Search Results (8,968)

Outlier detection in time series is traditionally formulated as the identification of rare or extreme observations with respect to global statistical properties. While effective for stationary processes, this perspective becomes insufficient in complex and non-stationary systems, where atypical behavior may manifest as disruptions of stable relationships rather than numerical extremeness. This paper proposes a rule-based framework for detecting structural outliers in non-stationary time series. Regular system behavior is represented by an interpretable set of deterministic IF–THEN rules describing stable relational patterns between features. Each rule defines a logical context and an admissible range of a diagnostic quantity, estimated nonparametrically from historical observations satisfying the rule condition. For a given observation, the set of active rules is identified and a structural inconsistency score is computed as the fraction of violated rule consequences. Additionally, observations lacking support from high-frequency contexts are treated as candidates for structural atypicality. The method is deterministic and avoids the need for explicit probabilistic modeling or iterative parameter learning, which simplifies interpretation and implementation. The framework is illustrated on daily EUR/USD data (2010–2022) using technical indicators (EMA, RSI) and absolute log-returns as the diagnostic measure. Results provide evidence that structurally atypical events can be identified even when global statistical thresholds remain unviolated, suggesting the practical relevance of relational analysis for non-stationary time series monitoring contexts. Full article

Wildfires are increasingly complex emergencies driven by climate variability, the expansion of wildland–urban interfaces, and the interaction between fire events and hazardous environments. These factors pose significant challenges for emergency management, particularly in the presence of cascading effects and multi-hazard interactions. This review examines the potential contribution of Chemical, Biological, Radiological, Nuclear, and Explosive (CBRNE) frameworks to wildfire emergency management, focusing on preparedness and operational response. A narrative analysis of interdisciplinary literature was conducted to identify conceptual and operational overlaps between fire science and CBRNE-based approaches, with particular attention to command structures, hazard assessment, and response coordination. The analysis indicates that wildfire management systems often remain fragmented, with variability in procedures, training, and the integration of monitoring technologies. Evidence from CBRNE operational models suggests that structured command systems, field-based analytical capabilities, and interoperable procedures support improved situational awareness and decision-making. The review highlights how selected CBRNE principles, including structured command systems, zoning strategies, hazard characterization, and interoperability mechanisms, may address persistent gaps in complex wildfire emergency management, providing a basis for improved coordination, operational effectiveness, and system resilience. Full article

Despite growing evidence linking cultural participation to subjective well-being, existing research has largely focused on individual-level participation, local communities, or single-event case studies, leaving the role of festival density insufficiently explored at the national level. This study addresses this gap by examining the relationship between festival density, operationalized as the number of festivals per population (NFP), and national well-being through a cross-country comparative framework. The analysis integrates data from 121 countries and 7859 festivals obtained from the Vibrate platform with national well-being indicators from the World Happiness Report (2025). Using Pearson correlation analysis and supplementary regression-based robustness checks, the study identifies a moderate positive association between festival density and national well-being. However, the strength of this relationship varies across geographical and contextual settings, and weakens when broader socioeconomic factors are taken into account. The findings further indicate that cultural attributes, particularly festival genre, are more strongly associated with well-being outcomes than structural characteristics such as festival size. Religious festivals exhibit the strongest observed correlation, although this result should be interpreted cautiously due to the limited number of observations within this category. The study contributes to the literature by conceptualizing festival density as a macro-level indicator of cultural opportunity structures and by providing one of the first systematic cross-country analyses of its relationship with national well-being. The findings advance current knowledge by suggesting that the cultural characteristics of festival ecosystems may be more relevant to well-being than their scale alone, while also highlighting the importance of broader socioeconomic conditions in shaping national well-being outcomes. The findings also contribute to the sustainability literature by highlighting the role of cultural ecosystems as components of social sustainability. By fostering opportunities for social interaction, collective identity, and cultural participation, festival environments may support sustainable well-being and strengthen the social and cultural dimensions of sustainable development. Full article

Social media studies often rely on one platform while drawing conclusions about online publics more generally. This study tests that inferential move through an event-centered comparison of El Niño discourse across X/Twitter, YouTube, Facebook, Reddit, TikTok, and LinkedIn. The observation window ran from 9 May through 17 May 2026, several days before and after the May 14 El Niño Watch issued by the National Oceanic and Atmospheric Administration (NOAA), which reported an 82 percent probability of El Niño emerging during May to July 2026 and a 96 percent probability of continuation through the 2026 to 2027 Northern Hemisphere winter. The corpus contains 8145 items classified as highly or moderately related to El Niño after platform-specific collection and common annotation. X/Twitter supplies 7075 items, YouTube 864, Facebook 66, Reddit 59, TikTok 50, and LinkedIn 31. Texts were annotated with a shared structured schema covering relevance, sentiment, emotion, topic, stance, likely misinformation, personal experience, humor, calls to action, language, engagement, and length. The results show that platform choice changes the empirical object. X/Twitter appears multilingual, fast-moving, and weather-heavy. YouTube is more negative, humorous, and personally experiential. Facebook is long-form and media/news oriented, with the highest model-flagged likely misinformation rate. Reddit is concentrated around weather concern. TikTok is short, playful, and personal. LinkedIn is small, professional, and mostly informational. These differences caution against generalizing from one platform to social media as a whole unless a study explicitly defines its scope, accounts for platform and genre differences, and recognizes that visible discourse may include organizational, algorithmically amplified, automated, or otherwise inauthentic activity alongside genuine human expression. Full article

GNSS-derived strain-rate analysis, geodetic earthquake recurrence modeling, and seismic potential estimations were integrated to investigate segment-scale deformation behavior along the central North Anatolian Fault Zone (NAFZ) using a high-resolution geodetic velocity field. The obtained strain rates reveal that deformation within the central NAFZ is distributed across a geometrically complex and kinematically heterogeneous fault network rather than being restricted to the main fault strand alone. While the main fault accommodates the majority of regional deformation, significant strain accumulation is also observed along major splay fault systems, including the Merzifon–Esençay, Ezinepazarı, Sungurlu, Eldivan, and Ekinveren faults. The derived strain patterns further indicate the coexistence of localized transtensional and transpressional deformation regimes controlled by fault geometry, segment boundaries, and structural discontinuities. Geodetically derived earthquake recurrence periods display pronounced spatial variability, with shorter recurrence periods concentrated along the main fault strand and comparatively longer earthquake cycles characterizing structurally complex splay systems. Among the investigated structures, the eastern and central segments of the Merzifon–Esençay Fault (MEF) exhibit relatively elevated strain accumulation and seismic potential. In particular, the estimated potential earthquake magnitudes reaching Mw 7.3–7.5, together with paleoseismological evidence indicating that the most recent major surface-rupturing event along the Esençay segment occurred approximately 3700 years ago, suggest that this fault system may represent a candidate seismic gap within the central NAFZ. Overall, the results demonstrate that deformation within the central NAFZ is strongly partitioned among interacting fault segments and highlight the importance of segment-scale geodetic analyses for improving seismic hazard assessments in complex strike-slip fault systems. Full article

Background: Agricultural logistics in Kazakhstan is critical for export-oriented supply chains, but its resilience is limited by infrastructure constraints, fluctuating export demand, and insufficient coordination between market and logistics processes. Methods: This study develops a conceptual multi-level model of the agricultural logistics system and a hybrid simulation model combining system dynamics and discrete-event simulation to analyze intermodal transportation under demand and capacity constraints. The model integrates demand formation, storage, transport, and export operations, as well as feedback mechanisms between fulfilled demand, repeat orders, and logistics performance. The model is implemented in AnyLogic 8.9. Results: The conceptual model structures the interaction of key participants, logistics facilities, and infrastructure levels within Kazakhstan’s agricultural logistics system. Simulation experiments reproduce cyclic logistics behavior and show that reduced logistics capacity increases the demand gap and system pressure, while stronger market signals intensify demand and infrastructure load. The results confirm that resilience depends on the balance between demand activation, logistics capacity, and replenishment policy. Conclusions: The proposed approach provides a tool for analyzing the resilience of agricultural intermodal logistics in Kazakhstan and supports scenario-based evaluation of infrastructure and market factors. The novelty lies in combining a conceptual multi-level logistics model with hybrid simulation of demand and logistics flows. Full article

Climate change is reshaping agricultural systems by altering temperature and rainfall regimes, increasing the frequency of extreme events, and intensifying risks to crop productivity, water use, and farm decision-making. As climate–agriculture research expands rapidly, it becomes increasingly difficult to identify consolidated knowledge domains, emerging priorities, and evidence gaps. This study maps the structure and evolution of this literature using 219,261 Scopus-indexed documents selected from 290,560 records published between 1990 and 2025. A text-mining workflow combined BERTopic-based semantic modeling with supervised thematic classification into 18 macro-themes, while annual shares, z-scores, and document-level primary–secondary co-framing were used to assess temporal salience and cross-theme coupling. The results show sustained growth in research output, with 53.67% of publications produced between 2016 and 2025, and strong geographical concentration in the United States and China, which together account for 41.98% of the corpus. Hydrology and water management, crop production, impact assessment, and atmospheric processes remain central pillars, while socio-economic vulnerability, food security, sustainability, biotechnology, and greenhouse gas mitigation have gained prominence. The resulting evidence map provides a reproducible overview of the climate–agriculture knowledge landscape and can support research prioritization and policy design for climate-resilient agrifood systems. Full article

Ochratoxin A (OTA) is a prevalent foodborne mycotoxin that has been increasingly recognized as a potential environmental contributor to neurodegenerative diseases. Despite extensive research, a systematic integration of how OTA replicates the specific pathological hallmarks of Alzheimer’s Disease (AD) is currently lacking. This study provides a comprehensive systematic review of the mechanistic evidence linking OTA exposure to AD-related pathways, utilizing the Adverse Outcome Pathway (AOP) framework to categorize complex toxicological data into biological key events (KEs). A systematic literature search was conducted across PubMed, Scopus, and Web of Science. A total of 24 peer-reviewed articles were selected for synthesis, comprising 14 in vitro studies and 10 in vivo investigations. The integrated evidence demonstrates that OTA exposure triggers a robust toxicological cascade that replicates several key mechanistic pathways associated with AD in experimental models. Early molecular triggers involve significant redox imbalance and mitochondrial bioenergetic failure, which serve as catalysts for sustained neuroinflammation and microglial activation. In vivo data, from multiple animal models, consistently show that these cellular dysfunctions culminate in structural damage. This systematic integration provides a clearer roadmap for future risk assessment and emphasizes the urgent need for refined regulatory guidelines to protect neurological health from chronic mycotoxin exposure. Full article

Background/Objective: Bisphenol AF (BPAF), a fluorinated analogue of bisphenol A, is an environmental contaminant associated with hepatotoxicity and metabolic disruption. However, the systematic molecular mechanisms linking early transcriptional events to metabolic dysfunction in the liver remain poorly defined. The aim of this study is to elucidate the association between BPAF exposure and hepatic lipid accumulation by integrating transcriptomics, cellular metabolomics, and targeted phenotypic assays. Methods: We performed RNA-sequencing on livers from mice exposed to BPAF (0.1–10 mg/kg/day, 28 days), and performed non-targeted metabolomics on AML12 murine hepatocytes co-cultured with RAW264.7 macrophages in a Transwell system (0–2500 nM BPAF, 48 h). Key metabolic pathways were identified through integrated bioinformatics and validated using enzymatic assays, qRT-PCR, Western blotting, and phenotypic staining (lipid droplets, ROS). Results: Multi-omics integration revealed significant disruption of PPAR signaling and the tricarboxylic acid (TCA) cycle. A striking dose-dependent accumulation of succinate was observed in exposed cells, concomitant with a significant inhibition of succinate dehydrogenase (SDH) activity (52% reduction at 2500 nM, p < 0.001). Transcriptomic data confirmed the downregulation of mitochondrial fatty acid β-oxidation genes. Phenotypic validation indicated that BPAF exposure is associated with oxidative stress, pro-inflammatory cytokine release (TNF-α, IL-6), and pronounced intracellular lipid droplet accumulation in hepatocytes. Conclusions: This study suggests that BPAF exposure is associated with SDH dysfunction, TCA cycle arrest, and lipid dysregulation. Whether BPAF directly inhibits SDH or acts through upstream mitochondrial targets warrants further structural and kinetic investigation. Full article

As extreme weather events increasingly threaten global food systems, accurately assessing climate risks and predicting regional crop yields remains a critical challenge. Conventional prediction models often rely on direct weather-to-yield relationships, bypassing continuous crop physiological responses and limiting their capacity to capture fine-grained temporal impacts of meteorological anomalies. To address this, we propose a novel two-stage spatiotemporal functional framework that integrates high-resolution daily weather trajectories with satellite-derived indicators, utilizing the Enhanced Vegetation Index (EVI) and Land Surface Water Index (LSWI) to represent canopy structural vigor and hydraulic status, respectively. In the first stage, a Historical Functional Linear Model (HFLM) dynamically maps daily meteorological trajectories (temperature, precipitation, and solar radiation) onto continuous physiological curves under strict temporal causality constraints. This generates bivariate coefficient surfaces that reveal dynamic windows of vulnerability and capture divergent, lagged physiological responses to climate stress. In the second stage, a spatially heterogeneous functional additive model integrates these weather-shaped physiological trajectories alongside raw meteorological dynamics as joint predictors for county-level yields. By extracting functional principal components and modeling flexible non-linear biological responses while accounting for continuous spatial heterogeneity, this dual-channel frameworkcaptures key aspects of both chronic physiological stress and acute meteorological shocks. Validated across a 25-year (2000–2024) U.S. Corn Belt panel, the proposed DC-FAM achieves a mean weighted mean squared prediction error (WMSPE) of 242.33 (bu/acre)² and a median out-of-sample $R_{\mathrm{cv}}^2$ of 0.422, outperforming all benchmarks including a random forest. Attribution of the 2012 flash drought further demonstrates the framework’s capacity to mechanistically trace the complete disaster propagation chain from anomalous spring warming to mid-summer hydraulic failure. The proposed framework provides a transparent, biophysically grounded tool for decoding dynamic climate stress trajectories and disaster propagation chains, offering potential implications for adaptive farm management and precision agricultural insurance. Full article

CRISPR-Cas9 has transformed microbial biotechnology by enabling precise genome modifications; however, achieving high editing efficiency remains a challenge due to multiple determinants, including on-target specificity, off-target events, PAM sequence, sgRNA scaffold composition, and RNA secondary structure. Our review foresees how artificial intelligence (AI) can address those challenges by enabling automated identification as well as highly active guide RNA (gRNA) optimisation. We highlight the influence of a data-driven training strategy that is focused on high-quality, diverse, and accurately labelled microbial datasets—mainly, given the limitations of models derived from mammalian systems that are not directly transferable to microbial organisms. Moreover, we discuss the key role of FAIR (Findable, Accessible, Interoperable, and Reusable) data principles and centralised, curated CRISPR-Cas databases as foundational elements for developing robust and predictive frameworks. Emerging directions are also explored, including generative AI approaches capable of supporting automated experimental planning. By considering the potential dual use of such technologies, the review further addresses bioethical considerations and regulatory frameworks necessary to ensure responsible genome engineering as a milestone, as well as the implementation of safeguards against misuse, particularly in pathogenic microorganisms. Furthermore, the convergence of standardised experimental data, specialised microbial datasets, and advanced AI architectures is paving the way to transform microbial biotechnology by accelerating metabolic engineering and synthetic biology applications. Full article

A massive number of early-constructed small-to-medium-span bridges are collectively entering an “aging” phase in China. Meanwhile, vast amounts of unstructured bottom-level inspection texts remain underutilized. To address them, this paper proposes a data governance method. Large Language Models were leveraged to process unstructured defect data from 18,238 real-world bridges nationwide. The data were structurally cleaned and mapped into discrete features, revealing multidimensional vulnerabilities. On this basis, the Stable Contrastive Pattern Risk (SCPR) intelligent decision-making model was developed. The results demonstrate that, following robust filtration, 6 nationwide common risk rules were extracted from 2064 initial candidate combinations. These rules converge into three core risk patterns: the heavy-duty aging pattern, the substructure-dominated pattern, and the over-water small-span low-seismic-design pattern. Guided by these robust rules and specific damage enrichment characteristics, risk stratification and differentiated management strategies were further formulated for Class III bridges. This research facilitates a paradigm shift in bridge maintenance. It moves from reactive, post-event symptom characterization toward data-driven, proactive early warnings. This shift provides a substantive scientific foundation for optimizing resource allocation and enabling precise investment decisions at the road network level. Full article

Background: Patients with type 2 diabetes mellitus (T2DM) who present with ST-segment elevation myocardial infarction (STEMI) remain at high risk of adverse remodeling after reperfusion. This observational study examined whether pre-admission glucose-lowering therapy class was associated with six-month left ventricular (LV) reverse remodeling and myocardial work recovery. Methods: We analyzed 253 patients with STEMI, baseline LV ejection fraction ≤ 50%, successful primary PCI, and complete baseline and six-month echocardiography. The primary inferential analyses focused on 75 patients with T2DM, grouped according to pre-admission therapy with SGLT-2 inhibitors, GLP-1 receptor agonists, DPP-4 inhibitors, or conventional therapy; non-diabetic patients were retained as a descriptive reference group. Clinical outcome, propensity-score, subgroup, and mediation analyses were considered exploratory because of small subgroup and event counts. Results: SGLT-2 inhibitor and GLP-1 receptor agonist exposure was associated with larger improvements in LVEF, LV volumes, and global work efficiency than DPP-4 inhibitors or conventional therapy. Crude MACE rates were highest in the conventional-therapy group, but event estimates were imprecise and confounded by baseline risk, revascularization status, and discharge therapy. Conclusions: In patients with T2DM recovering from STEMI, pre-admission exposure to SGLT-2 inhibitors and, to a lesser extent, GLP-1 receptor agonists was associated with more favorable structural and myocardial work recovery. These hypothesis-generating findings should be interpreted as associations and require confirmation in adequately powered prospective studies. Full article

Political violence poses a significant challenge to global stability, underscoring the need for comparative analytical models that support analytical interpretation of structured conflict data. This paper presents a comparative evaluation of 12 machine learning approaches, including traditional supervised models, deep learning architectures, and zero-shot large language models, for the classification of political violence events using the Armed Conflict Location and Event Data Project (ACLED) dataset (2010–2020, over 40,000 events). The results demonstrate that, on short structured event text represented via TF-IDF, fine-tuned traditional machine learning models achieve stronger performance than zero-shot LLM approaches and deep learning models on structured event data. We further introduce a multilingual classification framework for English and Urdu news content, illustrating cross-lingual transfer robustness using machine-translated Urdu data; results reflect translation-based evaluation conditions and should not be interpreted as performance on naturally occurring low-resource Urdu political-event text. As an exploratory extension, the framework is applied to 57,700 tweets related to the Article 370 crisis in Kashmir to illustrate applicability to unstructured social media text; given that the best Twitter model (55% accuracy) falls below the 69% majority-class baseline, these results should be interpreted solely as coarse discourse indicators and not as a validated classification component. Unlike prior work, this study systematically combines multilingual evaluation with zero-shot LLM analysis for political event classification. Geographic out-of-sample validation (leave-one-country-out or leave-one-region-out) was not conducted; the reported performance should therefore not be interpreted as evidence of cross-regional generalizability without further experimentation. The findings highlight practical considerations for designing data-driven analytical frameworks for conflict monitoring and analytical decision support. Full article

We investigate the onset of hyperbolicity in Jensen polynomials $J_{d,n}$ associated with the Riemann $\Xi$-function and identify a robust parity-driven bifurcation with a natural geometric interpretation. Numerical analysis for degrees $5\le d\le 16$ reveals two distinct regimes. For even d, the roots form a compact complex cluster whose imaginary extent decreases smoothly, and the transition to hyperbolicity is governed by a single complex-conjugate pair, consistent with a low-dimensional (tame) geometric structure. For odd d, a hierarchy of more intricate onset mechanisms emerges, including single-event transitions ($d=11$) and intermittent regimes ($d\ge 13$) with decoupled geometric invariants, suggestive of dynamics on decorated (wild) character varieties. We interpret this dichotomy through a connection with the $P_{\mathrm{III}}\left(D_6\right)$ tau-function arising in the Painlevé confluence diagram. Defining $\tau \left(t\right)=\Xi ({\displaystyle \frac{1}{2}}+\sqrt{-t})/\Xi \left({\displaystyle \frac{1}{2}}\right)$, we construct a generating function $B\left(w\right)={\sum }_{j\ge 0}{b}_j{w}^j$ from the cumulants of $log\Xi ({\displaystyle \frac{1}{2}}+z)$ using high-precision Cauchy/DFT methods (280–400-digit arithmetic), without explicit use of the zero expansion. Two independent numerical diagnostics indicate that B exhibits Stieltjes-type behavior: (i) positivity of Hankel determinants up to order $N=30$ and (ii) Padé approximants whose poles converge to ${\gamma }_k^2$ (squares of Riemann-zero ordinates) with stabilizing residues. These results provide strong evidence that the parity bifurcation observed in Jensen polynomial onset reflects a finite-dimensional manifestation of an underlying moment-based positivity structure. Motivated by this correspondence, we formulate a conjecture relating the Stieltjes nature of $B\left(w\right)$ to the eventual hyperbolicity of Jensen polynomials. This conjecture suggests a bridge between finite-dimensional root geometry and an infinite-dimensional kernel-based positivity framework, while leaving open the problem of establishing such positivity independently of the zero expansion. Full article