Search Results (10,953)

Cold stress can inhibit the growth of cucurbits, disrupt pollination and fertilization, induce fruit deformities, reduce plant resistance, and increase susceptibility to diseases, ultimately resulting in yield reduction, quality deterioration, or even complete crop failure. This review focuses on the main cucurbits, such as melon, cucumber, and watermelon, systematically expounding the roles of plant hormones, signaling molecules, soluble sugars, key regulatory factors, molecular mechanisms, and network interactions in their response to cold stress. Furthermore, it highlights future research directions and application potential. By analyzing existing challenges and prospective advancements in this field, the review aims to provide a comprehensive reference for facilitating genetic improvement in cold tolerance. Full article

Extracting road network information from satellite remote sensing images is an effective method of dealing with dynamic changes in road networks. At present, the use of deep learning methods to automatically segment road networks from remote sensing images has become mainstream. However, existing methods often produce fragmented extraction results. This is usually caused by insufficient feature extraction and occlusion. In order to solve these problems, we propose an enhanced feature extraction and multi-branch occlusion discrimination network (EFMOD-Net) based on an encoder–decoder architecture. Firstly, a multi-directional feature extraction (MFE) module was proposed as the input for the network, which utilizes multi-directional strip convolution for feature extraction to better capture the linear features of the road. Subsequently, an enhanced feature extraction (EFE) module was designed to enhance the performance of the model in the feature extraction stage by using a dual-branch structure. The proposed multi-branch occlusion discrimination (MOD) module combines the attention mechanism and strip convolution to learn the topological relationship between pixels, enhance the network’s detection of occlusion and complex backgrounds, and reduce the generation of road debris. On the public dataset, the proposed method is compared with other SOTA methods. The experimental results show that the network designed in this paper achieves an IoU of 64.73 and 63.58 on the DeepGlobe and CHN6-CUG datasets, respectively, which is 1.66% and 1.84% higher than the IoU of performance-based methods. The proposed method combines multi-directional bar convolution and a multi-branch structure for road extraction, which provides a new idea for linear object segmentation in complex backgrounds that could be applied directly to urban renewal, disaster assessment, and other application scenarios. Full article

We present the conceptual framework of the Astronomical Hub (AstroHub), a unified platform combining various optical instruments at a single observatory. Its major approach lies in arranging conditions for research groups to install telescopes and equipment and participate in joint projects. AstroHub is planned to integrate Virtual Observatory (VO) tools, FAIR data principles, and a telescope network to create a powerful and attractive ecosystem for both robust near-Earth object (NEO) monitoring and diverse deep space research. We provide an overview of the AstroHub development directions in the case study of the Assy-Turgen Observatory. Full article

In mammals, the suprachiasmatic nucleus (SCN), located in the hypothalamus serves as the master biological clock and precisely regulates circadian rhythms through a complex network structure. As a central pacemaker, the SCN has two primary functions: one is to synchronize the daily rhythms in physiological and behavioral activities; the other is to entrain the endogenous rhythms to the external light–dark cycle. A deep understanding of the SCN network structure is crucial for elucidating the functional mechanisms of the biological clock system. In this review, we systematically summarized the impact of the SCN network structure on functional regulation under light stimulation based on mathematical models. Studies have shown that the coupling between the light-sensitive subgroups in the left and right nuclei of the SCN can enhance the entrainment ability. As an integrated network structure, the SCN may have the characteristics of the small-world network or the scale-free network, as these properties are more conducive to the realization of functions. Additionally, the higher-order coupling mechanism within the SCN can effectively expand the entrainment range. These theoretical research results offer new insights into the relationship between the SCN network and functions and provide crucial theoretical guidance and validation directions for subsequent experimental research. Full article

This study presents a data-driven framework for modeling urban heat in a highland region of Quang Ngai Province, Vietnam—an area with limited prior research on heat stress. Using advanced machine learning methods, including Category Boosting (CatBoost) and deep convolutional neural network (CNN), the spatial distribution of urban land surface temperature (LST) is predicted based on topographical, land use/land cover, urban morphological, proximity, and compactness features. Our findings show that incorporating urban compactness metrics significantly enhances prediction accuracy, with CatBoost explaining 89% of LST variance. Based on Shapley Additive Explanations, built-up density, bare land density, distance to river, green space density, and built-up cluster compactness are identified as the most influential factors. Machine learning-based causal analysis further clarifies the direct effects of key urban features on LST. The proposed framework helps reveal distinct characteristics of the study area with respect to urban heat properties. The research findings can support sustainable urban planning and heat stress alleviation in the study area. Full article

Accurate imputation of missing data in air quality monitoring is essential for reliable environmental assessment and modeling. This study compares two imputation methods, namely Random Forest (RF) and Bidirectional Recurrent Imputation for Time Series (BRITS), using data from the Mexico City air quality monitoring network (2014–2023). The analysis focuses on stations with less than 30% missingness and includes both pollutant (CO, NO, NO₂, NO_x, SO₂, O₃, PM₁₀, PM_2.5, and PMCO) and meteorological (relative humidity, temperature, wind direction and speed) variables. Each station’s data was split into 80% for training and 20% for validation, with 20% artificial missingness. Performance was assessed through two perspectives: local accuracy (MAE and RMSE) on masked subsets and distributional similarity on complete datasets (Two One-Sided Tests and Wasserstein distance). RF achieved lower errors on masked subsets, whereas BRITS better preserved the complete distribution. Both methods struggled with highly variable features. On complete time series, BRITS produced more realistic imputations, while RF often generated extreme outliers. These findings demonstrate the advantages of deep learning for handling complex temporal dependencies and highlight the need for robust strategies for stations with extensive gaps. Enhancing the accuracy of imputations is crucial for improving forecasting, trend analysis, and public health decision-making. Full article

Lung cancer is the leading cause of cancer mortality globally, despite the advancements in screening and management. Survival rates for lung cancer remain suboptimal, largely due to late-stage diagnoses and tumor heterogeneity. Recent advancements in artificial intelligence and radiomics provide a promising outlook for lung cancer screening, diagnosis, personalized treatment, and prognosis. These advances use large-scale clinical and imaging datasets that help identify patterns and predictive features that may be missed by human interpretation. Artificial intelligence tools hold the potential to take clinical decision-making to another level, thus improving patient outcomes. This review summarizes current evidence on the applications, challenges, and future directions of artificial intelligence (AI) in lung cancer care, with an emphasis on early diagnosis and personalized treatment. We examine recent developments in AI-driven approaches, including machine learning and deep neural networks, applied to imaging (radiomics), histopathology, biomarker analysis, and multi-omic data integration. AI-based models demonstrate promising performance in early detection, risk stratification, molecular profiling (e.g., programmed death-ligand 1 (PD-L1) and epidermal growth factor receptor (EGFR) status), and outcome prediction. These tools may enhance diagnostic accuracy, optimize therapeutic decisions, and ultimately improve patient outcomes. However, significant challenges remain, including model heterogeneity, limited external validation, generalizability issues, and ethical concerns related to transparency and clinical accountability. AI holds transformative potential for lung cancer care but requires further validation, standardization, and integration into clinical workflows. Multicenter collaborations, regulatory frameworks, and explainable AI models will be essential for successful clinical adoption. Full article

Generative artificial intelligence (GAI) has emerged as a transformative force in the Internet of Vehicles (IoV), addressing limitations of traditional AI such as reliance on large labeled datasets and narrow task applicability. This paper aims to systematically review recent advances in applying GAI to the IoV, with a focus on training, decision-making, and security. We begin by introducing the fundamental concepts of vehicular networks and GAI, laying the groundwork for readers to better understand the subsequent sections. Methodologically, we adopt a structured literature review, covering developments in synthetic data generation, dynamic scene reconstruction, traffic flow prediction, anomaly detection, communication management, and resource allocation. In particular, we integrate multimodal GAI capabilities with 5G/6G-enabled edge computing to support low-latency, reliable, and adaptive vehicular network services. Our synthesis identifies key technical challenges, including lightweight model deployment, privacy preservation, and security assurance, and outlines promising future research directions. This review provides a comprehensive reference for advancing intelligent IoV systems through GAI. Full article

Background: Streptomyces bacteria are prolific producers of secondary metabolites (SMs), including many antibiotics. However, most biosynthetic gene clusters (BGCs) remain silent under laboratory conditions. Global transcriptional regulators, such as AdpA, can activate these BGCs, but their roles in secondary metabolism are not fully understood. This study investigates the regulatory function of AdpA in Streptomyces venezuelae (AdpA_Sv), a fast-growing model species and natural chloramphenicol producer that encodes over 30 BGCs. Methods: We applied RNA-seq and ChIP-seq at 12 and 20 h—corresponding to vegetative and aerial hyphae stages—to profile the AdpA_Sv regulatory network. Results: AdpA_Sv influenced the expression of approximately 3000 genes, including those involved in primary metabolism, quorum sensing, sulfur metabolism, ABC transporters, and all annotated BGCs, and it bound to around 200 genomic sites. Integration of RNA-seq and ChIP-seq data identified a core regulon of 49–91 directly regulated genes, with additional effects likely mediated indirectly via other transcription factors or non-canonical binding sites. Motif analysis confirmed similarity to the canonical Streptomyces griseus AdpA-binding sequence, with a novel 5-bp 3′ extension. AdpA_Sv directly regulated several SM pathways, including chloramphenicol biosynthesis, potentially alleviating Lsr2-mediated repression. Conclusions: This study defines, for the first time, the direct AdpA regulon in S. venezuelae and establishes AdpA_Sv as a central regulator of secondary metabolism. Our findings highlight S. venezuelae as a promising chassis strain for heterologous expression and suggest strategies for activating silent BGCs in other Streptomyces species. Full article

Graphic design and image processes have a vital role in information technologies and safe, memorable learning activities, which can meet the need for modern and visual aids in the field of education. In this article, the concepts of comparison and competition have been presented using grades or numbers obtained for two different intelligence quotient (IQ) classes of students. The two classes are categorized as learners having textual (un-visualized) and visualized aids. We use the results and outcomes of the random sampling data of the two classes in the parameters of four different, competitive, two-compartmental mathematical models. One of the compartments is for students who only learn through textual learning, and the other one is for students who have access to visualized text resources. Four of the mathematical models were solved numerically, and their grades were obtained by different iterations using the data of the mean of different random sampling tests taken for thirty months; each sampling involved thirty students. The said data are also drawn by using a neural network approach, showing the fitting curves for all the data, the training data, the validation data, and the testing data with histogram, aggression, mean square error, and absolute error. The obtained dynamics are also compared with neural network dynamics. The results of the scenario pointed out that the best results (determined through high grades) were obtained among the students of visual aid learners, as compared to textual and conventional learners. The visualized resources, constructed within the mathematics syllabus domain, may help to upgrade multidimensional mathematical education and the learning activities of intermediate-level students. For this, the findings of the present study are helpful for education policymakers: there is a directive to focus on visual-based learning, utilizing data from various surveys, profile checks, and questionnaires. Furthermore, the techniques presented in this article will be beneficial for those seeking to build a better understanding of the various methods and ideas related to mathematics education. Full article

To improve the traffic efficiency of connected and automated vehicles (CAVs) in on-ramp merging areas, this study proposes a novel region-level multi-agent reinforcement learning framework, Regionally Coordinated Nash-Advantage Decomposition Deep Q-Network with Conflict-Aware Q Fusion (RC-NashAD-DQN). Unlike existing vehicle-level control methods, which suffer from high computational overhead and poor scalability, our approach abstracts on-ramp and main road areas as region-level control agents, achieving coordinated yet independent decision-making while maintaining control precision and merging efficiency comparable to fine-grained vehicle-level approaches. Each agent adopts a value–advantage decomposition architecture to enhance policy stability and distinguish action values, while sharing state–action information to improve inter-agent awareness. A Nash equilibrium solver is applied to derive joint strategies, and a conflict-aware Q-fusion mechanism is introduced as a regularization term rather than a direct action-selection tool, enabling the system to resolve local conflicts—particularly at region boundaries—without compromising global coordination. This design reduces training complexity, accelerates convergence, and improves robustness against communication imperfections. The framework is evaluated using the SUMO simulator at the Taishan Road interchange on the S1 Yongtaiwen Expressway under heterogeneous traffic conditions involving both passenger cars and container trucks, and is compared with baseline models including C-DRL-VSL and MADDPG. Extensive simulations demonstrate that RC-NashAD-DQN significantly improves average traffic speed by 17.07% and reduces average delay by 12.68 s, outperforming all baselines in efficiency metrics while maintaining robust convergence performance. These improvements enhance cooperation and merging efficiency among vehicles, contributing to sustainable urban mobility and the advancement of intelligent transportation systems. Full article

Background/Objectives: Obesity and type 2 diabetes mellitus (T2DM) profoundly disrupt lipid metabolism within local microenvironments of skeletal muscle and its associated connective tissues, including adipose tissue, bone, and fascia. However, the role of local communication between skeletal muscle and its proximal connective tissues in propagating metabolic dysfunction is incompletely understood. This narrative review synthesizes current evidence on these local metabolic interactions, highlighting novel insights and existing gaps. Methods: We conducted a comprehensive literature analysis of primary research published in the last decade, sourced from PubMed, Web of Science, and ScienceDirect. Studies were selected for relevance to skeletal muscle, adipose tissue, fascia, and bone lipid metabolism in the context of obesity and T2DM, with emphasis on molecular, cellular, and paracrine mechanisms of local crosstalk. Findings were organized into thematic sections addressing physiological regulation, pathological remodeling, and inter-organ signaling pathways. Results: Our synthesis reveals that local lipid dysregulation in obesity and T2DM involves altered fatty acid transporter dynamics, mitochondrial overload, fibro-adipogenic remodeling, and compartment-specific adipose tissue dysfunction. Crosstalk via myokines, adipokines, osteokines, bioactive lipids, and exosomal miRNAs integrates metabolic responses across these tissues, amplifying insulin resistance and lipotoxic stress. Emerging evidence highlights the underappreciated roles of fascia and marrow adipocytes in regional lipid handling. Conclusions: Collectively, these insights underscore the pivotal role of inter-tissue crosstalk among skeletal muscle, adipose tissue, bone, and fascia in orchestrating lipid-induced insulin resistance, and highlight the need for integrative strategies that target this multicompartmental network to mitigate metabolic dysfunction in obesity and T2DM. Full article

Neurodegenerative disorders such as Alzheimer’s disease (AD), Parkinson’s disease (PD), and multiple sclerosis (MS) are marked by progressive network dysfunction that challenges conventional, protocol-based neurorehabilitation. In parallel, neuromodulation, encompassing deep brain stimulation (DBS), transcranial magnetic stimulation (TMS), transcranial direct current stimulation (tDCS), vagus nerve stimulation (VNS), and artificial intelligence (AI), has matured rapidly, offering complementary levers to tailor therapy in real time. This narrative review synthesizes current evidence at the intersection of AI and neuromodulation in neurorehabilitation, focusing on how data-driven models can personalize stimulation and improve functional outcomes. We conducted a targeted literature synthesis of peer-reviewed studies identified via PubMed, Embase, Scopus, and reference chaining, prioritizing recent clinical and translational reports on adaptive/closed-loop systems, predictive modeling, and biomarker-guided protocols. Across indications, convergent findings show that AI can optimize device programming, enable state-dependent stimulation, and support clinician decision-making through multimodal biomarkers derived from neural, kinematic, and behavioral signals. Key barriers include data quality and interoperability, model interpretability and safety, and ethical and regulatory oversight. Here we argue that AI-enhanced neuromodulation reframes neurorehabilitation from static dosing to adaptive, patient-specific care. Advancing this paradigm will require rigorous external validation, standardized reporting of control policies and artifacts, clinician-in-the-loop governance, and privacy-preserving analytics. Full article

Nowadays, integrating Artificial Intelligence (AI) in military communication systems is reshaping current defense strategies by enhancing secure data exchange, situational awareness, and autonomous decision-making. This survey examines advancements of AI in tactical communication networks, including UAV networks, radar-based transmission, and electronic warfare resilience, thereby addressing a key gap in the existing literature. This is the first comprehensive review of AI applied exclusively to current tactical communication systems, synthesizing fragmented literature into a unified defense-oriented framework. A key contribution of this survey is its cross-sectoral perspective, exploring how civilian AI techniques are applied in military contexts to enhance resilient and secure communication networks. We analyze state-of-the-art research, industry initiatives, and real-world implementations. Additionally, we introduce a three-criteria evaluation methodology to systematically assess AI applications based on system objectives, military communication constraints, and tactical environmental factors, enabling a study of AI strategies for multidomain interoperability. Finally, we draft future research directions, emphasizing the need for AI standardization, enhanced adversarial resilience, and AI-powered self-healing networks. This survey provides key insights into the evolving role of AI in modern military communications for researchers, policymakers, and defense professionals. Full article

Driving fatigued is a prevalent issue frequently contributing to traffic accidents, prompting the development of automated fatigue detection methods based on various data sources, particularly reliable physiological signals. However, challenges in accuracy, robustness, and practicality persist, especially for cross-subject detection. Multimodal data fusion can enhance the effective estimation of driver fatigue. In this work, we leverage the advantages of multimodal signals to propose a novel Multimodal Attention Network (TMU-Net) for driver fatigue detection, achieving precise fatigue assessment by integrating electroencephalogram (EEG) and electrooculogram (EOG) signals. The core innovation of TMU-Net lies in its unimodal feature extraction module, which combines causal convolution, ConvSparseAttention, and Transformer encoders to effectively capture spatiotemporal features, and a multimodal fusion module that employs cross-modal attention and uncertainty-weighted gating to dynamically integrate complementary information. By incorporating uncertainty quantification, TMU-Net significantly enhances robustness to noise and individual variability. Experimental validation on the SEED-VIG dataset demonstrates TMU-Net’s superior performance stability across 23 subjects in cross-subject testing, effectively leveraging the complementary strengths of EEG (2 Hz full-band and five-band features) and EOG signals for high-precision fatigue detection. Furthermore, attention heatmap visualization reveals the dynamic interaction mechanisms between EEG and EOG signals, confirming the physiological rationality of TMU-Net’s feature fusion strategy. Practical challenges and future research directions for fatigue detection methods are also discussed. Full article