Search Results (46)

The objective of this research was to design a method to assign topics to claims debunked by fact-checking agencies. During the fact-checking process, access to more structured knowledge is necessary; therefore, we aim to describe topics with semantic vocabulary. Classification of topics should go beyond simple connotations like instance-class and rather reflect broader phenomena that are recognized by fact checkers. The assignment of semantic entities is also crucial for the automatic verification of facts using the underlying knowledge graphs. Our method is based on sentence embeddings, various clustering methods (HDBSCAN, UMAP, K-means), semantic entity matching, and terms importance assessment based on TF-IDF. We represent our topics in semantic space using Wikidata Q-ids, DBpedia, Wikipedia topics, YAGO, and other relevant ontologies. Such an approach based on semantic entities also supports hierarchical navigation within topics. For evaluation, we compare topic modeling results with claims already tagged by fact checkers. The work presented in this paper is useful for researchers and practitioners interested in semantic topic modeling of fake news narratives. Full article

Background/Objectives: Topologically associating domains (TADs) are key structural units of the genome, playing a crucial role in gene regulation. TAD boundaries are enriched with specific biological markers and have been linked to genetic diseases, making consistent TAD detection essential. However, accurately identifying TADs remains challenging due to the lack of a definitive validation method. This study aims to develop a novel algorithm, termed coiTAD, which introduces an innovative approach for preprocessing Hi-C data to improve TAD prediction. This method employs a proposed “circle of influence” (COI) approach derived from Hi-C contact matrices. Methods: The coiTAD algorithm is based on the creation of novel features derived from the circle of influence in input contact matrices, which are subsequently clustered using the HDBSCAN clustering algorithm. The TADs are extracted from the clustered features based on intra-cluster interactions, thereby providing a more accurate method for identifying TADs. Results: Rigorous tests were conducted using both simulated and real Hi-C datasets. The algorithm’s validation included analysis of boundary proteins such as H3K4me1, RNAPII, and CTCF. coiTAD consistently matched other TAD prediction methods. Conclusions: The coiTAD algorithm represents a novel approach for detecting TADs. At its core, the circle-of-influence methodology introduces an innovative strategy for preparing Hi-C data, enabling the assessment of interaction strengths between genomic regions. This approach facilitates a nuanced analysis that effectively captures structural variations within chromatin. Ultimately, the coiTAD algorithm enhances our understanding of chromatin organization and offers a robust tool for genomic research. The source code for coiTAD is publicly available, and the URL can be found in the Data Availability Statement section. Full article

Tactical aerial reconnaissance missions using small unmanned aerial systems (UASs) have become a common scenario in the military. In particular, the detection of visually obscured objects such as camouflage materials and unexploded ordnance (UXO) is of great interest. Hyperspectral sensors, which provide detailed spectral information beyond the visible spectrum, are highly suitable for this type of reconnaissance mission. However, the additional spectral information places higher demands on system architectures to achieve efficient and robust data processing and object detection. To overcome these challenges, the concept of data reduction by band selection is introduced. In this paper, a specialized and robust concept of context-based hyperspectral sensor management with an implemented methodology of band selection for small and challenging UXO and camouflaged material detection is presented and evaluated with two hyperspectral datasets. For this purpose, several anomaly detectors such as LRX, NCC, HDBSCAN, and bandpass filters are introduced as part of the detection workflows and tested together with the sensor management in different system architectures. The results demonstrate how sensor management can significantly improve the detection performance for UXO compared to using all sensor bands or statistically selected bands. Furthermore, the implemented detection workflows and architectures yield strong performance results and improve the anomaly detection accuracy significantly compared to common approaches of processing hyperspectral images with a single, specialized anomaly detector. Full article

This study addresses the challenges of maritime traffic management in the western waters of Taiwan, a region characterized by substantial commercial shipping activity and ongoing environmental development. Using 2023 Automatic Identification System (AIS) data, this study develops a robust feature extraction framework involving data cleaning, anomaly trajectory point detection, trajectory compression, and advanced processing techniques. Dynamic Time Warping (DTW) and the Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) algorithms are applied to cluster the trajectory data, revealing 16 distinct maritime traffic patterns, key navigation routes, and intersections. The findings provide fresh perspectives on analyzing maritime traffic, identifying high-risk areas, and informing safety and spatial planning. In practical applications, the results help navigators optimize route planning, improve resource allocation for maritime authorities, and inform the development of infrastructure and navigational aids. Furthermore, these outcomes are essential for detecting abnormal ship behavior, and they highlight the potential of route extraction in maritime surveillance. Full article

Super-resolution single-molecule localization microscopy (SMLM) of presynaptic active zones (AZs) and postsynaptic densities contributed to the observation of protein nanoclusters that are involved in defining functional characteristics and in plasticity of synaptic connections. Among SMLM techniques, direct stochastic optical reconstruction microscopy (dSTORM) depends on organic fluorophores that exert high brightness and reliable photoswitching. While multicolor imaging is highly desirable, the requirements necessary for high-quality dSTORM make it challenging to identify combinations of equally performing, spectrally separated dyes. Red-excited carbocyanine dyes, e.g., Alexa Fluor 647 (AF647) or Cy5, are currently regarded as “gold standard” fluorophores for dSTORM imaging. However, a recent study introduced a set of chemically modified rhodamine dyes, including CF583R, that promise to display similar performance in dSTORM. In this study, we defined CF583R’s performance compared to AF647 and CF568 based on a nanoscopic analysis of Bruchpilot (Brp), a nanotopologically well-characterized scaffold protein at Drosophila melanogaster AZs. We demonstrate equal suitability of AF647, CF568 and CF583R for basal AZ morphometry, while in Brp subcluster analysis CF583R outperforms CF568 and is on par with AF647. Thus, the AF647/CF583R combination will be useful in future dSTORM-based analyses of AZs and other subcellularly located marker molecules and their role in physiological and pathophysiological contexts. Full article

Despite the significant impacts of biomass burning (BB) on global climate change and regional air pollution, there is a relative lack of research on the temporal trends and geographic patterns of BB in Northeast China (NEC). This study investigates the spatial–temporal distribution of BB and its impact on the atmospheric environment in the NEC region during 2004 to 2023 based on remote sensing satellite data and reanalyzed data, using the Siegel’s Repeated Median Estimator and Mann–Kendall test for trend analysis, HDBSCAN to identify significant BB change regions, and Moran’s Index to examine the spatial autocorrelation of BB. The obtained results indicate a fluctuating yet overall increasing BB trend, characterized by annual increases of 759 for fire point counts (FPC) and 12,000 MW for fire radiated power (FRP). BB predominantly occurs in the Songnen Plain (SNP), Sanjiang Plain (SJP), Liaohe Plain (LHP), and the transitional area between SNP and the adjacent Greater Khingan Mountains (GKM) and Lesser Khingan Mountains (LKM). Cropland and urban areas exhibit the highest growth in BB trends, each surpassing 60% (p < 0.05), with the most significant growth cluster spanning 68,634.9 km². Seasonal analysis shows that BB peaks in spring and autumn, with spring experiencing the highest severity. The most critical periods for BB are March–April and October–November, during which FPC and FRP contribute to over 80% of the annual total. This trend correlates with spring planting and autumn harvesting, where cropland FPC constitutes 71% of all land-cover types involved in BB. Comparative analysis of the aerosol extinction coefficient (AEC) between areas with increasing and decreasing BB indicates higher AEC in BB increasing regions, especially in spring, with the vertical transport of BB reaching up to 1.5 km. County-level spatial autocorrelation analysis indicates high–high clustering in the SNP and SJP, with a notable resurgence of autocorrelation in the SNP, suggesting the need for coordinated provincial prevention and control efforts. Finally, our analysis of the impact of BB on atmospheric pollutants shows that there is a correlation between FRP and pollutants, with correlations for PM_2.5, PM₁₀, and CO of 0.4, 0.4, and 0.5, respectively. In addition, the impacts of BB vary by region and season, with the most significant impacts occurring in the spring, especially in the SNP, which requires more attention. In summary, considering the escalating BB trend in NEC and its significant effect on air quality, this study highlights the urgent necessity for improved monitoring and strategic interventions. Full article

The current generation of cities with vast cultures and heritage is influenced by various factors like immigrants from different countries, religious heritage, tourism, and many more factors. Segregation in geographical regions is one of the ways to find patterns in cities influenced by gender, religion, age, income, and many more. In this study, an HDBSCAN-based activity segregation model using Google POI (Point of Interest) is proposed to study the multi-density patterns of reviewers, with possible Indian names, and activities in the Dublin metropolitan area. In this work, the POI dataset is used to study the activity segregation of Indian names in Dublin. This research uses the username to identify the possible gender and nationality of the reviewer using the NamSor app (a machine learning model for prediction of gender and nationality) with an accuracy of 92%. The result shows the proposed HDBSCAN models identify 16 unique segregations which is just nine clusters using the traditional DBSCAN classification model. Full article

This study utilizes taxi trajectory data to uncover urban residents’ travel patterns, offering critical insights into the spatial and temporal dynamics of urban mobility. A fusion clustering algorithm is introduced, enhancing the clustering accuracy of trajectory data. This approach integrates the hierarchical density-based spatial clustering of applications with noise (HDBSCAN) algorithm, modified to incorporate time factors, with kernel density analysis. The fusion algorithm demonstrates a higher noise point detection rate (15.85%) compared with the DBSCAN algorithm alone (7.31%), thus significantly reducing noise impact in kernel density analysis. Spatial correlation analysis between hotspot areas and paths uncovers distinct travel behaviors: During morning and afternoon peak hours on weekdays, travel times (19–40 min) exceed those on weekends (16–35 min). Morning peak hours see higher taxi utilization in residential and transportation hubs, with schools and commercial and government areas as primary destinations. Conversely, afternoon peaks show a trend towards dining and entertainment zones from the abovementioned places. In the evening rush, residents enjoy a vibrant nightlife, and there are numerous locations for picking up and dropping off people. A chi-square test on weekday travel data yields a p-value of 0.023, indicating a significant correlation between the distribution of travel hotspots and paths. Full article

The Chicago Array of Things (AoT) is a robust dataset taken from over 100 nodes over four years. Each node contains over a dozen sensors. The array contains a series of Internet of Things (IoT) devices with multiple heterogeneous sensors connected to a processing and storage backbone to collect data from across Chicago, IL, USA. The data collected include meteorological data such as temperature, humidity, and heat, as well as chemical data like CO₂ concentration, PM2.5, and light intensity. The AoT sensor network is one of the largest open IoT systems available for researchers to utilize its data. Anomaly detection (AD) in IoT and sensor networks is an important tool to ensure that the ever-growing IoT ecosystem is protected from faulty data and sensors, as well as from attacking threats. Interestingly, an in-depth analysis of the Chicago AoT for anomaly detection is rare. Here, we study the viability of the Chicago AoT dataset to be used in anomaly detection by utilizing clustering techniques. We utilized K-Means, DBSCAN, and Hierarchical DBSCAN (H-DBSCAN) to determine the viability of labeling an unlabeled dataset at the sensor level. The results show that the clustering algorithm best suited for this task varies based on the density of the anomalous readings and the variability of the data points being clustered; however, at the sensor level, the K-Means algorithm, though simple, is better suited for the task of determining specific, at-a-glance anomalies than the more complex DBSCAN and HDBSCAN algorithms, though it comes with drawbacks. Full article

Further reducing greenhouse gas and pollutant emissions from road vehicles is a major task for the automotive industry. Stricter regulations regarding emissions and fleet fuel consumption require the continuous development of new powertrains and methods. In particular, the combination of hybrid powertrains on the technical side and the focus on real driving emissions (RDE) on the legislative side pose significant challenges to the vehicle calibration process. Against this background, new test methods and environments are being investigated to counteract the high number of interactions between hybrid drive systems and quasi-infinite test conditions due to RDE. Complementary to new test environments, innovative methods for data analysis are needed that allow the exploitation of the complete potential of measurement data. The application of such a method in the field of emission calibration is presented in this paper. For this purpose, a clustering method (HDBSCAN) is applied to critical sequences from emission tests. Within this presentation, the clustering process is based on a single signal only. This paper shows how signals of various characteristics can be processed with dynamic time warping and generically structured with the clustering method used. Here, 959 single events are automatically categorized into 24 clusters. This provides a new basis for system evaluation, enabling the automatic identification, categorization, and prioritization of calibration weaknesses. Using twelve signals of different characteristics, the generic usability of the clustering method is demonstrated. Full article

In order to solve low-quality problems such as data anomalies and missing data in the condition monitoring data of hydropower units, this paper proposes a monitoring data quality enhancement method based on HDBSCAN-WSGAIN-GP, which improves the quality and usability of the condition monitoring data of hydropower units by combining the advantages of density clustering and a generative adversarial network. First, the monitoring data are grouped according to the density level by the HDBSCAN clustering method in combination with the working conditions, and the anomalies in this dataset are detected, recognized adaptively and cleaned. Further combining the superiority of the WSGAIN-GP model in data filling, the missing values in the cleaned data are automatically generated by the unsupervised learning of the features and the distribution of real monitoring data. The validation analysis is carried out by the online monitoring dataset of the actual operating units, and the comparison experiments show that the clustering contour coefficient (SCI) of the HDBSCAN-based anomaly detection model reaches 0.4935, which is higher than that of the other comparative models, indicating that the proposed model has superiority in distinguishing between the valid samples and anomalous samples. The probability density distribution of the data filling model based on WSGAIN-GP is similar to that of the measured data, and the KL dispersion, JS dispersion and Hellinger’s distance of the distribution between the filled data and the original data are close to 0. Compared with the filling methods such as SGAIN, GAIN, KNN, etc., the effect of data filling with different missing rates is verified, and the RMSE error of data filling with WSGAIN-GP is lower than that of other comparative models. The WSGAIN-GP method has the lowest RMSE error under different missing rates, which proves that the proposed filling model has good accuracy and generalization, and the research results in this paper provide a high-quality data basis for the subsequent trend prediction and state warning. Full article

The genetic architecture of ischemic stroke (IS), which is one of the leading causes of death worldwide, is complex and underexplored. The traditional approach for associative gene mapping is genome-wide association studies (GWASs), testing individual single-nucleotide polymorphisms (SNPs) across the genomes of case and control groups. The purpose of this research is to develop an alternative approach in which groups of SNPs are examined rather than individual ones. We proposed, validated and applied to real data a new workflow consisting of three key stages: grouping SNPs in clusters, inferring the haplotypes in the clusters and testing haplotypes for the association with phenotype. To group SNPs, we applied the clustering algorithms DBSCAN and HDBSCAN to linkage disequilibrium (LD) matrices, representing pairwise r² values between all genotyped SNPs. These clustering algorithms have never before been applied to genotype data as part of the workflow of associative studies. In total, 883,908 SNPs and insertion/deletion polymorphisms from people of European ancestry (4929 cases and 652 controls) were processed. The subsequent testing for frequencies of haplotypes restored in the clusters of SNPs revealed dozens of genes associated with IS and suggested the complex role that protocadherin molecules play in IS. The developed workflow was validated with the use of a simulated dataset of similar ancestry and the same sample sizes. The results of classic GWASs are also provided and discussed. The considered clustering algorithms can be applied to genotypic data to identify the genomic loci associated with different qualitative traits, using the workflow presented in this research. Full article

Membrane distillation (MD) is proposed as an environmentally friendly technology of emerging interest able to aid in the resolution of the worldwide water issue and brine processing by producing distilled water and treating high-saline solutions up to their saturation with a view toward reaching zero liquid discharge (ZLD) at relatively low temperature requirements and a low operating hydrostatic pressure. Topic modeling (TM), which is a Machine Learning (ML) method combined with Natural Language Processing (NLP), is a customizable approach that is ideal for researching massive datasets with unknown themes. In this study, we used BERTopic, a new cutting-edge Python library for topic modeling, to explore the global and local themes in the MD separation literature. By using the BERTopic model, the words describing the collected dataset were detected together with over- and underexplored research topics to guide MD researchers in planning their future works. The results indicated that two global themes are widely discussed and are relevant to MD scientists abroad. In brief, these topics are permeate flux, heat-energy recovery, surface modification, and polyvinylidene fluoride hydrophobic membranes. BERTopic discovered 62 local concepts. The most researched local topics were solar applications, membrane scaling, and electrospun membranes, while the least investigated were boron removal, dairy effluent applications, and nickel wastewater treatment. In addition, the topics were illustrated in a 2D plane to better understand the obtained results. Full article

Maritime transportation is one of the major contributors to the development of the global economy. To ensure its safety and reduce the occurrence of a maritime accident, intelligent maritime monitoring and ship behavior identification have been drawing much attention from industry and academia, among which, the accurate prediction of ship trajectory is one of the key questions. This paper proposed a trajectory prediction model integrating the Convolutional LSTM (ConvLSTM) and Sequence to Sequence (Seq2Seq) models to facilitate simultaneous extraction of temporal and spatial features of ship trajectories, thereby enhancing the accuracy of prediction. Firstly, the trajectories are preprocessed using kinematic-based anomaly removal and Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) to improve the data quality for the training process of trajectory prediction. Secondly, the ConvLSTM-based Seq2seq model is designed to extract temporal and spatial features of the ship trajectory and improve the performance of long-time prediction. Finally, by using real AIS data, the proposed model is compared with the Seq2Seq and Bidirectional LSTM based on attention mechanism (Bi-Attention-LSTM) models to verify its effectiveness. The experimental results demonstrate that the proposed model achieves excellent performance in predicting turning trajectories, good predictive accuracy on straight line motions, and greater improvement in prediction accuracy compared to the other two benchmark models. Overall, the proposed model represents a promising contribution to improving ship trajectory prediction accuracy and may enhance the safety and quality of ship navigation in complex and volatile marine environments. Full article

The study of managing risk in aviation is the key to improving flight safety. Compared to the other flight operation phases, the approach and landing phases are more critical and dangerous. This study aims to detect and analyze unstable approaches in Taiwan through historical flight data. In addition to weather factors such as low visibility and crosswinds, human factors also account for a large part of the risk. From the accidents studied in the stochastic report of the Flight Safety Foundation, nearly 70% of the accidents occurred during the approach and landing phases, which were caused by improper control of aircraft energy. Since the information of the flight data recorder (FDR) is regarded as the airline’s confidential information, this study calculates the aircraft’s energy-related metrics and investigates the influence of non-weather-related factors on unstable approaches through a publicly available source, automatic dependent surveillance-broadcast (ADS-B) flight data. To evaluate the influence of weather- and non-weather-related factors, the outliers of each group classified by weather labels are detected and eliminated from the analysis by applying hierarchical density-based spatial clustering of applications with noise (HDBSCAN), which is utilized for detecting abnormal flights that are spatial anomalies. The deep learning method was adopted to detect and predict unstable arrival flights landing at Taipei Songshan Airport. The accuracy of the prediction for the normalized total energy and trajectory deviation of all flights is 85.15% and 82.11%, respectively. The results show that in different kinds of weather conditions, or not considering the weather, the models have similar good performance. The input features were analyzed after the model was obtained, and the flights detected as abnormal are discussed. Full article