Data Point Research Articles

Shelf-life boundaries of Listeria monocytogenes in cold smoked salmon during refrigerated storage and temperature abuse

Cold smoked salmon (CSS) is a high-value ready-to-eat product, but it generally has a short shelf-life even under refrigeration and can support the growth of Listeria monocytogenes. Therefore, the objective of this study was to examine the growth and survival of L. monocytogenes in CSS during refrigerated storage and temperature abuse.The growth and survival data of L. monocytogenes (116 records, 465 data points) were retrieved from ComBase (https://www.combase.cc). All records contained storage time and temperature, but other information (aw, pH, and salt) was not fully documented. Each data point, normalized with the initial population to calculate relative growth (RG, log CFU/g), was used to classify the probability of growth. Eighty percent (80%) of the data were randomly sampled for examining the effect of storage time and temperature on growth of L. monocytogenes, while the remaining 20% were set aside for model validation.Logistic regression was used to develop a model for classifying L. monocytogenes growth according to 7 different control thresholds (CT), ranging from 0 to 3 log CFU/g in RG. A probability threshold was set to judge if the bacterial growth has exceeded a CT. The validation showed > 89% of true negative rate for not exceeding the control thresholds. A dynamic method was then developed and demonstrated to predict the growth probabilities under fluctuating temperature conditions. The result of this study suggested that storage time and temperature could be used to predict the growth of L. monocytogenes in CSS and to control listeriosis using a risk-based strategy. It can be used by the retailers and consumers to determine if a packaged product is safe to consume based on its time and temperature history.

Roughness Inversion of Water Transfer Channels from a Data-Driven Perspective

Manning’s roughness coefficient (nc) is an important parameter characterizing the flow capacity of water transfer channels, and it is also an important and sensitive parameter in one-dimensional (1D) flow simulation. This study focused on the roughness inversion for datasets with different sequence lengths, time steps and anomalous data points. A case study was performed with the datasets of the Shandong Jiaodong Water Transfer Project under steady-state conditions. For sequence lengths, the datasets of 6, 12, 24, 40, 88, and 142 h were selected, all with a time step of 1 min. Subsequently, the time step was changed to 5, 10, 15, 30, 60, and 120 min for the 40 h dataset mentioned above. Finally, the flow data point under a certain moment was selected and changed by 10%, 20%, 30%, and 40% respectively. The results show that there is a quadratic relationship between the nc value and the objective function value and the optimal nc value is nc=−b/2a. It is recommended that the nc value retains four decimal places and is inverted using high-frequency and cleaned datasets.

A study on the emotional and attitudinal behaviors of social media users under the sudden reopening policy of the Chinese government.

Since the outbreak of the COVID-19 pandemic, the Chinese government has implemented a series of strict prevention and control policies to prevent the spread of the virus. Recently, the Chinese government suddenly changed its approach and lifted all prevention and control measures. This sudden change in policy is expected to lead to a widespread outbreak of COVID-19 in China, and the public and local governments are not adequately prepared for the unknown impact on society. The change in the "emergency" prevention and control policy provides a unique research perspective for this study. The purpose of this study is to analyze the public's attitudes and emotional responses to COVID-19 under the sudden opening policy, identify the key factors that contribute to these attitudes and emotions, and propose solutions. In response to this sudden situation, we conducted data mining on topics and discussions related to the opening of the epidemic on Sina Weibo, collecting 125,686 interactive comments. We used artificial intelligence technology to analyze the attitudes and emotions reflected in each data point, identify the key factors that contribute to these attitudes and emotions, explore the underlying reasons, and find corresponding solutions. The results of the study show that in the face of the sudden release of the epidemic, the public mostly exhibited negative emotions and behaviors, with many people experiencing anxiety and panic. However, the gradual resumption of daily life and work has also led some people to exhibit positive attitudes. The significance of this study is to help the government and institutions understand the impact of policy implementation on users, and to enable them to adjust policies in a timely manner to respond to potential social risks. The government, emergency departments, and the public can all prepare for similar situations based on the conclusions of this study.

Searching Density-Increasing Path to Local Density Peaks for Unsupervised Anomaly Detection

Unsupervised anomaly detection (AD) is a challenging problem in the data mining community. Clustering-based AD methods aim to group normal data points into clusters and then regard a point belonging to none of the clusters as an anomaly. However, they may suffer from the problems of unknown cluster numbers and arbitrary cluster shapes. This paper presents a novel clustering-based AD method named Density-increasing Path (DIP) to tackle these challenges. DIP searches a path for each data point. The path starts at the data point itself, passes through several points with monotonically increasing densities, and ends at a density peak. Further, DIP defines the climbing difficulty of each path by combining the distance and density increment of each step along the path, which can be regarded as the anomaly score of the path starting point. DIP can adaptively decide the number of peaks to address the challenge of unknown cluster numbers. Since DIP requires the path to pass several points rather than directly reaching the peak, it handles arbitrary cluster shapes. We also propose the ensemble DIP to improve prediction accuracy. The experimental results on four synthetic datasets and eleven real-world benchmarks demonstrate that DIP outperforms existing methods.

Subspace Clustering via Adaptive Non-Negative Representation Learning and Its Application to Image Segmentation

Self-representation subspace clustering based on graphs has the merits of capability and efficiency. However, the graph built by the self-representation methods has two issues: (i) usually lacking conciseness and informativeness due to the negative representation coefficients. (ii) no guarantee of an overall optimal solution due to the separation of representation learning and graph construction. To alleviate these issues, we propose a novel subspace clustering via learning non-negative representation with an adaptive graph. Specifically, we explicitly impose the non-negative constraint on the self-representation learning, ensuring that each data point is approximated from a group of homogeneous samples and enhancing the distinguishability of data representation. Meanwhile, an adaptive graph is developed so that both representation and the geometric structure of data are simultaneously learned in a unified procedure. Moreover, the learned representation is less sensitive to data noise imposed by the ℓ <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2,1</sub> -norm, so the adaptive graph will be further improved. An efficient optimization procedure is developed to find the optimal solution. Extensive experiments on subspace clustering and the extension application to image segmentation validate the advantages of our method against state-of-the-art methods.

Changing material around (2060) Chiron revealed by an occultation on December 15, 2022

We were able to accurately predict the shadow path and successfully observe an occultation of a bright star by Chiron on December 15, 2022. The Kottamia Astronomical Observatory in Egypt did not detect the occultation by the solid body, but we found three extinction features in the light curve that had symmetrical counterparts with respect to the central time of the occultation. One of the features is broad and shallow, whereas the other two features are sharper, with a maximum extinction of ∼25% at the achieved spatial resolution of 19 km per data point. From the Wise Observatory in Israel, we detected the occultation caused by the main body and several extinction features surrounding the body. When all the secondary features are plotted in the sky plane, we find that they can be caused by a broad ∼580 km disk with concentrations at radii of 325 ± 16 km and 423 ± 11 km surrounding Chiron. At least one of these structures appears to be outside the Roche limit. The ecliptic coordinates of the pole of the disk are λ = 151° ±8° and β = 18° ±11°, in agreement with previous results. We also reveal our long-term photometry results, indicating that Chiron had suffered a brightness outburst of at least 0.6 mag between March and September 2021 and that Chiron was still somewhat brighter at the occultation date than at its nominal pre-outburst phase. The outermost extinction features might be consistent with a bound or temporarily bound structure associated with the brightness increase. However, the nature of the brightness outburst is unclear, and it is also unclear whether the dust or ice released in the outburst could be feeding a putative ring structure or whether it is emanating from it.

Explainable Deep Learning-Based Approach for Multilabel Classification of Electrocardiogram

Recently computer-aided diagnosis methods have been widely adopted to aid doctors in disease diagnosis making their decisions more reliable and error-free. Electrocardiogram (ECG) is the most commonly used, noninvasive diagnostic tool for investigating various cardiovascular diseases. In real life, patients suffer from more than one heart disease at a time. So any practical automated heart disease diagnosis system should identify multiple heart diseases present in a single ECG signal. In this article, we propose a novel deep learning-based method for the multilabel classification of ECG signals. The proposed method can accurately identify up to two labels of an ECG signal pertaining to eight rhythm or morphological abnormalities of the heart and also the normal heart condition. Also, the black-box nature of deep learning models prevents them from being applied to high-risk decisions like the automated heart disease diagnosis. So in this article, we also establish an explainable artificial intelligence (XAI) framework for ECG classification using class activation maps obtained from the Grad-CAM technique. In the proposed method, we train a convolutional neural network (CNN) with constructed ECG matrices. With the experiments conducted, we establish that training the CNN by taking only one label for each ECG signal data point is enough for the network to learn the features of an ECG point with multilabel information in it (multiple heart diseases at the same time). During classification, we apply thresholding on the output probabilities of the softmax layer of our CNN, to obtain the multilabel classification of ECG signals.We trained the model with 6311 ECG records and tested the model with 280 ECG records. During testing, the model achieved a subset accuracy of 96.2&#x0025; and a hamming loss of 0.037 and a precision of 0.986 and a recall of 0.949 and an F1-score of 0.967. Considering the fact that the model has performed very well in all the metrics of multilabel classification, the model can be directly used as a practical tool for automated heart disease diagnosis.

Leave-one-ion-out cross-validation for assisting in developing robust QSPR models of ionic liquids

Precisely determining thermodynamic properties such as density (ρ) and viscosity (η) at variable temperatures and pressures is necessary in the chemical process design of ionic liquids (ILs). Quantitative structure–property relationship (QSPR) models offer a quick and accurate route for obtaining the thermodynamic properties of ILs under variable environmental conditions. However, QSPR models employing the leave-one-out cross-validation (LOO-CV) method are often hindered by a “pseudo-high” robustness, weakened stability, and unbalanced data point distribution when used for determining the ρ and η of ILs. In this work, we propose a rigorous evaluation method for QSPR models, called leave-one-ion-out cross-validation (LOIO-CV), to evaluate the thermodynamic properties of ILs. By balancing the distribution of data points in ILs, two f(T,P,I)-QSPR models are developed with norm indexes (I) to predict ρ and η of ILs at variable temperatures and pressures. Our LOIO-CV method enhances the stability of QSPR models when predicting the properties of ILs with novel cations and anions, which is crucial for the data-driven design of new ILs.

An Analytical Performance Evaluation on Multiview Clustering Approaches

The concept of machine learning encompasses a wide variety of different approaches, one of which is called clustering. The data points are grouped together in this approach to the problem. Using a clustering method, it is feasible, given a collection of data points, to classify each data point as belonging to a specific group. This can be done if the algorithm is given the collection of data points. In theory, data points that constitute the same group ought to have attributes and characteristics that are equivalent to one another, however data points that belong to other groups ought to have properties and characteristics that are very different from one another. The generation of multiview data is made possible by recent developments in information collecting technologies. The data were collected from à variety of sources and were analysed using a variety of perspectives. The data in question are what are known as multiview data. On a single view, the conventional clustering algorithms are applied. In spite of this, real-world data are complicated and can be clustered in a variety of different ways, depending on how the data are interpreted. In practise, the real-world data are messy. In recent years, Multiview Clustering, often known as MVC, has garnered an increasing amount of attention due to its goal of utilising complimentary and consensus information derived from different points of view. On the other hand, the vast majority of the systems that are currently available only enable the single-clustering scenario, whereby only makes utilization of a single cluster to split the data. This is the case since there is only one cluster accessible. In light of this, it is absolutely necessary to carry out investigation on the multiview data format. The study work is centred on multiview clustering and how well it performs compared to these other strategies.

CSI: Contrastive data Stratification for Interaction prediction and its application to compound-protein interaction prediction.

Accurately predicting the likelihood of interaction between two objects (compound-protein sequence, user-item, author-paper, etc.) is a fundamental problem in Computer Science. Current deep-learning models rely on learning accurate representations of the interacting objects. Importantly, relationships between the interacting objects, or features of the interaction, offer an opportunity to partition the data to create multi-views of the interacting objects. The resulting congruent and non-congruent views can then be exploited via contrastive learning techniques to learn enhanced representations of the objects. We present a novel method, Contrastive Stratification for Interaction Prediction (CSI), to stratify (partition) a dataset in a manner that can be exploited via Contrastive Multiview Coding to learn embeddings that maximize the mutual information across congruent data views. CSI assigns a key and multiple views to each data point, where data partitions under a particular key form congruent views of the data. We showcase the effectiveness of CSI by applying it to the compound-protein sequence interaction prediction problem, a pressing problem whose solution promises to expedite drug delivery (drug-protein interaction prediction), metabolic engineering, and synthetic biology (compound-enzyme interaction prediction) applications. Comparing CSI with a baseline model that does not utilize data stratification and contrastive learning, and show gains in average precision ranging from 13.7% to 39% using compounds and sequences as keys across multiple drug-target and enzymatic datasets, and gains ranging from 16.9% to 63% using reaction features as keys across enzymatic datasets. Code and dataset available at https://github.com/HassounLab/CSI.

Insights into modeling refractive index of ionic liquids using chemical structure-based machine learning methods

Ionic liquids (ILs) have drawn much attention due to their extensive applications and environment-friendly nature. Refractive index prediction is valuable for ILs quality control and property characterization. This paper aims to predict refractive indices of pure ILs and identify factors influencing refractive index changes. Six chemical structure-based machine learning models called eXtreme Gradient Boosting (XGBoost), Light Gradient Boosting Machine (LightGBM), Categorical Boosting (CatBoost), Convolutional Neural Network (CNN), Adaptive Boosting-Decision Tree (Ada-DT), and Adaptive Boosting-Support Vector Machine (Ada-SVM) were developed to achieve this goal. An enormous dataset containing 6098 data points of 483 different ILs was exploited to train the machine learning models. Each data point’s chemical substructures, temperature, and wavelength were considered for the models’ inputs. Including wavelength as input is unprecedented among predictions done by machine learning methods. The results show that the best model was CatBoost, followed by XGBoost, LightGBM, Ada-DT, CNN, and Ada-SVM. The R2 and average absolute percent relative error (AAPRE) of the best model were 0.9973 and 0.0545, respectively. Comparing this study’s models with the literature shows two advantages regarding the dataset’s abundance and prediction accuracy. This study also reveals that the presence of the –F substructure in an ionic liquid has the most influence on its refractive index among all inputs. It was also found that the refractive index of imidazolium-based ILs increases with increasing alkyl chain length. In conclusion, chemical structure-based machine learning methods provide promising insights into predicting the refractive index of ILs in terms of accuracy and comprehensiveness.

The centimeter-to-submillimeter broad-band radio spectrum of the central compact component in a nearby type-II Seyfert galaxy NGC 1068

Abstract We analyze all the available Atacama Large Millimeter/submillimeter Array archival data for the nearby type-II Seyfert galaxy NGC 1068, including new 100 GHz data with an angular resolution of 005, which was not included in previous continuum spectral analyses. By combining with the literature data based on the Very Large Array, we investigate the broad-band radio continuum spectrum of the central ≲7 pc region of NGC 1068. We find that the flux density is between ≈10 and 20 mJy at 5–700 GHz. Due to the inability of the model in previous studies to account for the newly added 100 GHz data point, we proceeded to update the models and make the necessary adjustments to the parameters. One possible interpretation of this broad-band radio spectrum is a combination of emission from the jet base, the dusty torus, and the compact X-raying corona with a magnetic field strength of ≈20 G on scales of ≈30 Schwarzschild radii from the central black hole. In order to firmly identify the compact corona by omitting any other possible extended components (e.g., free–free emission from ionized gas in the vicinity), high-resolution/sensitivity observations achieved by next-generation interferometers will be necessary.

Another look at halfspace depth: flag halfspaces with applications

The halfspace depth is a well-studied tool of nonparametric statistics in multivariate spaces. We introduce a flag halfspace – an intermediary between a closed halfspace and its interior – and demonstrate that the halfspace depth can be equivalently formulated also in terms of flag halfspaces. Flag halfspaces allow us to derive theoretical results regarding the halfspace depth without the need to differentiate absolutely continuous measures from measures containing atoms, as was frequently done previously. Flag halfspaces are used to state results on the dimensionality of the halfspace median set for random samples. We prove that under mild conditions, the dimension of the sample halfspace median set of d-variate data cannot be d−1 and that for d = 2, the sample halfspace median set must be either a two-dimensional convex polygon or a data point.

Smoothed separable nonnegative matrix factorization

Given a set of data points belonging to the convex hull of a set of vertices, a key problem in linear algebra, signal processing, data analysis and machine learning is to estimate these vertices in the presence of noise. Many algorithms have been developed under the assumption that there is at least one nearby data point to each vertex; two of the most widely used ones are vertex component analysis (VCA) and the successive projection algorithm (SPA). This assumption is known as the pure-pixel assumption in blind hyperspectral unmixing, and as the separability assumption in nonnegative matrix factorization. More recently, Bhattacharyya and Kannan (ACM-SIAM Symposium on Discrete Algorithms, 2020) proposed an algorithm for learning a latent simplex (ALLS) that relies on the assumption that there is more than one nearby data point to each vertex. In that scenario, ALLS is probabilistically more robust to noise than algorithms based on the separability assumption. In this paper, inspired by ALLS, we propose smoothed VCA (SVCA) and smoothed SPA (SSPA) that generalize VCA and SPA by assuming the presence of several nearby data points to each vertex. We illustrate the effectiveness of SVCA and SSPA over VCA, SPA and ALLS on synthetic data sets, on the unmixing of hyperspectral images, and on feature extraction on facial images data sets. In addition, our study highlights new theoretical results for VCA.

Kalman filtering-enhanced short-delay self-heterodyne interferometry for linewidth measurement.

We demonstrate an extended Kalman filtering-enhanced linewidth measurement in short-delay self-heterodyne interferometry (SDSHI). We found that a modified SDSHI trace closely resembles a biased cosine wave, which would enable convenient linewidth estimation by its uniform envelope contrast without any correction factor. Experimentally, we adopted this approach for kHz laser linewidth measurement, taking advantages of extended Kalman filtering (EKF) to adaptively track the cosine wave. Apart from the measurement noise suppression, this approach could use as many data points as possible in the noisy trace to make a linewidth estimation at each tracked data point, from which we can deduce valuable statistical parameters such as the mean and standard deviation. This approach involves no more equipment than conventional SDSHI and sophisticated EKF so that it can be easily implemented. Therefore, we believe it will find wide applications in ultra-narrow laser linewidth measurement.

Probabilistic outlier detection for robust regression modeling of structural response for high-speed railway track monitoring

Outlier detection is an important procedure taken in structural health monitoring (SHM) to create clean and reliable data. A robust time series outlier detection method incorporating a Bayesian perspective and an extreme learning machine (ELM) neural network model is proposed, with application to long-term monitoring data of ballastless tracks for high-speed railway systems. A robust sparse Bayesian ELM (SBELM) model is first established by computing the posterior probability density function of the ELM weight parameters and then marginalizing over the prediction-error precision parameter to obtain a robust nonlinear regression model between the track temperature and structural response. Both the posterior mean and the associated uncertainties of the robust SBELM model are then taken into account to compute the outlier probability for each suspicious data point, which quantifies their degree of data “outlier-ness.” It effectively takes into account the prediction uncertainty of the SBELM regression model. The method is applied to long-term monitoring data for track temperatures, and track strain and relative displacement responses, from two high-speed rail track systems where there are both slight and serious outliers. The results demonstrate that the proposed method can reliably detect outliers by quantifying the outlier probability and that the final results are robust to the selection of the “thresholds.” It is also shown that our new algorithm produces significantly improved model prediction performance after the outliers are detected and removed.

Performance Assessment Comparison between Physically Based and Regression Hydrological Modelling: Case Study of the Euphrates–Tigris Basin

This paper presents two hydrological models developed for the Euphrates–Tigris Basin in Turkey. The first model is a linear regression-based model allowing the estimation of streamflow based on available hydroclimatic data (precipitation, temperature, evapotranspiration, etc.) with the use of clustering analysis. The second model consists of an elevation-based semi-distributed hydrological model (HBV model), allowing process-based modelling of the watershed. A set of performance metrics identified the HBV model as the best performance in terms of predicting streamflow (NSE = 0.752), while the RCA4-EU regression model of CORDEX showed the most robust performance. The results show the potential of regression models from a computational and data point of view in being integrated into physically based models wherein a hybrid approach might be beneficial. The comparison of conceptual models with statistical analyses of streamflow shows the potential of regression analysis when the regions are clustered in hydro-meteorologically homogeneous groups. The employment of the conceptual model HBV also provides significantly robust streamflow estimation for the region, which is especially important in estimating the hydropower potential of the region’s near future.

Sparsity Fuzzy C-Means Clustering With Principal Component Analysis Embedding

The clustering method has been widely used in data mining, pattern recognition, and image identification. Fuzzy c-means (FCM) is a soft clustering method that introduces the concept of membership. In this method, the fuzzy membership matrix is obtained by calculating the distance between data points in the original space. However, these methods may yield suboptimal results owing to the influence of redundant features. Moreover, FCM is always sensitive to noise points and heavily subject to outliers. In this paper, we propose a method called sparsity FCM clustering with principal component analysis embedding (P_SFCM). We simultaneously conduct principal component analysis (PCA) and membership learning, and then add an additional weighting factor for each data point. The goal of this operation is to identify the noise or outliers. Overall, the benefit of our framework is that it retains most of the information in the subspace while improving the robustness of the noise. In this paper, we employ an iterative optimization algorithm to efficiently solve our model. To verify the reliability of the proposed method, we conduct a convergence analysis, noise robustness analysis, and multi-cluster experiments. Furthermore, comparative experiments are conducted on both synthetic and real benchmark datasets. The experimental results show that the P_SFCM is competitive with comparable methods.

Guided Sampling for Multistructure Data via Neighborhood Consensus and Residual Sorting

Robust model fitting is a critical technique for artificial intelligence. The performance of most robust model fitting techniques heavily depends on the use of sampling algorithms. In this paper, we propose an efficient guided sampling algorithm for multi-structure data by using the neighborhood consensus and the residual sorting. Specifically, a Neighborhood Consensus based Strategy (NCS) is first proposed to select the first datum (i.e., seed datum) of a minimal subset, and then a Residual Sorting based Strategy (RSS) samples the rest data of the minimal subset based on the seed datum. This strategy effectively combines the benefits of neighborhood consensus and residual sorting, where neighborhood consensus can judge whether a selected data point is an inlier, and residual sorting encourages this strategy to select data points from the same structure of the first selected data point. Moreover, to achieve better fitting performance, the Markov Chain Monte Carlo process is used to combine NCS with the random selection strategy to select the seed datum, and an appropriate size is set to the initial block of randomly sampled hypotheses for RSS. Experimental results on three vision tasks (e.g., two-view motion segmentation and 3D motion segmentation) demonstrate that the proposed algorithm achieves superior performance to several state-of-the-art sampling algorithms.

A deep learning approach for optimize dynamic and required power in electric assisted bicycle under a structure and operating parameters

The purpose of this research is to study how the operating and structure parameter affect the dynamic, require power and electric consumption of electric assisted bicycle (EAB). The paper applied a combined an artificial neural network (ANN) with genetic algorithm (GA) method to predict the required power, electric consumption of EAB and find an effective performance area. The MATLAB-Simulink simulation model is established to create 1000 data point, that is applied in an ANN for training, testing and verifying. The ANN-GA method is applied to identify the optimized required power and electric consumption under four typical slope grades in the area of (0–0.65%), four typical wheel radius in the area of (0.3–0.0.39 m), four typical frontal areas in the range of (0.423–1.323 m2), four typical speed levels from speed level 1 to speed level 4. After the ANN is trained, it is applied into the genetic algorithm to identify the effective performance. The prediction results reflect the major physical mechanism that governs ES performance and are validated against the MATLAB-Simulink simulation model. Additionally, the electric assisted bicycle can reach an effective performance area at 27.16 km/h with the optimized required power of 545.5 W at slope grade of 0%, wheel radius of 0.39 m, speed level 4, frontal area of 0.423 m2. The results show that the ANN-GA method is suitable to identifying structure and operating parameters including various slope grades, frontal areas, wheel radius, speed level for optimizing effective performance of EAB, which contributes a helpful method for EAB design and control. Beside that, the experimental test was conducted on real road test at Taehwa river to verify the simulated results. The experiment results and simulation results have the same trend at the same conditions. (The short version of the paper was presented at ICAE2022,Bochum, Germany,Aug 8–11, 2022. This paper is a substantial extension of the short version of the conference paper.)