Search Results (559)

In the oil industry, the leakage of pipelines containing hydrocarbon fluids causes significant environmental and economic damage. Recently, there has been a growing trend in employing data mining techniques for detecting leaks. Among these methods is the Hidden Markov Model, which, despite good results with stationary data, becomes inefficient when a leak causes a drop in the pressure or flow, reducing its accuracy. This paper presents an adaptive Hidden Markov method. Previous methods had low accuracy due to insufficient information for accurate leak detection. They often classified the size and location of leaks broadly. In contrast, the proposed model extracts hidden features to accurately identify the location and size of leaks, even in noisy conditions. Simulating a leak in a section of an oil pipeline in the Iranian Oil Export Corridor demonstrates the proposed method’s superiority over common methods like K-NN, SVM, Naive Bayes, and logistic regression. Full article

Short-chain dehydrogenase/reductases (SDRs) are the largest NAD(H)-dependent oxidoreductase superfamilies and are involved in diverse metabolisms. This study presents a comprehensive genomic analysis of the SDR superfamily in Cinnamomum camphora, a species that is one of the most significant woody essential oil plants in southern China. We identify a total of 222 CcSDR proteins and classify them into five types based on their cofactor-binding and active sites: ‘atypical’, ‘classic’, ‘divergent’, ‘extended’, and ‘unknown’. Phylogenetic analysis reveals three evolutionary branches within the CcSDR proteins, and further categorization using the SDR-initiative Hidden Markov model resulted in 46 families, with the CcSDR110C, CcSDR108E, and CcSDR460A families being the most populous. Collinearity analysis identified 34 pairs of CcSDR paralogs in C. camphora, 141 pairs of SDR orthologs between C. camphora and Populus trichocarpa, and 59 pairs between C. camphora and Oryza sativa. Expression profile analysis indicates a preference for the expression of 77 CcSDR genes in specific organs such as flowers, bark, twigs, roots, leaves, or fruits. Moreover, 77 genes exhibit differential expression patterns during the four developmental stages of leaves, while 130 genes show variance across the five developmental stages of fruits. Additionally, to explore the biosynthetic mechanism of methyl eugenol, a key component of the leaf essential oil in the methyl eugenol chemotype, this study also identifies eugenol synthase (EGS) within the CcSDR460A family through an integrated strategy. Real-time quantitative PCR analysis demonstrates that the expression of CcEGS in the leaves of the methyl eugenol chemotype is more than fourfold higher compared to other chemotypes. When heterologously expressed in Escherichia coli, it catalyzes the conversion of coniferyl acetate into a mixture predominantly composed of eugenol (71.44%) and isoeugenol (21.35%). These insights pave the way for future research into the functional diversity of CcSDR genes, with a focus on secondary metabolism. Full article

Semantic ontologies have been widely utilized as crucial tools within natural language processing, underpinning applications such as knowledge extraction, question answering, machine translation, text comprehension, information retrieval, and text summarization. While the Kurdish language, a low-resource language, has been the subject of some ontological research in other dialects, a semantic web ontology for the Badini dialect remains conspicuously absent. This paper addresses this gap by presenting a methodology for constructing and utilizing a semantic web ontology for the Badini dialect of the Kurdish language. A Badini annotated corpus (UOZBDN) was created and manually annotated with part-of-speech (POS) tags. Subsequently, an HMM-based POS tagger model was developed using the UOZBDN corpus and applied to annotate additional text for ontology extraction. Ontology extraction was performed by employing predefined rules to identify nouns and verbs from the model-annotated corpus and subsequently forming semantic predicates. Robust methodologies were adopted for ontology development, resulting in a high degree of precision. The POS tagging model attained an accuracy of 95.04% when applied to the UOZBDN corpus. Furthermore, a manual evaluation conducted by Badini Kurdish language experts yielded a 97.42% accuracy rate for the extracted ontology. Full article

The Galapagos Marine Reserve is vital for cetaceans, serving as both a stopover and residency site. However, blue whales, occasionally sighted here, exhibit poorly understood migratory behavior within the Galapagos and the broader Eastern Tropical Pacific. This study, the first to satellite tag blue whales in the Galapagos (16 tagged between 2021 and 2023), explored their behavior in relation to environmental variables like chlorophyll-a concentration, sea surface temperature (SST), and productivity. Key findings show a strong correlation between foraging behavior, high chlorophyll-a levels, productivity, and lower SSTs, indicating a preference for food-rich areas. Additionally, there is a notable association with geomorphic features like ridges, which potentially enhance food abundance. Most tagged whales stayed near the Galapagos archipelago, with higher concentrations observed around Isabela Island, which is increasingly frequented by tourist vessels, posing heightened ship strike risks. Some whales ventured into Ecuador’s exclusive economic zone, while one migrated southward to Peru. The strong 2023 El Niño–Southern Oscillation event led to SST and primary production changes, likely impacting whale resource availability. Our study provides crucial insights into blue whale habitat utilization, informing adaptive management strategies to mitigate ship strike risks and address altered migration routes due to climate-driven environmental shifts. Full article

In recent years, time series land cover products have been developed rapidly. However, the traditional classification strategy rarely considers time continuity and spatial consistency, which leads to the existence of unreasonable changes among the multi-period products. In order to solve the existing problems, this paper proposes a matrix decomposition model and an optimized hidden Markov model (HMM) to improve the consistency of the time series land cover maps. It also compares the results with the spatio-temporal window filtering model. The spatial weight information is introduced into the singular value decomposition (SVD) model, and the regression model is constructed by combining the eigenvalues and eigenvectors of the image to predict the unreasonable variable pixels and complete the construction of the matrix decomposition model. To solve the two problems of reliance on expert experience and lack of spatial relationships, this paper optimizes the model and proposes the HMM Land Cover Transition (HMM_LCT) model. The overall accuracy of the matrix decomposition model and the HMM_LCT model is 90.74% and 89.87%, respectively. It is found that the matrix decomposition model has a better effect on consistency adjustment than the HMM_LCT model. The matrix decomposition model can also adjust the land cover trajectory to better express the changing trend of surface objects. After consistent adjustment by the matrix decomposition model, the cumulative proportion of the first 15 types of land cover trajectories reached 99.47%, of which 83.01% were stable land classes that had not changed for three years. Full article

Maneuvering motion prediction is central to the control and operation of ships, and the application of machine learning algorithms in this field is increasingly prevalent. However, challenges such as extensive training time, complex parameter tuning processes, and heavy reliance on mathematical models pose substantial obstacles to their application. To address these challenges, this paper proposes an LSTM-based modeling algorithm. First, a maneuvering motion model based on a real USV model was constructed, and typical operating conditions were simulated to obtain data. The Ornstein–Uhlenbeck process and the Hidden Markov Model were applied to the simulation data to generate noise and random data loss, respectively, thereby constructing a sample set that reflects real experiment characteristics. The sample data were then pre-processed for training, employing the MaxAbsScaler strategy for data normalization, Kalman filtering and RRF for data smoothing and noise reduction, and Lagrange interpolation for data resampling to enhance the robustness of the training data. Subsequently, based on the USV maneuvering motion model, an LSTM-based black-box motion prediction model was established. An in-depth comparative analysis and discussion of the model’s network structure and parameters were conducted, followed by the training of the ship maneuvering motion model using the optimized LSTM model. Generalization tests were then performed on a generalization set under Zigzag and turning conditions to validate the accuracy and generalization performance of the prediction model. Full article

Modern Automatic Speech Recognition (ASR) systems are primarily designed to recognize normal speech. Due to a considerable acoustic mismatch between normal speech and whisper, ASR systems suffer from a significant loss of performance in whisper recognition. Creating large databases of whispered speech is expensive and time-consuming, so research studies explore the synthetic generation using pre-existing normal or whispered speech databases. The impact of standard audio data augmentation techniques on the accuracy of isolated-word recognizers based on Hidden Markov Models (HMM) and Convolutional Neural Networks (CNN) is examined in this research study. Furthermore, the study explores the potential of inverse filtering as an augmentation strategy for producing pseudo-whisper speech. The Whi-Spe speech database, containing recordings in normal and whisper phonation, is utilized for data augmentation, while the internally recorded speech database, developed specifically for this study, is employed for testing purposes. Experimental results demonstrate statistically significant improvement in performance when employing data augmentation strategies and inverse filtering. Full article

Cysteine plays a pivotal role in the sulfur metabolism network of plants, intimately influencing the conversion rate of organic sulfur and the plant’s capacity to withstand abiotic stresses. In tea plants, the serine acetyltransferase (SAT) genes emerge as a crucial regulator of cysteine metabolism, albeit with a notable lack of comprehensive research. Utilizing Hidden Markov Models, we identified seven CssSATs genes within the tea plant genome. The results of the bioinformatics analysis indicate that these genes exhibit an average molecular weight of 33.22 kD and cluster into three distinct groups. Regarding gene structure, CssSAT1 stands out with ten exons, significantly more than its family members. In the promoter regions, cis-acting elements associated with environmental responsiveness and hormone induction predominate, accounting for 34.4% and 53.1%, respectively. Transcriptome data revealed intricate expression dynamics of CssSATs under various stress conditions (e.g., PEG, NaCl, Cold, MeJA) and their tissue-specific expression patterns in tea plants. Notably, qRT-PCR analysis indicated that under salt stress, CssSAT1 and CssSAT3 expression levels markedly increased, whereas CssSAT2 displayed a downregulatory trend. Furthermore, we cloned CssSAT1-CssSAT3 genes and constructed corresponding prokaryotic expression vectors. The resultant recombinant proteins, upon induction, significantly enhanced the NaCl tolerance of Escherichia coli BL21, suggesting the potential application of CssSATs in bolstering plant stress resistance. These findings have enriched our comprehension of the multifaceted roles played by CssSATs genes in stress tolerance mechanisms, laying a theoretical groundwork for future scientific endeavors and research pursuits. Full article

Face recognition (FR) uses a passive approach to person authentication that avoids face-to-face contact. Among different FR techniques, most FR approaches place little emphasis on reducing powerful cryptography and instead concentrate on increasing recognition rates. In this paper, we have proposed the Hidden Markov Model (HMM) and convolutional Neural Network (CNN) models for FR by using ORL and Yale datasets. Facial images from the given data sets are divided into 3 portions, 4 portions, 5 portions, and 6 portions corresponding to their respective HMM hidden states being used in the HMM model. Quantized levels of eigenvalues and eigenvector coefficients of overlapping blocks of facial images define the observation states of the HMM model. For image selection and rejection, a threshold is calculated using singular value decomposition (SVD). After training HMM on 3 states HMM, 4 states HMM, 5 states HMM, and 6 states HMM, the recognition accuracies are 96.5%, 98.5%, 98.5%, and 99.5%, respectively, on the ORL database and 90.6667%, 94.6667%, 94.6667%, and 94.6667% on the Yale database. The CNN model uses convolutional layers, a max-pooling layer, a flattening layer, a dense layer, and a dropout layer. Relu is used as the activation function in all layers except in the last layer, where softmax is used as the activation function. Cross entropy is used as a loss function, and we have used the Adam optimizer in our proposed algorithm. The proposed CNN model has given 100% training and testing accuracy on the ORL data set. While using the Yale data set, the CNN model has a training accuracy of 100% and a testing accuracy of 85.71%. In this paper, our proposed model showed that the HMM model is cost-effective with lesser accuracy, while the CNN model is more accurate as compared to HMM but has a higher computational cost. Full article

Tai Chi (TC) practice has been shown to improve both cognitive and physical function in older adults. However, the neural mechanisms underlying the benefits of TC remain unclear. Our primary aims are to explore whether distinct age-related and TC-practice-related relationships can be identified with respect to either temporal or spatial (within/between-network connectivity) differences. This cross-sectional study examined recurrent neural network dynamics, employing an adaptive, data-driven thresholding approach to source-localized resting-state EEG data in order to identify meaningful connections across time-varying graphs, using both temporal and spatial features derived from a hidden Markov model (HMM). Mann–Whitney U tests assessed between-group differences in temporal and spatial features by age and TC practice using either healthy younger adult controls (YACs, n = 15), healthy older adult controls (OACs, n = 15), or Tai Chi older adult practitioners (TCOAs, n = 15). Our results showed that aging is associated with decreased within-network and between-network functional connectivity (FC) across most brain networks. Conversely, TC practice appears to mitigate these age-related declines, showing increased FC within and between networks in older adults who practice TC compared to non-practicing older adults. These findings suggest that TC practice may abate age-related declines in neural network efficiency and stability, highlighting its potential as a non-pharmacological intervention for promoting healthy brain aging. This study furthers the triple-network model, showing that a balancing and reorientation of attention might be engaged not only through higher-order and top-down mechanisms (i.e., FPN/DAN) but also via the coupling of bottom-up, sensory–motor (i.e., SMN/VIN) networks. Full article

A fine-grained metro trip contains complete information on user mobility, including the original station, destination station, departure time, arrival time, transfer station(s), and corresponding transfer time during the metro journey. Understanding such detailed trip information within a city is crucial for various smart city applications, such as effective urban planning and public transportation system optimization. In this work, we study the problem of detecting fine-grained metro trips from cellular trajectory data. Existing trip-detection approaches designed for GPS trajectories are often not applicable to cellular data due to the issues of location noise and irregular data sampling in cellular data. Moreover, most cellular data-based methods focus on identifying coarse-grained transportation modes, failing to detect fine-grained metro trips accurately. To address the limitations of existing works, we propose a novel and efficient fine-grained metro-trip detection (FGMTD) model in this work. By considering both the local and global spatial–temporal characteristics of a trajectory and the metro network, FGMTD can effectively mitigate the effects of location noise and irregular data sampling, ultimately improving the accuracy and reliability of the detection process. In particular, FGMTD employs a spatial–temporal hidden Markov model with efficient index strategies to capture local spatial–temporal characteristics from individual positions and metro stations, and a weighted trip-route similarity measure to consider global spatial–temporal characteristics from the entire trajectory and metro route. We conduct extensive experiments on two real datasets to evaluate the effectiveness and efficiency of our proposed approaches. The first dataset contains cellular data from 30 volunteers, including their actual trip details, while the second dataset consists of data from 4 million users. The experiments illustrate the significant accuracy of our approach (with a precision of $87.80\%$ and a recall of $84.28\%$). Moreover, we demonstrate that FGMTD is efficient in detecting fine-grained trips from a large amount of cellular data, achieving this task within 90 min of processing a day’s data from 4 million users. Full article

In the face of escalating credit card fraud due to the surge in e-commerce activities, effectively distinguishing between legitimate and fraudulent transactions has become increasingly challenging. To address this, various machine learning (ML) techniques have been employed to safeguard cardholders and financial institutions. This article explores the use of the Ensemble Hidden Markov Model (EHMM) combined with two distinct feature extraction methods: principal component analysis (PCA) and a proposed statistical feature set termed MRE, comprising Mean, Relative Amplitude, and Entropy. Both the PCA-EHMM and MRE-EHMM approaches were evaluated using a dataset of European cardholders and demonstrated comparable performance in terms of recall (sensitivity), specificity, precision, and F1-score. Notably, the MRE-EHMM method exhibited significantly reduced computational complexity, making it more suitable for real-time credit card fraud detection. Results also demonstrated that the PCA and MRE approaches perform significantly better when integrated with the EHMM in contrast to the conventional HMM approach. In addition, the proposed MRE-EHMM and PCA-EHMM techniques outperform other classic ML models, including random forest (RF), linear regression (LR), decision trees (DT) and K-nearest neighbour (KNN). Full article

The phonocardiogram (PCG) is a crucial tool for the early detection, continuous monitoring, accurate diagnosis, and efficient management of cardiovascular diseases. It has the potential to revolutionize cardiovascular care and improve patient outcomes. The PhysioNet/CinC Challenge 2016 database, a large and influential resource, encourages contributions to accurate heart sound state classification (normal versus abnormal), achieving promising benchmark performance (accuracy: 99.80%; sensitivity: 99.70%; specificity: 99.10%; and score: 99.40%). This study reviews recent advances in analytical techniques applied to this database, and 104 publications on PCG signal analysis are retrieved. These techniques encompass heart sound preprocessing, signal segmentation, feature extraction, and heart sound state classification. Specifically, this study summarizes methods such as signal filtering and denoising; heart sound segmentation using hidden Markov models and machine learning; feature extraction in the time, frequency, and time-frequency domains; and state-of-the-art heart sound state recognition techniques. Additionally, it discusses electrocardiogram (ECG) feature extraction and joint PCG and ECG heart sound state recognition. Despite significant technical progress, challenges remain in large-scale high-quality data collection, model interpretability, and generalizability. Future directions include multi-modal signal fusion, standardization and validation, automated interpretation for decision support, real-time monitoring, and longitudinal data analysis. Continued exploration and innovation in heart sound signal analysis are essential for advancing cardiac care, improving patient outcomes, and enhancing user trust and acceptance. Full article

Single-particle tracking is a powerful technique to investigate the motion of molecules or particles. Here, we review the methods for analyzing the reconstructed trajectories, a fundamental step for deciphering the underlying mechanisms driving the motion. First, we review the traditional analysis based on the mean squared displacement (MSD), highlighting the sometimes-neglected factors potentially affecting the accuracy of the results. We then report methods that exploit the distribution of parameters other than displacements, e.g., angles, velocities, and times and probabilities of reaching a target, discussing how they are more sensitive in characterizing heterogeneities and transient behaviors masked in the MSD analysis. Hidden Markov Models are also used for this purpose, and these allow for the identification of different states, their populations and the switching kinetics. Finally, we discuss a rapidly expanding field—trajectory analysis based on machine learning. Various approaches, from random forest to deep learning, are used to classify trajectory motions, which can be identified by motion models or by model-free sets of trajectory features, either previously defined or automatically identified by the algorithms. We also review free software available for some of the analysis methods. We emphasize that approaches based on a combination of the different methods, including classical statistics and machine learning, may be the way to obtain the most informative and accurate results. Full article

Theobroma cacao plantations are of significant economic importance worldwide, primarily for chocolate production. During the harvest and processing of cocoa beans, they are subjected to fermentation either by microorganisms present in the environment (spontaneous fermentation) or the addition of starter cultures, with different strains directly contributing distinct flavor and color characteristics to the beans. In addition to fungi and bacteria, viruses are ubiquitous and can affect the quality of the fermentation process by infecting fermenting organisms, destabilizing microbial diversity, and consequently affecting fermentation quality. Therefore, in this study, we explored publicly available metatranscriptomic libraries of cocoa bean fermentation in Limon Province, Costa Rica, looking for viruses associated with fermenting microorganisms. Libraries were derived from the same sample at different time points: 7, 20, and 68 h of fermentation, corresponding to yeast- and lactic acid bacteria-driven phases. Using a comprehensive pipeline, we identified 68 viral sequences that could be assigned to 62 new viral species and 6 known viruses distributed among at least nine families, with particular abundance of elements from the Lenarviricota phylum. Interestingly, 44 of these sequences were specifically associated with ssRNA phages (Fiersviridae) and mostly fungi-infecting viral families (Botourmiaviridae, Narnaviridae, and Mitoviridae). Of note, viruses from those families show a complex evolutionary relationship, transitioning from infecting bacteria to infecting fungi. We also identified 10 and 3 viruses classified within the Totiviridae and Nodaviridae families, respectively. The quantification of the virus-derived RNAs shows a general pattern of decline, similar to the dynamic profile of some microorganism genera during the fermentation process. Unexpectedly, we identified narnavirus-related elements that showed similarity to segmented viral species. By exploring the molecular characteristics of these viral sequences and applying Hidden Markov Models, we were capable of associating these additional segments with a specific taxon. In summary, our study elucidates the complex virome associated with the microbial consortia engaged in cocoa bean fermentation that could contribute to organism/strain selection, altering metabolite production and, consequently, affecting the sensory characteristics of cocoa beans. Full article