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Intracranial aneurysms are common vascular lesions with a highly variable natural history. While most unruptured intracranial aneurysms remain stable throughout life, a biologically aggressive subset progresses to growth and rupture, resulting in aneurysmal subarachnoid hemorrhage with substantial morbidity and mortality. Contemporary evidence demonstrates that aneurysm behavior is dynamic rather than static and reflects the interaction of hemodynamic forces, inflammatory vascular remodeling, genetic susceptibility, and environmental risk factors. Rupture risk is not constant over time and may be highest early after aneurysm formation, followed by a period of relative quiescence in selected lesions. Traditional population-based risk estimates have therefore evolved toward individualized risk stratification incorporating aneurysm size, location, morphology, growth, patient-specific factors, and emerging imaging and computational biomarkers. This chapter reviews the epidemiology, pathobiology, growth patterns, and rupture risk of intracranial aneurysms, integrating foundational observational studies with recent advances in genetics, vessel wall imaging, and predictive modeling. Understanding the natural history of brain aneurysms is essential for balancing the risks of observation against intervention and for guiding future innovations in aneurysm management. Full article

Rapid urbanization and complex topography complicate Urban Heat Island (UHI) spatio-temporal dynamics. Traditional models and coarse-resolution imagery often fail to capture fine-scale, spatially non-stationary seasonal driving mechanisms. This study investigates the multi-dimensional drivers of surface thermal dynamics in Kunming, a typical low-latitude plateau city, using seasonal median LST composite (2018–2025). Integrating eXtreme Gradient Boosting (XGBoost) with eXplainable Artificial Intelligence (XAI) models decoupled the nonlinear impacts of these drivers. Results reveal a seasonal thermal dichotomy: Summer exhibits the most intense UHI effect with extreme peak temperatures, while Spring presents an anomaly where natural and vegetated Local Climate Zones (LCZs) show pronounced warming. SHapley Additive exPlanations (SHAP) analysis identified a seasonal rotation: anthropogenic and structural factors dominate Summer and Autumn warming, whereas natural and topographic regulators govern Spring and Winter. GeoShapley deconstruction demonstrated strong spatial non-stationarity. Building-density warming is amplified in poorly ventilated urban cores, and fragmented vegetation’s cooling is offset by anthropogenic heat during peak summer. This study provides new insights into the seasonal drivers of urban thermal environments in plateau cities. Full article

To accurately predict the fishing intensity of the Northwest Pacific squid fishing grounds and address the limitations of traditional models in capturing long-term temporal and spatial correlations and neglecting the coupling relationships of deep environmental factors, this study constructs a 3DCNN model and three fusion models incorporating residual, attention, and Transformer mechanisms. Using the 2017–2024 AIS fishing data and ocean environmental variables from the North Pacific squid fishing industry, the models’ performance is compared at 12 different temporal and spatial scales, and key core environmental variables are identified. The results show that the ResNet3D model exhibits the best overall performance, achieving an F1 score of 0.7909 at the 1.0°-7 days temporal–spatial scale. The residual connections effectively mitigate the gradient vanishing problem, balancing prediction accuracy and stability. The optimal spatial resolution is 1.0°, and the key environmental variables include S₁₀₀, Chl-a₁₀₀, PP₁₀₀, and DO₁₀₀. S₁₀₀ is the core driving variable, consistently exhibiting the highest feature importance value at all time scales. It should be noted that Chl-a is considered an indirect indicator of primary productivity, which may influence squid distribution through trophic transfer processes rather than direct biological effects. This study demonstrates the prediction accuracy and applicability of the multi-mechanism fusion 3DCNN model, reveals the temporal and spatial distribution patterns of fishing intensity in the Northwest Pacific squid fishing grounds, and provides scientific methods and technical support for dynamic monitoring, intelligent management, and sustainable utilization of squid resources. Full article

Background/Objectives: Bloodstream infections (BSIs) are a major cause of morbidity and mortality, particularly when delays in pathogen identification hinder timely and targeted antimicrobial therapy. Rapid diagnostic tests (RDTs) accelerate microbiological identification, yet their clinical impact remains heterogeneous across different healthcare settings. This study aimed to evaluate the real-world effect of implementing FAST microbiology, a diagnostic workflow that integrates RDTs into conventional blood culture processing, on diagnostic timeliness, antimicrobial decision-making, and patient management in a hospital specializing in complex infectious diseases. Methods: We conducted a quasi-experimental study comparing non-FAST and FAST workflows over a 24-month period, including 166 adult patients with sepsis admitted to ICU and non-ICU units, accounting for 231 BSIs. Microbiological outcomes, treatment dynamics, time to targeted therapy, and key clinical endpoints were compared between non-FAST and FAST groups. Results: FAST microbiology significantly reduced the time to initiation of targeted therapy across clinical settings. No statistically significant differences were observed in hospital length of stay, overall mortality, or 28-day mortality between the two groups. Baseline clinical severity, age, and comorbidity burden remained the main determinants of clinical outcomes. Conclusions: These real-world findings support the integration of rapid diagnostics into existing antimicrobial stewardship frameworks by improving diagnostic timeliness and supporting earlier microbiologically guided therapeutic decisions. However, the results also highlight that accelerating diagnostics alone may not be sufficient to improve survival in critically ill patients with complex infectious diseases, where outcomes are predominantly driven by patient- and disease-related factors. Full article

Cascading failures of clustered landslide dams can intensify downstream hazards not only by increasing peak flood magnitude but also by accelerating the rise of water level immediately upstream of successive dams, thereby shortening the available response time before overtopping. This study reports large-scale flume experiments on a three-dam cascade built with identical geometry and similar soil gradation, while systematically varying longitudinal spacing and inflow discharge. The principal measured variable, $C_w\left(t\right)$, is defined here as the local forebay run-up/water-level record measured at a fixed gauge position immediately upstream of each dam. The run-up hydrographs were summarized using peak run-up $C_w^{\mathrm{m}\mathrm{a}\mathrm{x}}$, threshold-arrival time $t_a$ defined at $0.1 C_w^{\mathrm{m}\mathrm{a}\mathrm{x}}$, time to peak $t_p$, maximum rising-stage rate $S^{\mathrm{m}\mathrm{a}\mathrm{x}}$, and above-threshold duration $T$. Across ten tests (five spacing configurations under low/high discharge), peak run-up at both downstream dams consistently exceeded that at Dam1, with amplification factors relative to Dam1 of 1.11–1.45 at Dam2 and 1.13–1.42 at Dam3; Dam3 was not always higher than Dam2. Amplification was much stronger in the rising-stage dynamics: $S^{\mathrm{m}\mathrm{a}\mathrm{x}}$ increased relative to Dam1 by factors of 1.56–11.0 at Dam2 and 2.27–14.0 at Dam3, demonstrating pronounced downstream wavefront steepening. Higher discharge produced earlier threshold arrivals and peaks throughout the cascade, whereas shorter spacing generally produced more impulsive downstream responses with sharper peaks and larger rate amplification. Overall, the dataset provides stage/run-up-based constraints on cascade amplification and indicates that, within the present experimental matrix, dam spacing is the dominant geometric control on flood propagation and downstream hazard escalation. Full article

Carbon is an essential component in the regulation of climate systems through the global biogeochemical cycle. However, changes in land use/land cover (LULC) have reduced the capacity of terrestrial ecosystems like watershed to store carbon. This shows the need for a policy framework that balances conservative objectives with agricultural demands, as watersheds are required to support carbon storage and food production. Previous studies have generally assessed carbon dynamics or LULC change separately, with limited integration of policy-driven scenarios. Therefore, this study aimed to conduct multi-scenario carbon storage modeling to evaluate LULC protection strategies in the Cimanuk Watershed, Indonesia, an area experiencing significant LULC pressures. The method used consisted of Support Vector Machine (SVM)–Markov, the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Geodetector, and Getis-Ord Gi*. A total of four scenarios were used to project LULC and carbon storage in 2042, which included Business as Usual (BAU), Paddy Field Protection (PFP), Forest Protection (FOP), and Paddy Field and Forest Protection (PFFOP). The results showed that forest area declined by 39,400 ha between 2015 and 2025, thereby reducing carbon storage. The PFFOP scenario was identified as the most viable, combining the protection of paddy fields and forests to balance agricultural production and carbon sequestration. Among the factors analyzed, slope exerted the greatest influence on carbon storage. Spatial cluster analysis showed that carbon hotspots were predominantly located in the upper Cimanuk sub-watershed. These results offered valuable insights into scenario-based sustainable watershed management to optimize carbon storage and maintain agricultural function. Furthermore, the proposed framework showed promising potential for application in other tropical watersheds, serving as a reference for decision-makers in sustainable watershed management. Full article

The rice leaf roller (RLR), Cnaphalocrocis medinalis (Guenée), is a major migratory pest that threatens rice production across East Asia. Effective management of migratory pests relies fundamentally on accurately identifying their source areas, population dynamics, and key environmental drivers. Western Hunan is a critical rice-growing region characterized by unique topography and varied climates, making it a principal pathway for RLR migration. Based on 14-year (2011–2024) monitoring datasets, we identified substantial interannual variability in July RLR abundance in Western Hunan, when the population typically peaks, highlighting the episodic and unstable nature of regional infestations. Back-trajectory simulations reveal that heavy occurrence years of RLR feature clear northward migration pathways from the Indo-China Peninsula and South China to Western Hunan in July, supported by strong southerly winds along the route. Multiple linear regression analysis further shows that spring warmth initially facilitates high population accumulation in source regions, and the synergistic effect of source-region precipitation deficits and abundant local rainfall triggers large-scale immigration into Western Hunan. These meteorological factors collectively account for up to 66% of the interannual variability in RLR population fluctuations, confirming that climatic conditions largely determine outbreak severity. This provides a robust quantitative framework for regional early-warning systems and sustainable pest management in migratory corridors. Full article

The shift from the central place paradigm to the network paradigm in regional relation research emphasizes the need to elucidate the factors and mechanisms driving urban network dynamics. Leveraging firm-level big data—including a headquarters–branch relationships database (29,359 headquarters and 114,679 branches) and an investment relationships database (21,843 investing firms and 69,733 recipients)—this study constructs an urban network integrating both vertical and horizontal enterprise connections. Using exponential random graph models (ERGMs), it analyzes the influencing factors and driving mechanisms of urban network dynamics in the Yellow River Basin (YRB). This study found that the urban network in the YRB is characterized by multiple isolated “core–periphery” radial networks. Strong connections are concentrated within each province’s major cities and their immediate surroundings, while horizontal connections across provincial borders are weaker. From 2000 to 2020, the urban network has evolved from isolated “core–periphery” radial networks to corridor networks where some core nodes are interconnected. The urban network dynamics in the YRB result from the combined influences of the preferential attachment mechanism, the network self-organization mechanism, the multi-dimensional proximity mechanisms, and the geographical boundary effect. Enterprises tend to establish branches or investments in cities with spatial proximity and larger economic scales. Reciprocal and transitive structures significantly facilitate urban network formation. Additionally, institutional proximity, geographical proximity, cultural proximity, cognitive proximity, and geomorphological division all exert varying degrees of influence on enterprise connections between cities. Full article

Instance segmentation accurately extracts the position and outline of ships, serving as the foundation for maritime safety tasks such as multi-object tracking, sensor fusion, and collision warning. This study focuses on single-frame segmentation and aims to address the challenge of multi-scale ship occlusion in congested ports, providing reliable observational data through high-precision recognition to ensure navigation safety. Existing methods suffer from performance degradation in complex maritime environments due to factors such as multi-scale distribution, low resolution of distant targets, and frequent occlusions. Among these, ship occlusion is particularly problematic as it leads to feature confusion between adjacent instances and inaccurate boundary segmentation. To address these challenges, we propose a novel instance segmentation algorithm (MAF-ORDNet) based on Multi-branch Adaptive Feature Fusion and Occluded Region Decoupling. Firstly, a multi-branch adaptive feature fusion module is designed to capture contextual information through different receptive fields and dynamically fuse multi-scale features, thereby restoring occluded semantics and enhancing robustness. Secondly, an occlusion region decoupling module is constructed to accurately localize occluded regions and enhance contour responses via adaptive sampling, achieving refined boundary processing. In addition, we constructed and annotated the Occlusion ShipSeg dataset, which contains 1969 real occlusion images, 2150 simulated occlusion images, and 1132 images under adverse weather conditions, totaling 17,352 fine instance annotations. Experimental results show that, compared with PatchDCT, YOLOv11s, and Mask2Former, our method improves AP by 2.7%, 3.2%, and 2.4%, respectively, while maintaining a comparable inference speed to YOLOv8s. These results confirm that MAF-ORDNet achieves a favorable balance between accuracy and efficiency in multi-scale occluded ship segmentation tasks. Full article

Motor Imagery (MI)-based brain–computer interfaces (BCIs) offer promising solutions for enhancing communication and motor functions in individuals with neurological impairments. However, decoding EEG signals accurately is difficult because of their poor signal-to-noise ratio and variability across subjects and sessions. In addition, EEG signals are sensitive to noise. Moreover, the low spatial resolution of EEG signals makes model generalization unreliable due to differences between signals across subjects. While several deep learning models have been developed, a fair comparison remains difficult due to differences in pre-processing, training procedures, and evaluation protocols. This study provides a systematic, controlled comparison of five deep learning approaches for subject-specific classification—EEGNet, EEG-TCNet, ShallowConvNet, DeepConvNet, and CTNet—using the BCI Competition IV datasets 2a and 2b. To enable an unbiased comparison, all models are trained using the same pipeline, with uniform pre-processing and training. Apart from classical accuracy scores, the effect of a constant set of hyper-parameters on the training dynamics, generalization capacity, and the susceptibility to overfitting is evaluated. The performance of the above-stated models is evaluated based on training dynamics, computational efficiency, accuracy, and the quality of the features learned by the models. Using the five-dimensional analysis framework consisting of quantitative performance metrics, training curves, confusion matrix analysis, ROC analysis, and t-SNE visualization techniques, the performance of the brain–computer interfaces is comprehensively analyzed. The experimental analysis confirms that CTNet outperforms other models, with accuracy values of 82.56% and 86.42% on the BCI competition IV datasets 2a and 2b, respectively. The EEGNet model is recognized as having the most potential in the field of real-time applications, owing to its light structure; meanwhile, the DeepConvNet model shows signs of overfitting, despite showing good accuracy. These findings highlight that model training characteristics and sensitivity to the hyper-parameters are important factors in evaluating deep learning models for MI-EEG classification problems. Full article

As essential components of the natural environment, urban green and blue spaces (UGBSs) hold significant potential to enhance public health and wellbeing. However, existing research is limited in understanding the spatiotemporal heterogeneity and nonlinear relationships characterizing how built environment (BE) features of UGBSs influence public happiness. This study takes Nanjing, China as a case study. It integrates multisource data (e.g., social media text, remote-sensing imagery, POI data, land use, etc.) and employs machine learning techniques (including sentiment analysis and random forest), to investigate the nonlinear effects and spatiotemporal dynamics of UGBSs’ BE on public happiness. The results show that nonlinear relationships (e.g., S-shaped and inverted U-shaped) commonly exist between UGBSs’ BE indicators and happiness. The influence of UGBSs’ BE on happiness demonstrates significant spatiotemporal dynamics. Diversity and destination accessibility were dominant factors from 2021 to 2023, whereas the importance of the design and density dimensions increased substantially after 2023. The influence varied across UGBS types; except for the diversity dimension, the BE’s density, design, and destination accessibility were significantly associated with happiness across all UGBS types. The study offers empirical evidence to inform planning and management of UGBS infrastructure, with the aim to maximize public health benefits and foster healthy cities. Full article

Background: Modern oncology views immune system dysfunction as a key factor in carcinogenesis. The induction of immunogenic cell death (ICD), a form of regulated cell death capable of activating adaptive immunity, represents a promising therapeutic strategy. Damage-associated molecular patterns (DAMPs) play a central role in this process. This review aims to summarize current knowledge of DAMPs, their release mechanisms during ICD, their classification, and their prognostic and therapeutic significance in antitumor immunity. Methods: We systematically reviewed and synthesized literature published in Pubmed and Google Scholar on ICD and DAMPs, focusing on distinct forms of DAMPs which were categorized based on recognition mechanisms (five classes) and cellular origin (extracellular, mitochondrial, nuclear, and cytosolic). Key molecules, their receptors, downstream signaling pathways, and clinical associations were analyzed. Results: The spatiotemporally coordinated release of the pattern of DAMPs promotes dendritic cell maturation, antigen presentation, activation of cytotoxic T lymphocytes, and elimination of tumor cells. DAMPs can exhibit a dual role: they are able to induce sterile inflammation essential for antitumor immunity, but may also contribute to metastasis and chronic inflammation. Among all DAMPs, high-mobility group box 1 (HMGB1, a nuclear DAMP) and calreticulin (CRT, a cytosolic protein) demonstrate the greatest prognostic value. Other DAMPs (e.g., extracellular matrix components, uric acid) act as signal amplifiers during various forms of cell death. Conclusions: Understanding the spatiotemporal dynamics of DAMP release is critical for activating immune responses against malignant cells. Monitoring DAMPs may improve patient stratification, predict therapeutic responses, and enable personalized immunotherapeutic strategies. Further investigation of ICD mechanisms and DAMP release represents a fundamental basis for developing novel anticancer therapies. Full article

Air–sea CO₂ flux (FCO₂) in the estuary–coastal continuum plays a vital role in global carbon sequestration; however, the mechanisms governing FCO₂ spatial heterogeneity during early spring remain poorly understood, particularly the roles of distinct dissolved inorganic carbon (DIC) sources. In March 2025, we investigated the FCO₂ spatial variability and DIC sources across the Yangtze River estuary and adjacent coastal areas using DIC concentration, pH, and δ¹³C_DIC analyses. The study area was a net CO₂ source (7.3 ± 8.7 mmol m⁻² d⁻¹), with the intensity declining progressively from the inner estuary to offshore areas. Physical mixing of three principal water masses established the following pattern: high-pCO₂ Changjiang Diluted Water and Yellow Sea Coastal Current drove CO₂ outgassing, while low-pCO₂ East China Sea Shelf Water weakened it. Quantitative apportionment revealed atmospheric CO₂ invasion as the dominant DIC source, followed by carbonate dissolution and organic matter degradation, with the latter declining from the inner estuary to offshore areas. The spatial variation in DIC source contributions further confirms that, superimposed on the physical mixing, biogeochemical processes—particularly biological activity—modulated reginal source intensities. This early-spring case captures a critical transitional window and highlights the necessity of integrating multi-factor regulation with DIC source partitioning to resolve carbon dynamics in the estuarine–coastal continuum. Full article

Urban ecological vulnerability has become an important perspective for understanding ecosystem stability under environmental change. However, its spatiotemporal dynamics and driving mechanisms remain insufficiently understood in high-latitude grassland cities. This study focuses on the central urban area of Hailar and examines how ecological vulnerability evolves and what factors shape its spatial differentiation. Using the sensitivity–resilience–pressure (SRP) framework, a multidimensional evaluation system was constructed based on statistical yearbooks and GIS-based spatial data. Ecological vulnerability was assessed on a 1 km grid from 2010 to 2020, and its evolution was analyzed in three stages. The spatial pattern remains relatively stable but shows increasing differentiation over time. High-vulnerability areas are persistently concentrated in built-up regions, while low-vulnerability areas are mainly located in surrounding forest and grassland ecosystems with higher ecological resilience. Over time, vulnerability gradually shifts outward from the urban core, with clear intensification along the urban fringe. The results indicate that ecological vulnerability is driven by the interaction of sensitivity, resilience, and pressure, while urban expansion plays a key role in intensifying ecological stress and reshaping spatial patterns. The study provides a framework for understanding ecological vulnerability dynamics in high-latitude resource-based grassland cities and supports zoning-based ecological management and land-use optimization. Full article

With the advancement of new-type power systems and smart grids, the structure of power protection and control systems has become increasingly complex, and their reliability exhibits dynamic evolution, multi-factor coupling, and full life cycle characteristics. Against this background, this paper presents a review of the evolution of reliability analysis methods for power protection and control systems. Early research has focused on parametric modeling based on statistical data and structural logic combination analysis, establishing a static reliability analysis framework grounded in the relationship between component failure probability and system structure. Subsequently, to characterize temporal process features such as state transitions, fault dependencies, and maintenance recovery, dynamic modeling methods such as state-space models and dynamic fault trees were developed and applied. In recent years, with the continuous accumulation of full life cycle operational data, multi-source information fusion and data-driven technologies have gradually been introduced into reliability research, promoting the expansion of the analysis framework from stage-based evaluation to full-process evolutionary modeling. On this basis, the modeling concepts, applicable scenarios, and inherent limitations of different methods are summarized and compared. Furthermore, the development trend of an integrated reliability analysis system that deeply combines mechanism models with data-driven methods is discussed, aiming to provide a theoretical foundation for the improvement of reliability analysis systems. Full article