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Here, a new‐rising technology (DNA storage) in which the binary digital information/data is converted into a DNA sequence composed of distinct nucleotides, providing a dense, stable, energy‐efficient, and sustainable data storage solution is studied. In principle, this technology offers substantial data density. However, the theoretical limit of DNA storage has not been achieved yet. In this study, a new DNA storage system is proposed based on hydrogels with SiO2 layer (their pore size can be regulated). The highest data density of the hydrogels is obtained to be 6.3 × 109 GB g−1. Further, by coupling them with digital polymerase chain reaction (dPCR), the recovery limit reaches five copies of DNA. Subsequently, three‐dimensional (3D) printing is used to explore the effect of macrostructures on the DNA storage capacity. Finally, bionic‐structure thermally‐responsive robust (Best) hydrogels with four different structures are developed, which achieve a data density of 1.04 × 1010 GB g−1. [ABSTRACT FROM AUTHOR]

Abstract Here, a new‐rising technology (DNA storage) in which the binary digital information/data is converted into a DNA sequence composed of distinct nucleotides, providing a dense, stable, energy‐efficient, and sustainable data storage solution is studied. In principle, this technology offers substantial data density. However, the theoretical limit of DNA storage has not been achieved yet. In this study, a new DNA storage system is proposed based on hydrogels with SiO2 layer (their pore size can be regulated). The highest data density of the hydrogels is obtained to be 6.3 × 109 GB g−1. Further, by coupling them with digital polymerase chain reaction (dPCR), the recovery limit reaches five copies of DNA. Subsequently, three‐dimensional (3D) printing is used to explore the effect of macrostructures on the DNA storage capacity. Finally, bionic‐structure thermally‐responsive robust (Best) hydrogels with four different structures are developed, which achieve a data density of 1.04 × 1010 GB g−1.

K-means is one of the most popular clustering algorithms. Its simplicity and efficiency enable it to be used in various industrial fields such as period partition of time-of-use (TOU) tariff. To solve the problems including insufficient determination of cluster number and initial cluster centers in K-means, we propose a modified K-means clustering algorithm based on fuzzy membership functions (FMF), gap statistic (GS), and data density (DD). First, the improved initial cluster centers are modeled to complete initialization through the first calculation based on FMF. Second, the cluster number of the dataset is determined by GS evaluation and further calculation based on DD is performed if necessary. Next, the efficiency and quality of the proposed clustering algorithm are examined by using three evaluation indexes: standard deviation, silhouette coefficient index, and Davies Boldin index. Finally, the effectiveness of the proposed method is verified under a typical case study of TOU period partition, and the results show that the proposed method can significantly shorten convergence time and improve clustering quality, compared to the traditional K-means clustering algorithm. [ABSTRACT FROM AUTHOR]

Current age estimation datasets often have a skewed long-tail distribution with significant data imbalance, rather than an ideal uniform distribution for each category. The existing age estimation algorithms that rely on label distribution do not leverage data density information to address the issue of data imbalance. To solve the aforementioned problem, this paper proposes a novel method based on cost-sensitive learning, namely Data-Imbalance Adaptive Age Regression (DIAAR), for age estimation. DIAAR consists of two main modules: the adaptive soft label (ASL) module and the Data Density Smoothing (DDS) module. The ASL module embeds soft labels in the form of probability in the age regression. It assigns different degrees of soft labels adaptively to head and tail data based on their density, which helps balance the dataset. The DDS module further addresses data imbalance by revising data density through kernel smoothing and reweighting the loss function accordingly. Experiments on two benchmark datasets show that DIAAR can effectively deal with data imbalance and improve the accuracy of age estimation, achieving an average improvement of 8% over the baseline models. Moreover, this approach can be applied to various methods based on convolutional neural network models. [ABSTRACT FROM AUTHOR]

Due to the lack of sufficient information about the density of subsurface masses, in the modeling of the earth gravity field, usually a constant global average density value is used for the entire studied area. However, any accuracy improvement of the mass density used in the modeling of topographic gravitation will increase the accuracy of gravity field modeling. To approve this quantitatively, a topographic density model with a resolution of 30×30 arc second is prepared from the processing of seismographic maps and satellite data of the layers of the lithosphere and used instead of a constant mass density in four study regions inside Iran with diverse topographic and data distribution. These four regions have the dimension of 2.5×3 degrees. The point intervals in the first and third regions (R1 and R3) are approximately 5 minutes, while in the second and fourth regions (R2 and R4) the intervals are approximately 13 minutes. In areas with the same point distribution, R1 has a relatively smoother topography than R3. The topography in R2 is relatively rougher than R4. In addition to the global average density value, the average value for Iran and the region is also included in modeling the gravity topographic masses. Owe to topography diversity, these areas seem to be suitable for an investigation of the RTM technique performance. In modeling the gravity field, the least squares collocation method and, consequently, the RTM technique are used in modeling the effect of topographic mass gravitation. In order to evaluate the effect of lateral density variations when using the RTM technique for gravity field modeling of the earth, the least squares collocation method is used in this research. The RCR technique is used for gravity field modeling by least squares collocation method and the effect of the global topography is removed. To remove the global gravitational effect from the observations, the EIGEN6C4 model degree and order 360 is used. To remove the effect of topography by RTM method, a digital elevation model with a point density of 1 second arc is used. In addition to density correction, the use of improved covariance algorithm in gravity field modeling is also evaluated in this research. The results show that in the areas with more topography, and hence more density variations, the effect of density modification in removing the effect of topography from the gravity anomalies signal of the region is more significant. Furthermore, comparison to the control points of this study shows that application of density correction and the improved covariance algorithm in areas with rough topography and lack of sufficient gravimetric data and proper distribution, the accuracy of gravity field modeling can be improved by 15.6%. Using the IC approach in four regions leads to an increase in modeling accuracy. Among these regions, the highest increase in accuracy is related to region 2 (with a decrease of 1 mGal of standard deviation) and relatively related to region 3 (with a decrease of 11.5% in standard deviation). These two areas have rougher topography than areas 1 and 4. [ABSTRACT FROM AUTHOR]

The hierarchical dimensionless method can effectively remove the effect of different index dimensions, and solve imbalanced data distribution and low discrimination caused by anomalous index values. However, when using this method, it is necessary to artificially specify the number of partition intervals so that the dimensionless results are interfered by human factors and lose objectivity. To solve this problem, a dimensionless method of density hierarchy is proposed considering the distribution characteristics of raw data. This method divides the interval according to the density of data distribution, objectively determines the hierarchical series, and takes into account the advantages of the hierarchical dimensionless method. The calculation is comparatively simple and reduces human factors. In addition, through the stochastic simulation method, it is found that the method has good anti-interference to outliers, and the balance of dimensionless results is affected by the scale of raw data. [ABSTRACT FROM AUTHOR]

Outlier detection, a critical process for enhancing data quality, has been applied to a broad spectrum of real-world applications. This study introduces a novel and effective outlier detection method that incorporates both local density and global structural information. Specifically, we employ the concept of potential energy, a well-established principle in physics, to represent local densities or neighbor distributions of data. Additionally, we also exploit the notion of hubness, a measure explored in network science, to further capture global structural information of data. To facilitate this, we construct a strongly connected digraph by introducing a virtual node along with an associated set of virtual edges. This digraph underpins a tailored Markov random walk process that is used to estimate the hubness scores of data objects. Subsequently, the importance degrees of data objects are estimated according to their potential energies and hubness scores. The data objects with lower importance degrees are deemed to be outliers. Extensive experiments conducted on thirteen publicly accessible real-world datasets against nine popular outlier detection algorithms show that the proposed method achieved encouraging and competitive performance in comparing the state-of-the-art outlier detection algorithms. • An outlier detection method that integrates both local densities and global structures of data. • It exploits potential energy, inherently consistent to distribution, to represent data densities. • The rarity degree of data is further derived from global graph structures of neighbors. [ABSTRACT FROM AUTHOR]

Current age estimation datasets often have a skewed long-tail distribution with significant data imbalance, rather than an ideal uniform distribution for each category. The existing age estimation algorithms that rely on label distribution do not leverage data density information to address the issue of data imbalance. To solve the aforementioned problem, this paper proposes a novel method based on cost-sensitive learning, namely Data-Imbalance Adaptive Age Regression (DIAAR), for age estimation. DIAAR consists of two main modules: the adaptive soft label (ASL) module and the Data Density Smoothing (DDS) module. The ASL module embeds soft labels in the form of probability in the age regression. It assigns different degrees of soft labels adaptively to head and tail data based on their density, which helps balance the dataset. The DDS module further addresses data imbalance by revising data density through kernel smoothing and reweighting the loss function accordingly. Experiments on two benchmark datasets show that DIAAR can effectively deal with data imbalance and improve the accuracy of age estimation, achieving an average improvement of 8% over the baseline models. Moreover, this approach can be applied to various methods based on convolutional neural network models.

The Landsat program has the longest collection of moderate-resolution satellite imagery, and the data are free to everyone. With the improvements of standardized image products, the flexibility of cloud computing platforms, and the development of time series approaches, it is now possible to conduct global-scale analyses of time series using Landsat data over multiple decades. Efforts in this regard are limited by the density of usable observations. The availability of usable Landsat Tier 1 observations at the scale of individual pixels from the perspective of time series analysis for land change monitoring is remarkably variable both in space (globally) and time (1985–2020), depending most immediately on which sensors were in operation, the technical capabilities of the mission, and the acquisition strategies and objectives of the satellite operators (e.g., USGS, commercial company) and the international ground receiving stations. Additionally, analysis of data density at the pixel scale allows for the integration of quality control data on clouds, cloud shadows, and snow as well as other properties returned from the atmospheric correction process. Maps for different time periods show the effect of excluding observations based on the presence of clouds, cloud shadows, snow, sensor saturation, hazy observations (based on atmospheric opacity), and lack of aerosol optical depth information. Two major discoveries are: 1) that filtering saturated and hazy pixels is helpful to reduce noise in the time series, although the impact may vary across different continents; 2) the atmospheric opacity band needs to be used with caution because many images are removed when no value is given in this band, when many of those observations are usable. The results provide guidance on when and where time series analysis is feasible, which will benefit many users of Landsat data.

This paper proposes an unsupervised way for Phonocardiogram (PCG) analysis, which uses a revised auto encoder based on distribution density estimation in the latent space. Auto encoders especially Variational Auto-Encoders (VAEs) and its variant β−VAE are considered as one of the state-of-the-art methodologies for PCG analysis. VAE based models for PCG analysis assume that normal PCG signals can be represented by latent vectors that obey a normal Gaussian Model, which may not be necessary true in PCG analysis. This paper proposes two methods DBVAE and DBAE that are based on estimating the density of latent vectors in latent space to improve the performance of VAE based PCG analysis systems. Examining the system performance with PCG data from the a single domain and multiple domains, the proposed systems outperform the VAE based methods. The representation of normal PCG signals in the latent space is also investigated by calculating the kurtosis and skewness where DBAE introduces normal PCG representation following Gaussian-like models but DBVAE does not introduce normal PCG representation following Gaussian-like models.

Monitoring the Earth by annually mapping land cover (LC) fractions helps to better understand the ongoing processes and changes of land use and land management. At 10 to 30 m spatial resolution, the combination of time-series data aggregation, specifically spectral-temporal metrics (STM), and regression-based unmixing models has been shown to be highly effective in quantifying LC fractions over large areas. However, STM are subject to variations in data densities within and between years, which may lead to variations in prediction accuracies and limit the transferability of models through time. To better understand the influence of annual data density on multi-year monitoring, we systematically tested the accuracy and spatial-temporal transferability of regression models for LC fraction mapping. Additionally, we introduced a novel strategy, Random Observation Selection, in the STM generation to enhance model transferability and compared its results to those obtained from regular STM. We used STM from multi-spectral Sentinel-2 satellite data to estimate Impervious surface, Woody and Low vegetation at a regional scale in northern Germany and Poland for the years 2017 to 2022. The study period is characterized by substantial inter-annual variation in data density and well suited to test temporal transfer: in the extremely hot and dry years of 2018 and 2019 data density is above average and considered relatively high, while in the wetter years of 2017 and 2021 it is low. The mapping quality of models trained in single years varied considerably between years with high and low data density. Specifically, models trained in years with high data density performed well in that same year but poorly when transferred to years with lower density. Conversely, models trained in years with low data density demonstrated less of a decrease in accuracy when transferred. A multi-year model trained in all six years performs the best for each individual year. The use of Random Observation Selection improved the transferability of all models, particularly for those trained with data from a high data density year when transferred to years with lower data density. Here, the mean absolute error showed the highest relative improvement of 16%. In conclusion, STM proved useful for multi-year regression-based monitoring, but care must be taken when annual data density varies. Incorporating Random Observation Selection reduced this influence and improved the spatial-temporal transferability of quantitative LC fraction monitoring. • Annual land cover fractions (2017–2022) mapped with regression-based unmixing. • Impact of varying temporal data densities on multi-year monitoring demonstrated. • Training data augmentation with Random Observation Selection (ROS) is introduced. • ROS improved both temporal and spatial transferability. • Recommendations for multi-year monitoring with varying data densities given. [ABSTRACT FROM AUTHOR]

Vegetation phenology has a great impact on land-atmosphere interactions like carbon cycling, albedo, and water and energy exchanges. To understand and predict these critical land-atmosphere feedbacks, it is crucial to measure and quantify phenological responses to climate variability, and ultimately climate change. Coarse-resolution sensors such as MODIS and AVHRR have been useful to study vegetation phenology from regional to global scales. These sensors are, however, not capable of discerning phenological variation at moderate spatial scales. By offering increased observation density and higher spatial resolution, the combination of Landsat and Sentinel-2 time series might provide the opportunity to overcome this limitation.In this study, we analyzed the potential of combined Sentinel-2 and Landsat time series for estimating start of season (SOS) of broadleaf forests across Germany for the year 2018. We tested two common statistical modeling approaches (logistic and generalized additive models using thin plate splines) and the two most commonly used vegetation indices, the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI).We found strong agreement between SOS estimates from logistic and spline models (rEVI = 0.86; rNDVI = 0.65), whereas agreement was higher for EVI than for NDVI (RMSDEVI = 3.07, RMSDNDVI = 5.26 days). The choice of vegetation index thus had a higher impact on the results than the fitting method. The EVI-based SOS also showed higher correlation with ground observations compared to NDVI (rEVI = 0.51, rNDVI = 0.42). Data density played an important role in estimating land surface phenology. Models combining Sentinel-2A/B, with an average cloud-free observation frequency of 12 days, were largely consistent with the combined Landsat and Sentinel-2 models, suggesting that Sentinel-2A/B may be sufficient to capture SOS for most areas in Germany in 2018. However, in non-overlapping swath areas and mountain areas, observation frequency was significantly lower, underlining the need to combine Landsat and Sentinel-2 for consistent SOS estimates over large areas. Our study demonstrates that estimating SOS of temperate broadleaf forests at medium spatial resolution has become feasible with combined Landsat and Sentinel-2 time series.

Superparamagnetic clustering (SPC) is an unsupervised classification technique in which clusters are self-organised based on data density and mutual interaction energy. Traditional SPC algorithm uses the Swendsen–Wang Monte Carlo approximation technique to significantly reduce the search space for reasonable clustering. However, Swendsen–Wang approximation is a Markov process which limits the conventional superparamagnetic technique to process data clustering in a sequential manner. Here the authors propose a parallel approach to replace the conventional appropriation to allow the algorithm to perform clustering in parallel. One synthetic and one open-source dataset were used to validate the accuracy of this parallel approach in which comparable clustering results were obtained as compared to the conventional implementation. The parallel method has an increase of clustering speed at least 8.7 times over the conventional approach, and the larger the sample size, the more increase in speed was observed. This can be explained by the higher degree of parallelism utilised for the increased data points. In addition, a hardware architecture was proposed to implement the parallel superparamagnetic algorithm using digital electronic technologies suitable for rapid or real-time neural spike sorting. [ABSTRACT FROM AUTHOR]

Tufte addresses two major objectives in The Visual Display of Quantitative Information—to identify many of the mistakes and abuses common to informational graphics and to develop a general theory of data graphics to increase their efficiency and effectiveness.

The challenge of reducing complexity, failures and time in software development are tasks found in the vast majority of information technology companies. The professionals seek to structure the development of applications through agile methodologies, but in reality, there are several difficulties in planning the time to make applications development tasks. To help in this situation, this paper proposes the use of fuzzy neural network composed by fuzzy rules to assist in the construction of a specialist system based on interpretable rules, facilitating the prediction of software development hours according to the complexity of the elements present in the project. To support in the data fuzzification process, an incremental density technique is proposed for the first layer of the model. To improve the sensitivity of the neuron present in the neural network a Leaky-ReLU type activation function is used to obtain the results. The set of rules to be created, through tests in a real database based on the technique of use case point, can help in the development of future expert systems, to be used by these professionals. The results of the tests were efficient to generate predictability about the efforts to build the software. [ABSTRACT FROM AUTHOR]

Abstract In order to make full use of the built wind farm, it is one of the most basic work to analyze the output characteristics. In this paper, a thought of mining wind power output characteristics with the perspective of descriptive statistical analysis and correlation is put forward. Based on the measured data, the output characteristics of the wind farm are analyzed from two aspects of descriptive statistics: the digital feature and the distribution characteristics. Then, in view of the wind speed-power relationship, an envelope domain is constructed to characterize it, and its rationality is verified by the defined data density index of wind power output. The method proposed in this paper is helpful to effectively characterize the output characteristics of wind farm connected to the network, and lays a foundation for better operation and management of wind farms. Highlights • The characteristics of wind farms are investigated by descriptive statistical analysis. • Weibull distribution is validated by comparing the cumulative probability of output ratio of wind farms. • Envelope domain is constructed to describe the relations between wind speed and power. • A data density index is developed to verify the rationality of envelope domain. • The applicability of envelope domain in three intervals of wind speed is compared. [ABSTRACT FROM AUTHOR]

Place-prioritization analyses are a means by which researchers can translate information on the geographic distributions of species into quantitative prioritizations of areas for biodiversity conservation action. Although several robust algorithms are now available to support this sort of analysis, their vulnerability to biases deriving from incomplete and imbalanced distributional information is not well understood. In this contribution, we took a well-sampled group (i.e., Icteridae or New World blackbirds) in an intensively sampled region (the contiguous continental United States), and developed a set of pseudo-experimental manipulations of occurrence data density—in effect, we created situations in which data density was reduced 10- or 100-fold, and situations in which data density varied 100-fold from region to region. The effects were marked: priority areas for conservation shifted, appeared, and disappeared as a function of our manipulations. That is, differences in density of data can affect the position and complexity of areas of high conservation priority that are identified using distributional areas of species derived from ecological niche modeling. The effects of data density on prioritizations become more diffuse when considerations of existing protected areas and costs related to human intervention are taken into account, but changes are still manifested. Appropriate considerations of sampling density when constructing ecological niche models to identify distributional areas of species are key to preventing artifactual biases from entering into and affecting results of analyses of conservation priority. • Conservation prioritization exercises are affected in significant ways by geographic occurrence data density. • Major conservation priority areas that are identified may shift, appear, or disappear, as a function of geographic occurrence data density. • The effects on conservation prioritization outcomes identified in this study mimic well-known geographic data density disparities that are manifested in many regions of the Earth. [ABSTRACT FROM AUTHOR]

There are some hotspot problems remaining in trajectory cluster for discovering mobile behavior regularity, such as the computation of distance between sub trajectories, the setting of parameter values in cluster algorithm and the uncertainty/boundary problem of data set. As a result, based on the time and space, this paper tries to define the calculation method of distance between sub trajectories. The significance of distance calculation for sub trajectories is to clearly reveal the differences in moving trajectories and to promote the accuracy of cluster algorithm. Besides, a novel adaptive density trajectory cluster algorithm is proposed, in which cluster radius is computed through using the density of data distribution. In addition, cluster centers and number are selected by a certain strategy automatically, and uncertainty/boundary problem of data set is solved by designed weighted rough c-means. Experimental results demonstrate that the proposed algorithm can perform the fuzzy trajectory cluster effectively on the basis of the time and space distance, and obtain the optimal cluster centers and rich cluster results information adaptably for excavating the features of mobile behavior in mobile and sociology network. [ABSTRACT FROM AUTHOR]

Doppler LiDARs have become flexible and versatile remote sensing devices for wind energy applications. The possibility to measure radial wind speed components contemporaneously at multiple distances is an advantage with respect to meteorological masts. However, these measurements must be filtered due to the measurement geometry, hard targets and atmospheric conditions. To ensure a maximum data availability while producing low measurement errors, we introduce a dynamic data filter approach that conditionally decouples the dependency of data availability with increasing range. The new filter approach is based on the assumption of self-similarity, that has not been used so far for LiDAR data filtering. We tested the accuracy of the dynamic data filter approach together with other commonly used filter approaches, from research and industry applications. This has been done with data from a long-range pulsed LiDAR installed at the offshore wind farm 'alpha ventus'. There, an ultrasonic anemometer located approximately 2.8 km from the LiDAR was used as reference. The analysis of around 1.5 weeks of data shows, that the error of mean radial velocity can be minimised for wake and free stream conditions. [ABSTRACT FROM AUTHOR]

Spectral clustering has become very popular in recent years, due to the simplicity of its implementation as well as the performance of the method, in comparison with other popular ones. But many studies show that clustering results are sensitive to the selection of the similarity graph and its parameters, e.g. and . To address this issue, inspired by density sensitive similarity measure, we propose an improved spectral graph clustering method that utilizes the similarity measure based on data density combined with DNA genetic algorithms (ISC-DNA-GA), making it increase the distance of the pairs of data in the high density areas, which are located in different spaces. The method can reduce the similarity degree among the pairs of data in the same density region to find the spatial distribution characteristics of the complex data. After computing the Laplacian matrix, we apply DNA-GAs to obtain the clustering centroids and assign all of the points to the centroids, so as to achieve better clustering results. Experiments have been conducted on the artificial and real-world datasets with various multi-dimensions, using evaluation methods based on external clustering criteria. The results show that the proposed method improves the spectral clustering quality, and it is superior to those competing approaches. [ABSTRACT FROM AUTHOR]

Aiming at the problems of the identification precision of traditional classifier was low for the minority class sample and the traditional under-sampling method was easy to lose information, the paper proposed an under-sampling method US-DD based on data density distribution, which could divide data into high density data cluster and low density data cluster. The two kinds of data were not only different on the sample quantity, but also were different on the influence of data classification. Therefore, it could divide the data set using the data density, performed different re-sampling strategy for the data cluster with different density, and achieved the purpose of improving data balance. Through selecting six IJC1 data sets, the experiment results show that the US-DD method is effective for unbalanced data classification, and can effectively improve the recognition performance of the classifier for the minority class by comparing with the method of the random under-sampling and KNN-NearMiss. [ABSTRACT FROM AUTHOR]

• HT surveys in 2019 and 2020 to test the reproducibility of estimated K and S s. • 3D K and S s fields are reproducible even when boundary conditions are varied. • Improved reproducibility observed when inverting with higher data density. • Accurate geological information included as initial guess improved reproducibility. • HT has the potential for mapping changes in K and S s heterogeneity over time. Over the past two decades, geostatistics-based hydraulic tomography (HT) has been proven to be a robust method for subsurface heterogeneity characterization. However, the reproducibility of estimated parameter fields, namely the hydraulic conductivity (K) and specific storage (S s), obtained over different data collection periods and under different flow scenarios have been rarely studied. In this study, we investigate the reproducibility and predictivity of HT estimates using data collected in 2019 and 2020 at a field site of varying boundary conditions underlain by sedimentary units in Japan, with different data density and prior geological information. Model performance of each case is evaluated qualitatively and quantitatively by examining results from model calibration, self-validation and cross-validation, in terms of parameter fields comparison, drawdown matches and cross-correlation distributions. Results of this study reveal that: 1) overall, the parameter fields for 2019 and 2020 HT estimates are generally reproducible, while fine-scale discrepancy exists; 2) they are also found to be reproducible in terms of consistency of estimated geostatistical parameters; 3) higher levels of reproducibility and predictivity associated with estimated K and S s fields are obtained when higher data density is included for geostatistical inverse modeling with more accurate prior geological information; and 4) the different spatial distributions of cross-correlation between head and parameter fields induced through the significant variation of the groundwater flow field from 2019 to 2020 strengthen the notion of reproducibility of HT results. These results collectively suggest that HT has the potential for mapping changes to K and S s heterogeneity over time broadening its future applications to various practical problems. [ABSTRACT FROM AUTHOR]

Multivariate statistical process control (MSPC) is an important means of monitoring multiple process variables and their interrelationships while controlling chemical and industrial plants efficiently and stably. To consider nonlinearities between process variables and multimodal data distributions, the data density can be used as an index for fault detection. Data domains with a low data density are considered abnormal states. However, after fault detection, faulty process variables cannot be diagnosed with an MSPC model based on the data density. Therefore, we have developed a new index to diagnose the process variables that contribute to process faults using a data density-based MSPC model. The proposed index uses the partial derivative of an MSPC model with respect to each process variable. We demonstrate the effectiveness of the proposed method using numerical simulation data, Tennessee Eastman process data, and real plant data analyses. [ABSTRACT FROM AUTHOR]

The knowledge of subsurface heterogeneity is a prerequisite to describe flow and transport in porous media. Of particular interest are the variance and the correlation scale of hydraulic conductivity. In this study, we present how these aquifer parameters can be inferred using empirical steady state pumping test data. We refer to a previously developed analytical solution of “effective well flow” and examine its applicability to pumping test data as under field conditions. It is examined how the accuracy and confidence of parameter estimates of variance and correlation length depend on the number and location of head measurements. Simulations of steady state pumping tests in a confined virtual aquifer are used to systematically reduce sampling size while determining the rating of the estimates at each level of data density. The method was then applied to estimate the statistical parameters of a fluvial heterogeneous aquifer at the test site Horkheimer Insel, Germany. We conclude that the “effective well flow” solution is a simple alternative to laboratory investigations to estimate the statistical heterogeneity parameter using steady state pumping tests. However, the accuracy and uncertainty of the estimates depend on the design of the field study. In this regard, our results can help to improve the conceptual design of pumping tests with regard to the parameter of interest. [ABSTRACT FROM AUTHOR]

Background: Precision in fit is crucial for dental crowns and bridges. Most analyses of fit are based on analog 2D techniques. Aim of this in-vitro study was to compare an analog and a digital quantitative and qualitative analysis for the fit of CAD/CAM fabricated dental copings. Methods: A prepared steel canine served as master die. CAD surface models, varying in data density, were purposely enlarged in height (Ez), circumference (Exy) and both of these aspects at once (Exyz). Two titanium copings for each variation were produced. The silicone-replica-technique was applied to analyze the fit by means of a 2D analog light microscope measurement (LMM) and a 3D computerassisted measurement using an optical digitizing system (ODKM97), respectively. Results: In most cases, restorations based on the low data density showed a better fit than those based on high data density. Original size low density data showed the lowest marginal and axial values in the quantitative 2D analyses (LMM and ODKM97). The 3D measurements (ODKM97) revealed best fit of the low density original size specimens, whereas the Ez specimens showed the highest values. Noticeable variations in fit were detected marginally and axially depending on the specific measurement point (mesial, distal, oral, or buccal) for both measurement systems. Discussion: The analog 2D replica technique revealed a loss of information due to the necessary cutting process. By contrast, the digital computer-based method provided 3D quantitative and qualitative results without data loss over the complete surface. [ABSTRACT FROM AUTHOR]