Combination Of Features Research Articles

A COMPARATIVE STUDY OF THE STRUCTURAL-SEMANTIC FEATURES OF ENGLISH AND UZBEK WORD COMBINATIONS

This study presents a comparative analysis of the structural-semantic features of word combinations in English and Uzbek, focusing on their formation, idiomaticity, and cultural-linguistic influences. English, as an analytic language with fixed word order and extensive phrasal verb usage, contrasts sharply with Uzbek, an agglutinative language that relies on suffixation, postpositions, and auxiliary verb constructions. The research examines key differences in syntax, morphology, and semantic transparency, highlighting challenges in translation and second-language acquisition. While English favors idiomatic expressions with opaque meanings (e.g., "kick the bucket"), Uzbek word combinations tend toward literalness, though Persian- and Russian-derived idioms exist (e.g., "dil kushodasi" [heart’s joy]). The study also explores how cultural and historical borrowings shape collocational patterns in both languages. By systematically comparing these features, the paper aims to enhance cross-linguistic understanding, aiding translators, linguists, and learners in navigating the complexities of both systems. Findings underscore the necessity of context-aware learning strategies to master these divergent structural-semantic frameworks.

Dual energy CT-derived quantitative parameters and hematological characteristics predict pathological complete response in neoadjuvant chemoradiotherapy esophageal squamous cell carcinoma patients

PurposeThere is no gold standard method to predict pathological complete response (pCR) in esophageal squamous cell carcinoma (ESCC) patients before surgery after neoadjuvant chemoradiotherapy (nCRT). This study aims to investigate whether dual layer detector dual energy CT (DECT) quantitative parameters and clinical features could predict pCR for ESCC patients after nCRT.Patients and methodsThis study retrospective recruited local advanced ESCC patients who underwent nCRT followed by surgical treatment from December 2019 to May 2023. According to pCR status (no visible cancer cells in primary cancer lesion and lymph nodes), patients were categorized into pCR group (N = 25) and non-pCR group (N = 28). DECT quantitative parameters were derived from conventional CT images, different monoenergetic (MonoE) images, virtual non-contrast (VNC) images, Z-effective (Zeff) images, iodine concentration (IC) images and electron density (ED) images. Slope of spectral curve (λHU), normalized iodine concentration (NIC), arterial enhancement fraction (AEF) and extracellular volume (ECV) were calculated. Difference tests and spearman correlation were used to select quantitative parameters for DECT model building. Multivariate logistic analysis was used to build clinical model, DECT model and combined model.ResultsA total of 53 patients with locally advanced ESCC were enrolled in this study who received nCRT combined with surgery and underwent DECT examination before treatment. After spearman correlation analysis and multivariate logistic analysis, AEF and ECV showed significant roles between pCR and non-pCR groups. These two quantitative parameters were selected for DECT model. Multivariate logistic analysis revealed that LMR and RBC were also independent predictors in clinical model. The combined model showed the highest sensitivity, specificity, PPV and NPV compared to the clinical and DECT model. The AUC of the combined model is 0.893 (95%CI: 0.802–0.983). Delong’s test revealed the combined model significantly different from clinical model (Z =-2.741, P = 0.006).ConclusionDual-layer DECT derived ECV fraction and AEF are valuable predictors for pCR in ESCC patients after nCRT. The model combined DECT quantitative parameters and clinical features might be used as a non-invasive tool for individualized treatment decision of those ESCC patients. This study validates the role of DECT in pCR assessment for ESCC and a large external cohort is warranted to ensure the robustness of the proposed DECT evaluation criteria.

MANAGEMENT THE FINANCIAL POTENTIAL OF ORGANIZATIONS THROUGH THE PRISM OF EFFECTIVENESS

The relevance of the research is determined by the significance of the processes of formation and growth of financial potential in modern organizations, which are driven by their implementation through systematic measures aimed at creating, accumulating, and increasing financial resources to ensure stable development, competitiveness, and the achievement of strategic goals. The article aims to specify the features of financial potential management in organizations, with a focus on the effective processes of its formation and growth. The research findings prove that the combination of well-known features of financial potential management in organizations should be sufficient for organizing a systematic analysis of the processes of its formation and growth, focusing on their overall effectiveness in terms of identifying the state (through the content of static and dynamic visualizations of the balance), possible scenarios of changes, and important areas for adjusting the overall variability.

Effects of Cash-Transfer Programs New Evidence From Uganda

Purpose: We explore the impact of a unique universal, unconditional cash-transfer project, the LIFE-project, in Welle, Uganda. Study Design: Employing mixed methods with difference-in-differences estimates and interviews, we focus on the effects of the LIFE-project on economic, health and well-being, as well as social cohesion outcomes. Findings: We find that the LIFE-project has positive effects on the residents of Welle along several dimensions, including sustainable livelihoods, total consumption, physical health, emotional well-being, and social cohesion. Contributions: The unique combination of features of the LIFE-project enables us to explore challenges outlined in the literature, as well as some novel questions, including those related to the inclusion of minors in cash-transfer programs and changes in social cohesion resulting from cash transfers. Implications: Local institutional incentives and enforcement mechanisms for tackling communal challenges and emerging conflicts, as well as community-managed funds, are of key importance for cash transfers to succeed.

Detection of Negative Emotions in Short Texts Using Deep Neural Networks.

Emotion detection is crucial in various domains, including psychology, health, social sciences, and marketing. Specifically, in psychology, identifying negative emotions in short Spanish texts, such as tweets, is vital for understanding individuals' emotional states. However, this process is challenging because of factors such as lack of context, cultural nuances, and ambiguous expressions. Although much research on emotion classification in tweets has focused on applications such as crisis analysis, mental health monitoring, and affective computing, most of it has been conducted in English, leaving a significant gap in addressing the emotional needs of Spanish-speaking communities. To address this gap, we used a corpus of 12,000 Spanish tweets tagged with Ekman's negative emotions (sadness, anger, fear, and disgust). Traditional features (n-grams of different types and sizes), syntactic n-grams, and combined features were evaluated. Different deep neural networks, including convolutional neural networks, Bidirectional Encoder Representations of Transformers (BERT), and the robust optimized BERT approach called RoBERTa, were implemented and compared with traditional machine learning methods to identify the most effective method. Extensive testing revealed that BERT achieved the best result, with a macro F1 score of 0.9973. Furthermore, we reported the carbon emissions generated during the training of each implemented method. This study makes a unique contribution by focusing on negative emotions in Spanish, leveraging one of the largest and highest-quality corpora available. It stands out for implementing advanced transformers such as RoBERTa and integrating combined and syntactic n-grams in traditional methods. Furthermore, it highlights how parameters, features, and preprocessing significantly influence performance.

A Study of Subacute Sclerosing Panencephalitis (SSPE): Clinical and Investigation Profile in a Tertiary Care Hospital

Subacute Sclerosing Panencephalitis (SSPE) is a rare and slowly progressive neurodegenerative disorder caused by persistence of mutant measles virus in the central nervous system. Myoclonic jerk is the predominant feature. Objective: To identify common clinical, demographic and investigation findings of diagnosed cases of Subacute Sclerosing Panencephalitis. Material and Methods: This is a Cross sectional observational study was done in Department of Paediatric Neuroscience, Bangladesh Shishu Hospital and Institute during the period from September 2024 to February 2025. Total 10 cases were included after diagnosis of SSPE. The diagnosis was based on a combination of clinical features and investigation findings. When clinical and investigation findings supported the diagnosis of SSPE then the case was included in this study. All data were recorded on a previously prepared standard data collection form Results: 90% cases were in 5-15 years age group. 80% cases were male and 20% were female. Among 10 patients, 60% patients had past history of measles infection and 80% patients were vaccinated against measles. Presenting symptoms were behavioral change (40%), cognitive decline (70%), decreased school performance (50%) and motor regression (100%), myoclonic jerks (80%), fall to ground (20%), altered speech (60%), vision loss (20%), spasticity (80%) and clonus (30%). Measles specific IgG in CSF was positive in (80%) cases and positive serum measles antibody was in (100%) cases. Patients had an abnormal EEG finding which includes periodic complexes (70%), generalized spike (20%) or slow wave (10%). Total 80% patients had abnormal findings on neuroimaging. Conclusion: Children presenting with developmental regression, myoclonic jerks, characteristics EEG changes, measles specific antibody in CSF and serum may help in the diagnosis of SSPE.

PET image nonuniformity texture features for metastasis risk prediction in osteosarcoma.

PET image analysis provides tumor heterogeneity data related to neoadjuvant chemotherapy response (NACR) and metastatic risk in osteosarcoma. Ki-67 expression is used to predict metastasis. The accuracy of prediction models with image quantitative features can be improved by including genetic information. Here, we aimed to evaluate the accuracy of a combination of heterogeneous 18F-fluorodeoxyglucose PET image texture features and Ki-67 expression as predictive indicators of metastasis. PET images and clinical data of 82 patients with osteosarcoma before and after treatment were collected. Quantitative features were extracted from the PET images obtained before treatment, and the area under the receiver operating characteristic curve (AUC) for NACR and metastatic event was calculated. Relative risk and odds analyses of the quantitative features of the entire image were performed. Kaplan-Meier survival analysis was performed to determine the relationship between image quantitative features and clinical information. The machine learning prediction model was evaluated using valid image quantitative features and various algorithms of the univariate analysis. Forty-seven image textures were obtained. The AUC values were 0.504-0.62 for NACR and 0.510-0.598 for metastatic events. The NACR and metastatic risk were related to the gray-level run length matrix (GLRLM) run length nonuniformity (RLNU) (relative risk: 1.3846, P = 0.0138 for NACR; relative risk: 2.1284, P = 0.049 for metastatic event) in the univariate analysis. The accuracy of the prediction model using the random forest algorithm with GLRLM RLNU, Ki-67 expression, and NACR was 0.91 for metastatic risk. NACR and metastatic risk were predicted with high accuracy using the nonuniformity in PET image texture. Combining PET image texture nonuniformity with Ki-67 expression and clinical data can enhance the accuracy of metastasis prediction in osteosarcoma. This multimodal approach may support metastasis risk prediction in osteosarcoma and aid in personalized treatment planning.

Neural signatures of depression: Classifying drug-naïve MDD patients with time- and frequency-domain EEG features during emotional processing

Abstract Accurate classification of major depressive disorder (MDD) remains a significant challenge, particularly because of the confounding effect of medications. This study bridges this gap by focusing on the classification of drug-naïve individuals diagnosed with MDD and healthy controls (HCs) using electroencephalogram (EEG) data recorded during emotional processing tasks. This study involved 14 HCs and 14 drug-naïve individuals diagnosed with MDD (aged 18–31, 12+ years of education, 12F/2M). The participants were presented with positive, neutral, and negative images collected from the International Affective Picture System. The mean power amplitudes of event-related potentials (ERP), including the P200, P300, early, middle, and late components of the late positive potential (LPP), were computed, along with band power features, and used as features for classifiers. A support vector machine model was employed for classification to evaluate the individual contributions of ERP components and band power features and explore the combined effects of ERP components and band power features within themselves. The alpha band power achieved the highest individual classification accuracy among the band power features for negative stimuli (92.86%). The late LPP component was the most discriminative ERP component for positive stimuli, yielding an accuracy rate of 89.29%. Combined analysis of the band power features exhibited high accuracy for both positive and negative stimuli (92.86% each). When the ERP components were combined, the classifier achieved the highest accuracy of 89.29% for both negative and neutral stimuli. Our findings suggest that alpha band power and LPP responses to negative and positive stimuli, respectively, can be used to detect MDD. The comparable performance of individual features to that of the combined feature sets indicates their strength as indicators of emotional processing in MDD. These findings provide valuable insights into the development of more reliable diagnostic tools and treatment monitoring strategies that focus on emotional processing in MDD.

ABOUT BLOG AS A GENRE OF PROFESSIONAL INTERNET COMMUNICATION IN THE COURSE OF «LANGUAGE OF BUSINESS COMMUNICATION»

The research focuses on exploring the structural and compositional features of blogs as a genre of professional Internet communication in the course of «Language of business communication». The tasks are outlined as follows: 1) to reveal the linguistic characteristics, functions, classification principles, and formatting rules of blogs; 2) to analyze blogs as a genre of professional Internet communication. The descriptive method and various types of linguistic analysis are applied. To develop students’ practical skills in professional blogging, analysis, and critical evaluation of posts by scientists, educators, and well-known public figures on the websites of reputable platforms in Ukraine, theoretical familiarization with linguistic features (stylistic, lexical, grammatical), functions (communicative, psychotherapeutic, educational, self-presentation, socialization, and others), classification principles (in particular, by authorship, by type of multimedia, by topic), and blog formatting rules (structural and compositional elements: profile, main page, individual entry/post page, friends’ feed), as well as practical analysis of the themes and structural and compositional features of the blogs by Oleksandr Ponomariv, Iryna Farion, and Taras Kremin are proposed. Posts by Oleksandr Ponomarev are grouped mainly with regard to the recommendations on language culture, discussing cases of violations of certain norms of literary language. Posts related to the history of language, sociolinguistics are also singled out. The posts demonstrate a combination of features from various functional styles of the Ukrainian language – scientific, journalistic, and colloquial. Posts by Iryna Farion are grouped in terms of the themes of the titles: about political life, problems of language policy; about prominent Ukrainians; letters, appeals; about scientific and teaching life, international relations. Such genre features of the posts as stylistic syncretism (features of journalistic, scientific, official-business, artistic, and colloquial styles), blurring the boundaries of taboo vocabulary, the use of stylistically reduced, jargon, and emotionally marked vocabulary, colloquial idioms, self-presentation (in particular, adherence to the orthographic norms of the Ukrainian spelling of 1928) are evidenced. Posts by Taras Kremin are proposed to be divided into three thematic groups: about the linguicide of the Ukrainian language by the Russian Federation, Russian aggression and propaganda, and counteraction to them; issues of the internal language situation and policy; international support for Ukraine, the Ukrainian language, and the status of the Ukrainian language in the world. The structural and compositional features of the posts, which demonstrate the important role of titles as external indicators of the text, a developed system of hyperlinks in the main body of the text, the accumulation of emotionally marked, evaluative vocabulary, grammatical and graphic markers, and the use of dialogization tools, are analyzed. Key words: «Language of Business Communication», b

EEG microstate biomarkers for schizophrenia: a novel approach using deep neural networks.

Schizophrenia remains a challenging neuropsychiatric disorder with complex diagnostic processes. Current clinical approaches often rely on subjective assessments, highlighting the critical need for objective, quantitative diagnostic methods. This study aimed to develop a robust classification approach for schizophrenia using EEG microstate analysis and advanced machine learning techniques. We analyzed EEG signals from 14 healthy individuals and 14 patients with schizophrenia during a 15-min resting-state session across 19 EEG channels. A data augmentation strategy expanded the dataset to 56 subjects in each group. The signals were preprocessed and segmented into five frequency bands (delta, theta, alpha, beta, gamma) and five microstates (A, B, C, D, E) using k-means clustering. Five key features were extracted from each microstate: duration, occurrence, standard deviation, coverage, and frequency. A Deep Neural Network (DNN) model, along with other machine learning classifiers, was developed to classify the data. A comprehensive fivefold cross-validation approach evaluated model performance across various EEG channels, frequency bands, and feature combinations. Significant alterations in microstate transition probabilities were observed, particularly in higher frequency bands. The gamma band showed the most pronounced differences, with a notable disruption in D → A transitions (absolute difference = 0.100). The Random Forest classifier achieved the highest accuracy of 99.94% ± 0.12%, utilizing theta band features from the F8 frontal channel. The deep neural network model demonstrated robust performance with 98.31% ± 0.68% accuracy, primarily in the occipital region. Feature size 2 consistently provided optimal classification across most models. Our study introduces a novel, high-precision EEG microstate analysis approach for schizophrenia diagnosis, offering an objective diagnostic tool with potential applications in neuropsychiatric disorders. The findings reveal critical insights into neural dynamics associated with schizophrenia, demonstrating the potential for transforming clinical diagnostic practices through advanced machine learning and neurophysiological feature extraction.

Harnessing unsupervised machine learning with [18F]FDG PET/CT to develop a composite model for predicting overall survival in cervical cancer patients undergoing concurrent chemoradiotherapy

Background and purpose:This study sought to develop an advanced composite model to enhance the prognostic accuracy for cervical cancer patients undergoing concurrent chemoradiotherapy (CCRT). The model integrated imaging features from [18F]FDG PET/CT scans with inflammatory markers using a novel unsupervised two-way clustering approach.MethodsIn this retrospective study, 154 patients diagnosed with primary cervical cancer and treated with CCRT were evaluated using [18F]FDG PET/CT scans. A total of 1,702 radiomic features were extracted from the imaging data. These features underwent rigorous selection based on reproducibility and non-redundancy. The unsupervised two-way clustering method was then employed to simultaneously stratify patients and reduce the dimensionality of features, resulting in the generation of meta-features that were subsequently used to predict overall survival.ResultsKaplan-Meier survival analysis demonstrated that the two-way clustering method successfully stratified patients into distinct risk groups with significant survival differences (P<0.001), outperforming traditional K-means clustering. Predictive models constructed using meta-features derived from two-way clustering showed superior performance compared to those using principal component analysis (PCA), particularly when more than four features were included. The highest C-index values for the COX, COX_Lasso, and RSF models were observed with nine meta-features, yielding results of 0.691 ± 0.026, 0.634 ± 0.018, and 0.684 ± 0.020, respectively. In contrast, models based solely on clinical variables exhibited lower predictive performance, with C-index values of 0.645 ± 0.041, 0.567 ± 0.016, and 0.561 ± 0.033. The combination of clinical data, inflammatory markers, and radiomic features achieved the highest predictive accuracy, with a mean AUC of 0.88 ± 0.07.ConclusionIntegrating radiomic data with inflammatory markers using unsupervised two-way clustering offered a robust approach for predicting survival outcomes in cervical cancer patients. This methodology presented a promising avenue for personalized patient management, potentially leading to more informed treatment decisions and improved outcomes.

Decorated Standing Stones – The Hagbards Galge Monument in Southwest Sweden

Abstract This article focuses on Hagbards galge (in English Hagbard's gallows), a burial site in south-west Sweden that consists of two stone settings with monumental paired standing stones decorated with rock art. The combination of these different features into one burial site makes the monument unique in a Scandinavian setting. This article aims to contextualize the monument in a European Bronze Age framework. We do this by discussing the stone settings and the standing stones and comparing these to other similar monuments to get an idea of their dating. Furthermore, a new laser scanning documentation has been carried out on the rock art revealing new details and images. Our findings suggest that the monument was constructed in Montelius’ period 4 (1100–900 BC) and that the rock art includes images of a sword and a shield which are atypical for Scandinavia but appear in Central Europe and in the British Isles. Furthermore, our analysis reveals that the people controlling this site held a key position in a communicative network including both land-based and sea-based transportation routes. Altogether, this suggests the burial to be a manifestation of wealth based on international trade networks which were intensified in the twelfth century BC.

MCDGLN: Masked connection-based dynamic graph learning network for autism spectrum disorder.

MCDGLN: Masked connection-based dynamic graph learning network for autism spectrum disorder.

GLEAM: A multimodal deep learning framework for chronic lower back pain detection using EEG and sEMG signals.

GLEAM: A multimodal deep learning framework for chronic lower back pain detection using EEG and sEMG signals.

When bipartite graph learning meets anomaly detection in attributed networks: Understand abnormalities from each attribute.

When bipartite graph learning meets anomaly detection in attributed networks: Understand abnormalities from each attribute.

Improving Crop Classification by Integrating Phenology Using Random Forests: Assessing the Role of Feature Selection

The increase of satellite images with high spatial resolution, short revisit period, and wide spatial coverage has brought an enormous amount of data; however, limited efforts have been made in feature selection for crop classification. Furthermore, different crop types have unique spectral, spatial, and phenological characteristics that have not been well understood and fully used for the expected results. This study established a crop-classification framework using the random forest model by integrating four types of features (i. e., spectral reflectance features, vegetation index features, spatial texture features, and crop phenological features) over 15 scenarios generated from Sentinel-2 images. The random forest model performed best (overall accuracy = 92.86%; Kappa coefficient = 0.8995) by integrating the four types of features. We systematically assessed the contribution of feature combinations on individual crop classification. Specifically, different feature combinations can effectively improve the recognition accuracy of different crop types. Our findings can provide great potential in choosing optimal features for crop classifications and benefit the application of machine learning in remote sensing–based crop mapping.

Graph vertex and spectral features for EEG-based motor imagery classification.

Graph vertex and spectral features for EEG-based motor imagery classification.

Radiomics Analysis on Computed Tomography Images for Prediction of Chemoradiation-induced Heart Failure in Breast Cancer by Machine Learning Models

Abstract Background: This study aimed to evaluate the effectiveness of clinical, dosimetric, and radiomic features from computed tomography (CT) scans in predicting the probability of heart failure in breast cancer patients undergoing chemoradiation treatment. Materials and Methods: We selected 54 breast cancer patients who received left-sided chemoradiation therapy and had a low risk of natural heart failure according to the Framingham score. We compared echocardiographic patterns and ejection fraction (EF) measurements before and 3 years after radiotherapy for each patient. Based on these comparisons, we evaluated the incidence of heart failure 3 years postchemoradiation therapy. For machine learning (ML) modeling, we first segmented the heart as the region of interest in CT images using a deep learning technique. We then extracted radiomic features from this region. We employed three widely used classifiers – decision tree, K-nearest neighbor, and random forest (RF) – using a combination of radiomic, dosimetric, and clinical features to predict chemoradiation-induced heart failure. The evaluation criteria included accuracy, sensitivity, specificity, and the area under the receiver operating characteristic curve (area under the curve [AUC]). Results: In this study, 46% of the patients experienced heart failure, as indicated by EF. A total of 873 radiomic features were extracted from the segmented area. Out of 890 combined radiomic, dosimetric, and clinical features, 15 were selected. The RF model demonstrated the best performance, with an accuracy of 0.85 and an AUC of 0.98. Patient age and V5 irradiated heart volume were identified as key predictors of chemoradiation-induced heart failure. Conclusion: Our quantitative findings indicate that employing ML methods and combining radiomic, dosimetric, and clinical features to identify breast cancer patients at risk of cardiotoxicity is feasible.

Integrating Molecular Testing With Clinical Criteria and Histopathology Improves Diagnostic Precision in Immune-Mediated Liver Diseases.

Integrating Molecular Testing With Clinical Criteria and Histopathology Improves Diagnostic Precision in Immune-Mediated Liver Diseases.

Radiomics from dual-energy CT-derived iodine maps for predicting lymph node metastases in patients with resectable rectal cancer.

BackgroundLymph node metastasis (LNM) is a poor prognostic predictor and is highly correlated with local recurrence in rectal cancer patients.ObjectiveTo investigate the value of radiomics from dual-energy CT-derived iodine maps for the preoperative prediction of LNM in rectal cancer patients.MethodsA total of 176 patients were enrolled in this study (training group, n = 123; validation group, n = 53). A radiomic signature was constructed via support vector machine (SVM) modeling. Seven models, including a clinical feature model (Model 1), an arterial model (Model 2), a venous model (Model 3), an arterial-venous model (Model 4), an arterial-clinical model (Model 5), a venous-clinical model (Model 6) and an arterial-venous-clinical model (Model 7), were established via logistic regression modeling. Diagnostic performance was assessed via receiver operating characteristic (ROC) curves.ResultsTumor location and carcinoembryonic antigen levels were used to construct Model 1 (training group, AUC [area under the ROC curve] = 0.721, 95% CI [confidence intervals], 0.630-0.813; validation group, AUC = 0.729, 95% CI, 0.593-0.865). Model 6 and Model 7 further improved the discriminatory performance in the training (AUC = 0.850 and 0.869, 95% CI, 0.782-0.919 and 0.807-0.932, respectively; p = 0.250) and validation groups (AUC = 0.780 and 0.716, 95% CI, 0.653-0.906 and 0.576-0.856, respectively; p = 0.115). Moreover, decision curve analysis revealed a greater net benefit with Model 6.ConclusionsThe combination of radiomic features based on dual-energy CT-derived iodine maps and clinical features provides better diagnostic performance for predicting LNM in rectal cancer patients.