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In this paper, we introduce the concept of S‐J‐ideals in both commutative and noncommutative rings. For a commutative ring R and a multiplicatively closed subset S, we show that many properties of J‐ideals apply to S‐J‐ideals and examine their characteristics in various ring constructions, such as homomorphic image rings, quotient rings, cartesian product rings, polynomial rings, power series rings, idealization rings, and amalgamation rings. In noncommutative rings, where S is an m‐system, we define right S‐J‐ideals. We demonstrate the equivalence of S‐J‐ideals and right S‐J‐ideals in commutative rings with identity and provide examples to illustrate the connections between right S‐prime ideals and J‐ideals. [ABSTRACT FROM AUTHOR]

Chimeric antigen receptor (CAR)-T cell therapy demonstrates substantial efficacy in various hematological malignancies. However, its application in solid tumors is still limited. Clinical studies report suboptimal outcomes such as reduced cytotoxicity of CAR-T cells and tumor evasion, underscoring the need to address the challenges of sliding cytotoxicity in CAR-T cells. Despite improvements from fourth and next-generation CAR-T cells, new challenges include systemic toxicity from continuously secreted proteins, low productivity, and elevated costs. Recent research targets genetic modifications to boost killing potential, metabolic interventions to hinder tumor progression, and diverse combination strategies to enhance CAR-T cell therapy. Efforts to reduce the duration and cost of CAR-T cell therapy include developing allogenic and in-vivo approaches, promising significant future advancements. Concurrently, innovative technologies and platforms enhance the potential of CAR-T cell therapy to overcome limitations in treating solid tumors. This review explores strategies to optimize CAR-T cell therapies for solid tumors, focusing on enhancing cytotoxicity and overcoming application restrictions. We summarize recent advances in T cell subset selection, CAR-T structural modifications, infiltration enhancement, genetic and metabolic interventions, production optimization, and the integration of novel technologies, presenting therapeutic approaches that could improve CAR-T cell therapy's efficacy and applicability in solid tumors. [ABSTRACT FROM AUTHOR]

Trilinearity is a property of some chemical data that leads to unique decompositions when curve resolution or multiway decomposition methods are used. Curve resolution algorithms, such as Multivariate Curve Resolution–Alternating Least Squares (MCR‐ALS), can provide trilinear models by implementing the trilinearity condition as a constraint. However, some trilinear analytical measurements, such as excitation–emission matrix (EEM) measurements, usually exhibit systematic patterns of missing data due to the nature of the technique, which imply a challenge to the classical implementation of the trilinearity constraint. In this instance, extrapolation or imputation methodologies may not provide optimal results. Recently, a novel algorithmic strategy to constrain trilinearity in MCR‐ALS in the presence of missing data was developed. This strategy relies on the sequential imposition of a classical trilinearity restriction on different submatrices of the original investigated dataset, but, although effective, was found to be particularly slow and requires a proper submatrix selection criterion. In this paper, a much simpler implementation of the trilinearity constraint in MCR‐ALS capable of handling systematic patterns of missing data and based on the principles of the Nonlinear Iterative Partial Least Squares (NIPALS) algorithm is proposed. This novel approach preserves the trilinearity of the retrieved component profiles without requiring data imputation or subset selection steps and, as with all other constraints designed for MCR‐ALS, offers the flexibility to be applied component‐wise or data block‐wise, providing hybrid bilinear/trilinear models. Furthermore, it can be easily extended to cope with any trilinear or higher‐order dataset with whatever pattern of missing values. [ABSTRACT FROM AUTHOR]

Background: Adaptive radiotherapy (ART) workflows have been increasingly adopted to achieve dose escalation and tissue sparing under shifting anatomic conditions, but the necessity of recontouring and the associated time burden hinders a real‐time or online ART workflow. In response to this challenge, approaches to auto‐segmentation involving deformable image registration, atlas‐based segmentation, and deep learning‐based segmentation (DLS) have been developed. Despite the particular promise shown by DLS methods, implementing these approaches in a clinical setting remains a challenge, namely due to the difficulty of curating a data set of sufficient size and quality so as to achieve generalizability in a trained model. Purpose: To address this challenge, we have developed an intentional deep overfit learning (IDOL) framework tailored to the auto‐segmentation task. However, certain limitations were identified, particularly the insufficiency of the personalized dataset to effectively overfit the model. In this study, we introduce a personalized hyperspace learning (PHL)‐IDOL segmentation framework capable of generating datasets that induce the model to overfit specific patient characteristics for medical image segmentation. Methods: The PHL‐IDOL model is trained in two stages. In the first, a conventional, general model is trained with a diverse set of patient data (n = 100 patients) consisting of CT images and clinical contours. Following this, the general model is tuned with a data set consisting of two components: (a) selection of a subset of the patient data (m < n) using the similarity metrics (mean square error (MSE), peak signal‐to‐noise ratio (PSNR), structural similarity index (SSIM), and the universal quality image index (UQI) values); (b) adjust the CT and the clinical contours using a deformed vector generated from the reference patient and the selected patients using (a). After training, the general model, the continual model, the conventional IDOL model, and the proposed PHL‐IDOL model were evaluated using the volumetric dice similarity coefficient (VDSC) and the Hausdorff distance 95% (HD95%) computed for 18 structures in 20 test patients. Results: Implementing the PHL‐IDOL framework resulted in improved segmentation performance for each patient. The Dice scores increased from 0.81±$ \pm $0.05 with the general model, 0.83±0.04$ \pm 0.04$ for the continual model, 0.83±0.04$ \pm 0.04$ for the conventional IDOL model to an average of 0.87±0.03$ \pm 0.03$ with the PHL‐IDOL model. Similarly, the Hausdorff distance decreased from 3.06±0.99$ \pm 0.99$ with the general model, 2.84±0.69$ \pm 0.69$ for the continual model, 2.79±0.79$ \pm 0.79$ for the conventional IDOL model and 2.36±0.52$ \pm 0.52$ for the PHL‐IDOL model. All the standard deviations were decreased by nearly half of the values comparing the general model and the PHL‐IDOL model. Conclusion: The PHL‐IDOL framework applied to the auto‐segmentation task achieves improved performance compared to the general DLS approach, demonstrating the promise of leveraging patient‐specific prior information in a task central to online ART workflows. [ABSTRACT FROM AUTHOR]

While there exists a rich array of matrix column subset selection problem (CSSP) algorithms for use with interpolative and CUR‐type decompositions, their use can often become prohibitive as the size of the input matrix increases. In an effort to address these issues, in earlier work we developed a general framework that pairs a column‐partitioning routine with a column‐selection algorithm. Two of the four algorithms presented in that work paired the Centroidal Voronoi Orthogonal Decomposition (CVOD; Centroidal Voronoi Tessellation Based Proper Orthogonal Decomposition Analysis, 2003, 137–150) and an adaptive variant (adaptCVOD) with the Discrete Empirical Interpolation Method (DEIM; SIAM J. Sci. Computer. 38 (2016), no. 3, A1454–A1482). In this work, we extend this framework and pair the CVOD‐type algorithms with any CSSP algorithm that returns linearly independent columns. Our results include detailed error bounds for the solutions provided by these paired algorithms, as well as expressions that explicitly characterize how the quality of the selected column partition affects the resulting CSSP solution. In addition to examples involving matrix approximation, we test several of our partition‐based constructions on tasks commonly encountered in model order reduction (MOR). [ABSTRACT FROM AUTHOR]

The high-dimensional nature of medical data frequently results in suboptimal performance of machine learning models. Applying feature selection before classification is necessary to improve the performance of classifiers. Although evolutionary-based wrapper feature selection methods are acknowledged for their superior performance in exploring optimal feature subsets, they have been demonstrated to carry the risk of overfitting and a potential loss of efficient search capability in the later stages of evolution. To address these issues, we propose a generalized wrapper feature selection method called Two Populations Based Feature Selection (TPBFS), which incorporates dual populations evolving in reverse directions to improve convergence speed. It introduces a probability-based crossover operation to mitigate overfitting and a record list to systematically track and replace optimal individuals, which helps to avoid getting stuck in local optima during later stages of evaluation. The experimental results demonstrate that TPBFS is effective in reducing the dimensionality of various medical datasets while guaranteeing the performance of classifiers. [ABSTRACT FROM AUTHOR]

The accuracy of Interferometric Synthetic Aperture Radar surface deformation solutions depends on the quality of the chosen interferogram subset. We present a method to select interferogram subsets based on unwrapping errors rather than temporal baseline thresholds. Using Sentinel‐1 interferograms over the Tulare Basin, California, we show that subtle phase noise can lead to up to 31.5 mm/yr line‐of‐sight (LOS) errors in short temporal baseline subset solutions, while decorrelation leads to a systematic underestimation of LOS rates (up to 92.3 mm/yr) in long temporal baseline subset solutions. Our new workflow better mitigates these noise sources at the same time. In the Eagle Ford region, Texas, our strategy better reconstructed up to 11.9 cm of cumulative LOS deformation between 2017 and 2021 over a ∼ ${\sim} $900 km2 $\mathrm{k}{\mathrm{m}}^{\mathrm{2}}$ region. This deformation feature can be linked to the total volume of produced oil and water through a linear relationship. Plain Language Summary: Surface deformation estimates derived from spaceborne imaging radar data are often impacted by weather conditions and surface vegetation changes. While it is common to select high‐quality data based on the time separation between two image passes, we designed a new data selection strategy based on measurable phase artifacts. We applied the new method to two vegetated regions that experienced land subsidence due to agricultural groundwater pumping (Tulare Basin, California) or oil and gas production (Eagle Ford region, Texas). In both cases, we show that surface deformation estimates can vary substantially depending on the choice of data subsets. Our strategy better mitigates different InSAR measurement noise terms at the same time. We showed that the observed subsidence in the Eagle Ford region is linearly proportional to the volume of produced oil and water from unconventional oil and gas resources exploitation. Furthermore, ongoing production activities have led to an increase in human‐induced earthquakes. Based on these findings, accurate surface deformation derived from InSAR data is now achievable in densely vegetated regions and can play an important role in future induced seismicity studies. Key Points: InSAR phase coherence does not always decrease with temporal baselinesChoosing interferograms based on phase unwrapping errors rather than temporal baselines better mitigates InSAR measurement noiseThe improved InSAR analysis strategy reveals up to 11.9 cm of LOS deformation signals related to oil and gas operations over the Eagle Ford [ABSTRACT FROM AUTHOR]

In this paper, for the sake of enhancing the security of wireless transmission, we proposed a novel system based on spatial and direction modulation (SDM) combined with generalized spatial modulation (GSM) which is aided by covert information mapping (CIM), termed as the CIM-GSDM system. In such a system, the legitimated user is equipped with distributed receivers so as to demodulate the conveyed signal by exploiting its indices while disturbing eavesdroppers for information security. More specifically, part of the information is modulated into the indices of the legitimated distributed receiver subsets with the aid of the mapped covert information and the interference matrix, while another part of the message is arranged by conventional amplitude-phase modulation. The proposed system can reap the benefits from both GSM and CIM to make eavesdropper suffer great mixture. Furthermore, the detection scheme and theoretical analysis of error performance are discussed as well. The simulation results exhibit that the bit error rate (BER) performance of legitimate user is much better than that of the eavesdropper while the proposed scheme improves the security compared to the original CIM-SDM system at the same spectral efficiency. [ABSTRACT FROM AUTHOR]

Depression is a severe neurological disorder characterized by a loss of interest and may lead to suicide. Electroencephalography (EEG) measurement is a non-invasive tool for neural electrical activities measurement which can be further used for different neurological disorder detection such as depression. The number of EEG electrodes used for measurement directly affects the instrumentation and measurement complexity of the experiment. This paper proposes a fisher score–based method for electrode ranking. This paper selects only those electrodes whose fisher score is greater than the mean of fisher scores of all electrodes. It results in a reduced set of electrodes. A deep learning–based model has been proposed which uses the reduced set of electrodes for depression detection. The performance of the proposed model is evaluated on two benchmark data sets having varying numbers of electrodes. The proposed model significantly reduces the number of electrodes to 68.42% and 60.93% for data sets 1 and 2, respectively. The accuracy of 98.73%, precision of 98.50%, recall of 98.75%, F1 score of 98.62% and AUC of 99.91% are obtained for data set 1 and accuracy of 95.48%, precision of 91.93%, recall of 96.11%, F1 score of 93.97% and AUC of 99.49% are obtained for data set 2. [ABSTRACT FROM AUTHOR]

Let G = (V, E) be a connected graph. A subset S of V is called a γ-free set if there exists a γ-set D of G such that S ∩ D = ∅. If further the induced subgraph H = G[V − S] is connected, then S is called a cc-γ-free set of G. We use this concept to identify connected induced subgraphs H of a given graph G such that γ(H) ≤ γ(G). We also introduce the concept of γ-totally-free and γ-fixed sets and present several basic results on the corresponding parameters. [ABSTRACT FROM AUTHOR]

Recently, human beings have been affected mainly by dreadful cancer diseases. Predicting cancer risk levels is a major challenge in biomedical research for feature selection and classification at the margins. To resolve this problem, we propose a Subset Clustering-Based Feature Selection using a Multi-Layer Perception Neural Network (SCFS-MLPNN). Initially, pre-processing is carried out with Intensive Mutual Disease Influence Rate (IMDIR) to identify the relational features. In addition, the Successive Disease Pattern Stimulus Rate (SDPSR) is carried out to create relative feature patterns. Based on the patterns, the features are selected and grouped into clustering. Inter-Class Sub-Space Clustering (ICSSC) is applied to split the features by class labels depending on the marginal rate. From the class labels, marginal features are obtained using spectral subset feature selection (SSFS). The selected features are then trained in a Multi-Layer Perception Neural Network (MLPNN) classifier to classify the patient features by risk. Its contribution is to exploit subset features to improve classification accuracy by clustering relational features. The proposed classifier yields higher classification accuracy than previous methods and observes cancer detection for early detection. Therefore, the proposed method achieved a risk analysis accuracy of 91.8% and an F-measure of 91.3% for early detection, which is recommended for early diagnosis. [ABSTRACT FROM AUTHOR]

Multi-objective optimization algorithms are crucial for addressing real-world problems, particularly with regard to optimizing well control parameters, which are often computationally expensive due to their reliance on numerical simulations. Surrogate-assisted models help to reduce this computational burden, but their effectiveness depends on the quality of the surrogates, which can be affected by candidate dimension and noise. This study proposes a novel surrogate-assisted multi-objective optimization framework (MOO-SESA) that combines selective ensemble support-vector regression with NSGA-II. The framework's uniqueness lies in its adaptive selection of a diverse subset of surrogates, established prior to iteration, to enhance accuracy, robustness, and computational efficiency. To our knowledge, this is the first instance in which selective ensemble techniques with multi-objective optimization have been applied to reservoir well control problems. Through employing an ensemble strategy for improving the quality of the surrogate model, MOO-SESA demonstrated superior well control scenarios and faster convergence compared to traditional surrogate-assisted models when applied to the SPE10 and Egg reservoir models. [ABSTRACT FROM AUTHOR]

AbstractWith the rapid development of quantum computers, researchers have shown quantum advantages in physics-oriented problems. Quantum algorithms tackling computational biology problems are still lacking. In this article, we demonstrate the quantum advantage in analyzing CITE-seq data. CITE-seq, a single-cell technology, enables researchers to simultaneously measure expressions of RNA and surface protein detected by antibody-derived tags (ADTs) in the same cells. CITE-seq data hold tremendous potential for identifying ADTs associated with targeted genes and identifying cell types effectively. However, both tasks are challenging since the best subset of ADTs needs to be identified from enormous candidate subsets. To surmount the challenge, we develop a quantum algorithm named bisection Grover’s search (BGS) for the best subset selection of ADT markers in CITE-seq data. BGS takes advantage of quantum parallelism by integrating binary search and Grover’s algorithm to enable fast computation. Theoretical results are provided to show the privilege of BGS in the estimation error and computational complexity. The empirical performance of the BGS algorithm is demonstrated on both the IBM quantum computer and simulator. Supplementary materials for this article are available online, including a standardized description of the materials available for reproducing the work. [ABSTRACT FROM AUTHOR]

One of the primary issues that arises in statistical modeling pertains to the assessment of the relative importance of each variable in the model. A variety of techniques have been proposed to quantify variable importance for regression models. However, in the context of best subset selection, fewer satisfactory methods are available. With this motivation, we here develop a variable importance measure expressly for this setting. We investigate and illustrate the properties of this measure, introduce algorithms for the efficient computation of its values, and propose a procedure for calculating p-values based on its sampling distributions. We present multiple simulation studies to examine the properties of the proposed methods, along with an application to demonstrate their practical utility. [ABSTRACT FROM AUTHOR]

Android malware has become an increasingly important threat to individuals, organizations, and society, posing significant risks to data security, privacy, and infrastructure. As malware evolves in sophistication and complexity, the detection and mitigation of these malicious software instances have become more challenging and time consuming since the required number of features to identify potential malware can be very high. To address this issue, we have developed an effective feature selection methodology for malware detection in Android. The critical concern in the field of malware detection is the complexity of algorithms and the use of features that are used to detect malware. The present paper delivers a methodology for pre-processing datasets to select the most optimal features that will allow detecting malware, while maintaining very high accuracy. The proposed methodology has been tested on two real world datasets and the results indicate that the number of features is significantly reduced from 489 to between 19 and 28 for the first dataset and from 9503 to between 9 and 27 for the second dataset, whilst the accuracy is maintained as if all features were used. [ABSTRACT FROM AUTHOR]

Urban plantation dynamics in different topographical and climatic conditions in Odisha were evaluated using linear model selection and regularisation techniques. The main objective was to evaluate how and to what extent the urban plantations respond to various climatic and edaphic conditions. The relationship between vegetation growth and climatic and soil parameters was studied using four statistical learning tools, subset selection, ridge regression, lasso, and partial least squares regression, and their performance was compared to a multiple regression model. The test MSE for the subset selection, ridge regression, lasso, and partial least squares regression models was evaluated to be 16,261.54, 12245.11, 16263.79 and 14,317.21, respectively. Results proved that statistical learning methods, namely subset selection, lasso, ridge regressions and partial least squares regression, were more accurate than multiple linear regression. From the results, it can be safely concluded that temperature shows greater correlation with the growth parameters. Precipitation also plays a vital role in vegetation dynamics. Soil parameters indicate a positive correlation with that of the growth. [ABSTRACT FROM AUTHOR]

Credit scoring models are crucial tools for lenders to assess credit risks. Researchers from academia and the financial industry have shown intense interest in these models. However, real credit datasets often have high dimensionality and class imbalance, making it challenging to develop accurate and effective credit scoring models. To address these challenges, a new approach called the Multiple-Optimized Ensemble Learning (MOEL) method has been proposed. In MOEL, a technique called Multiple Diverse Optimized Subsets (MDOS) generates multiple diverse optimized subsets from various weighted random forests. From each subset, more effective and relevant features are selected. Then, a new evaluation measure is applied to each subset to determine the more optimized subsets. These subsets are applied to a novel Mahalanobis-based oversampling (MOS) technique to provide balanced subsets for the base classifier, which lessens the detrimental effects of imbalanced datasets. Finally, a stacking-based ensemble method is applied to the balanced subsets for integration of the base models. The proposed model was evaluated against six high-dimensional imbalanced credit scoring datasets, and it outperformed state-of-the-art methods, exhibiting a mean rank of 1.5 and 1.333 in terms of F1_score and G-mean, respectively. [ABSTRACT FROM AUTHOR]

Feature subset selection problem is an NP hard problem and there is a need for computationally efficient algorithms that find near optimal feature subsets which improve the performance of a classifier. Two major challenges for feature subset selection are high-dimensional data, that is, data with a large number of features and large datasets. Scalability of the feature selection algorithms in terms of accuracy for high dimensional data and the time taken for large datasets are important issues. We propose a consensus clustering based approach to feature selection that addresses these issues. There exist many greedy feature ranking algorithms in the literature that are computationally efficient. Each algorithm assigns a different ranking order to the features. A consensus among these rankings may provide a feature ranking that performs well with respect to time as well as accuracy. The goal of this work is to propose efficient algorithms that work on small as well as large datasets. The contributions of this work include: i. A fast and scalable approach for feature selection Feature ranking based on consensus clustering(FRCC), has been designed using the available feature ranking algorithms from the literature. ii. A parallelizable version of FRCC, namely, Hybrid Feature Selection(HFS), is proposed to address the feature reduction in large datasets. The implementation results show that FRCC clearly outperforms many recent algorithms in the literature on small as well as large dimensional data sets. HFS has been implemented on datasets with lakhs of instances and dimensionality in hundreds and thousands. HFS proves to be very effective in terms of feature reduction and accuracy in comparison to the results obtained by recent algorithms in the literature. [ABSTRACT FROM AUTHOR]

Intrusion Detection System (IDS) is designed being help and safeguard IoT networks from potential threats. Distributed Denial of Service (DDoS) assaults are a pernicious kind of cyber-attacks causing server disruptions in modern cyber-security world. Detecting unauthorized and suspicious activities by observing data traffic flows is crucial for enhanced network security. So, this research paper proposes an innovative solution to IoT environment by designing effective intrusion detection module. It benefits from the working principle of different modules that are operated for data dimensionality reduction, feature optimization and deep classification to maximize network security by identifying normal and malicious traffic flows. The detection process commences with data pre-processing steps such as null set removal and redundant feature elimination that provide a clear and concise representation of the data. Next, we employ the Random Subset Feature Selection (RSFS) technique to minimize dimension of preprocessed information by eliminating duplicate or redundant features. The selected feature subsets are then used as the initial search space for the Mutation Boosted Golden Jackal Optimization (MBGJO) algorithm. It helps to predict optimal attributes that contribute most effectively to detection of different attack classes. Finally, the Light Gradient Boosting Machine (LGBM) algorithm is used to train the ideal feature set and detect various attack classes in the CIC-DDoS2019 dataset. By employing this algorithm, we ensure that our detection system remains scalable and capable of handling diverse attack scenarios. Experimental findings demonstrate that our IDS attains an impressive accuracy of 99.7%. Moreover, it surpasses other state-of-the-art mechanisms with regard to scalability and security. Our intrusion detection system thus provides an effective solution for safeguarding IoT networks against potential threats. [ABSTRACT FROM AUTHOR]

Ongoing modern computational advancements continue to make it easier to collect increasingly large and complex datasets, which can often only be realistically analyzed using models defined by intractable likelihood functions. This Stats invited feature article introduces and provides an extensive simulation study of a new approximate Bayesian computation (ABC) framework for estimating the posterior distribution and the maximum likelihood estimate (MLE) of the parameters of models defined by intractable likelihoods, that unifies and extends previous ABC methods proposed separately. This framework, copulaABCdrf, aims to accurately estimate and describe the possibly skewed and high-dimensional posterior distribution by a novel multivariate copula-based meta-t distribution based on univariate marginal posterior distributions that can be accurately estimated by distribution random forests (drf), while performing automatic summary statistics (covariates) selection, based on robustly estimated copula dependence parameters. The copulaABCdrf framework also provides a novel multivariate mode estimator to perform MLE and posterior mode estimation and an optional step to perform model selection from a given set of models using posterior probabilities estimated by drf. The posterior distribution estimation accuracy of the ABC framework is illustrated and compared with previous standard ABC methods through several simulation studies involving low- and high-dimensional models with computable posterior distributions, which are either unimodal, skewed, or multimodal; and exponential random graph and mechanistic network models, each defined by an intractable likelihood from which it is costly to simulate large network datasets. This paper also proposes and studies a new solution to the simulation cost problem in ABC involving the posterior estimation of parameters from datasets simulated from the given model that are smaller compared to the potentially large size of the dataset being analyzed. This proposal is motivated by the fact that, for many models defined by intractable likelihoods, such as the network models when they are applied to analyze massive networks, the repeated simulation of large datasets (networks) for posterior-based parameter estimation can be too computationally costly and vastly slow down or prohibit the use of standard ABC methods. The copulaABCdrf framework and standard ABC methods are further illustrated through analyses of large real-life networks of sizes ranging between 28,000 and 65.6 million nodes (between 3 million and 1.8 billion edges), including a large multilayer network with weighted directed edges. The results of the simulation studies show that, in settings where the true posterior distribution is not highly multimodal, copulaABCdrf usually produced similar point estimates from the posterior distribution for low-dimensional parametric models as previous ABC methods, but the copula-based method can produce more accurate estimates from the posterior distribution for high-dimensional models, and, in both dimensionality cases, usually produced more accurate estimates of univariate marginal posterior distributions of parameters. Also, posterior estimation accuracy was usually improved when pre-selecting the important summary statistics using drf compared to ABC employing no pre-selection of the subset of important summaries. For all ABC methods studied, accurate estimation of a highly multimodal posterior distribution was challenging. In light of the results of all the simulation studies, this article concludes by discussing how the copulaABCdrf framework can be improved for future research. [ABSTRACT FROM AUTHOR]

This study evaluated an optimized multi-model ensemble (MME) approach coupled with various objective functions to project the extreme precipitation. This study considered 60 stations in South Korea and used coupled model intercomparison project (CMIP) 6 GCMs under shared socioeconomic pathway (SSP) scenarios. The extreme precipitation characteristics were described by ten precipitation indices from expert team on climate change detection and indices (ETCCDI). The Generalized Reduced Gradient (GRG) algorithm was utilized to optimize the weights of all considered GCMs using four objective functions, namely Nash-Sutcliffe efficiency (NNSE), root mean square error (RMSE), Kling-Gupta efficiency (KGE), and percent bias (Pbias). The performance of optimized MME in extreme precipitation projection was compared to the equal-weighted MME. As a result, the optimized MME in this study demonstrated higher performances than the MME that simply averaged GCMs' output in most regions, regardless of the objective functions. These results indicated that the optimized MME with KGE generally performed better than MMEs using other objective functions. This study emphasized the importance of efficient GCMs subset selection, unequal weights, and the use of appropriate evaluation metrics for the MME approach in extreme precipitation projection. [ABSTRACT FROM AUTHOR]

The coresets approach, also called subsampling or subset selection, aims to select a subsample as a surrogate for the observed sample and has found extensive application in large-scale data analysis. Existing coresets methods construct the subsample using a subset of rows from the predictor matrix. Such methods can be significantly inefficient when the predictor matrix is sparse or numerically sparse. To overcome this limitation, we develop a novel element-wise subset selection approach, called core-elements, for large-scale least squares estimation. We provide a deterministic algorithm to construct the core-elements estimator, only requiring an O (nnz (X) + r p 2) computational cost, where X is an n × p predictor matrix, r is the number of elements selected from each column of X, and nnz (·) denotes the number of non-zero elements. Theoretically, we show that the proposed estimator is unbiased and approximately minimizes an upper bound of the estimation variance. We also provide an approximation guarantee by deriving a coresets-like finite sample bound for the proposed estimator. To handle potential outliers in the data, we further combine core-elements with the median-of-means procedure, resulting in an efficient and robust estimator with theoretical consistency guarantees. Numerical studies on various synthetic and real-world datasets demonstrate the proposed method’s superior performance compared to mainstream competitors. [ABSTRACT FROM AUTHOR]

BACKGROUND: Veterinary antibiotics are chemical compounds used to kill or inhibit the growth of pathogenic bacteria associated with animal diseases. These molecules can be defined by their retention times (tR) in liquid chromatography–mass spectrometry (LC–MS). One strategy to predict the tR of new veterinary antibiotics is the development of predictive quantitative structure–property relationships (QSPRs), which were used in this study. RESULTS: A database of 122 antibiotics was selected in which the tR was measured using a Hypersil GOLD column. An optimal three‐feature model was developed by integrating the unsupervised variable reduction, replacement method variable subset selection, and multiple linear regression. The negligible differences among the coefficient of determination and the root‐mean‐square error for the training set (R2 = 0.902 and RMSEC = 0.871) and test set (Q2 = 0.854 and RMSEP = 1.064) indicate a stable and predictive model. In a further step, a more in‐depth explanation of the mechanism of action of each descriptor in predicting the tR is provided, with the construction of the theoretical chemical space for accurate predictions of new antibiotics. CONCLUSION: The in silico model developed in this work identified three molecular descriptors associated with aqueous solubility, octanol–water partition coefficient, and the presence of negative and lipophilic atom pairs. The QSPR developed here could be implemented by agricultural and food chemists to identify and monitor existing and new antibiotics within the framework of LC–MS. The computational model was developed in accordance with five principles outlined by the Organization for Economic Co‐operation and Development. © 2024 Society of Chemical Industry. [ABSTRACT FROM AUTHOR]

Background: In recent years, significant morbidity and mortality in patients with severe inflammatory bowel disease (IBD) and cytomegalovirus (CMV) have drawn considerable attention to the status of CMV infection in the intestinal mucosa of IBD patients and its role in disease progression. However, there is currently no high-throughput sequencing data for ulcerative colitis patients with CMV infection (CMV + UC), and the immune microenvironment in CMV + UC patients have yet to be explored. Method: The xCell algorithm was used for evaluate the immune microenvironment of CMV + UC patients. Then, WGCNA analysis was explored to obtain the co-expression modules between abnormal immune cells and gene level or protein level. Next, three machine learning approach include Random Forest, SVM-rfe, and Lasso were used to filter candidate biomarkers. Finally, Best Subset Selection algorithms was performed to construct the diagnostic model. Results: In this study, we performed transcriptomic and proteomic sequencing on CMV + UC patients to establish a comprehensive immune microenvironment profile and found 11 specific abnormal immune cells in CMV + UC group. After using multi-omics integration algorithms, we identified seven co-expression gene modules and five co-expression protein modules. Subsequently, we utilized various machine learning algorithms to identify key biomarkers with diagnostic efficacy and constructed an early diagnostic model. We identified a total of eight biomarkers (PPP1R12B, CIRBP, CSNK2A2, DNAJB11, PIK3R4, RRBP1, STX5, TMEM214) that play crucial roles in the immune microenvironment of CMV + UC and exhibit superior diagnostic performance for CMV + UC. Conclusion: This 8 biomarkers model offers a new paradigm for the diagnosis and treatment of IBD patients post-CMV infection. Further research into this model will be significant for understanding the changes in the host immune microenvironment following CMV infection. [ABSTRACT FROM AUTHOR]

Background: Total neoadjuvant therapy (TNT) has been recommended by the National Comprehensive Cancer Network for treating locally advanced rectal cancer (LARC), but extremely rare studies have focused on establishing nomograms to predict the prognosis in these patients after TNT. We aimed to develop a nomogram to predict overall survival (OS) in rectal cancer patients who underwent TNT. Methods: In retrospective cohort study, we extract the data of the rectal cancer patients from the SEER database between 2010 and 2015, including demographic information and tumor characteristics. The cohort was divided into training set and validation set based on a ratio of 7:3. Univariate logistic regression analysis was utilized for the comparison of variables in training set. Candidate variables with P < 0.1 in training set was entered into the best subset selection, LASSO regression and Boruta feature selection. Finally, the selected variables significantly associated with the 3-year, 5-year, and 8-year OS were used to build a nomogram, followed by validation using receiver operating characteristic (ROC) curve, area under the curve (AUC), and calibration curve. Results: A total of 3265 rectal cancer patients (training set: 2285; test set: 980) were included in the present study. A nomogram was developed to predict the 3-year, 5-year, and 8-year OS based on age, household income, total number of in situ/malignant tumors, CEA, T stage, N stage and perineural invasion. The nomogram showed good efficiency in predicting the 3-year, 5-year and 8-year OS with good AUC for the training set and test set, respectively. Conclusion: We established a nomogram for predicting the 3-year, 5-year, and 8-year OS of the rectal cancer patients, which showed good prediction efficiency for the OS after TNT. [ABSTRACT FROM AUTHOR]

Due to the dynamic nature and limited resources in wireless networks, attack occurrence is inevitable. These attacks can damage or weaken the transmitted packets and threaten the entire system's efficiency. As a result, in such a situation, great and sometimes irreparable damage will be done to the business. Thus, security and attack prevention in wireless networks become a necessity and are very important. Essence intrusion detection systems determine whether a user's performance and behavior under the control or activity of a network traffic load is malicious. Since the characteristics of user behavior and network traffic are diverse and numerous, Selecting some features is necessary to improve the classification accuracy. Therefore, in this idea, a new model for estimating the penetration of wireless network-based networks is proposed based on a combination of feature subset selection based on firewall algorithm and fast neural learning networks. In this paper, the proposed idea will use the training set from the data set collected to test intrusion detection systems called KDD Cup to determine network intrusion detection methods and evaluate the proposed model. The proposed idea, based on the results obtained from the simulation and its performance in various experiments, has shown that it has improved significantly in terms of multiple criteria such as accuracy, F-criterion rate, and efficiency compared to the neural network pattern. In other words, the proposed idea performs better than the neural network method in identifying healthy nodes and new malicious intrusions in the target network. The simulation outputs also indicate that the proposed idea has a better classification rate and F-criteria than the FLN methods based on HSO, ATLBO, GA, and PSO. Vector backup machine, multilayer perceptron network, DBN, and S-NDAE have less time. [ABSTRACT FROM AUTHOR]

In the search for natural and non-toxic products alternatives to synthetic pesticides, the fumigant and repellent activities of 35 essential oils are predicted in the human head louse (Pediculus humanus capitis) through the Quantitative Structure-Activity Relationships (QSAR) theory. The number of constituents of essential oils with weight percentage composition greater than 1% varies from 1 to 15, encompassing up to 213 structurally diverse compounds in the entire dataset. The 27,976 structural descriptors used to characterizing these complex mixtures are calculated as linear combinations of non-conformational descriptors for the components. This approach is considered simple enough to evaluate the effects that changes in the composition of each component could have on the studied bioactivities. The best linear regression models found, obtained through the Replacement Method variable subset selection method, are applied to predict 13 essential oils from a previous study with unknown property data. The results show that the simple methodology applied here could be useful for predicting properties of interest in complex mixtures such as essential oils. [ABSTRACT FROM AUTHOR]

Selecting appropriate Global Climate Models (GCMs) presents a significant challenge for accurate climate projections. To address this, a novel framework based on information theory based minimum redundancy and maximum relevancy (MRMR) method identifies top-performing GCMs across the entire study region using multicriteria decision analysis methodology. A subset of the ten best-performing models out of twenty-two GCMs is chosen for multi-model ensemble analysis. Five MME methods are selected to assess the ensemble performance of the ten selected GCMs, categorized into simple, regression-based, geometric-based, and machine learning ensembles. This study evaluates the effectiveness of the MME method based on a comprehensive index called the extended distance between indices of simulation and observation. An Adaptive Multimodel Standardized Drought Index (AMSDI) has been developed based on the optimal MME method. For the application of the framework and the proposed index, historical precipitation data from 1950 to 2014 were utilized from 28 grid points in the Punjab province of Pakistan as the reference dataset. Additionally, simulations from 22 models of the Coupled Model Intercomparison Project phase 6, both past and future, were employed for the estimation procedure. In AMSDI indicator, we used improved multimodel ensemble of precipitation for future drought characterization under various future scenarios. Outcome associated with this research show that AMSDI effectively have ability to effectively identifiy extreme drought events for all three future scenarios. In conclusion, the AMSDI method is shown to be effective and flexible, improving accuracy in monitoring droughts. [ABSTRACT FROM AUTHOR]

One key aspect of the human experience is our ongoing stream of thoughts. These thoughts can be broadly categorized into various dimensions, which are associated with different impacts on mood, well-being, and productivity. While the past literature has often identified eye movements associated with a specific thought dimension (task-relatedness) during experimental tasks, few studies have determined if these various thought dimensions can be classified by oculomotor activity during naturalistic tasks. Employing thought sampling, eye tracking, and machine learning, we assessed the classification of nine thought dimensions (task-relatedness, freely moving, stickiness, goal-directedness, internal–external orientation, self-orientation, others orientation, visual modality, and auditory modality) across seven multi-day recordings of seven participants during self-selected computer tasks. Our analyses were based on a total of 1715 thought probes across 63 h of recordings. Automated binary-class classification of the thought dimensions was based on statistical features extracted from eye movement measures, including fixation and saccades. These features all served as input into a random forest (RF) classifier, which was then improved with particle swarm optimization (PSO)-based selection of the best subset of features for classifier performance. The mean Matthews correlation coefficient (MCC) values from the PSO-based RF classifier across the thought dimensions ranged from 0.25 to 0.54, indicating above-chance level performance in all nine thought dimensions across participants and improved performance compared to the RF classifier without feature selection. Our findings highlight the potential of machine learning approaches combined with eye movement measures for the real-time prediction of naturalistic ongoing thoughts, particularly in ecologically valid contexts. [ABSTRACT FROM AUTHOR]

Precise and accurate digital image correlation computed displacement data requires sufficient noise suppression and spatial resolution, which improve and diminish, respectively, with increased subset size. Furthermore, spatially varying speckle pattern quality and displacement field complexity ideally necessitate a location‐specific optimal subset size to obtain a favourable compromise between noise suppression and spatial resolution. Although dynamic subset selection (DSS) methods have been proposed based on speckle pattern quality metrics (SPQMs), they do not ensure such a favourable compromise. This work investigates using an artificial neural network (ANN) for DSS. An ANN is trained to predict the displacement error standard deviation of a subset from multiple SPQMs and the standard deviation of image noise, such that the smallest subset offering sufficient noise suppression, dictated by a displacement error standard deviation threshold, is appointed. Validation, both within and outside the domain of the training images, shows that the smallest subset providing sufficient noise suppression offers a favourable compromise for up to moderate displacement gradients. Additionally, the proposed method is shown to perform with greater consistency and reliability relative to existing SPQM‐based DSS methods. The novel proposition lies in utilising an ANN as an error prediction tool, based on multiple SPQMs, and hence, is an attractive alternative for DSS. [ABSTRACT FROM AUTHOR]

Based on an analysis of the information processing mechanism in the primary visual cortex of biological vision, this study proposes an integration method of bar-combination of shifted filter responses (B-COSFIRE) filter with the differential evolution (DE) algorithm for enhancing the precision of events extraction. First, the B-COSFIRE filter incorporates trainable and unsupervised features, utilizing the two-dimensional expression of the difference-of-Gaussians (DoG) model to simulate the receptive field model. By capitalizing on the blur and shift properties of the DoG response, the proposed approach enhances the continuous effective signal while attenuating discontinuous noise signal, thereby demonstrating superior noise robustness compared to conventional methods. Second, the selectivity of proposed filter is not predefined during the implementation but automatically determined based on the given prototype pattern during the configuration process, resulting in a universal solution adaptable to various target patterns. Lastly, we employ the DE algorithm to optimize the feature selection process, enabling the extraction of a minimum feature subset that maximizes the performance of events characterization. The B-COSFIRE method is widely used in the field of image processing. When applying it to seismic exploration, the seismic data used by this algorithm is in 'sgy' format, providing richer information than traditional image data. The proposed model can effectively detect the event in seismic data with significant data volume and substantial noise interference. The B-COSFIRE filter method outperforms conventional edge detection techniques by accurately capturing seismic events of varying widths, aligning with the principles observed in biological vision mechanisms. The extracted events exhibit enhanced continuity and accuracy compared to existing approaches. [ABSTRACT FROM AUTHOR]

The notion of complex interval-valued q-neutrosophic subbisemiring (CIVqNSBS) is developed and examined. Additionally, we examine the homomorphic features and significant attributes of CIVqNSBS. We suggest the CIVqNSBS level sets for bisemirings. Consider a complex neutrosophic subset of bisemiring Δ, denoted as-→ℵ, if and only if every non-empty level set--→Z(∂,♭) is a subbisemiring, where ∂, ♭ ∈ D[0, 1], then-→Z = (-→Z⊺ℵ,-→Z גℵ,-→ZℲℵ) is a CIVqNSBS of Δ. Let-→ℵ be the strongest complex neutrosophic relation of bisemiring Δ, and let -→Ψ be a CIVqNSBS of bisemiring Δ, if and only if -→Ψ is a CIVqNSBS of Δ × Δ, then -→ℵ is a CIVqNSBS of bisemiring Δ. We show that homomorphic images of all CIVqNSBSs are CIVqNSBSs, and homomorphic pre-images of all CIVqNSBSs are CIVqNSBSs. There are examples given to illustrate our results. [ABSTRACT FROM AUTHOR]

Machine learning (ML) has been extensively applied in various disciplines. However, not much attention has been paid to data heterogeneity in databases and number of samples used to train ML models in hydrology. In this study, we addressed these issues and their impacts on the accuracy and reliability of ML models in the estimation of saturated hydraulic conductivity, Ks. We selected 17,990 soil samples from the USKSAT database and created random subsets N = 2,000, 4,000, 6,000, 8,000, 10,000, 12,000, 14,000, 16,000, and 17,990, 80% of which were used for training. The random subset selection was repeated 50 times. The extreme gradient boosting (XGBoost) algorithm was used to estimate Ks from other soil properties, such as bulk density, soil depth, texture, and organic content. For each subset, we conducted the learning curve analysis on the training and cross‐validation data sets. Results showed that for all training sample sizes the number of samples was not enough for the training and cross‐validation curves to reach a plateau. We also applied the concept of representative elementary volume by plotting the average coefficient of determination, R2, and root mean square log‐transformed error, RMSLE, against the training sample size. For the testing data set, as the number of training sample size increased from 1,600 to 14,392 the average R2 value increased from 0.74 to 0.90, while the average RMSLE value decreased from 1.08 to 0.69. Either the learning curve or representative sample size analysis is required to investigate whether the number of samples is enough or not. Key Points: Learning curves were applied to address effects of data heterogeneity and number of samples on machine learning‐based model estimationsConcept of representative elementary volume was used to determine the representative sample size in machine learningThe number of samples was not enough for the training and cross‐validation curves to reach a plateau [ABSTRACT FROM AUTHOR]

Sparse regression and feature extraction are the cornerstones of knowledge discovery from massive data. Their goal is to discover interpretable and predictive models that provide simple relationships among scientific variables. While the statistical tools for model discovery are well established in the context of linear regression, their generalization to nonlinear regression in material modeling is highly problem‐specific and insufficiently understood. Here we explore the potential of neural networks for automatic model discovery and induce sparsity by a hybrid approach that combines two strategies: regularization and physical constraints. We integrate the concept of Lp regularization for subset selection with constitutive neural networks that leverage our domain knowledge in kinematics and thermodynamics. We train our networks with both, synthetic and real data, and perform several thousand discovery runs to infer common guidelines and trends: L2 regularization or ridge regression is unsuitable for model discovery; L1 regularization or lasso promotes sparsity, but induces strong bias that may aggressively change the results; only L0 regularization allows us to transparently fine‐tune the trade‐off between interpretability and predictability, simplicity and accuracy, and bias and variance. With these insights, we demonstrate that Lp regularized constitutive neural networks can simultaneously discover both, interpretable models and physically meaningful parameters. We anticipate that our findings will generalize to alternative discovery techniques such as sparse and symbolic regression, and to other domains such as biology, chemistry, or medicine. Our ability to automatically discover material models from data could have tremendous applications in generative material design and open new opportunities to manipulate matter, alter properties of existing materials, and discover new materials with user‐defined properties. [ABSTRACT FROM AUTHOR]

Cloud computing (CC) was extremely implemented by the application service providers (ASP) and enterprises for reducing either operational or capital expenditures. Services and applications before running on remote data centres can presently be transferred to public or private clouds. This alteration can manage the study community to analyze cloud platforms. But, a wide acceptance of such platforms can be blocked by main security concerns. Firewalls and typical rule-based security protection approaches could not be adequate for protecting user information in cloud environments. In recent times, advances in machine learning (ML) approaches are concerned the attention of research communities for building intrusion detection systems (IDS) that anomaly detection from the network traffic. This manuscript presents a Feature Subset Selection with Deer Hunting Optimizer based Deep Learning (FSS-DHODLAD) technique for Anomaly Detection in Secure Cloud Computing Environment. The purpose of the FSS-DHODLAD algorithm lies in the accurate identification and classification of anomalies that exist in the cloud platform. To reduce the high dimensionality of features, the FSS-DHODLAD technique designs a grasshopper optimization algorithm (GOA) to elect features. For anomaly detection, the FSS-DHODLAD technique utilizes Attention Convolutional Bidirectional Long Short Term Memory (AC-BLSTM) system. Eventually, the DHO system was utilized for the optimal hyperparameter selection of the AC-BLSTM model. To exhibit the higher performance of the FSS-DHODLAD system, a sequence of simulations was applied to the benchmark database and the outcomes are assessed concerning several evaluation measures. The simulation outcomes implied the greater performance of the FSS-DHODLAD algorithm over other recent approaches in terms of different metrics. [ABSTRACT FROM AUTHOR]

Tumor is one of the deadliest diseases; nowadays the cases of tumors are increasing rapidly. Researchers from worldwide are doing extensive research for the diagnosis and discernment of tumors by employing machine learning algorithms and performing experiments on the basis of observations which are stored in the form of datasets. The tumor-related dataset is high-dimensional and has many genes, most of which are not prognostic. Some of them are irrelevant and redundant. Here, we proposed a methodology named IG based on IWSSr-Random Forest(RF) which selects the most relevant prognostic genes by using Information Gain for gene ranking and evaluates the importance of genes by using RF after selecting the genes in an incremental manner in a wrapper. Furthermore, we use the RF for classification purposes. Experiments are performed on nine publicly available tumor-related datasets. Accuracy, Confusion matrix, Precision, F-measure, and Recall are used as performance evaluators. The proposed methodology selects 3 most relevant genes out of 2000 genes, 5 genes out of 7129 genes, 3 genes out of 7129 genes, 5 genes out of 24,481 genes, 7 genes out of 12,601 genes, 5 genes out of 15,154 genes, 2 genes out of 4026 genes, 5 genes out of 12,582 genes and 4 genes out of 2308 genes, and produces 88.71%, 71.67%, 98.61%, 79.38%, 93.60%, 99.60%, 92.42%, 95.83% and 92.77% accurate results in case of Colon, Central Nervous System, Leukemia, Breast Cancer, Lung Cancer, Ovarian Cancer, Lymphoma, MLL and SRBCT respectively. Experimental results present that IG based on IWSSr(RF) performs well compared to the state-of-the-art algorithms' results for instance RF, Naïve Bayes, KNN, and Decision Tree. IG based on IWSSr(RF) also has nominal time complexity compared to the time complexity of the above-mentioned classification algorithms. [ABSTRACT FROM AUTHOR]

The following problem has been known since the 80s. Let Γ be an Abelian group of order m (denoted |Γ| = m), and let t and {mi}ti =1, be positive integers such that Pt i=1 mi = m - 1. Determine when Γ∗ = Γ \ {0}, the set of non-zero elements of Γ, can be partitioned into disjoint subsets {Si}t i=1 such that |Si| = mi and P s∈Si s = 0 for every 1 ≤ i ≤ t. Such a subset partition is called a zero-sum partition. |I(Γ)| ̸= 1, where I(Γ) is the set of involutions in Γ, is a necessary condition for the existence of zero-sum partitions. In this paper, we show that the additional condition ofmi ≥ 4 for every 1 ≤ i ≤ t, is sufficient. Moreover, we present some applications of zero-sum partitions to magic- and antimagic-type labelings of graphs. [ABSTRACT FROM AUTHOR]

For problems of time-harmonic scattering by rational polygonal obstacles, embedding formulae express the far-field pattern induced by any incident plane wave in terms of the far-field patterns for a relatively small (frequency-independent) set of canonical incident angles. Although these remarkable formulae are exact in theory, here we demonstrate that (i) they are highly sensitive to numerical errors in practice, and (ii) direct calculation of the coefficients in these formulae may be impossible for particular sets of canonical incident angles, even in exact arithmetic. Only by overcoming these practical issues can embedding formulae provide a highly efficient approach to computing the far-field pattern induced by a large number of incident angles. Here we address challenges (i) and (ii), supporting our theory with numerical experiments. Challenge (i) is solved using techniques from computational complex analysis: we reformulate the embedding formula as a complex contour integral and prove that this is much less sensitive to numerical errors. In practice, this contour integral can be efficiently evaluated by residue calculus. Challenge (ii) is addressed using techniques from numerical linear algebra: we oversample, considering more canonical incident angles than are necessary, thus expanding the set of valid coefficient vectors. The coefficient vector can then be selected using either a least squares approach or column subset selection. [ABSTRACT FROM AUTHOR]

A sketch-and-select Arnoldi process to generate a well-conditioned basis of a Krylov space at low cost is proposed. At each iteration the procedure utilizes randomized sketching to select a limited number of previously computed basis vectors to project out of the current basis vector. The computational cost grows linearly with the dimension of the Krylov space. The subset selection problem for the projection step is approximately solved with a number of heuristic algorithms and greedy methods used in statistical learning and compressive sensing. [ABSTRACT FROM AUTHOR]

Background: Difficult airway management poses a high-risk procedure, potentially leading to patient mortality. Despite significant advancements in modern anesthesia over the years, there remains a lack of a single reliable tool for predicting difficult airways. Objectives: This study aims to examine neck fat diameter as a potential predictor of airway difficulty. Methods: Two hundred fifty patients participated in the study. Airway parameters, including Mallampati score, mandibular protrusion, upper lip bite test, thyromental distance, neck movements, and neck fat diameter, were documented. Results: Among our study population, 48 patients experienced difficult intubation. The Mann-Whitney test revealed that submental fat was significantly greater in the difficult intubation group (P-value < 0.001). Based on the best subset variable selection, submental fat, thyromental distance (model 1), and age (model 2) were selected to participate in the multivariate regression model. Each one mm increase in neck fat was associated with a 30% increase in the odds of difficult intubation (Pvalue < 0.001) in the univariate logistic model. After adjusting for thyromental distance, age, and gender, the odds of difficult intubation were approximately 2 times higher in patients with 1 mm more neck fat (P-value 0.007). The area under the curve of the Receiver Operating Characteristics curve and McFadden's pseudo-R squared of the multivariate model were 0.998 and 0.91, respectively. Conclusions: Neck fat diameter serves as a reliable and straightforward predictor of airway difficulty and can be used alone or in conjunction with other predictors for the prediction of difficult airway management. [ABSTRACT FROM AUTHOR]

A μ-way (v,k,t) trade T of volume m consists of μ pairwise disjoint collections Ti,...,Tμ, each of m blocks of size k such that for every t-subset of a v-set V, the number of blocks containing this t-subset is the same in each Ti for 1 ≤ i ≤ μ. If any t-subset of the v-set V occurs at most once in each Ti for 1 ≤ i ≤ μ, then T is called a μ-way (v, k, t) Steiner trade. In 2016, it was proved that there exists a 3-way (v, k, 2) Steiner trade of volume m when 12(k - 1) ≤ m for each k. Here we improve the lower bound to 8(k - 1) for even k, by using a recursive construction. [ABSTRACT FROM AUTHOR]

We introduce the notion of q-neutrosophic cubic vague subbisemiring (q-NSCVSBS) and level set of q-NSCVSBS of a bisemiring. The q-NSCVSBS is a new concept of subbisemirings of bisemirings. Let Ξ be a neutrosophic vague subset of Λ. Then 0 = ([T-Ξ, T+Ξ ], [I-Ξ, I+Ξ ], [F-Ξ, F+Ξ ]) is a q-NSCVSBS of Λ if and only if all non empty level set 0(ϱ1,ϱ2,s) is also a SBS of Λ for every ϱ1, ϱ2, s ∈ [0, 1]. Let Ξ be the q-NSCVSBS of Λ and Υ be the strongest cubic q-neutrosophic vague relation of Λ. Then Ξ is a q-NSCVSBS of Λ × Λ. Let Ξ be the q-NSCVSBS of Λ, show that pseudo cubic q-neutrosophic vague coset (ςΞ)p is also a q-NSCVSBS of Λ, for all ς ∈ Λ. Let Ξ1,Ξ2, ...,Ξn be the any family of q - NSCV SBSs of Λ1,Λ2, ...,Λn respectively, then Ξ1 × Ξ2 × ... × Ξn is also a q-NSCVSBS of Λ1 × Λ2 × ... × Λn. The homomorphic image of every q-NSCVSBS is also a q-NSCVSBS. The homomorphic pre-image of every q-NSCVSBS is also a q-NSCVSBS. [ABSTRACT FROM AUTHOR]

In this paper, a subset-optimized eight-dimensional trellis-coded quadrature amplitude modulation (SO-8DTCM-16QAM) format for higher-order constellations in high-speed optical communications is proposed. This scheme increases the number of subsets of base 2D constellation divisions. On this basis, it is further optimized by using 2D subsets for Cartesian product combinations to obtain 4D subsets and eliminate the combinations with small Euclidean distances. Finally, the 4D subsets are utilized to construct interrelated 8D subsets for trellis coding modulation and signal transmission. The proposed scheme can effectively reduce the decoding complexity and outperforms the conventional scheme at a high signal-to-noise ratio (SNR). Simulation verification of the proposed scheme is carried out, and the results show that the SO-8DTCM-16QAM achieves signal-to-noise ratio (SNR) gains of 1.60 dB, 1.56 dB, 1.51 dB, and 1.33 dB, respectively, compared with the conventional 8D-16QAM signals when BTB and 5/20/30 km optical signal transmission are performed. The SO-8DTCM-16QAM also achieves an SNR gain of 1.86 dB, 1.75 dB, and 1.22 dB at a net transmission rate of 14/21/28 GBaud. In addition, the SO-8DTCM-16/32/64QAM achieves an SNR gain of 1.27 dB, 0.80 dB, and 1.24 dB, respectively, when compared with the unoptimized 8DTCM-16/32/64QAM. Meanwhile, the proposed eight-subset SO-8DTCM-QAM scheme reduces the complexity of the decoding computation in the subset selection part and the constellation point selection part by 93.75% and 50%, respectively, compared with the unoptimized eight-subset and four-subset 8DTCM-QAM schemes. It can be seen that the proposed scheme simultaneously optimizes the transmission performance and complexity of high-speed optical communication systems and has practical application value. [ABSTRACT FROM AUTHOR]

We present a full Bayesian analysis of multiplicative double seasonal autoregressive (DSAR) models in a unified way, considering identification (best subset selection), estimation, and prediction problems. We assume that the DSAR model errors are normally distributed and introduce latent variables for the model lags, and then we embed the DSAR model in a hierarchical Bayes normal mixture structure. By employing the Bernoulli prior for each latent variable and the mixture normal and inverse gamma priors for the DSAR model coefficients and variance, respectively, we derive the full conditional posterior and predictive distributions in closed form. Using these derived conditional posterior and predictive distributions, we present the full Bayesian analysis of DSAR models by proposing the Gibbs sampling algorithm to approximate the posterior and predictive distributions and provide multi-step-ahead predictions. We evaluate the efficiency of the proposed full Bayesian analysis of DSAR models using an extensive simulation study, and we then apply our work to several real-world hourly electricity load time series datasets in 16 European countries. [ABSTRACT FROM AUTHOR]

Data science is an interdisciplinary domain that has vast applications. One among them is the bioinformatics domain. Bioinformatics is an essential field of science that incorporates computational, statistical and mathematical aspects for various experimental studies. Microarray experiments are vital in identifying the genetic cause of acute diseases. But the massive volume of data generated from microarray experiments makes analysis tedious and time-consuming for researchers. Hence, an efficient method for selecting relevant gene features is essential. This article proposes a new model for autism gene subset selection termed optimized reliefF (OReliefF). The proposed OReliefF employs a new fuzzy multi-verse optimizer (FMVO) to optimize the selection of neighbours. The filter-based feature selection model obtains the gene subset with high classification accuracy and a low error rate. Benchmark datasets of National Center for Biotechnology Information, USA with accession numbers GSE25507, GSE26415 and GSE2704 are used for implementation. The experimental findings show that the proposed autism gene selection model outperformed the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Rising temperatures due to global warming necessitate immediate evaluation of heatwave patterns in Peninsular Malaysia (PM). For this purpose, this study utilized a locally developed heatwave index and a gridded daily maximum temperature (Tmax) dataset from ERA5 (1950–2022). During validation, the ERA5 dataset accurately represented the spatial pattern of Level 1 heatwaves, showing widespread occurrence. Historically, Level 1 heatwaves prevailed at 63.0%, followed by Level 2 at 27.7%, concentrated in northwestern states and the enclave between the Tahan and Titiwangsa mountain ranges. During very strong El Niño events in 1982/83, 1997/98, and 2015/16, Level 2 heatwave distributions were 10.4%, 26.8%, and 15.0%, respectively. For future projection, the model ensemble was created by selecting top-performing Global Climate Models (GCMs) using Kling-Gupta efficiency (KGE), ranked re-aggregation with compromise programming index (CPI), and GCM subset selection via Fisher-Jenks. The linear scaling bias-corrected GCMs (BC-GCMs), NorESM2-LM, ACCESS-CM2, MPI-ESM1-2-LR, ACCESS-ESM1-5, and FGOALS-g3, were found to exhibit better performance, and then ensemble. March to May show the highest increase in all scenarios, ranging from 3.3 °C to 4.4 °C for Level 1 heatwaves and 4.1 °C to 10.7 °C for Level 2 heatwaves. In the near future, SSP5-8.5 projects up to a 40.5% spatial increase for Level 1 heatwaves and a 2.3% increase for Level 2 heatwaves, affecting 97.1% and 57.2% of the area, respectively. In the far future, under SSP2-4.5 and SSP5-8.5, Tmax is projected to rise rapidly (1.5–4.5 °C) in the northern, western, and central regions, with increasing population exposure anticipated in the northern and western regions. [ABSTRACT FROM AUTHOR]

Variable selection involves selecting truly important predictors from p-dimensional multivariate functional predictors in functional predictive models. In this paper, a variable selection method is designed for scalar-on-function predictions entangled with nonlinear joint associations among scalar response and multiple functional predictors. First, a nonparametric functional nonlinear conditional correlation coefficient, namely, the FunNCC coefficient, is proposed to measure complex dependencies, including the nonmonotonic marginal dependence, along with the conditional associations of redundancy, complement, and interaction. Then, a model-free feature ordering and selection method is designed, where the FunNCC is utilized to rank relevance, enabling the selection of a subset of predictors with the strongest joint dependence. Since this method allows for quantitatively evaluating the contributions of predictors in explaining responses, it achieves moderate model interpretability. Finally, extensive simulation studies and two real-data cases involving air pollution regression and hand gesture recognition are conducted to evaluate the finite sample performance of the proposed method, and the results show that the proposed FunNCC and variable selection methods outperform state-of-the-art baselines. [ABSTRACT FROM AUTHOR]

We develop a three-part approach to Verb Sense Disambiguation (VSD) in German. After considering a set of lexical resources and corpora, we arrive at a statistically motivated selection of a subset of verbs and their senses from GermaNet. This sub-inventory is then used to disambiguate the occurrences of the corresponding verbs in a corpus resulting from the union of TüBa-D/Z, Salsa, and E-VALBU. The corpus annotated in this way is called TGVCorp. It is used in the third part of the paper for training a classifier for VSD and for its comparative evaluation with a state-of-the-art approach in this research area, namely EWISER. Our simple classifier outperforms the transformer-based approach on the same data in both accuracy and speed in German but not in English and we discuss possible reasons. [ABSTRACT FROM AUTHOR]

In recent years, Artificial Intelligence has been used to assist healthcare professionals in detecting and diagnosing neurodegenerative diseases. In this study, we propose a methodology to analyze functional Magnetic Resonance Imaging signals and perform classification between Parkinson's disease patients and healthy participants using Machine Learning algorithms. In addition, the proposed approach provides insights into the brain regions affected by the disease. The functional Magnetic Resonance Imaging from the PPMI and 1000-FCP datasets were pre-processed to extract time series from 200 brain regions per participant, resulting in 11,600 features. Causal Forest and Wrapper Feature Subset Selection algorithms were used for dimensionality reduction, resulting in a subset of features based on their heterogeneity and association with the disease. We utilized Logistic Regression and XGBoost algorithms to perform PD detection, achieving 97.6% accuracy, 97.5% F1 score, 97.9% precision, and 97.7%recall by analyzing sets with fewer than 300 features in a population including men and women. Finally, Multiple Correspondence Analysis was employed to visualize the relationships between brain regions and each group (women with Parkinson, female controls, men with Parkinson, male controls). Associations between the Unified Parkinson's Disease Rating Scale questionnaire results and affected brain regions in different groups were also obtained to show another use case of the methodology. This work proposes a methodology to (1) classify patients and controls with Machine Learning and Causal Forest algorithm and (2) visualize associations between brain regions and groups, providing high-accuracy classification and enhanced interpretability of the correlation between specific brain regions and the disease across different groups. [ABSTRACT FROM AUTHOR]