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We examine the properties of oblique coordinates. The coordinates, introduced by Zúñiga et al. [J. Phys. B: At., Mol. Opt. Phys. 52, 055101, (2019)], reduce vibrational mode-mixing and enhance the quality of vibrational assignments in quantum mechanical investigations of two-dimensional model Hamiltonians. Oblique coordinates are obtained by making non-orthogonal rotations of the original coordinates that convert the matrix representation of the quadratic Hamiltonian operator into a block-diagonal matrix where the blocks are distinguished by the total quanta of vibrational excitation. Using techniques for the polar decomposition of matrices, we present a scheme for finding these coordinates for systems of arbitrary dimensions. Several molecular examples are presented that highlight the advantages of these coordinates. [ABSTRACT FROM AUTHOR]

Due to geological processes such as sedimentation, tectonic movement, and backfilling, natural soil often exhibits characteristics of rotated anisotropy. Recent studies have shown the significant impact of rotated anisotropy on slope stability. However, little research has explored how this rotated anisotropy affects the large deformations occurring after slope failure. Therefore, this study integrates rotated random field theory with smoothed particle hydrodynamics (SPH) to investigate its influence on post‐failure slope behavior. Focusing on a typical slope scenario, this research utilizes graphics processing unit (GPU)–accelerated covariance matrix decomposition (CMD) method to create rotated anisotropy random fields and applies the SPH framework for analysis. It examines the influence of rotated anisotropy angles and the cross‐correlation between cohesion and internal friction angle on landslides. The results indicate that the rotational anisotropy of the slope significantly influences post‐failure behavior. When the rotation angle is close to the slope surface, it tends to amplify both the magnitude and variability of slope failure. Furthermore, the study evaluates the efficiency of generating these random fields and emphasizes the substantial computational speed improvements achieved with GPU acceleration. These findings offer a robust approach for probabilistic analysis of slope large deformations considering rotated anisotropy. They provide a theoretical foundation for accurately assessing the risk of slope collapse, holding significant practical implications for geotechnical engineering. [ABSTRACT FROM AUTHOR]

The accurate estimation of respiratory rate (RR) is crucial for assessing the respiratory system's health in humans, particularly during auscultation processes. Despite the numerous automated RR estimation approaches proposed in the literature, challenges persist in accurately estimating RR in noisy environments, typical of real-life situations. This becomes especially critical when periodic noise patterns interfere with the target signal. In this study, we present a parallel driver designed to address the challenges of RR estimation in real-world environments, combining multi-core architectures with parallel and high-performance techniques. The proposed system employs a nonnegative matrix factorization (NMF) approach to mitigate the impact of noise interference in the input signal. This NMF approach is guided by pre-trained bases of respiratory sounds and incorporates an orthogonal constraint to enhance accuracy. The proposed solution is tailored for real-time processing on low-power hardware. Experimental results across various scenarios demonstrate promising outcomes in terms of accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

Summary: When selecting services from a cloud center to build applications, the quality of service (QoS) is an important nonfunctional attribute to be considered. However, in actual application scenarios, the QoS details for many services may not be available. This has led to a situation where prediction of the missing QoS records for services has become a key problem for service selection. This article presents a selective ensemble learning (SEL) framework for prefilling‐based matrix factorization (PFMF) predictors. In each PFMF predictor, the improved collaborative filtering is defined by examining the stability of the QoS records when measuring the similarity of users (or services), and then used to prefill empty records in the initial QoS matrix. To ensure the diversity of the basic PFMF predictors, various prefilled QoS matrices are constructed for the matrix factorization. In this process, different reference weights are assigned to the original and the prefilled QoS records. Finally, particle swarm optimization is used to set the ensemble weights for the basic PFMF predictors. The proposed SEL on PFMF (SEL‐PFMF) algorithm is validated on a public dataset, where its prediction performance outperforms the state‐of‐the‐art algorithms, and also shows good stability. [ABSTRACT FROM AUTHOR]

Emerging fields of quantum technologies and biomedical applications demand pure nanodiamonds (NDs) with well-defined surface chemistry. Therefore, an inexpensive, scalable and eco-friendly ND surface purification technology is required. In this study, we report our method, salt-coated air oxidation (SCAO) thermal annealing, to achieve uniform purification of a ND surface without the loss of diamond material. A photoluminescence (PL) spectroscopy quality control method is proposed to evaluate the degree of purification. The presence of an isoemission point in the set of nitrogen vacancy (NV) center PL spectra, obtained through the photochromic effect, is examined as a surface purity indicator. The ratio of the NV centers in NDs after the SCAO treatment was determined by decomposing the PL spectra using the non-negative matrix factorization technique. [ABSTRACT FROM AUTHOR]

The Bohai Sea is one of the most polluted sea areas in China. In this study, we used 2184 integrated concentrations of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) in the Bohai Sea of China during spring (March, April, and May), summer (June, July, and August), and autumn (October and November) from 2015 to 2022 to explore the trends and sources of nutrients variations. From 2015 to 2022, DIN showed a downward trend until 2020 and then an upward trend, whereas DIP exhibited a stable trend with a slight decrease. The concentrations of DIN and DIP had similar seasonal pattern which was the highest in autumn (0.292 ± 0.247 mg/L for DIN and 0.013 ± 0.016 mg/L for DIP) but lower in spring (0.267 ± 0.238 mg/L for DIN and 0.006 ± 0.010 mg/L for DIP) and summer (0.263 ± 0.324 mg/L for DIN and 0.008 ± 0.010 mg/L for DIP). Sources of DIN and DIP apportioned by the positive matrix factorization (PMF) model were riverine input, sediment resuspension, sewage discharge, atmospheric deposition, and underground input. During 2015–2022, the largest contributor to DIN was sewage discharge (28.7%) and the largest contributor to DIP was sediment resuspension (44.6%). Seasonally, DIN in spring and autumn was dominated by sewage discharge (45.4% and 27.8%, respectively). Whereas in summer, it was dominated by riverine input (32.4%) and atmospheric deposition (29.7%). DIP was dominated by sediment resuspension during all three seasons (35.8%–52.5%). In addition, the increase in DIN concentrations in 2021 and 2022 were mainly due to the incremental input of river discharge and atmospheric deposition caused by increased precipitation during summer and autumn. [ABSTRACT FROM AUTHOR]

We present a new decomposition of a Cauchy–Vandermonde matrix as a product of bidiagonal matrices which, unlike its existing bidiagonal decompositions, is now valid for a matrix of any rank. The new decompositions are insusceptible to the phenomenon known as subtractive cancellation in floating point arithmetic and are thus computable to high relative accuracy. In turn, other accurate matrix computations are also possible with these matrices, such as eigenvalue computation amongst others. [ABSTRACT FROM AUTHOR]

Fully homomorphic encryption (FHE) is capable of handling sensitive encrypted data in untrusted computing environments. The efficient application of FHE schemes in secure outsourced computation can effectively address security and privacy concerns. This paper presents a novel fully homomorphic encryption scheme called GMS, based on the n-secret learning with errors (LWE) assumption. By utilizing block matrix and decomposition technology, GMS achieves shorter encryption and decryption times and smaller ciphertext sizes compared to existing FHE schemes. For secure outsourced matrix multiplication A m × n · B n × l with arbitrary dimensions, GMS only requires O (max { m , n , l }) rotations and one homomorphic multiplication. Compared to the state-of-the-art methods, our approach stands out by achieving a significant reduction in the number of rotations by a factor of O (log max { n , l }) , along with a decrease in the number of homomorphic multiplications by a factor of n and O (min { m , n , l }) . The experimental results demonstrate that GMS shows superior performance for secure outsourced matrix multiplication of any dimension. For example, when encrypting a 64 × 64 -dimensional matrix, the size of the ciphertext is only 1.27 MB. The encryption and decryption process takes approximately 0.2 s. For matrix multiplication A 64 × 64 · B 64 × 64 , the runtime of our method is 39.98 s, achieving a speedup of up to 5X and 2X. [ABSTRACT FROM AUTHOR]

The use of organochlorine pesticides (OCPs) in specific regions is still prevalent. Moreover, the impact of past utilization can be observed in the present environmental matrices. The present study monitored the extent of contamination of OCPs in the soil and vegetable samples of Gaya, Bihar, India. For this, 63 soil and vegetable samples were collected from the vegetable cultivated area of Gaya. The collected samples were extracted using a Soxhlet extraction unit and OCPs were analysed with a gas chromatography–mass spectrometry detector. The concentration data generated from the analysis were interpreted using statistical tools and software. Mean concentration (μg/g) of Σ19OCPs in soil from residential, agricultural, commercial, and polyhouse sites were 0.69, 2.21, 0.17, and 0.72, respectively. Similarly, in vegetable samples, mean concentration (μg/g) of Σ19OCPs were 0.91, 0.96, 1.00, and 0.67, respectively. Among the monitored vegetable types, the concentration of OCPs increased in the order: pods > tubers > leaves > fruits > roots > stem. The bioconcentration factor of 19 OCPs showed that 61.90% of vegetable samples were hyperaccumulators. The results of molecular diagnostic ratio and positive matrix factorization reported the recent inputs of heptachlor, aldrin, endrin and methoxychlor; the past application of dichlorodimethyltrichloroethane (DDT), endosulfan, and chlordane; and the degradation of DDT to its metabolites and aldrin to dieldrin, which make up an overall source profile of OCPs in study area. The study found that incremental lifetime cancer risks and hazard quotients ranged from 6.98 × 10–8 to 1.31 × 10–5 and 4.25 × 10–2 to 4.63 × 10–1, respectively in vegetable samples which indicate low to high ILCR and low non-carcinogenic risk to populations exposed to OCPs. The study indicates the long lasting impact of past pesticide use by studying the contamination in soil and vegetables, and raises serious concerns about food safety. The contamination poses direct health risk to consumers related to potential carcinogenic and endocrine disrupting effects. Thus monitoring on the ground level could be a force to modify region specific policies, health, and remediation measures related to exposure to OCPs. [ABSTRACT FROM AUTHOR]

In recent years, industrial waste and agrochemicals have reduced soil fertility and productivity, significantly impacting food security and ecosystems. In China, areas near red mud deposits from the aluminum industry show severe heavy metal contamination. This study examines agricultural soil near a red mud site in Shanxi Province, analyzing Cd, Cr, Hg, Ni, Pb, As, Cu, and Zn levels and distribution. Geostatistical methods and GIS are utilized to assess heavy metal pollution using the single factor index, the Nemerow integrated index, and the Hakanson potential ecological risk index. Absolute Principal Component Scores-Multiple Linear Regression (APCS-MLR) and Positive Matrix Factorization (PMF) models are used for quantitative analysis of pollution sources. Research indicates that the average concentrations of eight heavy metals exceed the natural background values of Shanxi, placing them at a severe pollution level with moderate ecological risk. Specifically, indices for As, Pb, and Cr are 3.79, 3.38, and 3.26, indicating severe pollution; Cd, Cu, and Hg at 2.36, 2.62, and 3.00 suggest moderate pollution; Ni at 1.87 shows mild pollution, while Zn at 0.97 is not polluted. Hg presents the highest ecological risk with a coefficient of 120.00, followed by Cd (70.69) and As (37.92). Spatial analysis shows significant correlations among Pb, Zn, Cu, and Ni, while Cr, Cd, Hg, and As show greater variability and weaker correlations. Both models identify five main sources: industrial activities, agricultural fertilizers, red mud leachate, energy combustion, and natural geological backgrounds, with respective contribution rates in the APCS-MLR model at 27.7%, 24.6%, 18.1%, 15.2%, and 14.4%, and in the PMF model at 29.2%, 21.5%, 16.9%, 16.7%, and 15.7%. This study offers a scientific basis for controlling soil pollution in the region, filling a literature gap. [ABSTRACT FROM AUTHOR]

The construction of dams has caused disruptions to river connectivity, leading to alterations in the deposition of hydrophobic organic contaminants in reservoir sediments. Further investigation is warranted to explore the impact of cascade reservoirs with differing hydrological characteristics on polycyclic aromatic hydrocarbons (PAHs) distribution in sediment. This study examines the presence of 30 PAHs in the sediments collected from six cascade reservoirs situated in the Wujiang River basin during January and July 2017. The results showed that Σ30 PAHs ranged from 455–3000 ng/g dw (mean 1030 ng/g dw). Anthropogenic activities and reservoir hydrology determined the distribution trend of PAHs in sediments, with an overall increase from upstream to midstream and then a decrease downstream. The PAH levels were highly linked to the secondary industry (P < 0.05). This was further supported by the relationship between the PAH emissions from coal combustion and traffic sources analyzed by the positive matrix factorization model and economic parameters in the wet season (P < 0.01). At the same time, reservoir age (RA) showed a positive correlation with PAH concentrations (P < 0.05), while hydraulic retention time (HRT) exhibited a negative correlation with PAH levels (P = 0.03). The relationship between total organic carbon (TOC) and PAHs in stream sediments worldwide was nonlinear (P < 0.01), with PAH concentrations initially rising and then falling as TOC levels increased. Concerns regarding carcinogenic risk were raised due to contributions from coal and vehicular sources, with the risk increasing with RA. [ABSTRACT FROM AUTHOR]

Recommendation systems are ubiquitous in various domains, facilitating users in finding relevant items according to their preferences. Identifying pertinent items that meet their preferences enables users to target the right items. To predict ratings for more accurate forecasts, recommender systems often use collaborative filtering (CF) approaches to sparse user-rated item matrices. Due to a lack of knowledge regarding newly formed entities, the data sparsity of the user-rated item matrix has an enormous effect on collaborative filtering algorithms, which frequently face lazy learning issues. Real-world datasets with exponentially increasing users and reviews make this situation worse. Matrix factorization (MF) stands out as a key strategy in recommender systems, especially for CF tasks. This paper presents a neural network matrix factorization (NNMF) model through machine learning to overcome data sparsity challenges. This approach aims to enhance recommendation quality while mitigating the impact of data sparsity, a common issue in CF algorithms. A thorough comparative analysis was conducted on the well-known MovieLens dataset, spanning from 1.6 to 9.6 M records. The outcomes consistently favored the NNMF algorithm, showcasing superior performance compared to the state-of-the-art method in this domain in terms of precision, recall, F 1 score , MAE, and RMSE. [ABSTRACT FROM AUTHOR]

With the continuous accumulation of massive amounts of mobile data, point-of-interest (POI) recommendation has become a vital task for location-based social networks. Deep neural networks or matrix factorization (MF) alone are challenging to effectively learn user–POI interaction functions. Moreover, the user–POI interaction matrix is sparse, and the heterogeneous characteristics of auxiliary information are underused. Therefore, we propose an innovative POI recommendation method that integrates attention-aware meta-paths based on deep neural matrix factorization (DNMF-AM). First, we develop a multi-relational heterogeneous information network of "user–POI–geographic region–POI category." Multiple-weighted isomorphic information networks based on meta-paths are employed to obtain node-embedding vectors across different relationships. Attention networks are employed to aggregate node vectors across various relationships and serve as auxiliary information to mitigate the challenges of data sparsity. Subsequently, the internal embedding vectors of the users and POIs are extracted using feature embedding based on the user–POI interaction matrix. Second, these vectors are integrated with the embedding vectors obtained by aggregating the attention networks. Third, deep neural matrix factorization is used to learn linear and nonlinear user–POI interactions to mitigate the implicit feedback problem. This outcome is achieved using generalized matrix factorization and convolution-constrained multi-head self-attention mechanism deep neural networks. Extensive experiments conducted on two real-world datasets demonstrate that the DNMF-AM outperforms the optimal baseline NeuMF-CAA by 4.24% and 5.04% in terms of HR@10 and NDCG@10, respectively. [ABSTRACT FROM AUTHOR]

In recent years, recommendation explanation methods have received widespread attention due to their potentials to enhance user experience and streamline transactions. In scenarios where auxiliary information such as text and attributes are lacking, counterfactual explanation has emerged as a crucial technique for explaining recommendations. However, existing counterfactual explanation methods encounter two primary challenges. First, a substantial bias indeed exists in the calculation of the group impact function, leading to the inaccurate predictions as the counterfactual explanation group expands. In addition, the importance of collaborative filtering as a counterfactual explanation is overlooked, which results in lengthy, narrow, and inaccurate explanations. To address such issues, we propose a counterfactual explanation method based on Modified Group Influence Function for recommendation. In particular, via a rigorous formula derivation, we demonstrate that a simple summation of individual influence functions cannot reflect the group impact in recommendations. After that, building upon the improved influence function, we construct the counterfactual groups by iteratively incorporating the individuals from the training samples, which possess the greatest influence on the recommended results, and continuously adjusting the parameters to ensure accuracy. Finally, we expand the scope of searching for counterfactual groups by incorporating the collaborative filtering information from different users. To evaluate the effectiveness of our method, we employ it to explain the recommendations generated by two common recommendation models, i.e., Matrix Factorization and Neural Collaborative Filtering, on two publicly available datasets. The evaluation of the proposed counterfactual explanation method showcases its superior performance in providing counterfactual explanations. In the most significant case, our proposed method achieves a 17% lead in terms of Counterfactual precision compared to the best baseline explanation method. [ABSTRACT FROM AUTHOR]

ABSTRACT Little q$$ q $$‐Jacobi polynomials belong to the field of quantum calculus. This article obtains the bidiagonal decomposition of the collocation matrices of these polynomials, showing that, in many cases, it can be constructed to high relative accuracy (HRA). Then, it can be used to compute with HRA the inverses, eigenvalues, and singular values of these matrices. Numerical experiments are provided and illustrate the excellent results obtained when applying the presented methods. [ABSTRACT FROM AUTHOR]

The growing demand for ferrous metals and abundant scrap materials has fueled Nigeria’s scrap-iron smelting industry, leading to hazardous pollutant emissions. This study investigated the concentrations, seasonal and indoor-outdoor variations, origins, and health impacts of polycyclic aromatic hydrocarbons (PAHs) in dust samples around a scrap-iron smelting facility. Analyses of dust samples revealed that high molecular weight PAHs (HMWPAHs) dominated during both seasons, with 5-ring PAHs (34%) contributing most during the rainy season and 3-ring PAHs (36%) during the dry season. Carcinogenic PAHs were more prevalent in the rainy season compared to the dry season. Seven PAH sources were identified, with gasoline combustion being the dominant source during the rainy season and iron and steel production during the dry season. Incremental lifetime cancer risk (ILCR) assessments showed PAH concentrations within safe limits, with dermal contact identified as the primary exposure pathway for both children and adults in the study area. [ABSTRACT FROM AUTHOR]

A primary strategy for constructing click-through rate models based on deep learning involves combining a multi-layer perceptron (MLP) with custom networks that can effectively capture the interactions between different features. This is due to the widespread recognition that relying solely on a vanilla MLP network is not effective in acquiring knowledge about multiplicative feature interactions. These custom networks often employ product methods, such as inner, Hadamard, and outer products, to construct dedicated architectures for this purpose. Among these methods, the outer product has shown superiority in capturing feature interactions. However, the resulting quadratic form from the outer product operation limits the conveyance of informative higher-order interactions to the MLP. Efforts to address this limitation have led to models attempting to increase interaction degrees to higher orders. However, utilizing matrix factorization techniques to reduce learning parameters has resulted in information loss and decreased performance. Furthermore, previous studies have constrained the MLP's potential by providing it with inputs consisting of homogeneous outer products, thus limiting available information diversity. To overcome these challenges, we introduce DONN, a model that leverages a composite-wise bilinear module incorporating factorized bilinear pooling to mitigate information loss and facilitate higher-order interaction development. Additionally, DONN utilizes a feature-wise bilinear module for outer product computations between feature pairs, augmenting the MLP with combined information. By employing heterogeneous outer products, DONN enhances the MLP's prediction capabilities, enabling the recognition of additional nonlinear interdependencies. Our evaluation on two benchmark datasets demonstrates that DONN surpasses state-of-the-art models in terms of performance. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 crisis, higher education institutions increasingly rely on e-learning systems. Indeed, the higher education market has become increasingly competitive with the addition of open education models. However, the abundance of accessible online courses makes it challenging to deliver education that meets student needs. Learners have diverse profiles based on their traits, motivations, prior knowledge, and learning preferences. Recently, much research has given attention to the importance of using the contextual parameters to perform more accurate recommendations. In this context, context-aware recommendation of pedagogical resources can deal with the issue of information overload, cold start problem and meeting the learner's preferences. This paper describes a context-aware recommender system that harness the learner's contextual information. Our proposed approach is called Context Embedding Deep Collaborative Filtering (CEDCF), which enriches the learner profile with extracted sentiments from previous interactions. The proposed approach comprises three models, called Generalized Matrix Factorzation (GMF) , Multilayer Perceptron (MLP) and Neural Matrix Factorization (NeuMF). The GMF and the MLP are respectively applied to the rating matrix and the contextual parameters. The outputs of these models are then fed into a neural network to perform rating prediction. To put our proposal into shape, we model a real-world application of a merged coursera dataset to recommend courses. The experimental evaluation shows relevant results attesting the efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

Calcium imaging allows recording from hundreds of neurons in vivo with the ability to resolve single cell activity. Evaluating and analyzing neuronal responses, while also considering all dimensions of the data set to make specific conclusions, is extremely difficult. Often, descriptive statistics are used to analyze these forms of data. These analyses, however, remove variance by averaging the responses of single neurons across recording sessions, or across combinations of neurons, to create single quantitative metrics, losing the temporal dynamics of neuronal activity, and their responses relative to each other. Dimensionally Reduction (DR) methods serve as a good foundation for these analyses because they reduce the dimensions of the data into components, while still maintaining the variance. Nonnegative Matrix Factorization (NMF) is an especially promising DR analysis method for analyzing activity recorded in calcium imaging because of its mathematical constraints, which include positivity and linearity. We adapt NMF for our analyses and compare its performance to alternative dimensionality reduction methods on both artificial and in vivo data. We find that NMF is well-suited for analyzing calcium imaging recordings, accurately capturing the underlying dynamics of the data, and outperforming alternative methods in common use. [ABSTRACT FROM AUTHOR]

Radiation-induced lymphopenia (RIL) is common among patients undergoing radiation therapy (RT)' Severe RIL has been linked to adverse outcomes. The severity and risk of RIL can be predicted from baseline clinical characteristics and dosimetric parameters. However, dosimetric parameters, e.g. dose-volume (DV) indices, are highly correlated with one another and are only weakly associated with RIL. Here we introduce the novel concept of "composite dosimetric score" (CDS) as the index that condenses the dose distribution in immune tissues of interest to study the dosimetric dependence of RIL. We derived an improved multivariate classification scheme for risk of grade 4 RIL (G4RIL), based on this novel RT dosimetric feature, for patients receiving chemo RT for esophageal cancer. DV indices were extracted for 734 patients who received chemo RT for biopsy-proven esophageal cancer. Nonnegative matrix factorization was used to project the DV indices of lung, heart, and spleen into a single CDS; XGBoost was employed to explore significant interactions among predictors; and logistic regression was applied to combine the resultant CDS with baseline clinical factors and interaction terms to facilitate individualized prediction of immunotoxicity. Five-fold cross-validation was applied to evaluate the model performance. The CDS for selected immune organs at risk (ie, heart, lung, and spleen) (OR 1.791; 95 CI [1.350, 2.377]) was a statistically significant risk determinant for G4RIL. Pearson correlation coefficients for CDS versus G4RIL risk for individual immune organs at risk were greater than any single DV indicx. Personalized prediction of G4RIL based on CDS and 4 clinical risk factors yielded an area under the curve value of 0.78. Interaction between age and CDS revealed that G4RIL risk increased more sharply with increasing CDS for patients aged ≥65 years. Risk of immunotoxicity for patients undergoing chemo RT for esophageal cancer can be predicted by CDS. The CDS concept can be extended to immunotoxicity in other cancer types and in dose-response models currently based on DV indices. Personalized treatment planning should leverage composite dosimetric scoring methods rather than using individual or subsets of DV indices. [ABSTRACT FROM AUTHOR]

In recent years, factorization combined with attribute information has played an important role in attribute community detection. However, previous studies focused more on connecting the original shallow topological and attribute data. They ignored the potential representation structure of the network. To solve the problem that shallow information cannot fully represent the network structure, this paper proposes an attribute node classification method based on Attribute Edges weights Fusion and graph Embedding Factorization, called AEFEF. First, AEFEF converts topological information and attribute information into corresponding matrices representing node associations. Then, AEFEF constructs a new adjacency matrix by increasing the weights of shared edges between the topology structure and attribute structure. This operation can strengthen the tightness between nodes. Second, to explore the potential community structure, feature embedding is obtained by factorizing the attribute similarity matrix. Meanwhile, the new adjacency matrix is designed as a weight matrix to make the feature embedding between related nodes more similar. Finally, semi Non-negative Matrix Factorization (NMF) is introduced to modify the feature embedding by converting the negative values into positive values. Then the embedding is factorized to generate the membership matrix. At the same time, the network with a rich structure is decomposed with topological data as the main component. Otherwise, attribute information is the main component of NMF used to increase the accuracy of node classification. AEFEF is compared with 10 state-of-the-art algorithms on 7 real network datasets. The results reveal that AEFEF can improve the precision of attribute community detection. [ABSTRACT FROM AUTHOR]

Scars that form after skin injury can cause structural and functional skin damage. Currently, scar tissue determination relies mainly on doctors' subjective observations and judgments and lacks objectivity. However, current deep learning models can only achieve specific discrimination using unimodal data, which limits the comprehensive understanding of scar tissue and may reduce accuracy and stability. To solve these problems, in this study, a skin scar recognition platform based on advanced deep learning and a weighted aggregation network fusion method is proposed. It is implemented using a residual network-based CNN model and a logistic regression model with L1 regularization and is suitable for both unimodal and multimodal data. The experimental results showed that the proposed platform achieved a satisfactory accuracy of 98.26% for image discrimination. In the gene discrimination model test performed on a test dataset containing 17 gene expression samples, all samples were accurately discriminated. In addition, the proposed multimodal discrimination model achieved a discrimination accuracy of 98.23%. These results validate the effectiveness of deep feature extraction and multimodal feature fusion techniques for image discrimination tasks. On this basis, to deeply explore the pathogenesis of scar formation, a method with the ability to integrate regularization, sparsity, and orthogonality constraints, multiconstraint joint non-negative matrix factorization (MCJNMF), was used to explore the genetic correlation between collagen micrographic image features and gene expression data. In this study, we confirmed the association between the calcium signaling pathway, MAPK signaling pathway, and collagen fiber repair, and successfully identified 11 potential therapeutic targets, including TRIM59 and TBC1D9, which provide important clues for future scar treatment and prevention strategies. [ABSTRACT FROM AUTHOR]

ABSTRACT This work develops a generalized fused lasso (GFL) approach to fitting contrast‐based network meta‐analysis (NMA) models. The GFL method penalizes all pairwise differences between treatment effects, resulting in the pooling of treatments that are not sufficiently different. This approach offers an intriguing avenue for potentially mitigating biases in treatment rankings and reducing sparsity in networks. To fit contrast‐based NMA models within the GFL framework, we formulate the models as generalized least squares problems, where the precision matrix depends on the standard error in the data, the estimated between‐study heterogeneity and the correlation between contrasts in multi‐arm studies. By utilizing a Cholesky decomposition of the precision matrix, we linearly transform the data vector and design matrix to frame NMA within the GFL framework. We demonstrate how to construct the GFL penalty such that every pairwise difference is penalized similarly. The model is straightforward to implement in R via the “genlasso” package, and runs instantaneously, contrary to other regularization approaches that are Bayesian. A two‐step GFL‐NMA approach is recommended to obtain measures of uncertainty associated with the (pooled) relative treatment effects. Two simulation studies confirm the GFL approach's ability to pool treatments that have the same (or similar) effects while also revealing when incorrect pooling may occur, and its potential benefits against alternative methods. The novel GFL‐NMA method is successfully applied to a real‐world dataset on diabetes where the standard NMA model was not favored compared to the best‐fitting GFL‐NMA model with AICc selection of the tuning parameter (ΔAICc>13)$$ \Delta AICc>13\Big) $$. [ABSTRACT FROM AUTHOR]

Unlike in the field of visual scene recognition, where tremendous advances have taken place due to the availability of very large datasets to train deep neural networks, inference from medical images is often hampered by the fact that only small amounts of data may be available. When working with very small dataset problems, of the order of a few hundred items of data, the power of deep learning may still be exploited by using a pre-trained model as a feature extractor and carrying out classic pattern recognition techniques in this feature space, the so-called few-shot learning problem. However, medical images are highly complex and variable, making it difficult for few-shot learning to fully capture and model these features. To address these issues, we focus on the intrinsic characteristics of the data. We find that, in regimes where the dimension of the feature space is comparable to or even larger than the number of images in the data, dimensionality reduction is a necessity and is often achieved by principal component analysis or singular value decomposition (PCA/SVD). In this paper, noting the inappropriateness of using SVD for this setting we explore two alternatives based on discriminant analysis (DA) and non-negative matrix factorization (NMF). Using 14 different datasets spanning 11 distinct disease types we demonstrate that at low dimensions, discriminant subspaces achieve significant improvements over SVD-based subspaces and the original feature space. We also show that at modest dimensions, NMF is a competitive alternative to SVD in this setting. The implementation of the proposed method is accessible via the following link. [ABSTRACT FROM AUTHOR]

Highlights This work investigates the effects of laser processing parameters on basalt fiber‐reinforced composites for the optimization of surface processing without fiber damage. Various conditions of laser power, speed, and stand‐off distance were applied using a CO₂ laser. Consequently, a well‐processed morphology with full matrix removal and clear fiber exposure was achieved under optimized conditions of 32 W power, 200 mm/s speed, and 5 mm SOD. Those parameters resulted in a surface roughness Sa of 6.11 μm. Using the optimum parameters obtained from the experiments, the surface energy density was computed as 0.89 J/mm2, while ensuring adequate material removal without damage to the fiber. Lower power input of 20 W resulted in incomplete removal of the matrix; on the other extreme, unreasonably high powers resulted in fiber degradation. The experiments determined that optimum laser parameters indeed play a critical role in the processing of basalt fiber composites surfaces. Ideal laser settings: 32 W, 200 mm/s, 5 mm SOD. Full‐matrix removal and fiber exposition realized without any damage. The achieved surface roughness Sa is 6.11 μm for optimized laser parameters. Excessive laser power leads to a fiber damage. [ABSTRACT FROM AUTHOR]

Water quality monitoring reveals changing trends in the environmental condition of aquatic systems, elucidates the prevailing factors impacting a water body, and facilitates science-backed policymaking. A 2020 hiatus in water quality data tracking in the Lower Passaic River (LPR), New Jersey, has created a 5-year information gap. To gain insight into the LPR water quality status during this lag period and ahead, water quality indices computed with 16-year historical data available for 12 physical, chemical, nutrient, and microbiological parameters were used to predict water quality between 2020 and 2025 using seasonal autoregressive moving average (ARIMA) models. Average water quality ranged from good to very poor (34 ≤ µWQI ≤ 95), with noticeable spatial and seasonal variations detected in the historical and predicted data. Pollution source tracking with the positive matrix factorization (PMF) model yielded significant R2 values (0.9 < R2 ≤ 1) for the input parameters and revealed four major LPR pollution factors, i.e., combined sewer systems, surface runoff, tide-influenced sediment resuspension, and industrial wastewater with pollution contribution rates of 23–30.2% in the upstream and downstream study areas. Significant correlation of toxic metals, nutrients, and sewage indicators suggest similarities in their sources. [ABSTRACT FROM AUTHOR]

Seosan is a concentrated industrial complex in the midwestern region of Korea. A study was conducted from December 2020 to January 2021, measuring PM2.5 and chemical components in Seosan using online instruments every hour. The concentration of PM2.5 during the winter season was 31.4±17.8 μg/m3, exceeding the national ambient air quality standard of Korea. The mass fraction of organic matter, elemental carbon, three major ions, five minor ions, crustal elements, and trace elements in PM2.5 accounted for 24.5%, 4.36%, 32.0%, 2.82%, 4.11%, and 5.17% of the total PM2.5 mass concentration, respectively. Source identification was conducted using positive matrix factorization modeling, revealing eight sources of PM2.5: Secondary inorganic aerosol (SIA), vehicle exhaust, industry, coal combustion, biomass burning/incinerator, oil combustion, soil, and aged sea salt. Source contributions varied during high pollution episodes (HPE), with SIA dominating in HPE1 and soil and aged sea salt in HPE2. The potential source contribution function and conditional probability function were utilized to estimate the potential local and regional emission areas for the identified sources. In Seosan, vehicle exhaust and biomass burning/incinerator were primarily influenced by local sources. SIA, industry, and oil combustion sources were significantly affected by short-range transport from eastern China. Soil and aged sea salt, which exhibited high contributions during HPE2, were associated with long-range transport from Inner Mongolia. Coal combustion was attributed to both local sources, particularly large industrial complexes near Seosan, and long-range transport from Northeast China and Inner Mongolia. [ABSTRACT FROM AUTHOR]

We study the problem of estimating the -norms () of solutions to inhomogeneous difference equations. The difference equations are considered as bi-infinite (infinite in both directions) systems of linear equations. We establish estimates in the case of a diagonally dominant Laurent matrix. Using this result and the idea of matrix decomposition into the product of matrices related to the decomposition of the characteristic polynomial, we propose some estimates in the case of an arbitrary nonsingular band Laurent matrix. [ABSTRACT FROM AUTHOR]

The Whittaker–Henderson (WH) graduation is a smoothing method for equally spaced one-dimensional data such as time series. It includes the Bohlmann filter, the Hodrick–Prescott (HP) filter, and the Whittaker graduation as special cases. Among them, the HP filter is the most prominent trend-cycle decomposition method for macroeconomic time series such as real gross domestic product. Recently, a modification of the HP filter, the boosted HP (bHP) filter, has been developed, and several studies have been conducted. The basic idea of the modification is to achieve more desirable smoothing by extracting long-term fluctuations remaining in the smoothing residuals. Inspired by the modification, this paper develops the boosted version of the WH graduation, which includes the bHP filter as a special case. Then, we establish its properties that are fundamental for applied work. To investigate the properties, we use a spectral decomposition of the penalty matrix of the WH graduation [ABSTRACT FROM AUTHOR]

High-dimensional sparse matrix data frequently arise in various applications. A notable example is the weighted word–word co-occurrence count data, which summarizes the weighted frequency of word pairs appearing within the same context window. This type of data typically contains highly skewed non-negative values with an abundance of zeros. Another example is the co-occurrence of item–item or user–item pairs in e-commerce, which also generates high-dimensional data. The objective is to utilize these data to predict the relevance between items or users. In this paper, we assume that items or users can be represented by unknown dense vectors. The model treats the co-occurrence counts as arising from zero-inflated Gamma random variables and employs cosine similarity between the unknown vectors to summarize item–item relevance. The unknown values are estimated using the shared parameter alternating zero-inflated Gamma regression models (SA-ZIG). Both canonical link and log link models are considered. Two parameter updating schemes are proposed, along with an algorithm to estimate the unknown parameters. Convergence analysis is presented analytically. Numerical studies demonstrate that the SA-ZIG using Fisher scoring without learning rate adjustment may fail to find the maximum likelihood estimate. However, the SA-ZIG with learning rate adjustment performs satisfactorily in our simulation studies. [ABSTRACT FROM AUTHOR]

The patent serves as a vital component of scientific text, and over time, escalating competition has generated a substantial demand for patent analysis encompassing areas such as company strategy and legal services, necessitating fast, accurate, and easily applicable similarity estimators. At present, conducting natural language processing(NLP) on patent content, including titles, abstracts, etc., can serve as an effective method for estimating similarity. However, the traditional NLP approach has some disadvantages, such as the requirement for a huge amount of labeled data and poor explanation of deep-learning-based model internals, exacerbated by the high compression of patent content. On the other hand, most knowledge-based deep learning models require a vast amount of additional analysis results as training variables in similarity estimation, which are limited due to human participation in the analysis part. Thus, in this research, addressing these challenges, we introduce a novel estimator to enhance the transparency of similarity estimation. This approach integrates a patent's content with international patent classification (IPC), leveraging bidirectional encoder representations from transformers (BERT), and non-negative matrix factorization (NMF). By integrating these techniques, we aim to improve knowledge discovery transparency in NLP across various IPC dimensions and incorporate more background knowledge into context similarity estimation. The experimental results demonstrate that our model is reliable, explainable, highly accurate, and practically usable. [ABSTRACT FROM AUTHOR]

The advantage of employing mid-infrared spectrometry for milk analysis in breeding lies in its ability to quickly generate millions of records. However, these records may be biased if the calibration process does not account for their spectral variability when constructing the predictive model. Therefore, this study introduces a novel method for developing a world representative spectral database (WRSD) to reduce the risks of spectral extrapolation when predicting dairy traits in new samples. Using a 2-phase selection procedure that is both efficient and minimizes memory usage, we first generate a decomposition matrix via principal component analysis (PCA) on a dataset of 2,324,443 records. The next phase iterates spectral selection based on a location index from PCA scores, calculating spectra occurrence frequency for refined barycenter estimations. The chosen spectra's barycenter closely aligns with the entire dataset, proving the efficacy of using just 3 principal components. Applied to 4 varied datasets totaling over 21 million records, we select 583,440 spectra to represent spectral diversity, with selection rates between 2.00% and 14.88%. This selection illustrates the spectral variability across different dairy populations and data providers. Demonstrated through a hypothetical calibration set of 71 samples, the WRSD's utility for algorithm developers becomes apparent. This calibration set covers between 91.42% and 98.50% of the WRSD variability, except for the Irish dataset (3.50%), indicating a need for additional samples to accurately represent Irish variability and minimize spectral extrapolation. This study offers valuable insights into the representativeness of training sets for capturing spectral variability within targeted dairy populations. Although the current WRSD does not fully encompass global milk spectral diversity, its development underscores the importance of gathering more data and standardizing spectral information across spectrometer brands. Ultimately, the WRSD proves crucial not just for trait prediction but also for identifying abnormal milk samples, also marking a significant relevance in dairy science technology. The list of standard abbreviations for JDS is available at adsa.org/jds-abbreviations-24. Nonstandard abbreviations are available in the Notes. [ABSTRACT FROM AUTHOR]

This paper introduces the VDR decomposition of Chebyshev-Vandermonde matrices, where V represents an ordinary Vandermonde matrix, D is diagonal, and R is upper triangular. Our motivation for this work stems from the study by Brubeck et al. [Vandermonde with Arnoldi. SIAM Rev. 2021;63(2):405–415]. We explore the VDR decomposition and combine it with a QR decomposition of V for Chebyshev-Vandermonde matrices. Furthermore, we demonstrate that the upper triangular factor in the QR decomposition can be recursively obtained from the upper Hessenberg matrix in the Arnoldi process. To compare our approach with the results obtained using the default QR code in Matlab, we present experimental results. Finally, we provide an algorithm for solving linear systems with Chebyshev-Vandermonde matrices as coefficient matrices, and experimental results for an upper bound on the relative error by applying an a posteriori error analysis for the computed solutions of the systems. [ABSTRACT FROM AUTHOR]

Catalan–Stieltjes matrices and their corresponding Catalan-like numbers are very important in combinatorics. In this paper, we give a sufficient condition for the Catalan–Stieltjes matrix to be triangular strictly totally positive (Δ STP), that is, all its minors are positive except those which are zero due to the triangular structure. On this basis, we show that it is possible to compute the bidiagonal factorization of a class of Catalan–Stieltjes matrices with high relative accuracy (HRA). We also demonstrate that this class of matrices includes many well-known combinatorial triangles, such as the Catalan triangle of Aigner and the Bell triangle. Moreover, we construct an accurate algorithm to compute the bidiagonal factorization of some Catalan–Stieltjes matrices. The algorithm guarantees HRA for several linear algebraic computations associated with these matrices, like computing their singular values, their inverses, as well as the solutions of some linear systems. We show the accuracy and effectiveness of the proposed algorithm through numerical experiments. [ABSTRACT FROM AUTHOR]

We propose a new method of independent component analysis (ICA) in order to extract appropriate features from high-dimensional data. In general, matrix factorization methods including ICA have a problem regarding the interpretability of extracted features. For the improvement of interpretability, sparse constraint on a factorized matrix is helpful. With this background, we construct a new ICA method with sparsity. In our method, the ℓ 1 -regularization term is added to the cost function of ICA, and minimization of the cost function is performed by a difference of convex functions algorithm. For the validity of our proposed method, we apply it to synthetic data and real functional magnetic resonance imaging data. [ABSTRACT FROM AUTHOR]

Deep nonnegative matrix factorization (deep NMF) has recently emerged as a valuable technique for extracting multiple layers of features across different scales. However, all existing deep NMF models and algorithms have primarily centered their evaluation on the least squares error, which may not be the most appropriate metric for assessing the quality of approximations on diverse data sets. For instance, when dealing with data types such as audio signals and documents, it is widely acknowledged that ß-divergences offer a more suitable alternative. In this article, we develop new models and algorithms for deep NMF using some ß-divergences, with a focus on the Kullback-Leibler divergence. Subsequently, we apply these techniques to the extraction of facial features, the identification of topics within document collections, and the identification of materials within hyperspectral images. [ABSTRACT FROM AUTHOR]

Due to the increasing applications of dual quaternion and their matrices in recent years, as well as the significance of LU decomposition as a matrix decomposition technique, in this paper, we propose dual quaternion Gaussian transformation and obtain dual quaternion LU decomposition by using Gaussian transformation. We also use the total order of dual numbers to obtain the partial pivoting dual quaternion LU decomposition. Based on the real structure-preserving algorithm of quaternion matrix, we propose the real structure-preserving algorithms of LU decomposition and partial pivoting LU decomposition for dual quaternion matrix. Numerical experiments have verified the effectiveness of the new real structure-preserving approaches. [ABSTRACT FROM AUTHOR]

Corrosion behavior of (Cr2/3Ti1/3)3AlC2 and Ti3AlC2 in static oxygen‐saturated liquid lead–bismuth eutectic (LBE) at 550°C and (Cr2/3Ti1/3)3AlC2 in static oxygen‐depleted LBE at 500°C were investigated. In oxygen‐saturated corrosion, the loose and porous corrosion layer consisting of (PbTiO3 + TiO2) was generated on the surface of Ti3AlC2. In contrast, (Cr2/3Ti1/3)3AlC2 formed the protective Cr2O3 layer with better corrosion resistance. Moreover, dissolution corrosion of (Cr2/3Ti1/3)3AlC2 in oxygen‐depleted corrosion was intensified without a protective oxide film. And impurity phase TiC on the surface also caused the decomposition of matrix, thus impairing corrosion resistance. [ABSTRACT FROM AUTHOR]

Metal(loid) pollutants in road dust (road-deposited sediment) show obvious seasonal characteristics under the influence of sources. However, the short-term (days to weeks) variations of metal contamination in road dust in different functional areas with antecedent dry days (ADDs) and their relationships with the source factors were poorly understood, which reduced the comparability of researches and effectiveness of pollution remediation. Herein, temporal characteristics of As, Ba, Ca, Cd, Cr, Cu, Pb and Zn contamination in road dust were studied in two distinct functional areas in Nanjing, i.e. an urban park (Xuanwu, XW) and a stop light-controlled traffic intersection (Baima, BM), following a continuous 23-day sampling between two rainfall events. Mean concentrations of each metal at BM and XW were 1.6–29.5 and 1.3–10.8 times higher than those in the background soil, respectively. The enrichment factor (EF) was used to compensate for grain-size effect. EFs of metals were between 1.3 and 50.3, showing minor to extremely high contamination and different temporal trends. Combined with the EFs and positive matrix factorization (PMF) results, dry atmospheric deposition of industrial and construction sources was the main cause for comparable contamination levels of As, Ca, Cd and Zn between BM and XW and those stable temporal trends. Due to the traffic contribution, contamination levels of Cu, Ba, Pb and Cr at BM were higher (p <.05) than at XW. Different from the stable contamination levels at XW, contamination levels of Cu, Ba, Pb and Cr decreased at BM with the ADDs due to the dilution of traffic particles by less metal enriched atmospheric deposition. These results shed light on the quite different variations of metal contamination in road dust from different functional areas with ADDs under the influence sources, and have instructive significance on road dust sampling, metal contamination remediation and comparison between studies. [ABSTRACT FROM AUTHOR]

Herbicides (HBCs) are extensively used in modern agriculture. However, their potential negative impacts on environmental media have emerged as a significant environmental concern. In this study, we employed positive matrix factorization (PMF) to identify the potential sources of HBCs. Furthermore, we utilized a multi-matrix ecological risk model to assess the risks associated with HBCs in both surface water and groundwater in the black soil region of Northeast China. The findings revealed that the levels of ∑ 15 HBCs in surface water and groundwater ranged from 585.84 to 6466.96 ng/L and 4.80 to 11,774.64 ng/L, respectively. The PMF results indicated that surface runoff and erosion accounted for 50% of the total HBCs in water, serving as the primary sources. All tested HBCs exhibited acute risk values within acceptable levels. The risk index for the ∑ 15 HBCs was categorized as "moderate risk" in 31% of the surface waters and 13% of the groundwaters. However, 4% of the groundwater sampling sites reached the "high risk" level. The chronic risk quotient of ∑ 15 HBCs in surface water and groundwater was 92% and 62% at the "high risk" level, respectively. Interestingly, non-carcinogenic HBCs contributed more significantly to the ecotoxicology of the aquatic system than carcinogenic HBCs. This study provides comprehensive information on the legacy of HBCs in water bodies and emphasizes the potential risks posed by HBCs to aquatic systems. The results obtained from this study could help relevant management authorities in developing and implementing effective regulations to mitigate the ecological and environmental risks associated with HBCs. [Display omitted] [ABSTRACT FROM AUTHOR]

In this paper, we consider the graph regularization Q-weighted nonnegative matrix factorization problem in multi-view clustering. Based on the Q-weighted norm property, this problem is transformed into the minimization problem of the trace function. The necessary condition for the existence of a solution is given. The proximal alternating nonnegative least squares method and its acceleration method are designed to solve it. The convergence theorem is also given. The feasibility and effectiveness of the proposed methods are verified by numerical experiments. [ABSTRACT FROM AUTHOR]

We construct and investigate certain (unbalanced) superalgebra structures on End K (V) , with K a field of characteristic 0 and V a finite dimensional K -vector space (of dimension n ≥ 2). These structures are induced by a choice of non-degenerate symmetric bilinear form B on V and a choice of non-zero base vector w ∈ V. After exploring the construction further, we apply our results to certain questions concerning integer matrix factorization and isometry of integral lattices. [ABSTRACT FROM AUTHOR]

We study the Latent Dirichlet Allocation model, a popular Bayesian algorithm for text analysis. We show that the model's parameters are not identified, which suggests that the choice of prior matters. We characterize the range of values that the posterior mean of a given functional of the model's parameters can attain in response to a change in the prior, and we suggest two algorithms that report this range. Both of our algorithms rely on obtaining multiple Nonnegative Matrix Factorizations of either the posterior draws of the corpus' population term‐document frequency matrix or of its maximum likelihood estimator. The key idea is to maximize/minimize the functional of interest over all these nonnegative matrix factorizations. To illustrate the applicability of our results, we revisit recent work studying the effects of increased transparency on the communication structure of monetary policy discussions in the United States. [ABSTRACT FROM AUTHOR]

The contamination of heavy metal has permeated many parts of China, especially in densely populated and industrialized southern China. This study focused on the degree of pollution in farmland soil heavy metals (HMs), and its spatial distribution characteristics and source apportionment. Meanwhile, we conducted an evaluation of the health risks attributed to soil HMs and analyzed the factors that impact them. We found that the distribution of five heavy metals is mainly concentrated in the east-central and southern parts of the study area. Specifically, Cd and Hg have high levels of pollution and present potential ecological risks. The pollution sources of five HMs were analyzed utilizing positive matrix factorization. The results revealed that the contribution of different sources keeps the following order: natural source (42.42%), agricultural activities (29.93%), industrial pollution source (20.49%), and atmospheric deposition pollution (7.16%). The non-carcinogenic risks to residents were acceptable, whereas the carcinogenic risks were relatively high. Children and the elderly are more vulnerable to the negative effects of Cr, As. Using structural equation modeling, we found soil property is a vital factor affecting soil contamination, with the soil organic matter and cation exchange capacity having a relatively greater impact on heavy metals pollution. Our study provides some data reference and guidance for soil ecological protection and restoration. [ABSTRACT FROM AUTHOR]

Fast algorithms for type-five discrete cosine transform (DCT-V) for sequences of input data of short length in the range of two to eight are elaborated in the paper. A matrix–vector product representation of the DCT-V is the starting point for designing the algorithms. In each specific case, the DCT-V matrices have remarkable structural properties that follow from the localization of identical entries within the matrices. Each matrix of the DCT-V has only a few distinct entries that are repeated at different positions in its structure. Using simple transformations such as permutations of the rows and/or columns of this matrix or its favorable decomposition into two or more matrix components, it is possible to obtain efficient matrix structures that lead to useful factorization schemes. Based on the suitable factorization schemes we obtained, we developed fast algorithms that reduce the number of arithmetic operations when calculating the DCT-V. The correctness of the obtained algorithmic solutions was justified theoretically using a strict mathematical background of each of them. The developed algorithms were then further tested using MATLAB R2023b software to finally confirm their correctness. Finally, an evaluation of the computational complexity for each obtained solution is presented. The evaluation results were compared with the computational complexity of the direct calculation of matrix–vector products. The resulting factorizations of the matrices of the DCT-V reduce the average number of multiplications by 57% but increase the number of additions by 29%. [ABSTRACT FROM AUTHOR]

Telemedicine systems, while overcoming physical space constraints, often lack personalized interactions. By incorporating a push service and leveraging prediction-oriented algorithms, these systems can offer an improved user experience. Such enhancements enable timely treatment options and reduce unnecessary resource usage in on-site outpatient clinics. This research work starts by creating a robust algorithm using data mining techniques. Next, it establishes the foundation for a telemedicine push service. The service includes essential modules for disease differentiation, doctor recommendations, and diagnosis predictions. To optimize these modules, a merged algorithm combining k-nearest neighbor classification, nearest neighbor recommendation, and FP-growth is needed. This work aims to enhance treatment options for patients and streamline resource usage in on-site outpatient clinics. Moreover, this work has carried out empirical research for identification of algorithm by using available data at a public Chinese telemedicine system. The results of data analysis show the follows: 1. For disease diagnosis, the KNN model (k=1) is more accurate but less efficient, SVM and LibSVM are more efficient but less accurate than the KNN model; 2. In terms of doctor recommendation, nearest neighbor recommendation performs better but is not as efficient as matrix factorization; 3. in diagnostic prediction, the combination of introducing association mining and data segmentation can play a better role. The developed algorithm and its conclusions from this study could make easier and more efficient to provide treatment options for undecided-condition patients. [ABSTRACT FROM AUTHOR]

This paper investigates the stabilization problem for cyber‐physical systems subject to positive constraint and denial‐of‐service (DoS) attacks using the event‐triggered scheme. Unlike traditional modeling of cyber physical systems with DoS attack, this paper establishes a positive switched system model composed of two positive subsystems that according to the DoS attack is active or inactive. Considering the effect of positive constraint and DoS attack, a 1‐norm‐based event‐triggered mechanism is proposed in the sleep interval of DoS attacks. By incorporating switching theory with event‐triggered mechanism, the sufficient condition of positive property and exponential stabilization for the considered system is established. Thereafter, the gain matrix of the controller is calculated by matrix decomposition technique. Furthermore, the Zeno behavior is eliminated with a lower bound of the event‐triggered interval. Finally, an example is carried out to verify the validity of the theoretical results. [ABSTRACT FROM AUTHOR]

Network clustering is a critical task in data analysis, aimed at uncovering the underlying structure and patterns within complex networks. Traditional clustering methods often struggle with large-scale and noisy data, leading to suboptimal results. Also, the efficiency of positive samples in network clustering depends on the carefully constructed data augmentation, and the pre-training process of the model deals with large-scale data. To address these issues, in this paper, we introduce an efficient network clustering approach that leverages Graph-Boosting and Nonnegative Matrix Factorization to enhance clustering performance (GBNMF). Our algorithm addresses the limitations of traditional clustering techniques by incorporating the strengths of graph-boosting, which iteratively improves the quality of clusters, and Nonnegative Matrix Factorization (NMF), which effectively captures latent structures within the data. We validate our algorithm through extensive experiments on various benchmark network datasets, demonstrating significant improvements in clustering accuracy and robustness. The proposed algorithm not only achieves superior clustering results but also exhibits remarkable computational efficiency, making it a valuable tool for large-scale network analysis applications. [ABSTRACT FROM AUTHOR]

Understanding the health risks of polycyclic aromatic hydrocarbons (PAHs) in dust from city parks and prioritizing sources for control are essential for public health and pollution management. The combination of Source-specific and Monte Carlo not only reduces management costs, but also improves the accuracy of assessments. To evaluate the sources of PAHs in urban park dust and the possible health risks caused by different sources, dust samples from 13 popular parks in Kaifeng City were analyzed for PAHs using gas chromatograph-mass spectrometer (GC-MS). The results showed that the surface dust PAH content in the study area ranged from 332.34 µg·kg−1 to 7823.03 µg·kg−1, with a mean value of 1756.59 µg·kg−1. Nemerow Composite Pollution Index in the study area ranged from 0.32 to 14.41, with a mean of 2.24, indicating that the overall pollution warrants attention. Four pollution sources were identified using the positive matrix factorization (PMF) model: transportation source, transportation-coal and biomass combustion source, coke oven emission source, and petroleum source, with contributions of 33.74%, 25.59%, 22.14%, and 18.54%, respectively. The Monte Carlo cancer risk simulation results indicated that park dust PAHs pose a potential cancer risk to all three populations (children, adult male and adult female). Additionally, the cancer risk for children was generally higher than that for adult males and females, with transportation sources being the main contributor to the carcinogenic risk. Lastly, sensitivity analyses results showed that the toxic equivalent concentration (CS) is the parameter contributing the most to carcinogenic risk, followed by Exposure duration (ED). [ABSTRACT FROM AUTHOR]

Timely and accurate monitoring of typical coastal targets using remote sensing technology is crucial for maintainingmarine ecological stability. Hyperspectral target detection technology proves to be an effective tool in extracting various typical materials along the coastline. Traditional target detection methods using spectral domain information can effectively retain the intrinsic properties of the material. However, it is difficult to effectively recognize targets in homogeneous regions by using only spectral domain information, which may lead to insufficient utilization of spatial information. In this study, a detector based on signal-to-noise ratio fusion constrained energy minimization with low-rank sparse decomposition (SFLRSD) is proposed. This detector improves the separability of background and target by obtaining spatial domain information from hyperspectral images and fusing spectral domain information. First, total variation regularization and fractional Fourier transform are applied to process spatial and spectral domain information, respectively. The constrained energy minimization (CEM) detector is used to improve the separability between the target and background of the processed data. Then, the background and anomalies are represented as low-rank and sparse components, respectively, using low-rank sparse matrix factorization. This transforms the model solution into a covariance matrix problem, which is then solved using marginal distance difference (MDD) to isolate anomalous parts. Subsequently, the anomaly parts are fused with CEM detector results, weighted by their respective signal-to-noise ratios. This detection model leverages unified hyperspectral image features, enhancing spectral discreteness of anomalous targets and backgrounds. Finally, experiments on custom created hyperspectral dataset show that the proposed method outperforms other baseline methods in terms of visualization and quantitative performance. In this paper, we not only propose a new hyperspectral target detection method, but we also collect three typicalmarine litter of different materials bymeans of airborne hyperspectral remote sensing and construct four hyperspectral datasets in a real environment. All the simulation experiments in this paper are conducted in these four datasets. [ABSTRACT FROM AUTHOR]