Search Results (1,452)

The protection of Indigenous Territories - ITs in the state of Maranhão, located in the Northeast region of Brazil, represents a major challenge at the intersection of environmental conservation and territorial rights. Situated between the Amazon and Cerrado biomes and within the MATOPIBA agricultural frontier, the state faces increasing anthropogenic pressures that accelerate land use changes, intensify fire regimes, and increase greenhouse gas emissions. This study assessed the temporal dynamics of land use and land cover and their relationship with fire in officially recognized Indigenous Territories from 1985 to 2023 using remote sensing, geoprocessing, and spatial analysis in Google Earth Engine. Indigenous Territories lost 185,327 ha of native vegetation, of which 66.9% corresponded to forest and 33.1% to savanna, yet still retained 2028.755 ha in 2023, with 81.2% classified as forest. Fire recurrence reached up to 37 events per pixel, with Araribóia, Kanela, and Porquinhos dos Canela Apãnjekra exhibiting the highest frequencies. During the 2015–2016 El Niño, Araribóia recorded the largest fire episode, with 200,652 ha burned (48.5%). Between 2013 and 2023, total greenhouse gas emissions reached approximately 709 Mt CO₂eq, with 85% originating from fires and 15% from deforestation. The findings highlight the need to integrate traditional knowledge, territorial governance, and Integrated Fire Management strategies to strengthen the protection of Indigenous Territories and support the preservation of Indigenous livelihoods in Maranhão. Full article

As artificial intelligence systems proliferate across critical societal domains, understanding the nature, patterns, and evolution of AI-related harms has become essential for effective governance. Despite growing incident repositories, systematic computational analysis of AI incident discourse remains limited, with prior research constrained by small samples, single-method approaches, and absence of temporal analysis spanning major capability advances. This study addresses these gaps through a comprehensive multi-method text analysis of 3494 AI incident records from the OECD AI Policy Observatory, spanning January 2014 through October 2024. Six complementary analytical approaches were applied: Latent Dirichlet Allocation (LDA) and Non-negative Matrix Factorization (NMF) topic modeling to discover thematic structures; K-Means and BERTopic clustering for pattern identification; VADER sentiment analysis for emotional framing assessment; and LIWC psycholinguistic profiling for cognitive and communicative dimension analysis. Cross-method comparison quantified categorization robustness across all four clustering and topic modeling approaches. Key findings reveal dramatic temporal shifts and systematic risk patterns. Incident reporting increased 4.6-fold following ChatGPT’s (5.2) November 2022 release (from 12.0 to 95.9 monthly incidents), accompanied by vocabulary transformation from embodied AI terminology (facial recognition, autonomous vehicles) toward generative AI discourse (ChatGPT, hallucination, jailbreak). Six robust thematic categories emerged consistently across methods: autonomous vehicles (84–89% cross-method alignment), facial recognition (66–68%), deepfakes, ChatGPT/generative AI, social media platforms, and algorithmic bias. Risk concentration is pronounced: 49.7% of incidents fall within two harm categories (system safety 29.1%, physical harms 20.6%); private sector actors account for 70.3%; and 48% occur in the United States. Sentiment analysis reveals physical safety incidents receive notably negative framing (autonomous vehicles: −0.077; child safety: −0.326), while policy and generative AI coverage trend positive (+0.586 to +0.633). These findings have direct governance implications. The thematic concentration supports sector-specific regulatory frameworks—mandatory audit trails for hiring algorithms, simulation testing for autonomous vehicles, transparency requirements for recommender systems, accuracy standards for facial recognition, and output labeling for generative AI. Cross-method validation demonstrates which incident categories are robust enough for standardized regulatory classification versus those requiring context-dependent treatment. The rapid emergence of generative AI incidents underscores the need for governance mechanisms responsive to capability advances within months rather than years. Full article

Symmetry and asymmetry are foundational design principles in artificial intelligence, defining the balance between invariance and adaptability in multimodal learning systems. In audio-visual speaker verification, where speech and lip-motion features are jointly modeled to determine whether two utterances belong to the same individual, these principles govern both fairness and discriminative power. In this work, we analyze how symmetry and asymmetry emerge within a gated-fusion architecture that integrates Time-Delay Neural Networks and Bidirectional Long Short-Term Memory encoders for speech, ResNet-based visual lip encoders, and a shared Conformer-based temporal backbone. Structural symmetry is preserved through weight-sharing across paired utterances and symmetric cosine-based scoring, ensuring verification consistency regardless of input order. In contrast, asymmetry is intentionally introduced through modality-dependent temporal encoding, multi-head attention pooling, and a learnable gating mechanism that dynamically re-weights the contribution of audio and visual streams at each timestep. This controlled asymmetry allows the model to rely on visual cues when speech is noisy, and conversely on speech when lip visibility is degraded, yielding adaptive robustness under cross-modal degradation. Experimental results demonstrate that combining symmetric embedding space design with adaptive asymmetric fusion significantly improves generalization, reducing Equal Error Rate (EER) to 3.419% on VoxCeleb-2 test dataset without sacrificing interpretability. The findings show that symmetry ensures stable and fair decision-making, while learnable asymmetry enables modality awareness together forming a principled foundation for next-generation audio-visual speaker verification systems. Full article

Root-cause analysis (RCA) in large-scale microservice-based payment systems is challenging due to complex failure propagation along service dependencies, limited availability of labeled incident data, and heterogeneous service topologies across deployments. We propose ServiceGraph-FM, a pretrained graph-based model for RCA, where “foundation” denotes a self-supervised graph encoder pretrained on large-scale production cluster traces and then adapted to downstream diagnosis. ServiceGraph-FM introduces three components: (1) masked graph autoencoding pretraining to learn transferable service-dependency embeddings for cross-topology generalization; (2) a temporal relational diffusion module that models anomaly propagation as graph diffusion on dynamic service graphs (i.e., Laplacian-governed information flow with learnable edge propagation strengths); and (3) a causal attention mechanism that leverages multi-hop path signals to better separate likely causes from correlated downstream effects. Experiments on the Alibaba Cluster Trace and synthetic PayPal-style topologies show that ServiceGraph-FM outperforms state-of-the-art baselines, improving Top-1 accuracy by 23.7% and Top-3 accuracy by 18.4% on average, and reducing mean time to detection by 31.2%. In zero-shot deployment on unseen architectures, the pretrained model retains 78.3% of its fully fine-tuned performance, indicating strong transferability for practical incident management. Full article

The use of vibrational transport for granular materials has significantly increased in the technological industry due to its reliability, operational efficiency, cost-effectiveness, and relatively uncomplicated technological setup. These transportation methods typically utilize various forms of asymmetry, such as kinematic, temporal (time), wave, and power asymmetry, to induce controlled motion on oscillating surfaces. This study analyses the motion of the granular materials on an inclined plane, where the central innovation lies in the creation of an additional system asymmetry of frictional conditions that enables the granular materials to move upward. This asymmetry is created by introducing dry friction dynamic manipulations. A mathematical model has been developed to describe the motion of particles under these conditions. The modelling results proved that in an inclined transportation system, the asymmetry of frictional conditions during the oscillation cycle—created through dynamic dry friction manipulations—generates a net frictional force exceeding the gravitational force, thereby enabling the upward movement of granular particles. Additionally, the findings highlighted the key control parameters governing the motion of granular particles. λ, which represents the segment of the sinusoidal period over which the friction is dynamically louvered, serves as a parameter that controls the velocity of a moving particle on an inclined surface. The phase shift ϕ serves as a parameter that controls the direction of the particle’s motion at various inclination angles. Experimental investigations were conducted to assess the practicality of this method. The experimental results confirmed that the granular particles can be transported upward along the inclined surface with an inclination angle of up to 6 degrees, as well as provided both qualitative and quantitative validation of the model by illustrating that motion parameters exhibit comparable responses to the control parameters, and strongly agree with the theoretical findings. The primary advantage of the proposed vibrational transport method is the capacity for precise control of both the direction and velocity of granular particle transport using relatively simple mechanical setups. This method offers mechanical simplicity, low cost, and high reliability. It is well-suited to assembly line and manufacturing environments, as well as to industries involved in the processing and handling of granular materials, where controlled transport, repositioning, or recirculation of granular materials or small discrete components is required. Full article

Urban lakes, as critical components of urban ecosystems, provide essential ecological services but face water quality deterioration due to rapid urbanization and associated land use changes. This study investigated the temporal and spatial characteristics and evolution mechanisms of water quality in Wuhan city lakes, with a focus on the Great East Lake basin (GELB), a typical urban lake cluster in the middle Yangtze River basin. By integrating monthly water quality monitoring data (2017–2023) with high-resolution land use data (2020), we employed the Water Quality Index (WQI), Spearman correlation analysis, and Redundancy Analysis (RDA) to assess water quality and the impact of land use on major pollutants. The results revealed significant spatial heterogeneity: Sha Lake (SL) exhibited the best water quality, while Yangchun Lake (YCL) and North Lake (NL) showed the worst conditions. Seasonal variations in water quality were observed, influenced by the ecological functions of lakes and surrounding land use. Notably, understanding these seasonal dynamics provides insights into nutrient cycle operations and their effective management under varying climatic conditions. In addition, the correlation between chlorophyll-a concentration and nutrient elements in urban lakes was not consistent, with some lakes showing significant negative correlations. The water quality of urban lakes is influenced by both land use and human management. Land use analysis indicated high impervious surfaces in East Lake (EL), SL, and YCL exacerbated runoff-driven nutrient loads, the nitrogen elevation from agricultural runoff of Yan East Lake (YEL) and NL’s pollution from historical industrial discharge. This study highlights the urgent need for targeted water management strategies to mitigate the impact of urbanization on water quality and provide a scientific basis for effective governance and ecological restoration in rapidly urbanizing areas around the world. By adopting an integrated approach combining water quality assessments with land use data, this research offers valuable insights for sustainable urban lake management. Full article

The Ulva prolifera green tide in the South Yellow Sea has erupted annually for many years, posing significant threats to coastal ecology, the economy, and society. While environmental factors are widely acknowledged as prerequisites for these outbreaks, the asynchrony and complex coupling between their variations and disaster events have challenged traditional studies that rely on instantaneous correlations to uncover the underlying dynamic mechanisms. This study focuses on the Ulva prolifera disaster in the South Yellow Sea, systematically analyzing its spatiotemporal distribution patterns, the temporal accumulation and lag effects of environmental factors, and the coupled driving mechanisms using the Floating Algae Index (FAI). The results indicate that: (1) The disaster shows significant interannual variability, with 2019 experiencing the most severe outbreak. Monthly, the disaster begins offshore of Jiangsu in May, moves northward and peaks in June, expands northward with reduced scale in July, and largely dissipates in August. Years with large-scale outbreaks exhibit higher distribution frequency and broader spatial extent. (2) Environmental factors demonstrate significant accumulation and lag effects on Ulva prolifera disasters, with a mixed temporal mode of both accumulation and lag effects being dominant. Temporal parameters vary across different factors—nutrients generally have longer lag times, while light and temperature factors show longer accumulation times. These parameters change dynamically across disaster stages and display a clear inshore–offshore gradient, with shorter effects in coastal areas and longer durations in offshore waters, revealing significant spatiotemporal heterogeneity in temporal response patterns. (3) The driving mechanism of Ulva prolifera disasters follows a “nutrient-dominated, temporally relayed” pattern. Nutrient accumulation (PO₄, NO₃, SI) from the previous autumn and winter serves as the decisive factor, explaining 86.8% of interannual variation in disaster scale and 56.1% of the variation in first outbreak timing. Light and heat conditions play a secondary modulating role. A clear temporal relay occurs through three distinct stages: the initial outbreak triggered by nutrients, the peak outbreak governed by light–temperature–nutrient synergy, and the system decline characterized by the dissipation of all driving forces. These findings provide a mechanistic basis for developing predictive models and targeted control strategies. Full article

This work proposes an analysis of the capability of three deep learning models—the feedforward neural network (FFNN), long short-term memory (LSTM) network, and physics-informed neural network (PINN)—to identify the parameters of a flexible fixed-wing aircraft using in-flight data. These neural networks, composed of multiple hidden layers, are evaluated for their ability to perform system identification and to capture the nonlinear and dynamic behavior of the aircraft. The FNN and LSTM models are compared to assess the impact of temporal dependency learning on parameter estimation, while the PINN integrates prior knowledge of the system’s governing of ordinary differential equations (ODEs) to enhance physical consistency in the identification process. The objective is to exploit the generalization capability of neural network-based models while preserving the accurate estimation of the physical parameters that characterize the analyzed system. The neural networks are evaluated for their ability to perform system identification and capture the nonlinear behavior of the aircraft. The results show that the FFNN achieved the best overall performance, with average Theil’s inequality coefficient (TIC) values of 0.162 during training and 0.386 during testing, efficiently modeling the input-output relationships but tending to fit high-frequency measurement noise. The LSTM network demonstrated superior noise robustness due to its temporal filtering capability, producing smoother predictions with average TIC values of 0.398 (training) and 0.408 (testing), albeit with some amplitude underestimation. The PINN, while successfully integrating physical constraints through pretraining with target aerodynamic derivatives, showed more complex convergence, with average TIC values of 0.243 (training) and 0.475 (testing), and its estimated aerodynamic coefficients differed significantly from the conventional values. All three architectures effectively captured the coupled rigid-body and flexible dynamics when trained with distributed wing sensor data, demonstrating that neural network-based approaches can model aeroelastic phenomena without requiring explicit high-fidelity flexible-body models. This study provides a comparative framework for selecting appropriate neural network architectures based on the specific requirements of aircraft system identification tasks. Full article

Understanding the spatiotemporal evolution of soil salinization is essential for elucidating its driving mechanisms and supporting sustainable land and water management in arid regions. In this study, the Alar Reclamation Area in Xinjiang, a typical desert–oasis transition zone, was selected to investigate the drivers of spatiotemporal variation in soil salinization. GRACE gravity satellite observations for the period 2002–2022 were used to estimate groundwater storage (GWS) fluctuations. Contemporaneous Landsat multispectral imagery was employed to derive the normalized difference vegetation index (NDVI) and a salinity index (SI), which were further integrated to construct the salinization detection index (SDI). Pearson correlation analysis, variance inflation factor analysis, and a stepwise regression framework were employed to identify the dominant factors controlling the occurrence and evolution of soil salinization. The results showed that severe salinization was concentrated along the Tarim River and in low-lying downstream zones, while salinity levels in the middle and upper parts of the reclamation area had generally declined or shifted to non-salinized conditions. SDI exhibited a strong negative correlation with NDVI (p ≤ 0.01) and a significant positive correlation with both irrigation quota and GWS (p ≤ 0.01). A pronounced collinearity was observed between GWS and irrigation quota. NDVI and GWS were identified as the principal drivers governing spatial–temporal variations in SDI. The resulting regression model (SDI = 0.946 − 0.959 × NDVI + 0.318 × GWS) established a robust quantitative relationship between SDI, NDVI and GWS, characterized by a high coefficient of determination (R² = 0.998). These statistics indicated the absence of multicollinearity (variance inflation factor, VIF < 5) and autocorrelation (Durbin–Watson ≈ 1.876). These findings provide a theoretical basis for the management of saline–alkali lands in the upper Tarim River region and offer scientific support for regional ecological sustainability. Full article

Artificial intelligence (AI) is increasingly reshaping the control mechanisms that govern magnetic resonance imaging (MRI), enabling faster, safer, and more adaptive operation of the scanner’s physical subsystems. This review provides a comprehensive survey of recent AI-driven advances in core control domains: radio frequency (RF) pulse design and specific absorption rate (SAR) prediction, motion-dependent modeling of $B_1^{+}$ and $B_0$ fields, and gradient system characterization and correction. Across these domains, deep learning models—convolutional, recurrent, generative, and temporal convolutional networks—have emerged as powerful computational surrogates for numerical electromagnetic simulations, Bloch simulations, motion tracking, and gradient impulse response modeling. These networks achieve subject-specific field or SAR predictions within seconds or milliseconds, mitigating long-standing limitations associated with inter-subject variability, non-linear system behavior, and the need for extensive calibration. We highlight methodological themes such as physics-guided training, reinforcement learning for RF pulse design, subject-specific fine-tuning, uncertainty considerations, and the integration of learned models into real-time MRI workflows. Open challenges and future directions include unified multi-physics frameworks, deep learning approaches for generalizing across anatomies and coil configurations, robust validation across vendors and field strengths, and safety-aware AI design. Overall, AI-powered control strategies are poised to become foundational components of next-generation, high-performance, and personalized MRI systems. Full article

Three ageing reservoirs in Ekiti State, Nigeria (Ureje constructed in 1958, Egbe in 1982, and Ero in 1989), were comparatively assessed to evaluate fish assemblages and their conservation relevance. Despite the absence of formal fisheries governance, all three reservoirs supported temporally stable fish communities with low overall diversity. A core assemblage of six species dominated across sites, while species richness increased from seven species in the small urban Ureje reservoir to nine species in the larger and more rural Ero reservoir. Four native species that have become locally scarce in surrounding river systems (Heterotis niloticus, Parachanna obscura, Hepsetus odoe, and Hyperopisus bebe) persisted at low but consistent abundance. Aquatic environmental variables remained within suitable limits for freshwater fishes, and trophic structure appeared intact across the reservoirs. Catch density was substantially higher in the urban reservoir compared to the rural systems, reflecting spatial differences in fishing intensity. Overall, the findings demonstrate that small tropical reservoirs can function as important freshwater habitats that sustain fish biodiversity and fisheries production in modified landscapes. Full article

Youth unemployment remains one of the most persistent and structurally sensitive challenges in emerging economies, particularly in environments characterized by macroeconomic volatility and frequent shocks. This study investigates the dynamics and forecasting performance of youth unemployment in Turkey by adopting a symmetry-based multivariate framework that explicitly contrasts equilibrium-oriented and asymmetric temporal behaviors. Using monthly data covering the period 2009–2024, youth unemployment is modeled jointly with key macroeconomic indicators, including economic growth, inflation, overall unemployment, labor force participation, migration, exchange rates, and consumer confidence. The empirical strategy integrates traditional econometric models and modern machine learning approaches under a unified and leakage-free evaluation protocol. Stationarity and long-run properties of the series are examined using unit root tests and the Bayer–Hanck cointegration approach, followed by long-run coefficient estimation via FMOLS and DOLS. Forecasting performance is then compared across VARIMA, Prophet, and deep learning models (RNN, LSTM, and GRU), including both vanilla and hyperparameter-tuned specifications. The results reveal a clear performance hierarchy. VARIMA models, particularly the VARIMA (p = 2, q = 0) specification, consistently outperform all alternatives by a wide margin, achieving exceptionally low forecast errors. This finding indicates that youth unemployment in Türkiye is predominantly governed by symmetric co-movements and long-run equilibrium relationships among macroeconomic variables. Prophet and GRU models capture short-term and regime-sensitive fluctuations more flexibly, reflecting asymmetric temporal responses, but at the cost of higher forecast dispersion. In contrast, RNN and LSTM models exhibit limited generalization capability and are prone to overfitting in the small-sample macroeconomic context. As a result, this study positions the estimation of youth unemployment as both an econometric challenge and a symmetry-based analytical problem, offering new methodological and conceptual insights consistent with a fresh perspective. Full article

Fusarium verticillioides is a common pathogenic fungus of corn since it causes severe yield losses and produces mycotoxins to threaten the health of both humans and livestock. Although extensive research has characterized specific genetic and environmental factors influencing mycotoxin production, a systematic understanding of the temporal transcriptional dynamics governing its developmental progression remains lacking. This study addresses this critical knowledge gap through a time-series transcriptomic analysis of F. verticillioides at four key cultivation stages (3, 5, 7, and 9 days post-inoculation). Transcriptomic analysis identified 1928, 2818, and 1934 differentially expressed genes (DEGs) in the comparisons of FV3 vs. FV5, FV5 vs. FV7, and FV7 vs. FV9, respectively. Gene Ontology enrichment revealed 76, 106, and 56 significantly enriched terms across these comparisons, with “integral component of membrane” consistently being the most enriched cellular component. Pathway analysis demonstrated “amino acid metabolism” and “carbohydrate metabolism” as the most significantly enriched metabolic pathways. Notably, the fumonisin (FUM) and fusaric acid (FA) biosynthetic gene clusters exhibited coordinated peak expression during the early cultivation, followed by progressive decline. Mfuzz clustering further delineated 12 distinct expression trajectories, highlighting the dynamic transcriptional networks underlying fungal adaptation. This work provided the first comprehensive temporal transcriptome of F. verticillioides, establishing a foundational resource for understanding its stage-specific biology and revealing potential time-sensitive targets for future intervention strategies. Full article

The coordinated development of the agroforestry economy and the ecological environment is crucial for promoting the sustainable development and high-quality transformation of the agroforestry economy. Based on TOE theory and utilizing provincial-level panel data from China covering 2012–2023, this study comprehensively employs dynamic QCA and NCA methods to explore the multi-faceted driving pathways and supporting factors for the coordinated development of the agroforestry economy and ecological environment across temporal and spatial dimensions. Key findings include: (1) Coordinated development requires synergistic contributions from multiple factors—technological, organizational, and environmental—rather than isolated effects of any single element. While no single factor alone constitutes a necessary condition for coordination, the importance of technological innovation, market demand, and industrial support is progressively increasing; (2) The coordinated development of the agroforestry economy and ecological environment involves multiple pathways and complex mechanisms. Specifically, it encompasses four distinct approaches: enterprise-driven and industry-supported model, technology-innovation-led model, market-driven factor integration model, and government-led multi-stakeholder collaboration model; (3) No significant temporal effects emerged across all pathways, but pronounced spatial heterogeneity was evident. The enterprise-driven and industry-supported model suits Northeast and Central China; the technology-innovation-led model is suitable for South China and Northeast China; the market-driven factor integration model is suitable for East China, Central China, and Southwest China; the government-led multi-stakeholder collaboration model is suitable for Southwest China and Central China. Therefore, to enhance the coordinated development of the agroforestry economy and ecological environment, each region should adopt a holistic perspective, leverage its unique resource and factor endowments, strengthen the integrated matching of technological, organizational, and environmental factors, and explore development pathways tailored to local conditions. Full article

Oxidative stress is a defining feature of stroke pathology, but the magnitude, timing and impact of redox imbalance are not static. Emerging evidence indicates that physiological contexts, such as aging, metabolic stress, and circadian disruption, continuously reshape oxidative status and determine the brain’s vulnerability to ischemic and reperfusion injury. This review integrates recent insights into how these intrinsic modulators govern the transition from adaptive physiological redox signaling to pathological oxidative stress during stroke. Aging compromises mitochondrial quality control and blunts NRF2-driven antioxidant responses, heightening susceptibility to ROS-driven damage. Metabolic dysfunction, as seen in obesity and diabetes, amplifies oxidative burden through NADPH oxidase activation, lipid peroxidation, and impaired glutathione recycling, further aggravating post-ischemic inflammation. Circadian misalignment, meanwhile, disrupts the rhythmic expression of antioxidant enzymes and metabolic regulators such as BMAL1, REV-ERBα, and SIRT1, constricting the brain’s temporal window of resilience. We highlight convergent signaling hubs, NRF2/KEAP1, SIRT–PGC1α, and AMPK pathways, as integrators of these physiological inputs that collectively calibrate redox homeostasis. Recognizing oxidative stress as a dynamic, context-dependent process reframes it from a static pathological state to a dynamic outcome of systemic and temporal imbalance, offering new opportunities for time-sensitive and metabolism-informed redox interventions in stroke. Full article