Search Results (2,665)

Micro-motion features of targets, such as nutation and coning, play a crucial role in radar-based target recognition and classification. This paper addresses the challenge of extracting three-dimensional micro-motion parameters from smooth symmetric nutating cone targets using monostatic radar. Unlike conventional methods that rely on tail-fin structures, the proposed approach leverages the micro-Doppler characteristics of both fixed and sliding scattering points on the cone. The motion model of a nutating cone is established, and the expressions for micro-Doppler frequency shifts are derived. Based on the visibility of scattering points at the cone bottom, two categories of echoes are defined: those containing one or two scattering points. For each category, tailored signal processing methods are developed to estimate micro-motion parameters, including nutation angle, precession angle, coning frequency, wobble frequency, and geometric dimensions. Simulations under both noise-free and noisy conditions validate the effectiveness of the proposed method, demonstrating its robustness and accuracy in 3-D micro-motion feature extraction. Full article

Background: Comparing biological properties among related proteins has traditionally benefited research in areas such as biomedicine, phylogeny and evolution. Moreover, these kinds of properties have significantly increased as a result of the development of open-access resources and databases. In this context, the multiple sequence alignment (MSA) methods continue to be extensively applied to compare protein sequences and to identify evolutionarily conserved regions. Methods: In this work, we present INPROF, a novel web server designed to centralize and automate the computation of a large collection of features derived from protein sequences. This tool allows us to deeply analyze protein relationships and their conserved information by comparing them through their MSA. Specifically, this platform computes up to 46 different metrics including information at several proteomic levels (categories) like sequences, structures, domains or ontological terms. INPROF was designed to enable bioinformaticians and researchers to create a complete catalogue of features for subsequent prediction and artificial intelligence modeling based on proteins. The INPROF web server and source code are freely available. Results: INPROF were validated by predicting disease’s severity in several RNA-Seq datasets from COVID-19 patients. Specifically, INPROF were extracted from canonical proteins related to differentially expressed genes. Classification performance proved that INPROF were able to accurately classify COVID-19 severity, even outperforming classical classification with expression data. Conclusions: INPROF web server is a flexible platform designed to compute 46 quantitative metrics describing protein interactions which provide biologically meaningful characteristics applicable to downstream classification and prediction algorithms. Full article

The assessment of the degree of accessibility of urban green spaces for the population of the city of Timișoara (Romania) was carried out by taking into account the recommendations of the World Health Organization (WHO). These recommendations address the proximity accessibility of urban green spaces, operationalized through two main indicators: (1) proximity accessibility defined through two metrics–spatial distance and walking time between urban green spaces and residents’ dwellings; and (2) proximity accessibility defined by the area of urban green space available per urban resident capita. Based on the distance and walking time between residential areas and urban green spaces, accessibility classes were established, according to which the city’s green spaces were classified into distinct categories. Even under a simplified Euclidean centroid-to-centroid approach, the measured distances of urban green space accessibility exceed the World Health Organization’s recommended 300 m threshold for optimal access by a factor of 2 to 9 in the city of Timișoara. The measurements showed that none of the 48 studied neighborhoods of the city of Timișoara benefits from access to a public urban green space located at a distance of less than 200 m from the dwelling, according to the classification used in this study, and that only a single neighborhood has access to a public urban green space located at a distance of up to 300 m, as recommended by the WHO. The analysis indicated that for each resident of the city of Timișoara, an area of 8.4 m² of urban green space is allocated, a value below the WHO recommendation of 9 m² and below the legal threshold of 26 m² established by Romanian national legislation. Consequently, the city of Timișoara does not meet either the values established by national legislation or the authoritative international recommendations (WHO) regarding the standard of urban green space per capita, nor the accessibility criteria expressed as distance and walking time from residents’ dwellings to the nearest public urban green space. The results of the study show that, in relation to international standards and national obligations, Timișoara faces a severe deficit of urban green space, which affects the ecological, social, and health functions of the city. The obtained values highlight both a quantitative problem and a structural one, characterized by an uneven distribution and reduced accessibility of green spaces in most neighborhoods, with green spaces concentrated in the central area and limited access for many residents. This situation underscores the need for a strategic reconfiguration of urban policies, oriented toward increasing green capital and ensuring balanced, sustainable urban development aligned with contemporary standards. Urban policy implications include the strategic development of new green spaces in underserved neighborhoods, the establishment of pedestrian and green corridors to reduce travel time, and the redesign of pedestrian connectivity to major parks. These interventions would help reduce territorial inequalities and strengthen the city’s resilience. Full article

In this study, we present a unified symmetry-conservation solution analysis of a well-posed resonant nonlinear Schrödinger (NLS)-type equation incorporating spatio-temporal dispersion and inter-modal dispersion. Working within the truncated M-fractional derivative framework, we first construct exact traveling-wave solution families via the Kudryashov expansion method, together with the corresponding parameter constraints and limiting cases. We then determine the admitted Lie point symmetries and establish the associated Lie algebra, including the commutator structure, adjoint representation, and an optimal system of one-dimensional subalgebras for classification. Using the conservation theorem, we derive conserved vectors associated with the fundamental invariances of the model; in the NLS setting and under suitable conditions, these quantities can be interpreted as generalized power (mass), momentum, and energy-type invariants. Overall, the results provide explicit wave profiles and structural invariants that enhance the interpretability of the model and offer benchmark expressions useful for further qualitative, numerical, and stability investigations in nonlinear dispersive wave dynamics. Full article

Micro-expressions (MEs) are involuntary facial movements that reveal genuine emotions, holding significant value in fields like deception detection and psychological diagnosis. However, micro-expression recognition (MER) is fundamentally challenged by the entanglement of subtle emotional motions with identity-specific features. Traditional methods, such as those based on Robust Principal Component Analysis (RPCA), attempt to separate identity and motion components through fixed preprocessing and coarse decomposition. However, these methods can inadvertently remove subtle emotional cues and are disconnected from subsequent module training, limiting the discriminative power of features. Inspired by the Bruce–Young model of facial cognition, which suggests that facial identity and expression are processed via independent neural routes, we recognize the need for a more dynamic, learnable disentanglement paradigm for MER. We propose LFD-TCMEN, a novel network that introduces an end-to-end learnable feature disentanglement framework. The network is synergistically optimized by a multi-task objective unifying orthogonality, reconstruction, consistency, cycle, identity, and classification losses. Specifically, the Disentangle Representation Learning (DRL) module adaptively isolates pure motion patterns from subject-specific appearance, overcoming the limitations of static preprocessing, while the Temporal-Complemented Motion Enhancement (TCME) module integrates purified motion representations—highlighting subtle facial muscle activations—with optical flow dynamics to comprehensively model the spatiotemporal evolution of MEs. Extensive experiments on CAS(ME)³ and DFME benchmarks demonstrate that our method achieves state-of-the-art cross-subject performance, validating the efficacy of the proposed learnable disentanglement and synergistic optimization. Full article

Background: Preterm birth is a significant risk factor for neurodevelopmental delays, but the appropriate use and timing of age correction for developmental assessment remain debated. Objective: This study investigated psychomotor development in preterm children at two years of age, with the aim of clarifying whether age correction remains necessary at this stage, particularly across different gestational age groups. Methods: A total of 161 preterm infants were assessed at a mean chronological age of 25.4 months (mean corrected age: 23.3 months) and compared with two control groups of typically developing children matched for gender and either corrected age (Control–Corr, N = 88) or chronological age (Control–Chron, N = 87). The preterm group was further stratified by gestational age: extremely preterm (<28 weeks), very preterm (28–31 weeks), and moderate-to-late preterm (32–36 weeks). Cognitive, Language (Receptive, Expressive), and Motor (fine, gross) scales of Bayley-III were analysed using t-tests and MANOVAs. Results: Using corrected age, preterm children showed a selective profile, with deficits in Receptive Language, borderline mean score in Gross Motor, and preserved performance in Cognitive, Expressive Communication, and Fine Motor. When compared with controls of the same age, significant differences emerged in the Cognitive, Language, and Gross Motor, but not Fine Motor, domains. In contrast, scoring by chronological age produced a generalised delay, with preterm children performing significantly worse than chronological-age controls across all domains. Subgroup analyses further showed that extremely preterm children already displayed marked Language vulnerabilities at corrected age, which became more severe with chronological scoring and extended to other domains. Very preterm children also fell into the deficit range in Cognitive, Language, and Gross Motor scales/subscales when chronological age was applied, whereas moderate-to-late preterm children performed comparatively better. Conclusions: Developmental assessment using corrected age remains essential at least until 24 months, especially for extremely and very preterm children, to avoid substantial overestimation of developmental difficulties. Chronological scoring, while helpful to highlight persistent vulnerabilities, may inflate delay classification if used too early. Tailoring correction strategies by gestational age and developmental domain could provide a more accurate and clinically meaningful representation of preterm children’s developmental trajectories. Full article

Design philosophy by Steven Holl shows his interest in the spatial experience aspect of architecture in the way people perceive space. This study focuses on the composition of spatial connections in 18 residential projects. The objective is to clarify the continuity of the living room through floor plan classification and matrix analysis, which is highly relevant in that it helps bridge the gap in understanding the functional and structural mechanisms inherent in architectural design theory, particularly in the projects. As a result, the residential projects can be classified into four categories in terms of continuity of living room, and it has a unique type of expression in their residential projects. This study is limited to analyzing only the first-floor plan and does not examine other drawings, such as sectional or elevation views, nor does it consider other residential projects. Therefore, the analysis has limitations. This study classified and discussed the continuity and spatial connections within the living room, thereby contributing to the discourse on design methodology in relation to architectural theory and phenomenology. Full article

The classification of ship targets in remote sensing images holds significant application value in fields such as marine monitoring and national defence. Although existing research has yielded considerable achievements in ship classification, current methods struggle to distinguish highly similar ship categories for fine-grained classification tasks due to a lack of targeted design. Specifically, they exhibit the following shortcomings: limited ability to extract locally discriminative features; inadequate fusion of features at high and low levels of representation granularity; and sensitivity of model performance to background noise. To address this issue, this paper proposes a fine-grained classification framework for ship targets in remote sensing images based on Multi-Granularity Attention Network (MA-Net), specifically designed to tackle the aforementioned three major challenges encountered in fine-grained classification tasks for ship targets in remote sensing. This framework first performs multi-level feature extraction through a backbone network, subsequently introducing an Adaptive Local Feature Attention (ALFA) module. This module employs dynamic overlapping region segmentation techniques to assist the network in learning spatial structural combinations, thereby optimising the representation of local features. Secondly, a Dynamic Multi-Granularity Feature Fusion (DMGFF) module is designed to dynamically fuse feature maps of varying representational granularities and select key attribute features. Finally, a Feature-Based Data Augmentation (FBDA) method is developed to effectively highlight target detail features, thereby enhancing feature expression capabilities. On the public FGSC-23 and FGSCR-42 datasets, MA-Net attains top-performing accuracies of 93.12% and 98.40%, surpassing the previous best methods and establishing a new state of the art for fine-grained classification of ship targets in remote sensing images. Full article

Background/Objectives: There is an ongoing search for reliable biomarkers of lung cancer (LC) and its progression, including nervous system involvement. MicroRNAs (miRNAs) play a crucial role in the regulation of gene expression and represent a promising focus of investigation in this field. The aim of this study was to assess the profile of miRNA expression in patients diagnosed with lung cancer, with or without brain metastases. Methods: This study comprised 13 patients diagnosed with non-small cell lung cancer (mean age 64.8 years, 61.5% females): 6 with brain metastases (LC + BM) and 7 without them (LC), and a control group of 6 healthy volunteers (HC). The expression levels of 179 miRNAs were assessed and compared between the study groups using quantitative reverse-transcription PCR (qRT-PCR). Results: In LC + BM subgroup, two miRNAs were found to be downregulated in comparison with HC: miR-409-3p (logFC = −17.42, p = 0.029) and miR-485-3p (logFC = −17.30, p = 0.026). An exploratory, probe-based feature-ranking analysis identified eleven miRNAs that were repeatedly selected across the resampling runs: miR-363-3p, miR-210-3p, miR-194-5p, miR-409-3p, miR-22-3p, miR-2110, miR-326, miR-485-3p, miR-223-5p, miR-16-2-3p, and miR-139-5p. Among these, miR-363-3p, miR-210-3p, and miR-194-5p exhibited the highest empirical stability. Predictive modeling was subsequently evaluated using a fully nested cross-validation framework in which feature selection and model training were repeated within each training fold. Under this stringent evaluation, the classification performance was close to chance across all the evaluated algorithms, indicating a limited predictive utility of the identified miRNAs for distinguishing patients with and without brain metastases in the present dataset. Conclusions: Notable differences in miRNA expression profiles were revealed for the patients with brain metastases from lung cancer, suggesting the role of the selected miRNAs in cancer metastasis to the CNS. However, while our analysis provides exploratory insights, the findings should be interpreted with caution and require validation in larger, independent cohorts before any clinical or translational implications can be established. Full article

Various sensors are increasingly being adopted to support intelligent healthcare systems, which address the growing problem of staff shortages in assisted-living communities. In this context, detecting and assessing pain remain critical yet challenging tasks in both clinical and non-clinical settings. Traditional approaches such as self-reporting, physiological signal monitoring, and facial expression analysis often face limitations related to accessibility, equipment costs, and the need for professional support. To overcome these challenges in this work, we investigate a sensor-assisted system for pain detection and propose a lightweight framework that enables real-time classification of pain levels using acoustic sensors. Our system exploits the spectral features of voice signals that strongly correlate with pain to train Convolutional Neural Network (CNN) models. Our system has been validated through simulations in Jupiter Notebook and a Raspberry Pi-based hardware prototype. The experimental results demonstrate that the proposed three-level pain classification approach obtains an average accuracy of 72.74%, outperforming existing methods with the same pain-level granularity by 18.94–26.74% and achieving performance comparable to that of binary pain detection methods. Our hardware prototype, built from commercial off-the-shelf components for under 100 USD, achieves real-time processing speeds ranging from approximately 6 to 22 s. In addition to CNN models, our experiments demonstrate that other machine learning algorithms, such as Artificial Neural Networks, XGBoost, Random Forests, and Decision Trees, also prove to be applicable within our pain level classification framework. Full article

Music conveys emotion through a complex interplay of structural and acoustic cues, yet how these features map onto specific affective interpretations remains a key question in music cognition. This study explored how listeners, unaware of contextual information, categorized 110 emotionally diverse excerpts—varying in key, tempo, note density, acoustic energy, and expressive gestures—from works by Bach, Beethoven, and Chopin. Twenty classically trained participants labeled each excerpt using six predefined emotional categories. Emotion judgments were analyzed within a supervised multi-class classification framework, allowing systematic quantification of recognition accuracy, misclassification patterns, and category reliability. Behavioral responses were consistently above chance, indicating shared decoding strategies. Quantitative analyses of live performance recordings revealed systematic links between expressive features and emotional tone: high-arousal emotions showed increased acoustic intensity, faster gestures, and dominant right-hand activity, while low-arousal states involved softer dynamics and more left-hand involvement. Major-key excerpts were commonly associated with positive emotions—“Peacefulness” with slow tempos and low intensity, “Joy” with fast, energetic playing. Minor-key excerpts were linked to negative/ambivalent emotions, aligning with prior research on the emotional complexity of minor modality. Within the minor mode, a gradient of arousal emerged, from “Melancholy” to “Power,” the latter marked by heightened motor activity and sonic force. Results support an embodied view of musical emotion, where expressive meaning emerges through dynamic motor-acoustic patterns that transcend stylistic and cultural boundaries. Full article

With the continuous evolution of social media, users are increasingly inclined to express their personal emotions on digital platforms by integrating information presented in multiple modalities. Within this context, research on image–text sentiment analysis has garnered significant attention. Prior research efforts have made notable progress by leveraging shared emotional concepts across visual and textual modalities. However, existing cross-modal sentiment analysis methods face two key challenges: Previous approaches often focus excessively on fusion, resulting in learned features that may not achieve emotional alignment; traditional fusion strategies are not optimized for sentiment tasks, leading to insufficient robustness in final sentiment discrimination. To address the aforementioned issues, this paper proposes a Dual-path Interaction Network with Multi-level Consistency Learning (DINMCL). It employs a multi-level feature representation module to decouple the global and local features of both text and image. These decoupled features are then fed into the Global Congruity Learning (GCL) and Local Crossing-Congruity Learning (LCL) modules, respectively. GCL models global semantic associations using Crossing Prompter, while LCL captures local consistency in fine-grained emotional cues across modalities through cross-modal attention mechanisms and adaptive prompt injection. Finally, a CLIP-based adaptive fusion layer integrates the multi-modal representations in a sentiment-oriented manner. Experiments on the MVSA_Single, MVSA_Multiple, and TumEmo datasets with baseline models such as CTMWA and CLMLF demonstrate that DINMCL significantly outperforms mainstream models in sentiment classification accuracy and F1-score and exhibits strong robustness when handling samples containing highly noisy symbols. Full article

Reduced winter hair shedding in beef cows through the spring and summer months may contribute to heat stress and reduced performance in spring-calving herds. This study evaluated the relationship of hair shedding with the fertility and maternal performance of 72 Dexter cows. Hair shedding data for 20 May, 3 June, 17 June, and 1 July in 2019 were used to classify cows as high or low hair shedders. Hair shedding levels were lower (p < 0.05) for 2-year-old cows than for cows 7+ years of age for the first three dates and lower (p ≤ 0.05) for lactating cows than for dry cows on the first two dates. Concurrent and four years of historical performance records were used to assess the associations between hair shedding and cow–calf performance. Data from 230 natural matings in July and August from 2015 to 2019 were analyzed. Birth to weaning weight data were recorded from 2016 to 2019 on 124 spring-born calves. Cow fertility was higher (p < 0.05) for high-shed cows than for low-shed cows for the 1 July classification. When the records from cows that were dry in 2019 were excluded from testing, fertility was higher (p < 0.05) for high-shed cows than for low-shed cows at all four scoring dates. The associations of cow hair shedding levels with preweaning calf performance were minimal. Dexter cows exhibiting higher hair shedding levels in the spring and summer expressed higher summer fertility. Full article

The colored-leaf Osmanthus fragrans is a valuable ornamental tree species that integrates greenery, colorful leaves, and fragrance. At present, research on colored-leaf Osmanthus fragrans mainly focuses on cultivar breeding, classification and cultivation, and physiological resistance, while studies on leaf color variation remain limited. In this study, the colored-leaf Osmanthus cultivar ‘Jinyu Guihua’ and its female parent were used as materials. The leaf coloration mechanism was systematically investigated through a combined analysis of physiology, transcriptomics, and metabolomics. The results showed that compared with the female parent, the leaves of ‘Jinyu Guihua’ exhibited significantly reduced chlorophyll b and anthocyanin contents, fewer chloroplasts, and more plastoglobules. Transcriptomic analysis identified 3938 differentially expressed genes (DEGs), among which the key chlorophyll metabolism gene CAO was downregulated and NOL was upregulated; the key carotenoid synthesis gene PSY was downregulated and CYP97A3 was upregulated; the key anthocyanin synthesis gene ANS was downregulated; and the PetC2 gene in the photosynthesis-related Cytb6-f complex was upregulated. qRT-PCR validation results were consistent with the RNA-seq data. Metabolomic analysis detected 1290 metabolites, classified into 21 subcategories, with flavonoids being the most abundant (17.21%). Anthocyanin synthase (ANS) significantly downregulated the expression levels of cyanidin-3-O-rutinoside (Cy3R) and delphinidin-3-O-rutinoside (De3R). In conclusion, the leaf color variation in ‘Jinyu Guihua’ is closely related to changes in leaf pigment content and the regulation of key metabolic pathway gene expression. The findings of this study provide a theoretical basis for the molecular breeding of new colored-leaf Osmanthus varieties and serve as a reference for trait research in other ornamental plants. Full article

Soybean root rot, caused by diverse soil-borne pathogens, is a major constraint on production worldwide, with yield losses ranging from 10 to 60% under epidemic conditions. Symptomatic plants were collected from three locations in Harbin, Heilongjiang Province, China, and 23 fungal isolates were recovered using standard tissue isolation procedures. Integrated morphological characterization and rDNA-ITS sequencing identified these isolates as three Fusarium species: F. oxysporum (18 isolates, 78%), F. equiseti (3 isolates, 13%), and F. brachygibbosum (2 isolates, 9%). Pathogenicity assays following Koch’s postulates confirmed F. oxysporum as the predominant and most aggressive pathogen in this region. To identify resistance resources, 200 soybean germplasm accessions adapted to Northeast China were screened using an etiolated seedling hypocotyl inoculation method with Fusarium oxysporum isolate A3 (DSI = 68.5) as the test pathogen. Disease severity indices exhibited a continuous distribution (mean = 52.84, range = 0–100), suggesting quantitative inheritance. Accessions were classified as highly resistant (13, 6.5%), resistant (40, 20%), moderately susceptible (67, 33.5%), susceptible (43, 21.5%), or highly susceptible (37, 18.5%). To explore potential molecular mechanisms underlying resistance, RT-qPCR analysis was performed on two extreme genotypes—a highly resistant line (H9477F5, DSI = 15.3) and a highly susceptible line (HN91, DSI = 88.7) at 1, 3, and 5 days post-inoculation. The resistant line maintained consistently higher expression of positive regulators GmFER and GmSOD1, with GmFER reaching 15.89-fold induction at day 3. Conversely, expression of negative regulators GmJAZ1 and GmTAP1 remained lower in the resistant line, with susceptible plants showing 5.62-fold higher GmJAZ1 expression at day 3. These findings provide characterized pathogen isolates, resistant germplasm resources (53 accessions with HR or R classifications), and preliminary molecular insights that may inform breeding strategies for improving root rot resistance in Northeast China. Full article