·h)-1 to (91.8±10.0) μg·(g·h)-1. Plants from the Oleaceae (e.g., Fraxinus chinensis Roxb and Syringa oblata Lindl), Begonia (Malus prunifolia), Sapindaceae (Koelrenteria paniculate), and Aceraceae (Acer truncatum Bunge) families mainly emitted monoterpenes and sesquiterpenes. Among them, Fraxinus chinensis Roxb and Acer truncatum Bunge were the highest emitters with emission rates of (10.6±4.8) μg·(g·h)-1 and (11.8±6.4) μg·(g·h)-1, respectively. Ocimene and β-pinene were the two main monoterpenes emitted from greening tree species. No significant correlations were found between the emission of BVOCs and Pn or gs, while the emission rate of isoprene (r=0.681; P<0.01) and the total BVOC (r=0.698; P<0.01) from the Salicaceae family increased with increasing Tr. Moreover, leguminous plants showed a significant positive correlation between the total BVOC emission rate and ci (P=0.04). This study provides a scientific reference for the selection and configuration of urban greenery, and a theoretical basis for exploring the mechanism of BVOC emissions.

Correlation-based dynamic sampling for online high dimensional process monitoring

Effective process monitoring of high-dimensional data streams with embedded spatial structures has been an arising challenge for environments with limited resources. Utilizing the spatial structure is key to improve monitoring performance. This article proposes a correlation-based dynamic sampling technique for change detection. Our method borrows the idea of Upper Confidence Bound algorithm and uses the correlation structure not only to calculate a global statistic, but also to infer unobserved sensors from partial observations. Simulation studies and two case studies on solar flare detection and carbon nanotubes (CNTs) buckypaper process monitoring are used to validate the effectiveness of our method.

Inventory and Characteristics of Biogenic Volatile Organic Compounds (BVOCs) for 12 Deciduous Fruit Trees

To better understand the emission of biogenic volatile organic compounds (BVOCs) in suburbs, we selected 12 typical deciduous fruit trees and conducted field sampling and laboratory analysis of BVOCs using a dynamic sampling technique. To our knowledge, details of BVOC emissions for nine of the selected fruit tree species are reported here for the first time. Emissions of BVOCs from fruit trees contained nine kinds of compounds, including hydrocarbons, alcohols, and aldehydes, of which hydrocarbons accounted for up to 39.0%. All fruit trees were found to emit isoprene and monoterpenes (six species also emitted sesquiterpenes), of which three species were high emitters and nine species, such as Catalpa, were medium emitters. The emission rates of total BVOCs (including isoprene, monoterpenes, and sesquiterpenes) from different fruit trees ranged from (2.6±0.1) μg·(g·h)-1 to (14±0.8) μg·(g·h)-1. Analyses of BVOCs from different families and plant forms indicated that isoprene emission rates were significantly higher from woody fruit trees[(4.2±1.4) μg·(g·h)-1] than from vine fruit trees[(0.6±0.2) μg·(g·h)-1, P=0.03], whereas there was no significant difference between family or genus. This suggests that the emission level of BVOCs from fruit trees cannot be classified by family and genera. In contrast to coniferous plants, β-myrcene, D-limonene, and γ-terpinene-associated with floral or resinous aromas-were the main monoterpenes of fruit trees. The emission rate of β-myrcene was highest, accounting for 59.3% of the total monoterpene emissions. In addition, fruit trees may emit eight other fragrant VOCs listed as hazardous air pollutants (HAPs), e.g., fluorene, phenanthrene, and naphthalene. This study expands the field of BVOCs research and provides basic data for enriching the BVOCs database, as well as for evaluating the environmental effects of BVOCs.

A laboratory high-throughput glass chamber using dynamic headspace TD-GC/MS method for the analysis of whole Brassica napus L. plantlet volatiles under cadmium-related abiotic stress.

IntroductionThe dynamic headspace sampling technique using thermal desorption, gas chromatography‐mass spectrometry (TD‐GC/MS) is a powerful method for analysing plant emissions of volatile organic compounds (VOCs), and experiments performed in sterile and controlled conditions can be useful for VOC metabolism investigations.ObjectiveThe main purpose of this study was to set up a laboratory high‐throughput glass chamber for whole plant volatiles analysis. Brassica napus L. plantlets were tested with the developed system to better understand the relationship between low emission of induced terpene and cadmium (Cd)‐related abiotic stress.MethodologyVOCs emitted by 28‐day‐old Brassica napus L. plantlets cultivated in vitro were trapped with our device using adsorbent cartridges that were desorbed with a thermal desorption unit before cryofocusing with a cooled injection system and programmable temperature vaporising inlet into an HP‐5 ms GC column. Terpene detection and quantitation from chromatogram profiles were acquired using selected ion monitoring (SIM) mode during full scan analysis and mass spectra were obtained with a quadrupole‐type mass spectrometer.ResultsThe new trapping method produced reliable qualitative profiles of oilseed rape VOCs. Typical emissions of monoterpenes (myrcene, limonene) and sesquiterpenes (β‐elemene, (E,E)‐α‐farnesene) were found for the different concentrations tested. One‐way analysis of variance for quantitative results of (E,E)‐α‐farnesene emission rates showed a Cd concentration effect.ConclusionThis inexpensive glass chamber has potential for wide application in laboratory sterile approach and replicated research. Moreover, the non‐invasive dynamic sampling technique could also be used to analyse volatiles under both abiotic and biotic stresses.

Splinelike interpolation in particle tracking microrheology

Converting time dependent creep compliance to frequency dependent complex shear modulus is an important step in analyzing the results of particle tracking microrheology. Fitting a function to the whole time range and transforming it to calculate the shear modulus is one way of solving this problem. However, the creep compliance of many samples, such as gels of biopolymers, shows different trends under different time regimes. Fitting in these regimes segmentwise results in a function which usually cannot be transformed in a closed analytical form. In general, unlike for beta and cubic splines, also the continuity of the first derivative cannot be ensured. In this paper, we present a method for using segmentwise fitting and numerical conversion, discussing interpolation for improving the transition between the fitted ranges, and propose a dynamic sampling technique to control the accuracy of the resultant complex shear modulus.

Sample size selection in optimization methods for machine learning

This paper presents a methodology for using varying sample sizes in batch-type optimization methods for large-scale machine learning problems. The first part of the paper deals with the delicate issue of dynamic sample selection in the evaluation of the function and gradient. We propose a criterion for increasing the sample size based on variance estimates obtained during the computation of a batch gradient. We establish an $${O(1/\epsilon)}$$ complexity bound on the total cost of a gradient method. The second part of the paper describes a practical Newton method that uses a smaller sample to compute Hessian vector-products than to evaluate the function and the gradient, and that also employs a dynamic sampling technique. The focus of the paper shifts in the third part of the paper to L 1-regularized problems designed to produce sparse solutions. We propose a Newton-like method that consists of two phases: a (minimalistic) gradient projection phase that identifies zero variables, and subspace phase that applies a subsampled Hessian Newton iteration in the free variables. Numerical tests on speech recognition problems illustrate the performance of the algorithms.

Organic Matter—Solid Phase Interactions Are Critical for Predicting Arsenic Release and Plant Uptake in Bangladesh Paddy Soils

Agroecological zones within Bangladesh with low levels of arsenic in groundwater and soils produce rice that is high in arsenic with respect to other producing regions of the globe. Little is known about arsenic cycling in these soils and the labile fractions relevant for plant uptake when flooded. Soil porewater dynamics of field soils (n = 39) were recreated under standardized laboratory conditions to investigate the mobility and interplay of arsenic, Fe, Si, C, and other elements, in relation to rice grain element composition, using the dynamic sampling technique diffusive gradients in thin films (DGT). Based on a simple model using only labile DGT measured arsenic and dissolved organic carbon (DOC), concentrations of arsenic in Aman (Monsoon season) rice grain were predicted reliably. DOC was the strongest determinant of arsenic solid-solution phase partitioning, while arsenic release to the soil porewater was shown to be decoupled from that of Fe. This study demonstrates the dual importance of organic matter (OM), in terms of enhancing arsenic release from soils, while reducing bioavailability by sequestering arsenic in solution.

The use of dynamic (window) sampling in the site investigation of potentially contaminated ground

Dynamic sampling techniques in ground investigation have been used in continental Europe for many years. In the UK the use of this sampling method has increased rapidly over the last few years and has now become an established method for taking samples for the assessment of contamination potential. This widespread use has been due to the cost-effectiveness, flexibility and speed of operation. Dynamic sampling has also a number of health and safety advantages relative to the conventional investigation techniques (e.g., trial pitting and light cable percussive boring) that have historically been used in the UK for investigating potentially contaminated land. The method can also be used for the installation of a variety of instrumentation including piezometers and gas monitoring wells. This paper reviews the capability and limitations of dynamic sampling as used in the UK and discusses the advantages and disadvantages of the technique. A number of case histories are presented to illustrate its operation.

A dynamic sampling technique for the simulation of probabilistic and generalized activity networks

Most probabilistic activity networks (e.g. PERT) of any reasomable size are practically impossible to analyse mathematically in an acceptable time. This problem is augmented when stochastic branching is introduced to form generalized activity networks. For this reason simulation has proved to be one of the more popular and ‘accurate’ techniques available for network attribute analysis. In this paper a dynamic sampling technique is introduced that improves on the standard simulation approach used in popular project management software tools. A comparison is also made between the simulation requirements of standard probabilistic activity networks and a finite sample set of generalized activity networks in which activities are assigned either dependent or independent probability generations.

The activity-composition relationship of oxygen and hydrogen isotopes in aqueous salt solutions: III. Vapor-liquid water equilibration of NaCl solutions to 350°C

The effect of dissolved NaCl on equilibrium oxygen and hydrogen isotope fractionation factors between liquid water and water vapor was precisely determined in the temperature range from 130–350°C, using two different types of apparatus with static or dynamic sampling techniques of the vapor phase. The magnitude of the oxygen and hydrogen isotope effects of NaCl is proportional to the molality of liquid NaCl solutions at a given temperature. Dissolved NaCl lowers appreciably the hydrogen isotope fractionation factor between liquid water and water vapor over the entire temperature range. NaCl has little effect on the oxygen isotope fractionation factor at temperatures below about 200°C, with the magnitude of the salt effect gradually increasing from 200–350°C. Our results are at notable variance with those of Truesdell (1974) and Kazahaya (1986), who reported large oxygen and hydrogen isotope effects of NaCl with very complex dependencies on temperature and NaCl molality. Our high-temperature results have been regressed along with our previous results between 50 and 100°C (Horita et al., 1993a) and the low-temperature literature data to simple equations which are valid for NaCl solutions from 0 to at least 5 molal NaCl in the temperature range from 10–350°C. Our preliminary results of oxygen isotope fractionation in the system CaCO3-water ± NaCl at 300°C and 1 kbar are consistent with those obtained from the liquid-vapor equilibration experiments, suggesting that the isotope salt effects are common to systems involving brines and any other coexisting phases or species (gases, minerals, dissolved species, etc.). The observed NaCl isotope effects at elevated temperatures should be taken into account in the interpretation of isotopic data of brine-dominated natural systems.