Search Results (744)

Reliable classification of brain sequence cases is a challenging problem due to signal ambiguity and noise. Personal concentration is primarily determined by the base frequency of electroencephalogram (EEG) waves, i.e., the task rests on appropriate modelling and recognition of patterns in the corresponding human (in)activity (e.g., reading, relaxation, solving maths problems, etc.). Five underlying types of frequency (alpha, beta, gamma, delta, and theta) were considered as secondary input wave parameters in complex-valued node extensions to the prime amplitude in processing signals. Self-optimisable Artificial Intelligence (AI) methods can process, statistically analyse, and model the series-specific character and time behaviour to recognise untrained session assigned labels. This procedure involves signal pre-processing (transformation) and feature extraction to enhance the representation in time variability, eliminate uncertain cases, and reduce the unacceptable large raw format of data in detailed frequency band recording. This study focuses on improving AI modelling through brain-inspired doubled amplitude/frequency signal processing. This extended concept is based on an analogy with neural activity that generates dynamic frequency pulses as the main information holder in response to time excitations. The model is obtained in partial differential equation (PDE) solutions of evolutionary tree structure nodes—self-computational terms, using the optimal sine/cosine or rational expression. It enables the representation of periodic patterns in their intrinsic form related to primary wave characteristics. Two different machine learning methodologies were compared: the first evolutionary PDE transform and deep learning-based recurrent processing applied to all session records in bloc, assessing only one final class assessment, achieving predictive accuracy above 90% on untrained 1/3 data. The second group of regular modelling techniques evaluates each data row separately to compute its bound-label output in a time-lagged frame, reaching accuracy above 70%. An executable parametric software with a link to the public EEG data repository is available. Full article

This paper reports the design, development and laboratory validation of a low-cost multi-sensor system for bridge Structural Health Monitoring with complementary scour-related environmental sensing. The proposed sensing apparatus is intended for continuous dynamic (vibration-based) and quasi-static structural monitoring, in particular for bridge applications. It employs a distributed wired architecture comprising two custom printed circuit boards, BridgeWatch and EnvironMonitor. BridgeWatch nodes integrate MEMS accelerometers and gyroscopes, enabling acceleration measurements, quasi-static inclination estimates from gravity components, angular-rate measurements and structural surface-temperature acquisition. The EnvironMonitor node acquires wind, rainfall, air temperature, humidity and bridge water level; these variables provide environmental and hydrological context relevant to scour risk, without directly measuring riverbed elevation or scour depth. A Raspberry Pi coordinates acquisition, synchronisation and communication through RS485 and RS232 links. The hardware was implemented through custom PCB design and dedicated firmware. The principal contribution is the system-level integration of structural and environmental measurements in one open architecture. Laboratory validation comprised communication and synchronisation checks, shaker tests, a scaled structural benchmark, climatic-cell tests and environmental-input simulations. The results demonstrate functional acquisition and relevant dynamic-feature extraction under controlled conditions. These outcomes enable future full-scale in situ validation and testing on full-size case studies. Full article

Satellite-altimetry-derived marine gravity models often exhibit limited regional adaptability and region-dependent residual errors relative to shipborne observations, particularly in coastal, shelf, and slope areas. This study proposes a regional marine gravity refinement method that integrates region-adaptive background-field fusion with multi-relational graph neural network residual learning in the northern South China Sea and adjacent waters. Four background models—SIO/UCSD, SDUST2022GRA, NSOAS24, and SWOT05—were used together with shipborne gravity, bathymetry, distance-to-coast, and survey-line information. A region-adaptive initial field was first constructed according to the error characteristics of the background models in different subregions, and its difference from the shipborne observations was taken as the residual-learning target. The matched shipborne points were then represented as graph nodes, with spatial, terrain, model-response, survey-line, and regional relations used to construct a multi-relational graph. Spatially disjoint blocks were used to separate the training, validation, and test samples. The Multi-relational GNN predicted local residuals, which were added back to the region-adaptive initial field to obtain the refined gravity anomalies. On the held-out test set, the proposed method achieved an RMSE of 6.10 mGal, an MAE of 3.83 mGal, a 95th-percentile absolute error of 13.08 mGal, a bias of −0.19 mGal, and a squared Pearson correlation coefficient r² of 0.945, outperforming the interpolation, machine-learning, and neural-network baselines. Ablation experiments showed that the survey-line relation provided the largest individual contribution. However, the present validation is limited to spatially held-out samples within the existing shipborne survey network; generalization to completely unseen cruises, other marine regions, and areas without nearby shipborne constraints remains to be further verified. Overall, the results indicate that combining regional background-model adaptation with structured residual learning can improve regional marine gravity field refinement in spatially heterogeneous environments. Full article

Gangrenous mastitis is a severe form of mastitis in dairy goats that causes extensive tissue damage and has a poor prognosis, thereby substantially affecting the dairy goat industry; however, its molecular pathogenesis remains unclear. In this study, Staphylococcus aureus was used to establish a model of gangrenous mastitis in dairy goats. Mammary gland tissues were collected at 72 h post-inoculation for transcriptomic and proteomic sequencing, followed by integrated multi-omics analyses to systematically identify key regulatory pathways and candidate molecules associated with S. aureus-induced gangrenous mastitis. The results showed that clinical mastitis was characterized mainly by activation of pathways related to the acute inflammatory response, pathogen recognition, neutrophil chemotaxis, and phagocytic defense. In contrast, gangrenous mastitis involved broader molecular reprogramming, with significant enrichment of the TNF, IL-17, and NF-κB signaling pathways, complement and coagulation cascades, platelet activation, and extracellular matrix (ECM) remodeling. Protein–protein interaction (PPI) analysis, gene set enrichment analysis (GSEA), and validation of key molecules indicated that IL6, S100A8, THBS1, SERPINE1, and MMP9 may represent important nodes in disease progression. Collectively, these findings indicate that gangrenous mastitis is a complex infectious tissue-injury process driven by inflammatory amplification, aberrant immune-cell activation, complement–coagulation dysregulation, and tissue structural disruption. The identified molecules and pathways may facilitate the development of biomarker panels for early diagnosis and risk stratification and inform preventive and adjunctive therapeutic strategies targeting excessive inflammation, microcirculatory dysfunction, and ECM damage in dairy goats. Full article

Background and Clinical Significance: Cervical lymphadenopathy with an occult primary tumor represents a common yet diagnostically challenging clinical scenario in head and neck oncology. While endoscopic evaluation is typically relied upon for tumor localization, submucosal lesions may remain undetected. Case Presentation: We report a 41-year-old woman presenting with an isolated right level II neck mass without constitutional symptoms. Initial fiberscopic examination revealed no mucosal abnormalities; however, contrast-enhanced computed tomography demonstrated asymmetric enlargement of the right palatine tonsil with effacement of the adjacent parapharyngeal fat plane, alongside multiple enlarged cervical lymph nodes. Subsequent positron emission tomography supported focal metabolic activity in the tonsillar region. Histopathological examination confirmed a low-grade mucoepidermoid carcinoma arising from the minor salivary glands of the palatine tonsil. Immunohistochemistry showed strong p16 positivity (70%), while MAML2 rearrangement was not detected. Conclusions: This case highlights a critical pitfall where imaging–endoscopy discordance signals a submucosal malignancy. Furthermore, strong p16 expression should be interpreted in conjunction with tumor morphology and clinical context, as p16 positivity is not entirely specific for HPV-related squamous cell carcinoma. Through integration of the current case with a focused literature review, we propose a structured diagnostic framework emphasizing imaging prioritization and morphology-based interpretation to improve recognition of occult submucosal tonsillar malignancies. Full article

Early prediction of epileptic seizures remains an active research area in scalp electroencephalography (EEG) analysis. Current methods focused on this topic often rely on handcrafted features that insufficiently capture the multiscale nonlinear dynamics of preictal activity, process EEG channels independently without modeling brain connectivity, or demand computationally expensive manual hyperparameter tuning. To address these limitations, this study presents a framework integrating multifractal analysis (MFA), graph neural networks (GNNs), and a differential evolution algorithm (DEA) to classify non-overlapping one-minute EEG segments as preictal (within 60 min before onset) or reference (interictal) states. Five complementary MFA techniques extract nonlinear descriptors from each segment, which serve as node attributes in a graph of the 21 EEG channels. Unlike conventional approaches, this graph explicitly encodes neuroanatomical proximity to capture spatial brain dynamics and inter-channel topological dependencies. This graph-based representation allows the GNN to learn from the relational structure of the brain network, capturing spatiotemporal interactions critical for early prediction, while the DEA systematically optimizes the GNN architecture, eliminating subjective manual tuning. Under a segment-level validation protocol on the CHB-MIT database, the framework achieved 96.38% accuracy, 94.36% sensitivity, 98.41% specificity, and an AUC-ROC of 99.37%. These results demonstrate the effectiveness of combining multifractal descriptors, graph-based processing, and evolutionary optimization for segment-level discrimination. Nevertheless, patient-wise validation remains essential for clinical translation, and this work is consequently presented as a proof-of-concept demonstration. Full article

Urban anomalies such as rare facilities, unusual spatial configurations, and atypical functional patterns can provide valuable insights into urban dynamics and planning processes. However, graph-based anomaly discovery methods often suffer from instability, producing substantially different results across training runs and making the detected anomalies difficult to reproduce and interpret. In this paper, we use the term anomaly to denote automatically detected abnormal urban entities, while the term urban irregularity refers to their interpretation within the urban context. We present a consensus-driven framework for stable anomaly discovery using multi-relational point-of-interest (POI) graphs. Urban facilities are represented as nodes connected through multiple spatial and semantic relations, including geographic proximity, shared categories, shared facility types, and region-based associations. Four graph autoencoder architectures (GAE, ResGAE, VGAE, and SAGEAE) are employed to learn node representations, while reconstruction-, cluster-, neighborhood-, and relation-based anomaly scoring strategies are combined with multi-seed stability analysis to identify consensus anomalies. Experiments conducted on five large-scale cities (Baku, Turin, Vienna, Prague, and Kuala Lumpur) show that the proposed framework identifies recurring anomaly patterns across repeated runs and analytical configurations. Comparisons with representative anomaly detectors reveal partial but method-dependent overlap, while cross-city control experiments indicate that a subset of the detected anomalies exhibits non-random semantic and structural correspondence across different urban environments. Additional analyses suggest that consensus anomalies are frequently associated with semantically distinctive urban entities, including recreational areas, utility infrastructure, institutional facilities, specialized services, and cultural landmarks. Overall, the results indicate that stability-aware consensus provides a more reproducible and consistent basis for graph-based urban anomaly discovery and supports the interpretation of recurrent anomaly patterns in large-scale urban POI graphs, without requiring ground-truth anomaly labels. Full article

Low adult density can suppress recruitment through mate limitation or cooperative reproductive failure, even when a host improves adult survival. Motivated by this ecological tension, we introduce a fecundity-related component Allee effect into a stage-structured commensalism model with a Holling type II host benefit. The nonlinear recruitment term is $f\left(x_2\right)=\alpha x_2^2/(x_2+A)$, and the independently growing host converges to its carrying capacity, yielding an asymptotically autonomous commensal subsystem. We obtain a complete analytical classification. If the long-term host benefit D exceeds mature mortality ${\delta }_2$, a unique coexistence equilibrium attracts every nontrivial commensal population. If the benefit is insufficient, fecundity limitation can instead create two positive equilibria: a low-density saddle whose stable set is the basin boundary and a stable high-density coexistence state. Thus, extinction and coexistence are both possible, depending on initial stage composition and host abundance. Stronger fecundity limitation enlarges the extinction region, whereas greater recruitment, maturation or host benefit promotes persistence. At the critical boundary, the positive equilibria merge in a non-degenerate saddle-node bifurcation. Rigorous global arguments and numerical comparisons with the linear-recruitment model show how a component Allee effect can induce strong-Allee-type demographic behavior, including bistability and threshold-mediated extinction. Full article

Background/Objectives: Evidence regarding changes in oral and oropharyngeal squamous cell carcinoma (SCC) care across the COVID-19 pandemic remains limited, particularly in Eastern Europe. This study evaluated temporal changes in surgical admission volume, operative complexity, and comorbidity burden before, during, and after the pandemic at a Romanian tertiary maxillofacial center. Methods: This retrospective mixed-methods study included 248 admissions for oral and oropharyngeal SCC between 2018 and 2024. A structured administrative database was deterministically linked to narrative discharge summaries. Admissions were grouped into pre-pandemic (n = 117), pandemic (n = 93), and post-pandemic (n = 38) periods. Statistical analyses included between-period comparisons, segmented interrupted time-series analysis, and multivariable logistic regression. Qualitative thematic analysis was used to contextualize surgical and perioperative complexity. Results: Mean monthly admissions decreased from 4.9 before the pandemic to 2.6 during the pandemic and 1.6 in the post-pandemic period. Interrupted time-series analysis identified a significant declining baseline monthly trend (IRR 0.973, 95% CI 0.950–0.996; p = 0.024), without a significant immediate level change or post-onset slope change. Neck dissection increased from 23.9% before the pandemic to 39.8% during the pandemic (p = 0.045) and was independently associated with the pandemic/post-pandemic period (adjusted OR 1.87, 95% CI 1.06–3.29; p = 0.030). Qualitative findings identified recurrent codes related to surgical complexity, reconstruction, extensive nodal surgery, comorbidity burden, and perioperative management. Conclusions: Oral and oropharyngeal SCC surgical admissions showed a significant declining baseline trend between 2018 and 2024, while patients treated during and after the pandemic had higher adjusted odds of undergoing neck dissection. The findings support sustained oncological surveillance, timely referral, and multidisciplinary perioperative planning. Further multicenter studies incorporating standardized tumor–node–metastasis (TNM) staging and human papillomavirus (HPV) documentation are required to confirm these observations. Full article

Background/Objectives: Lymph-node metastasis remains central to staging, prognosis, surveillance, and treatment planning in malignant melanoma. At present, most statistical models assessing nodal metastatic risk in malignant melanoma consider pathological descriptors, inflammatory markers, metabolic alterations, clinical data, and related variables independently of each other. Therefore, we developed a transparent artificial intelligence (AI)-based approach to assess whether the propensity for nodal metastasis is determined by a single layer of local histopathological aggressiveness or by the integration of different biological levels, including local histopathological aggressiveness, systemic inflammatory–metabolic dysregulation, biological heterogeneity, or a clinicobiological pattern. Methods: In this retrospective study, we assessed 73 adult patients undergoing surgical removal of malignant melanoma. The primary endpoint was histopathologically confirmed lymph-node metastasis. Routinely collected patient-related data, including clinical, anatomical, operative, histopathological, nodal, comorbidity, biological, clinical course, and available staging data, were structured into interpretable constructs. These included the Histopathologic Aggressiveness Index (HAI), the Inflammatory–Metabolic Dysregulation Index (IMDI), the Biological–Histological Discordance Score (BHDS), model-estimated nodal metastatic probability, integrated clinicobiological risk, and explanation stability. The AI-based framework was evaluated by applying bias-reduced and penalized logistic regression, machine learning benchmarking, leave-one-out cross-validation, bootstrap estimation, permutation testing, decision curve analysis, rule extraction, feature stability evaluation, network analysis, similarity-based retrieval, conformal uncertainty estimation, and unsupervised phenomapping. Results: For 72 out of 73 patients, nodal histopathology results were available. Among these patients, 22 had positive nodal status. Positive nodal status was associated with a higher Breslow thickness, an increased mitotic rate, ulceration, lymphovascular invasion, a nodular subtype, and palpable adenopathy. The HAI demonstrated the strongest discriminative signal between node-positive and node-negative patients (median values of 67.8 vs. 45.3; p < 0.001) and retained an independent association with nodal metastasis within the bias-reduced logistic model (odds ratio [OR] per 10-point increase: 2.74; 95% confidence interval [CI]: 1.58–4.75; p < 0.001). The IMDI showed a weak exploratory relationship and did not retain an independent association after adjustment. Similarly, the BHDS did not show significant differences in separating the two endpoint groups. The penalized logistic model including only the HAI showed good performance under leave-one-out cross-validation, with ROC AUC = 0.889, PR-AUC = 0.706, and Brier score = 0.137. Through rule extraction, we found a cohort-specific HAI threshold value > 58.6, above which all node-positive cases were located. With respect to explainability, feature stability, network analysis, similarity retrieval, conformal prediction, and phenomapping, there was convergence toward a dominant high-risk phenotype defined primarily by histopathological criteria. Conclusions: Routine melanoma registries may be transformed into internally evaluated melanoma intelligence frameworks. Histopathologically confirmed lymph-node metastasis among patients with malignant melanoma was organized primarily along an axis of local histopathological aggressiveness, while systemic inflammatory–metabolic dysregulation provided subordinate contextual biological information. Full article

Marine remote sensing combines broad-coverage satellite observations of the ocean surface with sparse in-situ and underwater observations. However, reconstructing continuous three-dimensional environmental fields remains challenging because of irregular sampling, vertical non-stationarity, and bathymetric barriers. In this paper, a multi-granularity graph collaborative neural network (MG-GCNN) is proposed for high-fidelity field reconstruction and situational awareness in sparse monitoring scenarios. The network abstracts discrete observation points as heterogeneous nodes in the topological graph structure. By incorporating high-resolution bathymetry as a geometric prior, terrain-aware graph construction, vertical feature integration, multi-granularity aggregation, and self-supervised masked node reconstruction are jointly used to capture spatial and vertical dependencies under limited observation availability. Experimental results show that MG-GCNN significantly outperforms baseline interpolation and convolution models in terms of reconstruction accuracy, especially in regions with complex underwater terrain and extreme sampling sparsity. The reconstructed environmental fields can potentially provide three-dimensional environmental inputs for subsequent ocean-acoustic propagation modeling, underwater sensing, and related marine applications. Full article

Installation art has become a common intervention in industrial heritage creative districts, where it contributes to place identification, pedestrian orientation, and the everyday use of external spaces. Its spatial performance, however, is closely related to the location and spatial hierarchy in which it is placed. Taking the Zhengzhou Oil & Chemical Plant Creative District as a case study, this research examines 18 external-space units through manual pixel-level street-view semantic annotation, 320 on-site questionnaires, behavioral observation, and Grey Relational Analysis (GRA). The Installation Visibility Index (IVI), Openness Index (OPN), and Visual Focality Index (FOC) were used to describe pedestrian-scale visual structure; the Subjective Perception Index (PI) and Spatial Vitality Index (VPI) were used to evaluate perceived quality and observed public-space use. The four installation art placement paths presented distinct visual profiles. Visual identification-oriented units had the highest mean IVI and FOC, whereas functional activation-oriented and spatial guidance-oriented units were characterized by greater openness. Spatial vitality followed a clear hierarchy, with mean VPI values of 0.9563 in entrance and core node spaces, 0.5201 in main-axis spaces, 0.3660 in secondary-axis spaces, and 0.2001 in edge spaces. The visual identification-oriented path had the highest grouped VPI (0.7731), although four of its six units were located at entrances or core nodes. Grey Relational Analysis indicated that users’ overall spatial perception had the strongest association with observed vitality, while installation visibility, visual focality, and openness showed moderate and broadly comparable associations. The findings demonstrate that the contribution of installation art depends on its visual organization and its coordination with spatial hierarchy, surrounding functions, and pedestrian use. The study provides a micro-spatial framework and hierarchy-responsive design guidance for installation art in industrial heritage regeneration. Full article

Critically ill patients frequently move between the intensive care unit (ICU) and the operating room while remaining dependent on ventilatory, hemodynamic, antimicrobial, sedative, analgesic, and renal support. Although extensive perioperative data are generated, clinically relevant information may remain fragmented across devices and electronic systems. This narrative review synthesizes the literature published primarily between 2016 and 2026 on perioperative digital continuity, structured ICU–operating room handoff, interoperability, artificial intelligence (AI)-supported decision support, continuous renal replacement therapy (CRRT)-related pharmacokinetic personalization, and digital twin concepts. Based on this synthesis, we develop a conceptual framework in which the Intelligent Operating Room and Smart ICU function as connected clinical information nodes. The framework is organized around a minimum perioperative continuity dataset, a perioperative delta view, and the author-proposed concept of Time-to-Truth at ICU readmission. Current evidence supports structured handoff and selected AI-assisted physiological prediction, but evidence for improvement in major patient-centered outcomes remains limited and heterogeneous. CRRT illustrates the importance of preserving information on delivered therapy, interruptions, residual kidney function, and treatment-related changes when interpreting drug exposure. The proposed framework is not clinically validated and is intended as a basis for future implementation and prospective evaluation. Overall, the review suggests that improving digital continuity should precede more complex forms of automation in perioperative critical care. Full article

Background/Objectives: Nursing research on children with disabilities spans diverse disability groups, care settings, and areas of practice; however, its development and structure have not been comprehensively examined. This study aimed to examine publication trends, major research themes, temporal development, and collaboration patterns. Methods: The Web of Science Core Collection was searched on 29 June 2026 without publication-year restrictions. English-language articles and reviews indexed in Science Citation Index Expanded (SCI-EXPANDED) and Social Sciences Citation Index (SSCI) were included, yielding 1915 publications from 1977 to 2026. VOSviewer and Biblioshiny were used to analyze publication trends, keyword co-occurrence, thematic structure and evolution, trend topics, and country and institutional collaboration. Results: Research output increased substantially, particularly during the last decade. Across the study period, the United States had the highest publication output and co-authorship connectivity, followed by England, Canada, and Australia. Nursing, children, autism spectrum disorder, intellectual disability, and cerebral palsy were among the largest nodes in the keyword network. The thematic map positioned the autism–pediatrics–developmental disability cluster slightly within the motor themes quadrant, while the disability–children with disabilities–qualitative research, adolescents–communication–transition, and children–nursing–intellectual disability clusters were positioned among the basic themes. Thematic evolution showed both continuity and diversification, with autism, nursing, children, and intellectual disability represented across multiple periods, while quality of life, education, and mental health were represented in the most recent period. Trend topic analysis further showed that well-being, implementation, pediatric nursing, anxiety, and mental health were among the topics with more recent median publication years. Conclusions: Nursing research on children with disabilities has expanded and diversified, with recurring disability-specific topics alongside more recent topics related to psychosocial issues, pediatric nursing, and implementation. Future research could build on the thematic patterns identified in this study through systematic reviews and primary nursing research, while bibliometric studies incorporating additional databases could provide a more comprehensive view of the field. Full article

Audio–visual depression recognition in real-world scenarios is often challenged by temporal sparsity and cross-modal asynchrony, where depression-related cues may appear only in short segments and may not align precisely across modalities. Under such conditions, global pooling or dense attention tends to dilute sparse discriminative evidence with redundant context, leading to unstable utterance-level representations. To address this issue, we propose an audio–visual depression recognition framework that integrates modality feature adaptation, bidirectional cross-modal interaction, and graph-based prototype abstraction. Specifically, heterogeneous audio and visual streams are first transformed into compatible representations, after which bidirectional cross-attention models content-dependent dependencies across modalities without requiring index-wise correspondence. The fused tokens are then interpreted as graph nodes and aggregated into a compact set of semantic prototypes through graph convolution and differentiable soft clustering. In addition, audio perturbation is introduced during training as a task-oriented regularisation strategy for partial acoustic evidence loss and temporal misalignment. Experiments on the LMVD dataset demonstrate clear improvements on the primary depression-recognition task, while auxiliary evaluations on MIntRec and CMU-MOSI suggest that the structured prototype representation is beneficial for other temporally sparse audio–visual recognition tasks. These results indicate that structured prototype learning is effective for preserving sparse depression-related cues, while training-time perturbation provides a complementary regularisation effect under asynchronous multimodal conditions. Full article