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Criteria selection significantly impacts the reliability and utility of multicriteria decision making (MCDM) models. While criteria may vary across industries, a formalised criteria selection process is influential in determining MCDM model outcomes. This article analyses and compares the criteria selection approaches used in 62 articles that apply MCDM-based inventory classification models, contrasting them with methodologies outside the field. Our findings reveal a conspicuous absence of formal criteria selection methods within MCDM-based inventory classification research. The limited application of quantitative and qualitative approaches indicates that this field has not kept pace with methodological advances in criteria selection. To bridge this gap, we advocate for further research aimed at developing a conceptual framework for criteria selection tailored to inventory classification. We also suggest evaluating the impact of formal criteria selection processes on inventory management decisions and exploring the benefits of integrating artificial intelligence into criteria selection for inventory classification studies. Additionally, this article identifies several limitations related to criteria selection for practitioners employing MCDM-based inventory classification models. [ABSTRACT FROM AUTHOR]

Conventional low-pass filtering of 3D motion capture signals prior to estimating knee joint moments and ACL injury risk has known limitations. This study aimed to evaluate the fractional Fourier filter (FrFF), which employs a time-varying cut-off frequency, for assessing peak knee moments during common ACL injury risk screening tasks. Ground reaction force and motion data were collected from 23 team sport athletes performing 45° unanticipated sidesteps and drop jumps. Peak knee abduction, internal rotation and non-sagittal moments were estimated using inverse dynamics after five different low-pass filter approaches were applied (FrFF vs. four variations of a fourth-order Butterworth filter). The FrFF produced peak knee moments larger than "matched" (i.e. force and motion cut-off frequencies were equivalent) and closer to "unmatched" (i.e. force and motion cut-offs were different) Butterworth filter approaches and removed problems with representing foot-to-ground impact peaks. Participants with larger peak moments were identified as "at risk" of injury irrespective of filter approach, but the FrFF identified "at risk" classifications conventional approaches did not. Preliminary evidence suggests that the FrFF displays enhanced sensitivity to movement strategies that induce high knee loads. This was most evident for sidestepping, with more research warranted to optimise the FrFF for drop jumps. [ABSTRACT FROM AUTHOR]

Hyperspectral images (HSIs) are rich in spatial and spectral information, making them crucial for accurate classification. While existing convolutional neural network (CNN) methods have shown promise in HSI classification, they primarily focus on local features and struggle with establishing long-range dependencies. To address these limitations, we propose a Local-Global Feature Fusion Network (LGFFN). LGFFN effectively learns joint local-global information from HSIs. Firstly, we employ a group-general convolution parallel module (GGCPM) to extract both local and global spectral features. Next, we utilize parallel branches: a CNN branch for capturing fine-grained spatial information and an improved transformer encoder (ITE) branch for capturing long-distance spatial-spectral features and exploiting semantic information. Finally, we elegantly integrate the diverse features obtained from these branches to obtain classification results. We validate our method on four datasets and compare it with six state-of-the-art hyperspectral classification methods. Our experimental results demonstrate that LGFFN achieves high classification accuracy, outperforming existing methods. [ABSTRACT FROM AUTHOR]

High-resolution remote sensing (HRRS) scene classification is based on the semantic information in the image and its contextual spatial correlation, labelling different semantic categories for different HRRS. Convolutional neural networks (CNN) have been widely studied and applied in remote sensing scene classification in recent years. However, most of the existing CNN models focus on the global and high-level semantic features, ignoring the shallower feature information. In addition, complex background information and variable scales lead to a series of problems with large intra-class differences and high inter-class similarities, which also brings challenges to scene classification. To address the above problems and challenges, a scenario classification model based on the contextual spatial attention and channel attention mechanism of Lie Group manifold space learning is proposed in this study. In this model, we fully explore the multi-scale features of the scene (shallower-level and high-level) and propose a novel contextual spatial attention mechanism and channel attention mechanism. Extensive experimentation was carried out on the Union Remote Sensing Image Data Set (URSIS), which improved by 7.13% compared with the classical model. The experimental results showed that compared with other state-of-the-art remote sensing scene classification models, our proposed method has achieved significant improvement in classification accuracy and performance. [ABSTRACT FROM AUTHOR]

A study of land use and land cover (LULC) mapping using satellite data was conducted in the Congo Basin rainforest in the Sangha area of the northern Congo Republic, which is crucial for sustainable land management. However, the heterogeneous nature of LULC features makes data classification difficult in this area because persistent cloud cover is challenging for observing the land surface and tropical humid conditions characterizing the study area. This study compares and evaluates the potential of two of the most widely used and popular satellite image sources, the recently launched Landsat 9 and Sentinel-2, to generate land-use land cover (LULC) maps in the Sangha area using pixel-based supervised machine learning algorithms, Random Forest (RF), for classification. All procedures were performed using ArcGIS Pro. The potential to improve Landsat 9 performance was tested using the nearest resampling technique, and different bands were resampled from 30 × 30 to 10 × 10 m. Owing to the heterogeneous characteristics of the Sangha study area, six classes were defined: dense forest, open forest, wetland forest, water bodies, urban areas, and bare soils. To classify and compare Sentinel-2 and Landsat 9, the shortwave infrared (SWIR), Near Infrared (NIR), and Red, Green, Blue (RGB) bands were used. These two satellite images were compared to test the quality of their results, particularly for assessing the accuracy of the LULC classifications and identifying which dataset had the highest accuracy. Results show that overall accuracy was 93.80% for Sentinel-2 while it was 91.60% for Landsat 9, similarly, the Kappa coefficient was calculated 0.89 and 0.85 for Sentinel-2 and Landsat 9, respectively. Therefore, Sentinel-2 exhibits significant classification ability compared to the nearest resample technique Landsat 9, despite the improved resolution of the latter, which offers a scientific basis for choosing the appropriate satellite imagery to create accurate LULC maps. [ABSTRACT FROM AUTHOR]

Recent studies have focused on identifying the optimal seismic and energy upgrading interventions, among different alternatives, for poorly performing existing buildings. Most of such studies use detailed methodologies to assess the seismic and energy performance of buildings, which are time and computationally demanding, making it nearly unfeasible for practitioners and decision-makers to use them, rendering practice-oriented approaches extremely needed. To overcome the current limitations of more research-oriented methods, this study employs different available simplified approaches, namely SPO2FRAG, cloud-based capacity spectrum method and the Italian seismic risk classification guidelines (Sismabonus), to estimate some of the variables considered when using a multi-criteria decision-making (MCDM) framework to select the optimal combined retrofitting strategy, among a set of different options. To this end, a case-study school building was selected and four seismic retrofitting solutions, combined with three energy-based interventions, were identified. The performance of the building, in its as-built and retrofitted conditions, was analysed considering both simplified and detailed approaches for the estimation of two of the seismic-related variables, namely annual probability of failure and expected annual losses. Such simplified and refined estimates were used as MCDM input, comparing the ranking of retrofitting combined alternatives obtained in both cases, demonstrating the suitability of the proposed simplified methodology for engineering practice. [ABSTRACT FROM AUTHOR]

Support matrix machine (SMM) is a matrix classification method that effectively utilises information between rows and columns in matrix-form data. However, the presence of abnormal data in the feature matrix fed into the model can result in model non-convergence and hyperplane shift, greatly affecting classification accuracy. To address this issue, this paper introduces a matrix classification method, called asymmetric robust least squares support matrix machine (ARLSSMM). To construct ARLSSMM, a novel optimisation model based on a matrix with asymmetric constraints is designed to obtain the hyperplane, which can not only use the data set to dynamically determine the position of the hyperplane to obtain the maximum margin space but also reduce the influence of abnormal data on the model classification performance. Two different roller bearing types of test data are used for experimental validation, and the results show that ARLSSMM demonstrates excellent classification performance. [ABSTRACT FROM AUTHOR]

AbstractZNF384 gene rearrangements are a distinct subtype of adult B cell acute lymphoblastic leukemia (B-ALL). We screened 46 B-other ALL patients for ZNF384 fusions using fluorescent in situ hybridization (FISH) and reverse transcription-polymerase chain reaction (RT-PCR). Clinical data, treatment response, and minimal residual disease (MRD) status were analyzed. Ten (21.7%) patients were ZNF384-r positive (nine by FISH, nine by RT-PCR, eight by both). FISH showed atypical signals, including 3’ signal gain and 5’ signal deletion. EP300 was the main fusion partner (n = 5). TAF15::ZNF384, SYNRG::ZNF384, CREBBP::ZNF384, and TCF3::ZNF384 fusions were found in one patient each; one case’s partner gene is unknown. One patient was MRD-negative at the end of the first induction, lower than in patients without ZNF384-r. ZNF384-r incidence matched B-other ALL incidence in Chinese patients. Combined FISH and RT-PCR improved detection. ALL with ZNF384-r has unique features, and lower MRD-negative response may indicate a negative impact on traditional treatments. [ABSTRACT FROM AUTHOR]

This study introduces a novel method specifically designed to detect clusters of points within linear networks. This method extends a classification approach used for point processes in spatial contexts. Unlike traditional methods that operate on planar spaces, our approach adapts to the unique geometric challenges of linear networks, where classical properties of point processes are altered, and intuitive data visualisation becomes more complex. Our method utilises the distribution of the Kth nearest neighbour volumes, extending planar-based clustering techniques to identify regions of increased point density within a network. This approach is particularly effective for distinguishing overlapping Poisson processes within the same linear network. We demonstrate the practical utility of our method through applications to road traffic accident data from two Colombian cities, Bogota and Medellin. Our results reveal distinct clusters of high-density points in road segments where severe traffic accidents (resulting in injuries or fatalities) are most likely to occur, highlighting areas of increased risk. These clusters were primarily located on major arterial roads with high traffic volumes. In contrast, low-density points corresponded to areas with fewer accidents, likely due to lower traffic flow or other mitigating factors. Our findings provide valuable insights for urban planning and road safety management. [ABSTRACT FROM AUTHOR]

Amyotrophic Lateral Sclerosis (ALS) is a neurodegenerative disease that leads to progressive muscle weakness and paralysis, ultimately resulting in the loss of ability to communicate and control the environment. EEG-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control with the aim of rehabilitating ALS patients. In particular, P300-based BCI has been widely studied and used for ALS rehabilitation. Other EEG-based BCI methods, such as Motor Imagery (MI) based BCI and Hybrid BCI, have also shown promise in ALS rehabilitation. Nonetheless, EEG-based BCI methods hold great potential for improvement. This review article introduces and reviews FFT, WPD, CSP, CSSP, CSP, and GC feature extraction methods. The Common Spatial Pattern (CSP) is an efficient and common technique for extracting data properties used in BCI systems. In addition, Linear Discriminant Analysis (LDA), Support Vector Machine (SVM), Neural Networks (NN), and Deep Learning (DL) classification methods were introduced and reviewed. SVM is the most appropriate classifier due to its insensitivity to the curse of dimensionality. Also, DL is used in the design of BCI systems and is a good choice for BCI systems based on motor imagery with big datasets. Despite the progress made in the field, there are still challenges to overcome, such as improving the accuracy and reliability of EEG signal detection and developing more intuitive and user-friendly interfaces By using BCI, disabled patients can communicate with their caregivers and control their environment using various devices, including wheelchairs, and robotic arms. IMPLICATIONS FOR REHABILITATION: Electroencephalography (EEG)-based Brain-Computer Interface (BCI) methods have shown promise in providing communication and control for Amyotrophic Lateral Sclerosis (ALS) patients. EEG constitutes the most significant input in BCIs and can be successfully used in the neuro-rehabilitation of patients with stroke symptoms and amyotrophic lateral sclerosis. EEG based BCIs have the potential to provide a means of communication and control for individuals with severe disabilities.a variety of EEG-based BCI methods have been developed with the aim of rehabilitating disabled patients. [ABSTRACT FROM AUTHOR]

Ever since Brockett, Clark and Mitter introduced the estimation algebra method, it becomes a powerful tool to classify the finite-dimensional filtering system. In this paper, we investigate finite-dimensional estimation algebra with non-maximal rank. The structure of Wong matrix Ω will be focused on since it plays a critical role in the classification of finite-dimensional estimation algebras. In this paper, we first consider general estimation algebra with non-maximal rank and determine the linear structure of the submatrix of Ω by using rank condition and property of Euler operator. In the second part, we proceed to consider the case of linear rank n−1 and prove the linear structure of Ω. Finally, we give the structure of finite-dimensional filters which implies the drift term must be a quadratic function plus a gradient of a smooth function. [ABSTRACT FROM AUTHOR]

Purpose: This study describes the development of four age-based item code sets from WHO's International classification of functioning disability and health, children and youth version (ICF-CY). Given the continuing goal of universal implementation of the ICF-CY, a reduced set of codes was identified from more than 1600 codes to facilitate the use of the classification for clinical, research and policy applications of the ICF-CY Methods: The ICF-CY developmental code sets were developed by consensus using the Delphi method. A multi-disciplinary group of international experts representing 27 countries from 5 WHO world regions completed a series of iterative online surveys to rate categories of child functioning essential for inclusion in the respective age-based code set. Results: Four age-based code sets covering 37, 52, 60 and 57 codes across four domains of the ICF-CY were successfully derived with a high level of participant consensus. The code sets align with developmental theory and represent essential indicators of functioning defining key stages of child development. Conclusion: The ICF-CY developmental code sets offer a common, universal language of childhood functioning and disability with global application for multidisciplinary research, clinical practice and policy. IMPLICATIONS FOR REHABILITATION: Broad umbrella terms, such as children with disabilities, neurodiversity and neurodevelopmental disabilities are widely used in the literature but not defined in a consistent, universal language. Codes from the International Classification of Functioning, Disability and Health-Children and Youth Version (ICF-CY) offer a universal language of health and functioning. ICF-CY item codes were developed by consensus for four age groups of children, providing a universal language of disability for: epidemiological studies of disability prevalence screening and assessment tools documenting intervention goals and outcomes [ABSTRACT FROM AUTHOR]

Divergence, and particularly Transformed Divergence, are key separability metrics in remote sensing, predominantly utilized for digital image classification to achieve thematic mapping. Despite the widespread use of Transformed Divergence, the mathematical expression of Divergence – essential for calculating Transformed Divergence – appears in the literature in two different formulations, leading to inconsistent results and confusion within the remote sensing community. This study addresses the original source of Divergence, clarifies its correct formulation, and examines implementations that calculate Divergence and/or Transformed Divergence based on the original formula. Additionally, it provides data to verify the accuracy of future implementations and quantitatively shows the erratic implications of incorrect calculations. [ABSTRACT FROM AUTHOR]

Recycling, reusing, and reducing electronic garbage (E-waste) may be the sole methods for managing E-waste in use today. In essence, there is no ideal method of managing E-waste. This process required more labor and resources. E-waste classification is fulfilled using fractional Henry gas optimization-based deep convolutional neural network (FHGO-based deep CNN) is shown in this manuscript. To predict the optimal path the E-waste images are conversed through the energy-aware FHGO routing algorithm. The feature extraction procedure is performed to cut out the features for example the gray level co-occurrence matrix (GLCM) feature, local Gabor binary pattern (LGBP) and histogram of oriented gradient (HOG) and the pre-processing phase is concluded with a median filter. To supplement the extracted feature size the data augmentation is fulfilled. Moreover, the E-waste classification is done based on deep CNN, which is trained using a FHGO algorithm. FHGO is exhibited by the merging of Henry gas solubility optimization (HGSO) algorithm, fractional calculus (FC). Comparing to the existing approaches like deep learning, tensor flow deep learning, Cuckoo search-based neural network and machine learning the accuracy of the proposed method is 19.49%, 18.05%, 12.77%, and 7.89% privileged. [ABSTRACT FROM AUTHOR]

Accurate detection plays a critical role in improving the safety situation of vulnerable road users. This study extends infrastructure-based LiDAR application to all three major vulnerable road user groups including pedestrians, cyclists, and wheelchair users. Two critical problems for accurate detection of small-sized road users are scanning angle variability and feature fluctuation. To address these issues, a feature-based classification method combined with prior LiDAR trajectory information is developed. Effective dimension-related features are proposed and five classifiers including artificial neural network (ANN), random forest (RF), adaptive boosting (AdaBoost), random under-sampling boosting (RUSBoost), and long short-term memory (LSTM) are tested with a novel feature engineering process. A total of seven features are selected from the point cloud of clusters for vehicle/pedestrian/cyclist/wheelchair classification. By updating these significant features based on prior information of the entire trajectory, the performance of road user classification (imbalanced datasets) has been significantly improved. Experimental study is conducted to examine the recall rate, F1-score, and AUC of vehicles, pedestrians, cyclists, and wheelchairs before and after integration with prior trajectory information. The result shows the trained AdaBoost, RUSBoost, and LSTM classifiers with prior trajectory information can achieve recall/F1-score/AUC: (1) Low traffic volumes – vehicles (100%/99.96%/99.96%), pedestrians (99.96%/99.96%/99.97%), cyclists (99.74%/99.45%/99.67%), and wheelchairs (99.22%/99.68%/99.01%) and (2) Moderate traffic volumes – vehicles (99.39%/99.44%/99.69%), pedestrians (98.33%/97.99%/98.64%), and cyclists (95.41%/94.29%/94.40%), using 32-laser LiDAR sensors (10 Hz). [ABSTRACT FROM AUTHOR]

This article continues the series of works by the authors on the approximation of the electronic terms of a diatomic molecules and their vibrational structure by the Morse formula, which is the simplest anharmonic approximation of the real term U (r). Depending on the choice of parameters, the approximation has two alternative solutions M 1 (r) and M 2 (r) , with different patterns of deviations from the real term and its vibrational structure; some typical deviations for M 1 (r) and M 2 (r) are described. The difference δ (r) = U (r) − M (r) quantitatively shows the changes in the shape of the terms during approximation and can serve as a reference point when choosing its type for solving a specific problem. We introduced an empirical anharmonicity function − 2 ω e x (v) , which characterizes the positions of vibrational levels in the potential well; it demonstrates the distortion of the vibrational structure of the term U (r) during the approximation. Based on the data from literature, the functions δ (r) and − 2 ω e x (v) were constructed for more than 20 molecules and a preliminary classification of electronic terms according to their characteristic features is described. Here we present a group of simple terms with minimal deviations from the Morse shape. [ABSTRACT FROM AUTHOR]

Convolutional Neural Networks (CNN) for ship classification in multi-spectral images (RGB, IR, etc.) is proposed in this paper. Recent developments in deep learning have significantly advanced the field of ship recognition. However, since maritime light intensity is frequently disturbed, multispectral imaging is considered a more robust substitute for RGB imaging. The proposed architectures were fine-tuned after being trained from scratch on the publicly available dataset VAIS (RGB-IR pairs). Unfortunately, the classification results plateaued at 59.74% accuracy, which is unsatisfactory for most real-life applications. Such an accuracy wall was due to the small number of training images. In order to overcome the scarcity of IR ship images, we proposed a novel image data augmentation strategy that translates RGB images to IR. A Pix2Pix model and a Generative Adversarial Network (GAN) network were modified to carry out the generation process as an RGB to IR translator. The KAIST general-purpose RGB-IR image pairs dataset was used to train our RGB-to-IR image translator, whereas the VAIS dataset was held aside for validation purposes. Our proposed network improved the accuracy of the native network by 8% (from 59.74% to 67.74%), which is fairly satisfactory in the field of ship recognition. [ABSTRACT FROM AUTHOR]

Crack localisation is a fundamental step of damage identification. Most of the published research on crack localisation based on natural frequency consists in model updating which requires a great deal of calculation. This paper presents a simple method of crack localisation in composite cantilever beam-type structures. The proposed method consists of classifying the reduction of the first four measured natural frequencies and deducing the damage position from a table. For these purposes, a finite element model is first used to study the direct problem: the vibration behaviour of an intact beam and the changes caused in the damaged state. Then it is shown that the classification of the frequency reduction ratio is different for each damaged zone. Consequently, the beam is discretized into several areas according to the classifications of the first four frequency ratios. Therefore, this discretization is represented in a table to be used in damage localisation. The robustness of the suggested technique is first tested numerically by using the Abaqus/CAE finite element software environment. Then, the applicability of the localisation method is validated experimentally on a cracked glass fibre-reinforced epoxy beam. The results show that the proposed method can successfully localise damage in composite beams. [ABSTRACT FROM AUTHOR]

Road network selection plays a key role in map generalization for creating multi-scale road network maps. Existing methods usually determine road importance based on road geometric and topological features, few evaluate road importance from the perspective of road utilization based on human travel data, ignoring the functional values of roads, which leads to a mismatch between the generated results and people's needs. This paper develops two functional semantic features (i.e. travel path selection probability and regional attractiveness) to measure the functional importance of roads and proposes an automatic road network selection method based on graph convolutional networks (GCN), which models road network selection as a binary classification. Firstly, we create a dual graph representing the source road network and extract road features including six graphical and two functional semantic features. Then, we develop an extended GCN model with connectivity loss for generating multi-scale road networks and propose a refinement strategy based on the road continuity principle to ensure road topology. Experiments demonstrate the proposed model with functional features improves the quality of selection results, particularly for large and medium scale maps. The proposed method outperforms state-of-the-art methods and provides a meaningful attempt for artificial intelligence models empowering cartography. [ABSTRACT FROM AUTHOR]

Most of the existing adaptive classification algorithms in non-stationary data streams require recent labelled data for their updates. Such recent labels are often missing. For stream classification under verification latency only few approaches exist. Most of them assume clustered data or homogeneous drift in all features, which limits their applicability. We address this by proposing Anticipative Bayesian stream Classifier (ABClass), an approach that is capable of integrating and automatically selecting from different components. In its Bayesian classification framework, ABClass combines density estimation techniques, extended to extrapolate drift patterns over time, with unsupervised parameter tuning and unsupervised model selection. ABClass allows for multivariate density estimation and extrapolation techniques. In this work, we assume conditional independence between features given the class label for modelling feature-specific drift patterns. ABClass is generative and can also be used for explaining and visualising concept drift patterns. It is generic, making it easy to include further types of drift models, both for the class-conditional feature distribution and for the class prior distribution. The experimental evaluation on several real-world data streams shows its competitiveness compared to other state-of-the-art approaches. ABClass is in most cases ten- to hundred-times faster than its competitors, both for model fitting and for prediction. [ABSTRACT FROM AUTHOR]

Let FI(X, K) be the finitary incidence algebra of a non-connected partially ordered set X over a field K of characteristic different from 2. For the case where every multiplicative automorphism of FI(X, K) is inner, we present necessary and sufficient conditions for two involutions on FI(X, K) to be equivalent. [ABSTRACT FROM AUTHOR]

Objective: Many patients who are attended by paramedics do not require conveyance to an emergency department (ED). Our study focuses on comparing the characteristics and outcomes of patients who were advised to follow up with a general practitioner (GP) by an attending paramedic with those of patients who were discharged at scene or transported to hospital. Methods: This was a retrospective data linkage cohort study of ambulance, ED, hospital admission, and death records for all adults attended by paramedics in Victoria, Australia between the 1st of January 2015 and 30th of June 2019. Patients were excluded if they presented in cardiac arrest, resided in a residential aged care facility, or were receiving palliative care services. Outcomes of interest included reattendance by ambulance, ED presentation; and, a high acuity outcome which we defined as a patient who (1) presented to ED and received an Australasian Triage Scale of category 1 (Resuscitation) or 2 (Emergency) AND was admitted to a ward OR (2) was admitted to an Intensive Care Unit, Coronary Care Unit or Catheter laboratory (regardless of triage category) OR (3) died. Outcomes of interest were considered within 48-h of initial EMS attendance. Results: A total of 1,777,950 cases were included in the study of which 3.1% were referred to a GP, 9.0% were discharged at scene without a follow-up recommendation, and 87.9% were transported to hospital. Patients referred to a GP were more likely than those discharged at scene to subsequently present to an ED within 48 h of their attendance (5.3% vs 3.8%). However, GP referral was not associated with any change to high acuity outcome (0.3% vs 0.2%) or ambulance reattendance (6.0% vs 6.0%) compared to discharge at scene. The only factors that were associated with ambulance reattendance, ED presentation, and a high acuity outcome were male gender and elevated temperature. Conclusions: Despite increasing low and medium-acuity casework in this EMS system, paramedic referral to a GP is not common practice. Referring a patient to a GP did not reduce the likelihood of patients experiencing a high acuity outcome or recalling an ambulance within 48 h, suggesting opportunity exists to refine paramedic to GP referral practices. [ABSTRACT FROM AUTHOR]

This review article examines past and current issues related to Functional Voice Disorders (FVDs) and their classification. FVDs are commonly seen in voice clinics but are diagnosed by exclusion of other pathology. Because of this, FVD etiology is unclear and frequently debated, resulting in inconsistent classification methods. Voice teachers frequently encounter vocalists with FVDs, so a thorough understanding of FVD symptoms is crucial, in order to aid in referral to specialized care and to be supportive when vocalists are undergoing treatment. A narrative literature review of FVD labels and contributing factors is provided, along with new findings and issues with traditional classification systems. Direction on addressing these conditions holistically is provided. Traditional classification systems for FVDs based on supraglottic muscle tension or psychological factors often do not sufficiently explain FVDs for the entirety of this patient population. New studies on neurological underpinnings for FVDs are intriguing, but not enough is known yet to apply them clinically. This review provides insight into the ongoing FVD classification discussion. Further research should center on neurological factors in FVDs and ways to approach FVDs as multifactorial conditions. Further discussions on accurate FVD diagnostic criteria and labels should continue. [ABSTRACT FROM AUTHOR]

AbstractIn this work, we develop a new ensemble learning framework, multi-label Random Subspace Ensemble (mRaSE), for multi-label classification. Given a base classifier (e.g., multinomial logistic regression, classification tree, K-nearest neighbors), mRaSE works by first randomly sampling a collection of subspaces, then choosing the best ones that achieve the minimum cross-validation errors and, finally, aggregating the chosen weak learners. In addition to its superior prediction performance, mRaSE also provides a model-free feature ranking depending on the given base classifier. An iterative version of mRaSE is also developed to further improve the performance. A model-free extension is pursued on the iterative version, leading to the so-called Super mRaSE, which accepts a collection of base classifiers as input to the algorithm. We show the proposed algorithms compared favorably with the state-of-the-art classification algorithm including random forest and deep neural network, via extensive simulation studies and two real data applications. The new algorithms are implemented in an updated version of the R package RaSEn. [ABSTRACT FROM AUTHOR]

AbstractCombining multiple predictors obtained from distributed data sources to an accurate meta-learner is promising to achieve enhanced performance in lots of prediction problems. As the accuracy of each predictor is usually unknown, integrating the predictors to achieve better performance is challenging. Conventional ensemble learning methods assess the accuracy of predictors based on extensive labeled data. In practical applications, however, the acquisition of such labeled data can prove to be an arduous task. Furthermore, the predictors under consideration may exhibit high degrees of correlation, particularly when similar data sources or machine learning algorithms were employed during their model training. In response to these challenges, this paper introduces a novel structured unsupervised ensemble learning model (SUEL) to exploit the dependency between a set of predictors with continuous predictive scores, rank the predictors without labeled data and combine them to an ensembled score with weights. Two novel correlation-based decomposition algorithms are further proposed to estimate the SUEL model, constrained quadratic optimization (SUEL.CQO) and matrix-factorization-based (SUEL.MF) approaches. The efficacy of the proposed methods is rigorously assessed through both simulation studies and real-world application of risk genes discovery. The results compellingly demonstrate that the proposed methods can efficiently integrate the dependent predictors to an ensemble model without the need of ground truth data. [ABSTRACT FROM AUTHOR]

AbstractWe study the concept of extended derivations of algebras which expands diverse definitions of generalized derivations given in the literature. We concentrate on the family of the anti-commutative algebras and classify such spaces of derivations by considering a suitable notion of equivalence when we restrict attention to sucssh algebras. Afterwards, we investigate a link between the well-known (α,β,γ) -derivations and some new extended derivations obtained in our classification. Finally, we present applications of extended derivations to study degenerations of algebras. [ABSTRACT FROM AUTHOR]

AbstractCarpal tunnel syndrome (CTS) is a common condition diagnosed using physical exams and electromyography (EMG) data. This study aimed to classify CTS severity using machine learning techniques. EMG data from 154 patients, including measurements of motor and sensory latency, velocity, and amplitude, were used to form a six-dimensional feature space. Classifiers such as DT, LDA, NB, SVM, k-NN, and ANN were applied, and the feature space was reduced using ANOVA, MRMR, Relieff, and PCA. The DT classifier with ANOVA feature selection showed the best performance for both full and reduced feature spaces. [ABSTRACT FROM AUTHOR]

This study investigates the neural processing of other-race and other-species faces, using spatial frequencies (SFs) to probe into early visual processing mechanisms. We conducted a basic classification task to reveal fundamental aspects of facial processing without the complexity of higher-order recognition tasks. This approach utilized SFs set at low (LSF, 0.53 cycles per degree [cpd]), broadband (BB), and high (HSF, 2.1 cpd) SFs to examine their impact on the both P100 and N170 components of event-related potentials. Our findings revealed a significant latency difference in the N170 component under HSF conditions, indicating race-specific efficiency in processing fine details between Asian and Caucasian faces. Furthermore, N170 amplitudes were consistently larger for human than monkey faces, with this difference more pronounced in LSF and BB conditions. This finding indicates that the other-species effect in early visual processing is more pronounced in holistic processing (LSF and BB conditions) rather than in fine-detail processing. [ABSTRACT FROM AUTHOR]

This article considers the problem of how to elucidate the concept of nonsense. Viewed from a general philosophical standpoint this matters because the concept is used by certain philosophers in their criticism of philosophical questions and theses. I start with a presentation of examples of utterances considered nonsense, along with Baier's classification of kinds of nonsense. I then present various approaches, pointing out that none of them are completely satisfactory. I subsequently propose an approach that is a modification of the austere conception: while consistent with the latter in that it treats nonsense as a lack of something, it differs by holding that a determination of the nature of various instances of nonsense can only be accomplished by examining how we show that some given utterance is nonsense. This approach places the emphasis on distinguishing the question of the meaninglessness of an utterance from the question of the incorrectness of linguistic expressions. The advantage of my conception is that on the one hand, it does not deny that there is an important non-psychological difference between philosophical nonsense and gibberish, while on the other it avoids the absurdity that philosophical utterances are nonsense because what they say is nonsensical. [ABSTRACT FROM AUTHOR]

Problems related to the classification of species are common in the field of palaeontology. The family Homalonotidae (Phacopida; Trilobites), in the Devonian period of the Malvinoxhosan Realm, can be included in this issue, since of its 22 species described since the 19th century, only half are considered valid today. This is mainly due to the existence of not very distinct diagnoses, the lack of comparisons between species and/or the lack of more up-to-date concepts. In order to demonstrate some of these problems and propose plausible solutions, this work was based on a bibliographic survey of the literature on the subject. The surveys showed the existence of 11 species of Homalonotidae, of the genera Burmeisteria, Burmeisterella, Digonus and Dipleura. The analyses indicated that some species were very similar (e.g. Burmeisteria accraensis and B. notica), while others had no distinguishable characteristics (e.g. Burmeisteria oiara). Thus, new analyses and classifications were proposed based on the specificity of each case and the use of more up-to-date concepts (e.g. taphotaxon), as well as the application of a phylogeny of morphological characters, in order to better understand the relationship between the taxa studied. [ABSTRACT FROM AUTHOR]

AbstractObjectiveMethodResultsConclusionsPerformance validity test (PVT) misclassification is an important concern for neuropsychologists. The present study determined whether expanding PVT analysis from 4-PVTs to 8-PVTs could lead to elevated rates of false positive performance validity misclassifications.Retrospective analysis of 443 patients who underwent a fixed neuropsychological test battery in a mixed clinical and forensic setting. Rates of failing two PVTs were compared to those predicted by Monte Carlo simulations when PVT analysis extended from 4-PVTs to 8-PVTs. Indeterminate performers (IDT; n = 42; those who failed two PVTs only after PVT analysis extended from 4-PVTs to 8-PVTs) were compared to a PVT-Fail group (n = 148; those who failed two PVTs in the 4-PVT battery or failed >2 PVTs).Rate of failing two PVTs remained stable when PVT analysis extended from 4- to 8-PVTs (12.9 to 11.9%) and was significantly lower than those predicted by Monte Carlo simulations. Compared to PVT-Fail, the IDT group was significantly younger, had stronger neuropsychological test performance, and demonstrated comparable rates of forensic referral and conditions with known neurocognitive sequelae (e.g. stroke, moderate-to-severe TBI).Monte Carlo simulations significantly overestimated rates of individuals failing two PVTs as PVT battery length doubled. IDT did not differ from PVT-Fail across variables with known PVT effects (e.g. age, referral context, neurologic diagnoses), lowering concern that this group is comprised entirely of false-positive PVT classifications. More research is needed to determine the effect of PVT battery length on validity classification accuracy. [ABSTRACT FROM AUTHOR]

In this introduction to the special issue Contested Categories in the Context of International Migration, we argue for the importance of critically engaging with state-created categories and classification systems. We advocate doing so in the particularly convoluted context of international migration, where states use categories to manage migrants’ entry, reception, and residence, making categorisation extremely consequential for individual migrants, even though bureaucratic documentation and everyday understandings of categories often differ greatly between migrants’ home countries and receiving states. We outline some of the scholarship on categorisation that has inspired our thinking, discuss why migration is such an important and generative site for examining categories and processes of categorisation, and finally consider how the contributions in this special issue attest to this. [ABSTRACT FROM AUTHOR]

AbstractIn this paper, we generalize the classical method used to classify nilpotent low-dimensional Lie algebras to serve for nilpotent Bol algebras. The algebraic classification of the nilpotent Bol algebras up to dimension four is given. [ABSTRACT FROM AUTHOR]

Over the past decades, numerous studies have enhanced the understanding of the Salafi milieu, revealing a diversity within the movement that goes beyond the traditional classifications of quietist, jihadist, and political Salafism. This research builds on recent scholarly work by exploring the porous boundaries of the Salafi realm, emphasizing the concept of hybridization. It examines the blending of registers and repertoires of action from other social worlds with Salafism, utilizing a social movement perspective and first-hand field data. The study investigates Salafi trajectories of engagement beyond the religious sphere in Tunisia and Algeria during perceived periods of constraint. This comparative analysis is both empirically and theoretically significant, given the distinct socio-political landscapes of the two countries. Tunisia was recognized as a democratic exception in the MENA region in 2011. In contrast, Algeria, rising from the ashes of its civil war, is regarded as a resilient rentier state largely unaffected by the Arab uprisings but significantly impacted by the Hirak movement in 2019. Despite these differences, both countries have experienced revolutionary moments and repressive campaigns, offering opportunities for the transformation of Salafism. [ABSTRACT FROM AUTHOR]

The Zrt/Irt-like protein (ZIP) family consists of ubiquitously expressed divalent d-block metal transporters that play central roles in the uptake, secretion, excretion, and distribution of several essential and toxic metals in living organisms. The past few years has witnessed rapid progress in the molecular basis of these membrane transport proteins. In this critical review, we summarize the research progress at the molecular level of the ZIP family and discuss the future prospects. Furthermore, an evolutionary path for the unique ZIP fold and a new classification of the ZIP family are proposed based on the presented structural and sequence analyses. [ABSTRACT FROM AUTHOR]

Currently, all eligible goalball players compete together irrespective of their level of vision impairment, yet it remains unclear whether those with more impairment are disadvantaged during competition. Following the International Paralympic Committee's requirement for evidence-based, sport-specific classification, this study assessed whether individual goalball performance relates to the level of visual impairment. Using results from the 2016 and 2020 Paralympic Games, players' sport classes and in-competition key performance statistics (minutes played, throws per minute, goals per minute, penalties conceded per minute, blocks per minute, and goals per throw) were extracted. Players' visual acuity and visual field results were obtained through the IBSA Sport Administration System. Results showed no statistically significant differences in performance between classes. Further, there were no significant relationships between vision and performance for all six variables for female players. A small but significant positive correlation was found between visual acuity and the number of penalties conceded for male players. Collectively, the results suggest that currently eligible players compete fairly against one another during competitive goalball matches. Results provide support for the existing system of classification whereby all eligible athletes compete against each other irrespective of their level of impairment. [ABSTRACT FROM AUTHOR]

AbstractThis article focuses on indecomposable involutive non-degenerate set-theoretical solutions to the Yang–Baxter equation. More specifically, we give a full classification of those solutions which are of size p2, for p a prime. We do this through a thorough analysis of their associated permutation braces and using the language of cycle sets. [ABSTRACT FROM AUTHOR]

The Transformer has become pivotal for the integrated analysis of multi-source remote-sensing (RS) data in Earth observation, particularly in applications such as the fusion classification of hyperspectral images (HSI) and Light Detection and Ranging (LiDAR) data. However, Transformers are often employed as effective feature extractors by adopting similar processing blocks for different modalities from multi-source sensors, overlooking differences in imaging principles and data characteristics. Moreover, in the feature extraction process across different sensor data, there is a lack of necessary cross-modal information interaction, leading to insufficient utilization of complementary information between different sensors and resulting in suboptimal fusion outcomes. In this paper, we propose an interactive Transformer and CNN network for the fusion classification of HSI and LiDAR data. Specifically, a heterogeneous three-branch network architecture is designed for HSI and LiDAR data, where Transformers and CNNs encapsulate global contextual spatial and spectral information for HSI and capture geometric elevation patterns for LiDAR data, respectively. Elevation-Spatial Interaction (ESI) and Spectral-Spatial Interaction (SSI) modules are then introduced for multi-stage feature interaction. ESI enables the CNN-Transformer network to focus on essential local elevation details while simultaneously modelling global contextual spatial information. SSI facilitates the Transformer-Transformer network to cyclically intertwine spectral and spatial information for long-range spectral-spatial feature fusion. Finally, the interacted elevation, spatial, and spectral features undergo the Gated Fusion module to achieve hierarchical fusion adaptively, resulting in an elevation-spatial-spectral representation. Experiments conducted on three benchmark HSI-LiDAR datasets demonstrate the effectiveness of our proposed approach. [ABSTRACT FROM AUTHOR]

We investigate the Möbius groups theory in the framework of bicomplex numbers, which are pairs of complex numbers making up a commutative ring with zero-divisors. In this paper, we first generalize classical conjugacy classification to bicomplex analysis. Then we have a discussion of the iterates of a bicomplex Möbius transformation and study the attractive and repulsive fixed points in bicomplex setting. Finally, we shall prove two useful results concerning fixed point. [ABSTRACT FROM AUTHOR]

The integration of multi-modal data allows for a more accurate representation of the ground characteristics. For a comprehensive interpretation of remote sensing data, existing multi-modal data fusion research mainly focuses on the joint utilization of 3D Light Detection and Ranging (LiDAR) and 2D Hyperspectral Image (HSI) data. However, existing algorithms do not pay much attention to the interaction of high-level semantic information between different modal data before fusion. This paper proposes a novel multi-modal data fusion deep learning network with the Cross-Modal Self-Attentive Feature Fusion Transformer (SAFFT). The framework employs a multi-head self-attention layer to fuse various attention information from multiple heads, effectively enhancing advanced feature information from different modalities for comprehensive integration. Experimental results on the Houston 2013 dataset demonstrate the effectiveness of the proposed method, which achieves an overall accuracy (OA) of 94.3757% in classifying 15 semantic classes. [ABSTRACT FROM AUTHOR]

In this paper, we mainly devote to investigate the classification of traveling waves to the generalized Camassa–Holm equation with dual-power nonlinearities. Utilizing the celebrated approach of classification of traveling wave solution which was proposed by Jonatan Lenells [Traveling wave solutions of the Camassa-Holm equation. J Diff Equ. 2005;217(2):393–430]. We show a result concerns the regularity of traveling waves and then classify all traveling wave solutions. [ABSTRACT FROM AUTHOR]

The result of this paper contributes to the classification of triangular maps of the square with zero topological entropy stated by A. N. Sharkovsky in the 1980s. The problem was if a triangular map of the square such that its any ω-limit set contains unique minimal set can be distributionally chaotic. So far such result was disproved only for the class of triangular maps non-decreasing on fibres [L. Paganoni, J. Smítal, Strange distributionally chaotic triangular maps, Chaos Solitons Fractals 26(2) (2005), pp. 581–589]. In this paper, we solve the problem in negative for all triangular maps of the square, correcting the original result from Balibrea and Smítal [Strong distributional chaos and minimal sets, Topology appl. 156 (2009), pp. 1673–1678]. [ABSTRACT FROM AUTHOR]

Attributed network embedding (ANE) maps nodes in a network into a low-dimensional space while preserving the intrinsic essence of node attribute and network topology. Incorporating node attribute and network topology with more deeply and more harmoniously is a critical and challenging issue in the ANE, because node attribute and network topology are two kinds of heterogeneous information. Existing approaches fuse two kinds of heterogeneous information at different stages: i.e. before, during or after the learning process. In fact, fusions at different stages have their own advantages and disadvantages. To maximise the profit of utilising the attributed and networked information in ANE, we propose an Attributed Network Embedding model with Dual Fusion strategies (abbr. ANEDF), which consists of both mutually beneficial components: early fusion component for capturing the latent complementarity and late fusion component for extracting the unique and distinctive information from node attribute and network topology. The two components are co-trained during the learning process, which promotes information interaction and captures the consensus of heterogeneous information. Extensive experiments with the tasks of node classification, node clustering, link prediction and visualisation on eight publicly available networks have been conducted to evaluate the effectiveness and rationality of the proposed model. The experimental results demonstrate that ANEDF obtains the best classification, clustering and link prediction performance on 6–7 of 8 datasets, respectively. [ABSTRACT FROM AUTHOR]

Background: Epidermoid tumors of the cerebellopontine angle and posterior fossa account for approximately 1% of all intracranial tumors. Classifications that may guide surgical planning in the current neurosurgical practice are lacking. This study aimed to focus on the surgical outcome and suggest a classification system that may aid neurosurgeons in determining the goal of resection to minimize morbidity and mortality rates. Methods: The study population comprised patients who underwent surgery and follow-up for tissue-proven epidermoid tumors between 2015 and 2020. Patients' data, including demographic features, clinical symptomatology, the extent of surgical resection, and postoperative outcomes, were retrospectively evaluated. A new classification system was designed based on the anatomical–radiological findings and was evaluated in terms of clinical symptomatology, radiological features, surgical approach, and postoperative outcomes. Results: The patient population comprised 22 women (57.9%) and 16 men (42.1%), with a mean age of 34.9 years. A practical classification system based on the radiological–anatomical vertical (1, 2, and 3) and horizontal (a, b, and c) tumor extensions was designed. No significant differences were found in the patients in terms of sex/age. The most commonly observed symptom was gait disturbance (34.2%). The preoperative tumor diameter was significantly larger in the subtotal resection (STR) group than in the gross total resection (GTR) and near-total resection (NTR) groups. Significantly more cistern involvement was observed in the STR group than in the GTR group. The GTR, NTR, and STR rates were higher in grade 1, 3, and 2 cases, respectively. The subgroup 'a' was correlated with higher resection rates (GTR and NTR), whereas the subgroup 'b' was correlated with STR. Conclusions: Our suggested classification system represents a simple and practical model that may guide neurosurgeons in predicting the goal of resection during surgical planning and in minimizing potential morbidity. [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]

Optimizing text preprocessing and text classification algorithms is an important, everyday task in large organizations and companies and it usually involves a labor-intensive and time-consuming effort. For example, the filtering and sorting of a large number of electronic mails (emails) are crucial to keeping track of the received information and converting it automatically into useful and profitable knowledge. Business emails are often unstructured, noisy, and with many abbreviations and acronyms, which makes their handling a challenging procedure. To overcome those challenges, a two-step classification approach is proposed, along with a two-cycle labeling procedure in order to speed up the labeling process. Every step incorporates a heuristic classification approach to assign emails to predefined classes by comparing several classification and text vectorization algorithms. These algorithms are compared and evaluated using the F1 score and balanced accuracy. The implementation of the proposed algorithm is demonstrated in a shipbroker agent operating in Greece with excellent performance, improving organization and administration while reducing expenses. [ABSTRACT FROM AUTHOR]

Skin cancer is a serious cancer caused by the uncontrollable growth of damaged DNA that leads to death. It is essential to identify the disease at the initial stage and eliminate it from spreading. Hence, this research introduces an automated hybrid deep learning (DL) technique for improving the accuracy of cancer diagnostic systems. In the pre-processing, histogram stretching, colour constancy, hair removal and noise elimination process are undertaken. Then, the Adaptive fuzzy c-means clustering (AFC) is introduced for segmenting the tumour portion. Then, the feature extraction and classification stage is performed using an attention-based deep convolutional capsule weighted auto-encoder classifier network (A-DCCN-WAE) technique. For experimentation, the dataset is collected from International Skin Imaging Collaboration (ISIC) 2019 dataset and is implemented in the PYTHON platform. The proposed method obtains an accuracy of 97%, Precision of 95.6%, F-measure of 97.2%, Mathew's correlation coefficient (MCC) of 90.6% and specificity of 96.9%. [ABSTRACT FROM AUTHOR]

An evaluation of forensic hair analysisbetween experts, novices and the recently developed machine learning platform, HairNet, was conducted to assess accuracy and reliability. Our hypothesis stated experts and the machine learning platform will outperform novices in classifications of hair as human or non-human and suitability for nDNA analysis based on specialist knowledge and from training of the model. Statistically significant differences between novices and experts were found and attributed to training and experience for more complex classifications. For more simplistic classifications, no statistically significant difference between the novice and the experts was found. HairNet proved responses similar to expert responses in all classifications. Encouraging feedback was received regarding the use of technology and machine learning. The utilization of technology undoubtedly holds great promise to become part of the forensic tool kit for improving the efficiency and reliability of forensic hair analysis and in research, education and competency testing. [ABSTRACT FROM AUTHOR]

In this paper, we give a complete classification of torsion pairs in m-cluster categories of type D when m is odd, denoted by C D n m , via a bijection to combinatorial objects called Ptolemy diagrams of type D. As applications, we classify m-rigid subcategories of C D n m , which gives Jacquet-Malo's classification of m-cluster tilting subcategories of C D n m . When m = 1, we generalizes the work of Holm, Jørgensen and Rubey [14] for the classification of torsion pairs in cluster categories of type Dn. Communicated by Steffen Oppermann [ABSTRACT FROM AUTHOR]

Examining fabric weave patterns (FWPs) is connected to image-based surface texture feature (STF) acquisition, which can be difficult due to the structural complexity of woven fabrics. Randomly capturing static images may not correlate with the entire STF of a fabric. Traditionally, FWPs analysis is conducted by human vision, which causes an intensive cognitive load. Ultimately, the human vision-based cognitive load leads to ineffective quality inspection and error-prone FWPs analysis results. Given the above challenges, this study proposes a new streamlined video-based FWPs recognition method by incorporating Bayesian-optimized convolutional neural network (Bayes Opt-CNN). Essentially, this method is capable of leveraging the spatiotemporal features of the fabric's intricate surface structure. In this study, to validate the effectiveness of the proposed method, seven types of fabric structures were captured as streamline videos, which were then converted into sequences of image frames. Subsequently, the Bayesian optimization process was introduced to select the best hyperparameters during CNN-based supervised learning for pattern recognition. The evaluation demonstrates that the proposed method outperforms the benchmark method for identifying FWPs. [ABSTRACT FROM AUTHOR]