Search Results (822)

The proliferation of Internet of Things (IoT) devices has intensified the need for efficient real-time anomaly and intrusion detection, making the selection of an appropriate Complex Event Processing (CEP) engine a critical architectural decision for security-aware data pipelines. Python-based CEP frameworks offer compelling advantages through the seamless integration with data science and machine learning ecosystems; however, rigorous comparative evaluations of such frameworks under realistic IoT security workloads remain absent from the literature. This study presents the first systematic comparative evaluation of Faust and Streamz—two Python-native CEP engines representing fundamentally different architectural philosophies—specifically in the context of IoT network intrusion detection. Faust was selected for its actor-based stateful processing model with native Kafka integration and distributed table support, while Streamz was selected for its reactive, lightweight pipeline design targeting high-throughput stateless processing, making them representative of the two dominant paradigms in Python stream processing. Although both engines target different application niches, their performance characteristics under realistic CEP workloads have never been rigorously compared, leaving practitioners without empirical guidance. The primary evaluation employs an IoT network intrusion dataset comprising 583,485 events from 83 heterogeneous devices. To assess whether the observed performance characteristics are specific to this single dataset or generalize across different workload profiles, a secondary IoT-adjacent benchmark is included: the PaySim financial transaction dataset (6.4 million records), selected because its event schema, fraud-pattern temporal structure, and volume differ substantially from the intrusion dataset, providing a stress test for cross-workload robustness rather than a claim of domain equivalence. We acknowledge the reviewer’s valid point that a second IoT-specific intrusion dataset (such as TON_IoT or Bot-IoT) would constitute a more directly comparable validation; this is identified as a priority for future work. The load levels used in scalability experiments (up to 5000 events per second) intentionally exceed the dataset’s natural rate to stress-test each engine’s architectural ceiling and identify saturation thresholds relevant to large-scale or multi-sensor IoT deployments. We conducted controlled experiments with comprehensive statistical analysis. Our results demonstrate that Streamz achieves superior throughput at 4450 events per second with 89% efficiency and minimal resource consumption (40 MB memory, 12 ms median latency), while Faust provides robust intrusion pattern detection with 93–98% accuracy and stable, predictable resource utilization (1.4% CPU standard deviation). A multi-framework comparison including Apache Kafka Streams and offline scikit-learn baselines confirms that Faust achieves detection quality competitive with JVM-based alternatives (Faust: 96.2%; Kafka Streams: 96.8%; absolute difference of 0.6 percentage points, not statistically significant at $p=0.318$) while retaining the Python ecosystem advantages. Statistical analysis confirms significant performance differences across all metrics ($p<0.001$, Cohen’s $d>0.8$). Critical scalability thresholds are identified: Streamz maintains efficiency above 95% up to 3500 events per second, while Faust degrades beyond 2500 events per second. These findings provide IoT security engineers and system architects with actionable, empirically grounded guidance for CEP engine selection, establish reproducible benchmarking methodology applicable to future Python-based stream processing evaluations, and advance theoretical understanding of the accuracy–throughput trade-off in stateful versus stateless Python CEP architectures. Full article

The generative mechanisms underlying multifractal scaling in complex systems remain a fundamental unsolved problem, limiting our ability to distinguish healthy from pathological dynamics, predict system failures, or understand how scale-invariant organization emerges across vastly different physical domains. We resolve this challenge by introducing threshold sensitivity analysis—an extension of Chhabra–Jensen’s direct method—as a framework that classifies cascade types by examining how scaling reliability varies across moment orders q. Different q values systematically probe weak fluctuations (negative q) versus strong fluctuations (positive q), and the coefficient of determination ($r^2$) of partition function regressions quantifies scaling reliability at each q. Analyzing $r^2\left(q\right)$ patterns in 280 cardiac recordings (healthy controls through fatal heart failure), 200 financial time series (global equity markets and currencies, 2000–2025), and 80 climate stations (tropical to continental zones, 2000–2025), we discover a universal diagnostic signature: symmetric expansion of valid scaling behavior under relaxed $r^2$ thresholds, spanning both weak and strong fluctuations. This threshold sensitivity fingerprint—predicted by synthetic cascade simulations but never before validated empirically—uniquely identifies additomultiplicative cascades, hybrid processes that randomly alternate between additive stabilization and multiplicative amplification. Critically, this symmetric signature persists universally across domains: cardiac dynamics maintain consistent patterns across health and disease states, financial markets show varying robustness across asset classes (currencies more variable than US equities) while preserving a hybrid structure, and climate systems exhibit geographical variations (subtropical/continental stronger than tropical) without altering fundamental cascade type. These findings suggest that additomultiplicative organization is a unifying feature of complex adaptive systems, offering a resolution to decades of debate between additive and multiplicative models. The $r^2\left(q\right)$ profiling provides a mechanistic diagnostic capable of detecting early dysfunction, assessing system resilience, and revealing how environmental constraints shape—but do not determine—the fundamental principles governing multifractal complexity. Full article

Econophysics is an interdisciplinary field that applies physics concepts to economic and financial systems. By utilizing tools such as statistical physics, including fractal analysis and entropy measures, econophysics helps model the complex and non-linear dynamics of equity markets. This paper examines the intrinsic dynamics and regularity in information content in green finance markets (carbon, clean energy, and sustainability markets) by means of range scale analysis (R/S), detrended fluctuation analysis (DFA), fractionally integrated generalized auto-regressive conditionally heteroskedastic (FIGARCH) process, and Shannon entropy (SE). The empirical results can be summarized as follows. First, prices in all markets are persistent; however, returns are likely random as estimated Hurst exponents are close to 0.5. Second, the FIGARCH process shows that volatility series in carbon and sustainability markets are persistent, whilst volatility in clean energy is anti-persistent. Third, in carbon and sustainability markets, entropy is high in prices compared to returns and volatility series. On the contrary, the clean energy market shows lower entropy for prices than for returns and volatility. In sum, it is concluded that price and volatility series are predictable, whilst return series are not. Finally, based on a rolling window framework, it is concluded that the COVID-19 pandemic and the Russia–Ukraine war have altered long memory and randomness in all three green finance markets. Full article

Indoor air quality (IAQ) in educational buildings plays a critical role in the health, cognitive performance, and well-being of occupants. Aging university facilities often rely on outdated ventilation systems that are not designed to meet current demands or respond to dynamic occupancy levels. This study investigates the performance and feasibility of various advanced ventilation strategies in comparison to an existing balanced mechanical ventilation (BMV) system in a university classroom accommodating 100 students. Using a Dynamic Building Energy Simulation Program, simulations were conducted to evaluate IAQ (using CO₂ levels), energy consumption, and thermal comfort under three retrofitting scenarios: BMV, demand-controlled ventilation (DCV), and hybrid ventilation combining natural and mechanical airflow. The simulations indicate that DCV cuts annual HVAC energy use by 33% relative to the baseline, while the hybrid strategy achieves the greatest reduction of 42% and maintains CO₂ levels and thermal comfort within recommended limits. Although hybrid systems provide seasonal advantages, their complexity may limit applicability. In addition to technical analysis, this study also explores the financial and tax-related challenges associated with retrofitting ventilation systems in university buildings. Investment payback periods, operational costs, and potential tax incentives are discussed to evaluate economic viability. Overall, the endorse hybrid ventilation as the most cost-effective strategy where mixed-mode control is feasible, and DCV as a practical alternative for buildings unable to employ natural ventilation. Full article

The deployment of distributed renewable energy systems at the neighborhood scale is a key lever for urban decarbonization. In Europe, the regulatory framework now enables collective self-consumption, allowing multiple end-users to share locally produced energy. However, the complexity and early-stage uncertainties of such projects, especially in new district development, pose challenges for feasibility assessment and investor confidence. This study proposes a method to identify the impact of numerous technical, economic, and social parameters that may affect the feasibility of a project and that are uncertain at the early design stage, across multiple key performance indicators, thus addressing the concerns of various stakeholders. A key objective is to provide an integrated method applicable during the early stages of district development, when the integration of a collective self-consumption scheme is under consideration. The developed tools and methods are compatible with the available data at this stage and provide a basis for multi-criteria analysis. The simulation workflow was built around URBANopt and enhanced with probabilistic occupancy modeling, energy sharing mechanisms, and financial analysis modules. It was further complemented by sensitivity and risk analysis layers. The method was applied to a pre-design case study, illustrating how key design and operational uncertainties influence project viability. The results showed that despite the uncertainties on a wide array of parameters, reliable risk assessment per KPI could be performed on only a handful of parameters, which were identified through a sensitivity analysis using the Morris screening method. Full article

This paper analyzes financial market interdependence from a statistical-physics perspective by comparing Ising and spin glass representations of asset interactions. Financial markets are modeled as complex systems in which collective behavior emerges from time-varying interaction structures. Using daily data for a diversified 15-asset commodity system, including precious metals, energy commodities, industrial metals and soft commodities, over the period 2020–2024, we construct rolling coupling matrices based on both linear correlations and nonlinear mutual information and embed them into Ising and Sherrington–Kirkpatrick-type interaction frameworks. While aggregate synchronization indicators—such as average coupling strength and the largest eigenvalue—exhibit similar dynamics across the two representations, the spin glass framework reveals substantially richer structural heterogeneity. Preserving the sign structure of the interactions leads to wider dispersion, higher variability and nontrivial network configurations that are suppressed in the Ising representation. The results identify the Ising model as a benchmark for market coherence. The spin glass model is essential for capturing heterogeneous interactions and nonlinear dependence in financial markets. Full article

In the past five years, China has continuously strengthened its policy support for the development of new-type agricultural business entities. However, the implementation of these policies has faced various complexities, and the specific effectiveness of such support remains unclear. Additionally, many aspects of rural support policies need improvement. This study, based on survey data from 1349 rural households in Shandong Province, a major agricultural region in China, employs Pareto analysis and a multinomial ordered Probit model to examine the effectiveness and influencing factors of policies supporting new-type agricultural business entities. The results show the following: (1) the overall satisfaction of farmers with the support policies falls between “neutral” and “relatively satisfied,” with a mean score of 3.69; (2) factors such as the ease of obtaining financial support, the amount of government subsidies, policy awareness, evaluations of tax policies, and land use regulations have a positive influence on satisfaction with the support policies. Based on these findings, we recommend innovating fiscal and tax subsidy mechanisms and strengthening the dissemination and publicity of agricultural support policies to ensure their effective and orderly implementation. Additionally, it is essential to improve land use regulation policies and rural property rights trading mechanisms to promote the rational allocation of resources, enhance policy-oriented agricultural insurance to fully harness its income-enhancing effects for agricultural producers, thereby ensuring the long-term stability and sustainable development of the policy support system for new-type agricultural business entities. Full article

This study provides a structured review of statistical arbitrage research in the context of artificial intelligence, with a particular focus on machine learning based methods. The reviewed literature highlights the evolution from linear, rule-based strategies to increasingly complex data-driven models, while also documenting persistent challenges related to tail-risk exposure, regime instability, limited interpretability, and regulatory and governance constraints in practical applications. Building on this literature synthesis, the paper develops a conceptual AI-led, human-in-the-loop statistical arbitrage framework that integrates ML-generated signal modeling with structured human oversight—encompassing risk calibration, discretionary intervention, and interpretability review. This framework resonates with human-AI collaboration systems across other financial domains, collectively supporting the proposition that collaborative systems show potential to enhance resilience compared to purely AI-driven alternatives under specific market stress scenarios. It is positioned as a governance-oriented synthesis that qualitatively extends existing human-in-the-loop concepts by structurally embedding adaptive oversight within the statistical arbitrage decision architecture. Full article

Accurately forecasting the risk matrix and constructing a well-controlled portfolio based on these forecasts is the core objective of effective asset allocation. This paper takes the Chinese stock market as the research object, employing multiple machine learning algorithms to systematically compare the predictive performance of the Financial Stress (FS) indicator and the Economic Policy Uncertainty (EPU) index in sectoral risk management. The forecast results are subsequently applied to portfolio construction and optimization. The findings indicate that, in terms of predictive dimensions, EPU demonstrates strong performance in short-term forecasts, but its explanatory power decays rapidly as the forecasting horizon extends. In contrast, the FS factor achieves forecasting accuracy that is significantly superior to both the EPU factor and traditional price series across all time horizons, exhibiting robust long-memory characteristics and cross-period stability. At the portfolio application level, the minimum variance strategy constructed based on FS forecasts effectively reduces out-of-sample portfolio variance, achieving superior risk control performance compared to strategies based on EPU factor forecasts. This result reveals the differentiated mechanisms of the two factor types: EPU acts as a driving force for short-term risk structure reshaping, while financial stress serves as the core variable driving the evolution of long-term risk structures. Machine learning methods provide an effective technical pathway for capturing these complex nonlinear relationships. The research conclusions offer new empirical evidence for investors to optimize asset allocation decisions and for regulatory authorities to improve risk monitoring systems. Full article

To examine how digitally empowered rural e-commerce affects the market-oriented allocation of urban–rural factors at the county level and the underlying mechanism, this study treats the National E-commerce into Rural Counties Demonstration Program as a quasi-natural experiment. Using a panel of 1898 Chinese counties from 2000 to 2022, we conduct multi-period DID with staggered adoption and mediation analyses. The results show that rural e-commerce significantly raises the marketization level of factor allocation; the effect grows stronger over time and is most pronounced during the rapid-expansion phase, in agriculture-oriented e-commerce counties, in poverty-stricken counties, and in the Central and Western regions. The impact operates mainly through three channels: enlarging market size, upgrading industrial structure, and deepening digital financial usage. Notably, the digital finance channel exhibits a suppression effect, suggesting a complex role of financial digitalization in the early stages of rural development. To further ensure the robustness of our findings, we also conduct rigorous checks using the CSDID method and alternative proxy variables, consistently reaffirming the policy’s significant positive impact. These findings offer actionable evidence for deepening county-level factor-market reforms and advancing common prosperity, leading to policy recommendations on strengthening county digital infrastructure, tailoring e-commerce support systems, and improving the institutional environment for factor mobility. Full article

Modeling nonstationary time series in financial and energy markets remains challenging due to nonlinear dynamics, volatility clustering, and frequent regime shifts that distort the underlying probabilistic structure of the data. This study introduces a novel probabilistic–statistical decomposition framework, termed Robust Adaptive Decomposition (RAD), designed to preserve probabilistic symmetry between deterministic and stochastic components. In this context, symmetry refers to maintaining statistical balance—particularly in the means, variances, and distributional structures—between the extracted modes and the residual series, thereby preventing artificial bias or variance distortion during decomposition. The RAD framework adaptively determines the optimal number of modes needed to effectively separate short-term fluctuations from long-term structural movements. Unlike conventional techniques, such as Empirical Mode Decomposition (EMD), Ensemble EMD (EEMD), and CEEMDAN, the proposed method incorporates a robustness mechanism that mitigates mode mixing and reduces distortions induced by extreme shocks and regime transitions. The empirical evaluation is conducted on six oil-related energy commodities—Brent crude oil, kerosene, propane, sulfur diesel, heating oil, and gasoline—whose price dynamics exhibit pronounced nonlinearity and structural volatility. When integrated with ARIMA forecasting models, the RAD-based framework consistently outperforms benchmark decomposition approaches. Across all datasets, RAD–ARIMA achieves reductions of approximately 65–90% in MAE, 60–85% in RMSE, and up to 95% in MAPE relative to CEEMDAN-based models. These results demonstrate that RAD provides a mathematically rigorous and computationally efficient preprocessing mechanism that preserves statistical equilibrium while effectively disentangling deterministic structures from stochastic noise. Beyond oil markets, the framework offers broad applicability in econometric modeling, financial forecasting, and risk management, contributing to probability- and statistics-driven symmetry analysis in complex dynamic systems. Full article

Objectives: Medication adherence in elderly patients is shaped by cognitive, behavioral, systemic, and socioeconomic factors. This review aimed to identify determinants and effective strategies to improve adherence in older adults. Methods: A systematic search of PubMed, Scopus, and ScienceDirect (2015–2025) followed PRISMA 2020 guidelines. From 5116 records, 53 studies met inclusion criteria. Randomized controlled trials were meta-analyzed using standardized mean differences under a random-effects model. Risk of bias in the 10 pooled trials was assessed using the Cochrane RoB 2 tool, and certainty of evidence was evaluated using the GRADE framework. Results: Adherence ranged from 25.3% in institutionalized patients to 97.6% in pharmacist-led schizophrenia programs. Cognitive impairment and frailty reduced adherence (54.2%), while caregiver involvement improved rates, especially in dementia and schizophrenia (77.4–97.6%). Socioeconomic barriers, including medication cost, contributed to nonadherence but were mitigated by subsidies. Digital tools enhanced adherence in chronic disease, and machine learning models accurately predicted nonadherence (AUC up to 0.935). Effective interventions—caregiver support, digital platforms, and single-pill regimens—increased adherence by 25–59% and reduced cardiovascular events. The meta-analysis demonstrated a significant pooled effect (Standardized Mean Difference, SMD = 0.71, 95% CI: 0.11–1.54), although heterogeneity was high (I² = 99%). The RoB 2 assessment of the 10 pooled trials identified 2 at low risk, 4 with some concerns, and 4 at high risk of bias; the GRADE certainty of evidence was rated Very Low. Conclusions: Multiple factors, including frailty, cognitive deficits, socioeconomic barriers, regimen complexity, and the level of caregiver support, appear to be consistently associated with medication adherence in older adults. Strategies such as caregiver engagement, digital health tools, regimen simplification, and mental health support may contribute to improved adherence, although effect sizes vary considerably across study contexts. Given the substantial heterogeneity, Very Low certainty of evidence (GRADE), and variable study quality, findings should be interpreted with caution. System-level reforms, financial assistance programs, and culturally tailored approaches may further support adherence, while the successful implementation of digital health solutions will require addressing literacy, accessibility, and integration challenges. Full article

This research challenges the macro-centric narrative of crisis management by examining the socially embedded responses of small business owners during the global COVID-19 pandemic. While the literature frequently prioritizes the structural resilience of large firms, this study utilizes a novel conceptual framework to analyze how social networks, collective identities, and normative motivations shaped the adaptation strategies of over 27,000 entrepreneurs across 43 countries. Our analysis reveals that entrepreneurial agencies are deeply tied to interpersonal influence; expectations for future opportunities were significantly molded by peer effects, while the social contagion of nearby business closures exacerbated perceived impediments to growth. Furthermore, the study highlights a critical divergence based on entrepreneurial identity: family and purpose-driven actors—whose logic is rooted in social stability—suffered a more pronounced decline in innovation following income shocks compared to their wealth-driven counterparts. Finally, the study quantifies a significant structural shift in the entrepreneurial pipeline. While the pandemic triggered a 1.5% increase in potential entrepreneurs (reflecting a shift in societal aspirations), it caused a 2.3% contraction in emerging entrepreneurs, signaling a breakdown in the transition from individual intent to formal social organization. These findings suggest that crisis adaptation is not merely a financial calculation, but a complex negotiation of social support systems, peer-group benchmarking, and institutional trust. Full article

Forest and landscape restoration (FLR) represents a critical nexus of climate change mitigation, biodiversity conservation, and sustainable development. Despite substantial federal investments and commitments, empirical subnational research quantifying the relationships between governance structures, funding mechanisms, and restoration outcomes remains scarce, and integrated implementation frameworks bridging institutional, technical, and socio-economic dimensions are largely absent from the literature. This study presents a mixed-methods analysis of FLR implementation gaps across Maryland, Virginia, and West Virginia. Three Mid-Atlantic Appalachian states selected for their contrasting ecological conditions, governance structures, and restoration trajectories that collectively represent the heterogeneity of subnational restoration challenges. We examined 147 restoration projects (2019–2024), conducted 25 stakeholder interviews, and analyzed federal funding allocations ($428 million) through spatial and temporal frameworks. Our findings reveal five critical implementation barriers: (1) policy incoherence across federal–state–local jurisdictions creating 34% project delays; (2) chronic underfunding with 63% of projects receiving less than 60% of planned budgets; (3) technical capacity deficits affecting 71% of rural communities; (4) inadequate stakeholder engagement mechanisms reducing project sustainability by 45%; and (5) insufficient monitoring frameworks limiting adaptive management. We introduce an Integrated Restoration Implementation Framework (IRIF) that uniquely integrates policy coordination, sustainable financing, technical capacity building, and community engagement within a unified adaptive management cycle, operationalized through empirically derived thresholds, to guide evidence-based interventions. Quantitative analyses demonstrate that multi-stakeholder governance models increase restoration success rates by 2.3-fold (p < 0.001), while integrated funding mechanisms improve long-term sustainability by 67%. Theoretically, this study advances socio-ecological systems scholarship by providing empirical evidence that multi-scalar governance configurations and integrated stakeholder engagement mechanisms are principal determinants of restoration success, advancing the evidence base for adaptive governance approaches in complex federal systems. Our findings provide actionable intelligence for policymakers and practitioners, while underscoring that sustainable FLR in complex federal systems depends on coherent multi-level governance architectures coordinating institutional mandates, financial resources, technical capacity, and community agency across jurisdictional scales. Full article

Achieving breakthroughs in key core technologies is an inherent requirement for attaining a high level of scientific and technological self-reliance. The construction of a unified market (market integration construction) reshapes the rules of the innovation system and drives enterprises to tackle key core technologies. Based on the theory of complex adaptive systems, this paper uses the data of China’s A-share listed companies from 2008 to 2023 and the statistical yearbook to study the impact of market integration construction on the key core technological innovation of enterprises and its mechanism. The empirical research results show that: (1) Market integration construction reconstructs the rules governing resource flow, competitive incentives, and collaborative networks, guiding enterprises to achieve the emergence of key core technologies through nonlinear interactions. (2) Market integration construction exerts distinct effects on key core technological innovation by enhancing industrial investment and financial investment. (3) Agile responsiveness positively moderates the relationship between market integration construction and key core technological innovation. (4) The positive impact of market integration construction on key core technological innovation is more pronounced in non-state-owned, follower, and large enterprises. This study provides a theoretical basis and practical insights for advancing market integration construction and tackling key core technologies. Full article