Search Results (1,344)

This study introduces a hybrid Brain–Computer (BCI) system with a robust and secure activation mechanism between sessions, aiming to minimize the negative effects of visual stimulus-based BCI systems on user eye health. The system is based on the integration of Electroencephalography (EEG) signals and Electrooculography (EOG) artefacts, and includes an LED stimulus operating at a frequency of 7 Hz for safe activation and objects moving in different directions. While the LED functions as an activation switch that reduces visual fatigue caused by traditional visual stimuli, moving objects provide command generation depending on the user’s intention. In order to evaluate the stability of the system against physiological and psychological conditions, data were collected from 15 participants in two different sessions. The Correlation Alignment (CORAL) method was applied to the data to reduce the variance between sessions and to increase stability. A Bootstrap Aggregating algorithm was used in the classification processes, and with the CORAL method, the system accuracy rate was increased from 81.54% to 94.29%. Compared to similar BCI approaches, the proposed system offers a safe activation mechanism that effectively adapts to users’ changing cognitive states throughout the day by reducing visual fatigue, despite using a low number of EEG channels, and demonstrates its practicality and effectiveness by performing on par or superior to other systems in terms of high accuracy and robust stability. Full article

This paper addresses the low-carbon operation of integrated energy systems (PIESs) by proposing a carbon-aware demand response strategy with synergistic participation from consumers and energy storage. Initially, two typical scenarios—“electricity–carbon peak alignment” and “electricity–carbon peak misalignment”—are generated based on uncertainties in renewable generation and load profiles. These scenarios aim to characterise the coupling relationship between electricity and carbon emissions, providing a contextual basis for guiding responsive behaviours of consumers and storage systems. Subsequently, a carbon emission flow model incorporating energy conversion and storage is developed to quantify the carbon emission impacts of both consumers and energy storage units. Furthermore, a carbon-aware demand response strategy is formulated using dynamic carbon signals, coupled with an assessment model for carbon reduction benefits. Experimental validation across both scenarios demonstrates the efficacy of the proposed strategy in promoting low-carbon PIES operation. Compared to traditional electricity demand response, the proposed low-carbon demand response strategy enhances carbon emission reduction by 21.5% under the “electricity–carbon peak alignment” scenario, and this reduction even doubles under the “electricity–carbon peak misalignment” scenario. Additionally, the integration of energy storage for response increases the park’s average carbon reduction by 15%. This demonstrates that the strategy proposed in this paper significantly improves the park’s capability for carbon emission reduction. Full article

Drones are increasingly being used in maritime engineering for ship maintenance, emergency rescue, and safety monitoring tasks. In these tasks, action recognition is important for human–drone interaction and for detecting abnormal situations such as falls or distress signals. However, the maritime environment is highly challenging, with illumination variations, water spray, and dynamic backgrounds often leading to ambiguity between similar actions. To address this issue, we propose MSG-GCN, a multi-semantic guided graph convolutional network for human action recognition. Specifically, MSG-GCN integrates structured prior semantic information and further introduces a textual–semantic alignment mechanism to improve the consistency and expressiveness of multimodal features. Benefiting from its lightweight hierarchical design, our model offers excellent deployment flexibility, making it well suited for resource-constrained UAV applications. Experimental results on large-scale benchmark datasets, including NTU60, NTU120 and UAV-human, demonstrate that MSG-GCN surpasses state-of-the-art methods in both classification accuracy and computational efficiency. Full article

Inverse Synthetic Aperture Radar (ISAR) plays a vital role in the high-resolution imaging of marine targets, particularly under non-cooperative scenarios. However, resolution degradation due to limited observation angles and marine clutter such as wave-induced disturbances remains a major challenge. In this work, we propose a novel deep learning-based framework to enhance ISAR resolution in the presence of marine clutter and additive Gaussian noise, which performs direct restoration in the ISAR image domain after an IFFT2 back projection. Under small aspect sweeps with coarse range alignment, the network implicitly compensates for residual defocus and cross-range blur, while suppressing clutter and noise, to recover high-resolution complex ISAR images. Our approach leverages a residual neural network trained to learn a non-linear mapping between low-resolution and high-resolution ISAR images. The network is designed to preserve both magnitude and phase components, thereby maintaining the physical integrity of radar returns. Extensive simulations on synthetic marine vessel data demonstrate significant improvements in cross-range, outperforming conventional sparsity-driven methods. The proposed method also exhibits robust performance under conditions of low signal-to-noise ratio (SNR) and signal-to-wave ratio (SWR), effectively recovering weak scatterers and suppressing false artifacts. This work establishes a promising direction for data-driven ISAR image enhancement in noisy and cluttered maritime environments with minimal pre-processing. Full article

Diet influences brain health through many connected metabolic and molecular pathways, and these effects are stronger in obesity. This review links diet quality with cognitive decline and dementia risk. Ultra-processed, high-fat, high-sugar diets drive weight gain, insulin resistance, and chronic inflammation. These changes trigger brain oxidative stress, reduce DNA repair, deplete NAD⁺, disturb sirtuin/PARP balance, and alter epigenetic marks. Gut dysbiosis and leaky gut add inflammatory signals, weaken the blood–brain barrier, and disrupt microglia. Mediterranean and MIND diets, rich in plants, fiber, polyphenols, and omega-3 fats, slow cognitive decline and lower dementia risk. Trials show extra benefit when diet improves alongside exercise and vascular risk control. Specific nutrients can help in certain settings. DHA and EPA support brain health in people with low omega-3 status or early disease. B-vitamins slow brain shrinkage in mild cognitive impairment when homocysteine is high. Vitamin D correction is beneficial when levels are low. A practical plan emphasizes healthy eating and good metabolic control. It includes screening for deficiencies and supporting the microbiome with fiber and fermented foods. Mechanism-based add-ons, such as NAD⁺ boosters, deserve testing in lifestyle-focused trials. Together, these measures may reduce diet-related brain risk across the life span. At the same time, artificial intelligence can integrate diet exposures, adiposity, metabolic markers, multi-omics, neuroimaging, and digital phenotyping. This can identify high-risk phenotypes, refine causal links along the diet–obesity–brain axis, and personalize nutrition-plus-lifestyle interventions. It can also highlight safety, equity, and privacy considerations. Translationally, a pattern-first strategy can support early screening and personalized risk reduction by integrating diet quality, adiposity, vascular risk, micronutrient status, and microbiome-responsive behaviors. AI can aid measurement and risk stratification when developed with privacy, equity, and interpretability safeguards, but clinical decisions should remain mechanism-aligned and trial-anchored. Full article

This paper examines how the fractional Fourier transform (FrFT) can be used to form and analyze acoustic holograms produced by a moving, linear, frequency-modulated (LFM) source in a shallow water waveguide. In these environments, the source sound field creates an interference pattern, referred to as a two-dimensional interferogram, which represents the distribution of acoustic intensity in the frequency–time domain. This interferogram consists of parallel interference fringes. Consequently, focal points are formed and aligned along a straight line in the source hologram, which is represented by the two-dimensional Fourier transform of the interferogram. We have developed a holographic method for constructing the interferogram of an LFM source signal and transforming it into a Fourier hologram based on FrFT in the presence of strong noise. A key finding of this study is that the FrFT-based holographic method enables localized focal regions to emerge from modal interference even under high-intensity noise conditions. The positions of these focal spots are directly related to the source parameters, enabling the estimation of key characteristics such as the distance and velocity of the LFM source. We analyzed the effectiveness of the FrFT-based holographic method through numerical experiments in the 100–150 Hz frequency band. The results demonstrate the method’s high noise immunity for source localization in realistic shallow water environments under strong noise. Full article

The regulatory mechanism of brassinolide (BR) signaling in cucurbitaceae crops remains incompletely understood. Previous research demonstrated that the rice genes GW5 and GW5L modulate seed morphology via the BR pathway. However, the conservation of their orthologs in watermelon and their evolutionary trajectory are yet to be elucidated. In this study utilizing the watermelon 97103v2 genome, we identified 15 GW5-LIKE genes. Through structure, phylogenetic tree construction, collinearity, promoter and spatiotemporal expression analysis, we determined that ClGL1 to ClGL3 are the most closely related to GW5 and GW5L. Subsequently, two crucial materials were acquired: the inbred line Jing L6M harboring the homozygous mutant Clgl1, and the near-isogenic line Changhong, a Jing L6M backcross containing the wild-type allele ClGL1. Apart from the disparity in fruit morphology, a clear difference in seed shape was observed between the two. Furthermore, exogenous BR treatment demonstrated that ClGL1 positively regulated the BR signal, aligning with the positive impact of GW5 and GW5L. In conclusion, ClGL1 modulates the morphology of watermelon fruit and seed by enhancing BR signaling, which provides a key gene and theoretical basis for BR signaling evolution and molecular design breeding in Cucurbitaceae. Full article

Myocardial infarction (MI) remains one of the most critical causes of death worldwide, demanding predictive models that balance accuracy with clinical interpretability. This study introduces an explainable artificial intelligence (XAI) framework that integrates least absolute shrinkage and selection operator (LASSO) regression for feature selection, logistic regression for prediction, and Shapley additive explanations (SHAP) for interpretability. Using a dataset of 918 patients and 12 signal-derived clinical variables, the model achieved an accuracy of 87.7%, a recall of 0.87, and an F1 score of 0.89, confirming its robust performance. The key risk factors identified were age, fasting blood sugar, ST depression, flat ST slope, and exercise-induced angina, while the maximum heart rate and upward ST slope served as protective factors. Comparative analyses showed that the SHAP and p-value methods largely aligned, consistently highlighting ST_Slope_Flat and ExerciseAngina_Y, though discrepancies emerged for ST_Slope_Up, which showed limited statistical significance but high SHAP contribution. By combining predictive strength with transparent interpretation, this study addresses the black-box limitations of conventional models and offers actionable insights for clinicians. The findings highlight the potential of signal-driven XAI approaches to improve early detection and patient-centered prevention of MI. Future work should validate these models on larger and more diverse datasets to enhance generalizability and clinical adoption. Full article

Fractal dimension (Frac) and lacunarity (Lac) are frequently proposed as biomarkers of multiscale image complexity, but their incremental value over standardized radiomics remains uncertain. We position both measures within the Image Biomarker Standardisation Initiative (IBSI) feature space by running a fully reproducible comparison in two settings. In a baseline experiment, we analyze $N=1000$ simulated $64\times 64$ textured ROIs discretized to $N_g=64$, computing 92 IBSI descriptors together with Frac (box counting) and Lac (gliding box), for 94 features per ROI. In a wavelet-augmented experiment, we analyze $N=1000$ ROIs and add level-1 wavelet descriptors by recomputing first-order and GLCM features in each sub-band (LL, LH, HL, and HH), contributing $4\times (19+19)=152$ additional features and yielding 246 features per ROI. Feature similarity is summarized by a consensus score that averages z-scored absolute Pearson and Spearman correlations, distance correlation, maximal information coefficient, and cosine similarity, and is visualized with clustered heatmaps, dendrograms, sparse networks, PCA loadings, and UMAP and t-SNE embeddings. Across both settings a stable two-block organization emerges. Frac co-locates with contrast, difference, and short-run statistics that capture high-frequency variation; when wavelets are included, detail-band terms from LH, HL, and HH join this group. Lac co-locates with measures of large, coherent structure—GLSZM zone size, GLRLM long-run, and high-gray-level emphases—and with GLCM homogeneity and correlation; LL (approximation) wavelet features align with this block. Pairwise associations are modest in the baseline but become very strong with wavelets (for example, Frac versus GLCM difference entropy, which summarizes the randomness of gray-level differences, with $\left|r\right|\approx 0.98$; and Lac versus GLCM inverse difference normalized (IDN), a homogeneity measure that weights small intensity differences more heavily, with $\left|r\right|\approx 0.96$). The multimetric consensus and geometric embeddings consistently place Frac and Lac in overlapping yet separable neighborhoods, indicating related but non-duplicative information. Practically, Frac and Lac are most useful when multiscale heterogeneity is central and they add a measurable signal beyond strong IBSI baselines (with or without wavelets); otherwise, closely related variance can be absorbed by standard texture families. Full article

Breast cancer remains a leading cause of mortality among women despite advances in early detection and targeted therapies, underscoring the need for safer and more effective treatment options. Drug repurposing offers a promising strategy by leveraging existing pharmacological agents with established safety profiles. Desloratadine, a second-generation H1-histamine receptor antagonist widely prescribed for allergic conditions, has attracted interest in oncology because histamine signaling influences proliferation, angiogenesis, and immune responses, yet its anticancer potential remains poorly understood. In this study, we investigated its effects in MCF-7 breast cancer cells, which harbor wild-type TP53. Desloratadine inhibited cell viability in a dose-dependent manner, with an IC₅₀ of 14.2 µg/mL. Mechanistic analyses revealed that growth inhibition was primarily mediated through apoptosis, confirmed by Hoechst 33342 staining, ROS generation, annexin V/PI staining, and caspase-dependent pathways. Gene expression profiling demonstrated upregulation of TP53, FAS, and BAX, alongside reduced PARP-1 and NF-κB expression, with no detectable STAT3 or BCL2 expression. Flow cytometry indicated accumulation of cells in the sub-G₁ phase and G₂/M arrest, consistent with apoptosis induction. Molecular docking further supported these findings, showing that Desloratadine binds with high affinity to p53 (−7.0 kcal/mol), FAS (−6.8 kcal/mol), and NF-κB (−6.5 kcal/mol), forming stabilizing hydrogen bonds and hydrophobic interactions aligned with the observed gene expression changes. To confirm the functional role of TP53, we generated CRISPR-Cas9 knockout MCF-7 cells. Compared with wild-type cells, these knockout cells displayed markedly reduced sensitivity to Desloratadine, with the IC₅₀ shifting from 14.2 µg/mL to 36.4 µg/mL, demonstrating that p53 is a key mediator of the drug’s cytotoxic effect. Collectively, these findings identify Desloratadine as a potential repurposed drug candidate for breast cancer therapy, acting at least in part through a p53-dependent apoptotic pathway. Full article

Deepfake-style AI tutors are emerging in online education, offering personalized and multilingual instruction while introducing risks to integrity, privacy, and trust. This study aims to understand their pedagogical potential and governance needs for responsible integration. A PRISMA-guided, systematic review of 42 peer-reviewed studies (2015–early 2025) was conducted from 362 screened records, complemented by semi-structured questionnaires with 12 assistant professors (mean experience = 7 years). Thematic analysis using deductive codes achieved strong inter-coder reliability (κ = 0.81). Four major themes were identified: personalization and engagement, detection challenges and integrity risks, governance and policy gaps, and ethical and societal implications. The results indicate that while deepfake AI tutors enhance engagement, adaptability, and scalability, they also pose risks of impersonation, assessment fraud, and algorithmic bias. Current detection approaches based on pixel-level artifacts, frequency features, and physiological signals remain imperfect. To mitigate these challenges, a four-pillar governance framework is proposed, encompassing Transparency and Disclosure, Data Governance and Privacy, Integrity and Detection, and Ethical Oversight and Accountability, supported by a policy checklist, responsibility matrix, and risk-tier model. Deepfake AI tutors hold promise for expanding access to education, but fairness-aware detection, robust safeguards, and AI literacy initiatives are essential to sustain trust and ensure equitable adoption. These findings not only strengthen the ethical and governance foundations for generative AI in higher education but also contribute to the broader agenda of sustainable digital education. By promoting transparency, fairness, and equitable access, the proposed framework advances the long-term sustainability of learning ecosystems and aligns with the United Nations Sustainable Development Goal 4 (Quality Education) through responsible innovation and institutional resilience. Full article

Generation of idler pulses via the four-wave mixing (FWM) effect in a nonlinear optical ring resonator (NORR) was investigated numerically. It was found that the relative delay $\tau$ between input pump pulses significantly affects both the energy and temporal–spectral characteristics of generated idler pulses. Specifically, idler pulse energy decreases with increasing $\tau$ due to phase-matching conditions in FWM. Maximum energy transfer occurs for $\tau$ = 0, where optimal phase alignment among pump, signal, and idler waves is achieved. Temporal and spectral analysis at different relative delays reveals a change from a symmetric, multi-subpulse structure with a comb-like spectrum (for $\tau$ = 0) to a near-single-pulse form with a single-comb-line spectrum (for $\tau$ = 9 ps). These findings demonstrate the critical dependence of FWM efficiency on pump pulse synchronization. Therefore, precise control of the relative delay is essential for optimizing idler pulse generation in NORRs. Full article

Severe asthma is a heterogeneous condition often resistant to conventional corticosteroid therapy, necessitating the identification of novel biomarkers and therapeutic targets. This study investigates immunological, transcriptional, and proteomic biomarkers in severe asthma patients from the Brazilian ProAR cohort. Cytokines were measured using a multiplex technology and the differential sputum cell count was performed by cytospin preparations. Sputum transcriptomics was performed by RNA-seq using Ion S5 next-generation sequencing platform. The proteomic study of sputum was performed by liquid chromatography–tandem mass spectrometry (LC-MS/MS) using Q Exactive Orbitrap technology. Compared to mild-to-moderate asthma (MMA) and treatment-controlled severe asthma (SAC), the treatment-resistant severe asthma (SAR) group exhibited increased sputum neutrophilia, eosinophilia, and elevated IL-6 and TNF levels, correlating with impaired lung function. Transcriptomic and proteomic analyses revealed a Th2-independent molecular signature characterized by downregulation of the Hippo signaling pathway and upregulation of JAK–STAT inflammatory cascades. Distinctive microRNA profiles suggest regulatory involvement in inflammatory and proliferative processes. These findings align with prior studies, reinforcing the presence of an IL-6- and TNF-high severe asthma phenotype across diverse populations. Our results highlight key inflammatory pathways that may underlie corticosteroid resistance, offering potential targets for personalized therapeutic interventions in severe asthma. Full article

This paper introduces the Bateson Game, a signaling game in which ambiguity over the governing rules of interaction (interpretive frames), rather than asymmetry of information about player types, drives strategic outcomes. We formalize the communication paradox of the “double bind” by defining a class of games where a Receiver acts under uncertainty about the operative frame, while the Sender possesses private information about the true frame, benefits from manipulation, and penalizes attempts at meta-communication (clarification). We prove that the game’s core axioms preclude the existence of a separating Perfect Bayesian Equilibrium. More significantly, we show that under boundedly rational learning dynamics, the Receiver’s beliefs can become locked into one of two pathological states, depending on the structure of the Sender’s incentives. If the Sender’s incentives are cyclical, the system enters a persistent oscillatory state (an “ambiguity trap”). If the Sender’s incentives align with reinforcing a specific belief or if the Sender has a dominant strategy, the system settles into a stable equilibrium (a “certainty trap”), characterized by stable beliefs dictated by the Sender. We present a computational analysis contrasting these outcomes, demonstrating empirically how different parametrizations lead to either trap. The Bateson Game provides a novel game-theoretic foundation for analyzing phenomena such as deceptive AI alignment and institutional gaslighting, demonstrating how ambiguity can be weaponized to create durable, exploitative strategic environments. Full article

Background/Objectives: Bacterial blight (BB) represents one of the most devastating diseases threatening global rice production. Exploring and characterizing disease resistance (R) genes provides an effective strategy for controlling BB and enhancing rice resilience. Common wild rice (Oryza rufipogon) serves as a valuable reservoir of genetic diversity and disease resistance resources. In this study, we identified and functionally characterized a novel NLR gene, YPR1, from common wild rice (Oryza rufipogon), which exhibited significant spatial, temporal, and tissue-specific expression patterns. Methods: Using a combination of conventional PCR, RT-PCR, bioinformatics, transgenic analysis, and CRISPR/Cas9 gene-editing approaches, the full-length YPR1 sequence was successfully cloned. Results: The gene spans 4689 bp with a coding sequence (CDS) of 2979 bp, encoding a 992-amino acid protein. Protein domain prediction revealed that YPR1 is a typical CNL-type NLR protein, comprising RX-CC_like, NB-ARC, and LRR domains. The predicted molecular weight of the protein is 112.43 kDa, and the theoretical isoelectric point (pI) is 8.36. The absence of both signal peptide and transmembrane domains suggests that YPR1 functions intracellularly. Furthermore, the presence of multiple phosphorylation sites across diverse residues implies a potential role for post-translational regulation in its signal transduction function. Sequence alignment showed that YPR1 shared 94.02% similarity with Os09g34160 and up to 96.47% identity with its closest homolog in the NCBI database, confirming that YPR1 is a previously unreported gene. To verify its role in disease resistance, an overexpression vector (Ubi–YPR1) was constructed and introduced into the BB-susceptible rice cultivar JG30 via Agrobacterium tumefaciens-mediated transformation. T₁ transgenic lines were subsequently inoculated with 15 highly virulent Xanthomonas oryzae pv. oryzae (Xoo) strains. The transgenic plants exhibited strong resistance to eight strains (YM1, YM187, C1, C5, C6, T7147, PB, and HZhj19), demonstrating a broad-spectrum resistance pattern. Conversely, CRISPR/Cas9-mediated knockout of YPR1 in common wild rice resulted in increased susceptibility to most Xoo strains. Although the resistance of knockout lines to strains C7 and YM187 was comparable to that of the wild type (YP^WT), the majority of knockout plants exhibited more severe symptoms and significantly lower YPR1 expression levels compared with YP^WT. Conclusions: Collectively, these findings demonstrate that YPR1 plays a crucial role in bacterial blight resistance in common wild rice. As a novel CNL-type NLR gene conferring specific resistance to multiple Xoo strains, YPR1 provides a promising genetic resource for the molecular breeding of BB-resistant rice varieties. Full article