Search Results (64)

This study investigates the dynamic relationship between bearing gearbox condition indicators (BGCIs) and the lubrication oil temperature within the framework of health and usage monitoring system (HUMS) applications. Using the dynamic autoregressive distributed lag (DARDL) simulation model, we quantified both the short- and long-term responses of condition indicators to shocks in oil temperature, offering a robust framework for a counterfactual analysis. To complement the time-domain perspective, we applied a wavelet coherence analysis (WCA) to explore time–frequency co-movements and phase relationships between the condition indicators under varying operational regimes. The DARDL results revealed that the ball energy, cage energy, and inner and outer race indicators significantly increased in response to the oil temperature in the long run. The WCA results further confirmed the positive association between oil temperature and the condition indicators under examination, aligning with the DARDL estimations. The DARDL model revealed that the ball energy and the inner race energy have statistically significant long-term effects on the oil temperature, with p-values < 0.01. The adjusted R² of 0.785 and the root mean square error (MSE) of 0.008 confirm the model’s robustness. The wavelet coherence analysis showed strong time–frequency correlations, especially in the 8–16 scale range, while the frequency-domain causality (FDC) tests confirmed a bidirectional influence between the oil temperature and several condition indicators. The FDC analysis showed that the oil temperature significantly affected the BGCIs, with evidence of feedback effects, suggesting a mutual dependency. These findings contribute to the advancement of predictive maintenance frameworks in HUMSs by providing practical insights for enhancing system reliability and optimizing maintenance schedules. The integration of dynamic econometric approaches demonstrates a robust methodology for monitoring critical mechanical components and encourages further research in broader aerospace and industrial contexts. Full article

The Segment Anything Model 2 (SAM2) has achieved state-of-the-art performance in pixel-level object segmentation for both static and dynamic visual content. Its streaming memory architecture maintains spatial context across video sequences, yet struggles with long-term tracking due to its static inference framework. SAM 2’s fixed temporal window approach indiscriminately retains historical frames, failing to account for frame quality or dynamic motion patterns. This leads to error propagation and tracking instability in challenging scenarios involving fast-moving objects, partial occlusions, or crowded environments. To overcome these limitations, this paper proposes SAM2Plus, a zero-shot enhancement framework that integrates Kalman filter prediction, dynamic quality thresholds, and adaptive memory management. The Kalman filter models object motion using physical constraints to predict trajectories and dynamically refine segmentation states, mitigating positional drift during occlusions or velocity changes. Dynamic thresholds, combined with multi-criteria evaluation metrics (e.g., motion coherence, appearance consistency), prioritize high-quality frames while adaptively balancing confidence scores and temporal smoothness. This reduces ambiguities among similar objects in complex scenes. SAM2Plus further employs an optimized memory system that prunes outdated or low-confidence entries and retains temporally coherent context, ensuring constant computational resources even for infinitely long videos. Extensive experiments on two video object segmentation (VOS) benchmarks demonstrate SAM2Plus’s superiority over SAM 2. It achieves an average improvement of 1.0 in J&F metrics across all 24 direct comparisons, with gains exceeding 2.3 points on SA-V and LVOS datasets for long-term tracking. The method delivers real-time performance and strong generalization without fine-tuning or additional parameters, effectively addressing occlusion recovery and viewpoint changes. By unifying motion-aware physics-based prediction with spatial segmentation, SAM2Plus bridges the gap between static and dynamic reasoning, offering a scalable solution for real-world applications such as autonomous driving and surveillance systems. Full article

Environmental sustainability constitutes a strategic priority for Germany, with the circular economy serving a crucial function in its realization. Circular practices foster sustainable development by decreasing reliance on finite resources, minimizing waste, and reducing greenhouse gas (GHG) emissions. The circular economy provides ecological advantages and strengthens economic resilience through the promotion of innovation, enhancement of supply chain efficiency, and creation of green jobs. Complementary measures, including the preservation of natural capital, the enactment of structural economic reforms, and the implementation of environmental taxes, enhance sustainability objectives. Ecosystem conservation enhances carbon absorption, structural changes facilitate low-emission industries, and environmental taxes incorporate environmental costs. In contrast, industrial activity continues to be a significant contributor to GHG emissions, necessitating policy examination. This study analyzes the relationships between the circular economy, natural capital, structural change, environmental taxation, and industrial activities on GHG emissions in Germany from the first quarter of 2010 to the fourth quarter of 2022. The study employs wavelet coherence analysis (WCA), fully modified ordinary least squares (FMOLS), and dynamic ordinary least squares (DOLS), demonstrating that circular economy practices, natural capital, structural changes, and environmental taxes significantly reduce GHG emissions. Conversely, industrial activities continually elevate GHG emissions in Germany. Moreover, WCA further reveals the time–frequency dynamics and co-movement patterns between key variables and GHG emissions, enabling the detection of both short-term and long-term dependencies. The results indicate that enhancing environmental sustainability in Germany could be effectively achieved by mandating the integration of recycled materials within key industrial sectors to improve environmental sustainability, which would help lower resource extraction and related GHG emissions. Full article

The semiconductor industry currently lacks a robust, holistic method for detecting parameter drifts in wide-bandgap (WBG) manufacturing, where conventional fault detection and classification (FDC) practices often rely on static thresholds or isolated data modalities. Such legacy approaches cannot fully capture the intricate, multi-modal shifts that either gradually erode product quality or trigger abrupt process disruptions. To surmount these challenges, we present M2D2 (Multi-Machine and Multi-Modal Drift Detection), an end-to-end framework that integrates data preprocessing, baseline modeling, short- and long-term drift detection, interpretability, and a drift-aware federated paradigm. By leveraging self-supervised or unsupervised learning, M2D2 constructs a resilient baseline of nominal behavior across numeric, textual, and categorical features, thereby facilitating the early detection of both rapid spikes and slow-onset deviations. An interpretability layer—using attention visualization or SHAP/LIME—delineates which sensors, logs, or batch identifiers precipitate each drift alert, accelerating root-cause analysis. An active learning loop dynamically refines threshold settings and model parameters in response to real-time feedback, reducing false positives while adapting to evolving production conditions. Crucially, M2D2’s drift-aware federated learning mechanism reweights local updates based on each site’s drift severity, preserving global model integrity at scale. The key scientific breakthrough of this work lies in combining advanced multi-modal processing, short- and long-term anomaly detection, transparent model explainability, and an adaptive federated infrastructure—all within a single, coherent framework. Evaluations of real WBG fabrication data confirm that M2D2 substantially improves drift detection accuracy, broadens anomaly coverage, and offers a transparent, scalable solution for next-generation semiconductor manufacturing. Full article

Addressing the escalating complexity of global sustainability challenges requires interventions that are not only technically effective but also cognitively and philosophically grounded. While the leverage points perspective has provided a useful framework for understanding systemic change, it can be enhanced through more operational coherence and cultural pluralism. This paper introduces the Deep and Shallow Sustainability Intervention (DSSI) framework, a novel conceptual model that integrates Taoist philosophical insights with contemporary systems thinking and the leverage points literature. Structured across five interconnected Taoist-inspired domains and ten leverage points, the framework extends and enriches Meadows’ leverage point theory by integrating pre-paradigmatic meta-cognitions, systemic momentum, and context-sensitive action. It emphasizes that sustainable transitions require the dynamic interplay between foundational source-code shifts and operational implementation. This framework contributes to the growing field of transformative sustainability science by (1) embedding non-Western epistemologies into systems transformation theory, (2) offering a structured yet flexible model for multi-level intervention design, and (3) enabling transdisciplinary dialogue between philosophy, paradigmatic shift, meta-systemic logic, governance, and practice. Preliminary applications in European rural transition contexts suggest its potential to enhance context-sensitive action and value-aligned systems innovation. The DSSI framework thus offers a timely and integrative approach for guiding long-term, systemic, and culturally responsive sustainability transitions. Full article

Accurate long-term electricity load forecasting is critical for energy planning, infrastructure development, and risk management, especially under increasing uncertainty from climate and economic shifts. This study proposes a multi-resolution probabilistic load forecasting framework that leverages temporal hierarchies to generate coherent forecasts at hourly, daily, monthly, and yearly levels. The model integrates climate and economic indicators and employs tailored forecasting techniques at each resolution, including XGBoost and ARIMAX. Initially incoherent forecasts across time scales are reconciled using advanced methods such as Ordinary Least Squares (OLS), Weighted Least Squares with Series Variance Scaling (WLS_V), and Structural Scaling (WLS_S) to ensure consistency. Using historical data from Alberta, Canada, the proposed approach improves the accuracy of deterministic forecasts and enhances the reliability of probabilistic forecasts, particularly when using the OLS reconciliation method. These results highlight the value of temporal hierarchy structures in producing high-resolution long-horizon load forecasts, providing actionable insights for utilities and policymakers involved in long-term energy planning and system optimization. Full article

This paper solves the challenge of precise dual-frequency laser control in Airborne Coherent Doppler LiDAR systems by implementing an innovative laser driver architecture, which integrates compact hardware design with cascade Proportional-Integral-Derivative (PID) control and a frequency–temperature compensation mechanism. The experimental results demonstrate eminent performance with long-term temperature fluctuation below 0.007 °C, temperature stabilizing time under 4 s and long-term power fluctuation of the linear constant current source being <1%. The system enables wide-range temperature–frequency adjustment for individual lasers and dynamically adjusts the dual-laser beat frequencies between −1 GHz and +2 GHz, achieving the frequency difference fluctuation within 3 MHz. These achievements greatly enhance LiDAR performance and create possibilities for broader applications in dynamic environmental sensing, atmospheric monitoring, deep-space exploration, and autonomous systems. Full article

Floating high-frequency surface-wave radar provides an effective solution to widening the range of detection by such radar systems. However, for high-frequency radars with long coherence integration times, the yaw angle variations during this period can have a significant impact on the accuracy of direction-of-arrival estimation. Although adaptive beamforming methods are applicable to yaw angle compensation, their effectiveness can be significantly reduced when the measured distortion of antenna patterns is considered. To solve this problem, an iterative adaptive beamforming of focusing concept is proposed in this paper to compensate for yaw rotation. Firstly, an adaptive beamforming technique, called balanced-focusing pseudo-fixed beamforming, is developed to improve the ability of beam shape control by shortening the constraint range of the azimuth. Then, the shortened focusing range is determined by one iterative strategy that iteratively reduces the focusing length and selects the focusing center. The simulation results demonstrate that the proposed algorithm is applicable to significantly improve the precision and stability of direction-of-arrival estimation. This algorithm is also validated against the results obtained from two cooperative signals and ship echoes in a field experiment. Full article

This advanced exploration of integrating cross-modal Artificial-Intelligence-Generated Content (AIGC) within the Unity3D game engine seeks to elevate the diversity and coherence of image generation in game art creation. The theoretical framework proposed dives into the seamless incorporation of generated visuals within Unity3D, introducing a novel Generative Adversarial Network (GAN) structure. In this architecture, both the Generator and Discriminator embrace a Transformer model, adeptly managing sequential data and long-range dependencies. Furthermore, the introduction of a cross-modal attention module enables the dynamic calculation of attention weights between text descriptors and generated imagery, allowing for real-time modulation of modal inputs, ultimately refining the quality and variety of generated visuals. The experimental results show outstanding performance on technical benchmarks, with an inception score reaching 8.95 and a Frechet Inception Distance plummeting to 20.1, signifying exceptional diversity and image quality. Surveys reveal that users rated the model’s output highly, citing both its adherence to text prompts and its strong visual allure. Moreover, the model demonstrates impressive stylistic variety, producing imagery with intricate and varied aesthetics. Though training demands are extended, the payoff in quality and diversity holds substantial practical value. This method exhibits substantial transformative potential in Unity3D development, simultaneously improving development efficiency and optimizing the visual fidelity of game assets. Full article

Jason Allen-Paisant in Self-Portrait as Othello moves unflinchingly through complex histories and genealogies that widen to include Jamaica, Venice, Italy, France, and elsewhere and to locate the duppy manifestations of an unburied past in the pervasive precariousness of Black life. Across his poems, he tracks the chaotic reverberations of intergenerational traumas that persist across time, space and collective memory. This paper contends that the poet, through his use of allusion evident in his grafting and borrowings of other stories, literary syncretism, the symbolism of foreignness and its mysterious power, back and forth journeys through Europe and into homelands (Jamaica), procures an integrated circuit of Black meaning and kindred relations. This interconnectedness lays bare the sociohistorical conditions that have and continue to circumscribe and assault Black lives and deconstructs the perpetuity of anti-Black systems in the modern Western world. For all his worldly travels, the poet-narrator situates himself in an interstitial zone where each crossroad leads to new possibilities and affirmative energy. Allen-Paisant thus offers a way to reconcile a vicious history of Black xenophobia while procuring moments and processes to make peace with rupturous spaces, which necessitates a return to his homeland. However, homecoming complicates the search for self and the idea of return draws him into a dialogue with the fragmented inheritances of his past. He ultimately achieves coherence and fresh understandings through images of sterility and barrenness which he re-purposes as a foundation to make bold leaps of faith across uncertain chasms. This paper thus argues that for the poet of the African diaspora, who aspires to recover a long and complex spiritual history, the interface between domestic and international dramas highlights the luminous transcendence embodied in the journey along complicated routes and the steadfast pursuit of ideas that illuminate the deepest insights about identity, culture and the Black experience. Full article

In recent years, long-time coherent integration techniques have gained significant attention in maneuvering target detection due to their ability to effectively enhance the signal-to-noise ratio (SNR) and improve detection performance. However, for space targets, challenges such as micromotion phenomena and complex scattering characteristics make envelope alignment and phase compensation difficult, thereby limiting integration gain. To address these issues, in this study, we conducted an in-depth analysis of the echo model of cylindrical space targets (CSTs) based on different types of scattering centers. Building on this foundation, the multi-scale subaperture coherent integration Transformer (MsSCIFormer) was proposed, which integrates MsSCI with a Transformer architecture to achieve precise detection and motion parameter estimation of space targets in low-SNR environments. The core of the method lies in the introduction of a convolutional neural network (CNN) feature extractor and a dual-attention mechanism, covering both intra-subaperture attention (Intra-SA) and inter-subaperture attention (Inter-SA). This design efficiently captures the spatial distribution and motion patterns of the scattering centers of space targets. By aggregating multi-scale features, MsSCIFormer significantly enhances the detection performance and improves the accuracy of motion parameter estimation. Simulation experiments demonstrated that MsSCIFormer outperforms traditional moving target detection (MTD) methods and other deep learning-based algorithms in both detection and estimation tasks. Furthermore, each module proposed in this study was proven to contribute positively to the overall performance of the network. Full article

Wavelet coherence analysis (WCA) examines the dynamic interactions between economic policy uncertainty (EPU) in Brazil, Chile, Colombia, and Mexico and key external variables, using monthly data from 2010 to 2022. The findings reveal the following: (i) medium-term co-movements (4–16 months) between EPU and global financial indicators, including the Chicago Board Options Exchange (CBOE) Market Volatility Index (RVIX), Merrill Lynch Option Volatility Estimate Index (RMOVE), and Global EPU Index (RGEPU), emphasizing the sustained influence of financial volatility on domestic policy environments, particularly during global turbulence; (ii) significant interactions between EPU and the Climate Policy Uncertainty Index (RCPU) in resource-dependent economies like Brazil and Colombia, with pronounced effects in medium- and long-term horizons; (iii) bidirectional relationships between Brent crude oil prices (RBRENT) and EPU in Brazil, Colombia, and Mexico, where oil price fluctuations shape policy uncertainty, especially during global market disruptions; and (iv) notable co-movements between EPU and the Dow Jones Sustainability World Index (RW1SGI) in Brazil, Chile, and Mexico, highlighting sensitivity to shifts in sustainability-driven markets. These results underscore the need for economic diversification, strengthened financial safeguards, and integrated climate risk management to mitigate external shocks. By exploring the time–frequency dynamics of global uncertainties and domestic policy environments, this study provides actionable insights for fostering resilience and stability in Latin America’s interconnected economies while addressing vulnerabilities to global market volatility and sustainability transitions. Full article

This research explores the least explored domain concerning the impact of monetary tactics on carbon dioxide emissions in China, thereby adding depth to environmental economics. The analysis spans 1982–2022 and explores the interplay between monetary instruments, ecological footprint, CO₂ emissions, and factors like human capital, GDP per capita, remittances, urbanization, and fossil fuel dependence. The investigation utilizes a multifaceted approach, including wavelet analysis techniques, like wavelet power spectrum, wavelet coherence analysis, quantile on quantile, and quantile regression. The findings unveil critical insights. The results demonstrate that monetary policy has had differing effects on emissions depending on the time. Contractionary policies are good for lowering medium- and long-term emissions, even if they cause a short-term increase in emissions. Furthermore, the study emphasizes the beneficial impact of human capital development on CO₂ emissions. Fossil fuels, per capita income, and population significantly contribute to environmental damage beyond monetary policy. This research contributes original insights by integrating monetary policy and socio-economic factors in a comprehensive, multi-methodological framework, offering valuable guidance for crafting policies that balance economic growth with environmental sustainability. Full article

The radar monopulse angle measurement can obtain a target’s angle information within a single pulse, meaning that factors such as target motion and amplitude fluctuations, which vary over time, do not affect the angle measurement accuracy. However, in practical applications, when a target’s signal-to-noise ratio (SNR) is low, the single pulse signal is severely affected by noise, leading to a significant deterioration in angle measurement accuracy. Therefore, it is usually necessary to coherently integrate multiple pulses before estimating the angle. This paper constructs an angle expansion model for a multi-pulse angle measurement under coherent integration. The analysis reveals that even under noise-free conditions, after coherently integrating multiple pulses, the coupling of target amplitude fluctuations and motion state can still cause significant errors in the angle measurement. Subsequently, this paper conducts a detailed analysis of the impact of the amplitude fluctuations and target maneuvers on the random angle measurement error. It also derives approximate probability density functions of angle measurement errors under various fluctuation and motion scenarios based on the Lindeberg–Feller central limit theorem. In addition, based on the angle expansion model and the random error distribution, this paper proposes an angle correction algorithm based on multi-pulse integration and long-term estimation. Numerical experiments and radar data in the field verify the impact of target characteristics on the angle measurement under multi-pulse integration and the effectiveness of the angle correction algorithm. Full article

Sap flow within a leaf is a critical indicator of plant vitality and health. This paper introduces an easy-to-use, non-invasive and real-time imaging method for sap microcirculation imaging. From the coherent backscattering of light on a leaf, we show that the acquisition frequency of dynamic speckle can be linked to the microcirculation speed inside the leaf. Unlike conventional methods based on speckle contrast, which use integration times long enough to observe temporal decorrelation within a single image, our approach operates in a regime where speckle patterns appear ‘frozen’ in each frame of a given sequence. This ‘frozen’ state implies that any decorrelation of the speckle pattern within a frame is negligible. However, between successive frames, decorrelation becomes substantial, and it is this inter-frame decorrelation that enables the extraction of dynamic information. In this context, the integration time primarily influences the radiometric levels, while the frame acquisition rate emerges as the key parameter for generating activity index maps. Thus, by accessing different ranges of sap flow activity levels by varying the frame acquisition rate, we reveal, in a non-invasive way, the anatomy of the leaf’s circulatory network with unprecedented richness. We experimentally validate the ability of the method to characterize the vitality of a fig leaf in real time by observing the continuous decrease in sap circulation, first in the smaller vessels and then in the larger ones, following the cutting of the leaf over a 48 h period. Full article