Search Results (452)

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

Photogrammetric reconstruction from indoor imagery requires either labor-intensive ground control points (GCPs) or positioning sensor integration. While global navigation satellite system technology revolutionized aerial photogrammetry by enabling direct georeferencing through integrated sensor orientation (ISO), indoor environments lack an equivalent positioning solution. Before indoor positioning systems can be adopted for photogrammetric applications, their fundamental measurement precision must be established. This study characterizes the repeatability and temporal stability of the ZeroKey Quantum real-time location system (RTLS) as a prerequisite to testing reconstruction accuracy when RTLS measurements provide camera pose constraints in photogrammetric bundle adjustment. Through systematic tripod-mounted observations across 30 test locations in a controlled laboratory environment, optimal data collection protocols were determined, temporal stability was investigated, and measurement precision was quantified. An automated position-based stationary detection algorithm using a 20 mm threshold successfully identified all 30 stationary periods for durations of 30 s or less. Optimal duration analysis revealed that 1 s observation windows achieve 3 mm position precision and 1° orientation precision after brief settling, enabling practical workflows with worst-case total collection time of 2.5 s per station. Per-axis uncertainties were quantified as 1.6 mm, 1.7 mm, and 1.1 mm root mean square (RMS) for position and 0.08°, 0.09°, and 0.07° RMS for orientation. These findings demonstrate that ultrasonic RTLS achieves millimeter-level position repeatability and sub-degree orientation repeatability, establishing the measurement precision necessary to justify subsequent accuracy testing through photogrammetric bundle adjustment. Full article

Biogas production plays a key role in the development of renewable energy systems; however, forecasting biomethane yield remains challenging due to the nonlinear nature of anaerobic digestion. The objective of this study was to develop a predictive model based on Radial Basis Function Neural Networks (RBF-NN) to approximate biomethane production using operational data from the Przybroda biogas plant in Poland. Two separate models were constructed: (1) the relationship between process temperature and daily methane production, and (2) the relationship between methane fraction and total biogas flow. Both models were trained using Gaussian activation functions, individually adjusted neuron parameters, and a zero-level correction algorithm. The developed RBF-NN models demonstrated high approximation accuracy. For the temperature-based model, root mean square error (RMSE) decreased from 531 m³ CH₄·day⁻¹ to 52 m³ CH₄·day⁻¹, while for the methane-fraction model, RMSE decreased from 244 m³ CH₄·day⁻¹ to 27 m³ CH₄·day⁻¹. The determination coefficients reached R² = 0.99 for both models. These results confirm that RBF-NN provides an effective and flexible tool for modeling complex nonlinear dependencies in anaerobic digestion, even when only limited datasets are available, and can support real-time monitoring and optimization in biogas plant operations. Full article

The Rician distribution, which arises in radar, communications, and magnetic resonance imaging, is characterized by a noncentrality parameter and a scale parameter. The Rayleigh distribution is a special case of the Rician distribution with a noncentrality parameter of zero. This paper considers generalized hypothesis testing for Rayleigh and Rician distributions using Rissanen’s stochastic complexity, particularly his approximation employing Fisher information matrices. The Rayleigh distribution is a member of the exponential family, so its normalized maximum likelihood density is readily computed, and shown to asymptotically match the Fisher information approximation. Since the Rician distribution is not a member of the exponential family, its normalizing term is difficult to compute directly, so the Fisher information approximation is employed. Because the square root of the determinant of the Fisher information matrix is not integrable, we restrict the integral to a subset of its range, and separately encode the choice of subset. Full article

High-resolution Digital Terrain Models (DTMs) are essential for precise terrain analysis, yet their production remains constrained by the high cost and limited coverage of LiDAR surveys. This study introduces a deep learning framework based on a modified Residual Channel Attention Network (RCAN) to super-resolve 10 m DTMs to 1 m resolution. The model was trained and validated on a 568 km² LiDAR-derived dataset using custom elevation-aware loss functions that integrate elevation accuracy (L1), slope gradients, and multi-scale structural components to preserve terrain realism and vertical precision. Performance was evaluated across 257 independent test tiles representing flat, hilly, and mountainous terrains. A balanced loss configuration (α = 0.5, γ = 0.5) achieved the best results, yielding Mean Absolute Error (MAE) as low as 0.83 m and Root Mean Square Error (RMSE) of 1.14–1.15 m, with near-zero bias (−0.04 m). Errors increased moderately in mountainous areas (MAE = 1.29–1.41 m, RMSE = 1.84 m), confirming the greater difficulty of rugged terrain. Overall, the approach demonstrates strong potential for operational applications in geomorphology, hydrology, and landscape monitoring, offering an effective solution for high-resolution DTM generation where LiDAR data are unavailable. Full article

This study investigates vibration signals generated during end milling of thin-walled EN AW-7075 aluminum alloy components using a set of 24 tools with distinct cutting edge microgeometries. Five characteristic parameters describing the dynamic response of the process, including both energy-related and statistical indicators, were extracted and analyzed. The results clearly demonstrate the critical influence of tool microgeometry on process dynamics. In particular, the introduction of an additional zero-clearance flank land at the cutting edge proved decisive in suppressing vibrations. For the most favorable geometries, the root mean square (RMS) value of vibration was reduced by more than 50%, while the spectral power density (PSD) decreased by up to 70–75% compared with the least favorable configurations. Simultaneously, both time- and frequency-domain responses exhibited complex and irregular patterns, highlighting the limitations of intuitive interpretation and the need for multi-parameter evaluation. To enable a synthetic comparison of tools, the Vibration Severity Index (VSI), which integrates RMS and kurtosis into a single composite metric, was introduced. VSI-based ranking allowed the clear identification of the most dynamically stable geometry. For the selected tool, additional analysis was conducted to evaluate the influence of cutting parameters, namely feed per tooth and radial depth of cut. The results showed that the most favorable dynamic behavior was achieved at a feed of 0.08 mm/tooth and a radial depth of cut of 1.0 mm, whereas boundary conditions resulted in higher kurtosis and a more impulsive signal structure. Overall, the findings confirm that properly engineered cutting-edge microgeometry, especially the formation of additional zero-clearance flank land significantly enhances the dynamic of thin-wall milling, demonstrating its potential as an effective strategy for vibration suppression and process optimization in precision machining of lightweight structural materials. Full article

Research on interference suppression technology for enhanced long-range navigation (eLoran) signals is crucial for enhancing receiver performance. To address the zero-point drift phenomenon in eLoran signals during adaptive filtering, we propose a segmented inaction algorithm based on normal time–frequency transform (NTFT), which is designed for challenging environments, such as low signal-to-noise ratio (SNR) and complex noise conditions. The algorithm splits the 20 kHz frequency band of the eLoran signal into 200 equal sub-bands, then applies the inaction algorithm sequentially to each sub-band, which exhibits strong noise resistance and high robustness. It is regarded as a pre-filter of the adaptive filter, ensuring a cleaner input signal for subsequent processing. Simulation results indicate that, when processing low-SNR eLoran signals affected by multi-frequency narrow-band interference and band-limited Gaussian noise, the combined algorithm significantly improves root mean square error (RMSE) by 33.3% and relative root mean square error (R-RMSE) by 39.1% compared to the single VSS-LMS method. Additionally, it compensates for zero-point drift (the deviation observed in the time series between the positive zero-crossing point of the third period of the reconstructed signal and that of the original signal) by 79.3% and maintains third-week forward over-zero error at a very low level. The effectiveness of the combined algorithm was further validated through actual measurement experiments. Full article

Cold winter injury is a significant challenge in cultivating tropical trees in temperate regions. The conventional solution involves heating the entire greenhouse to protect the plants; however, this approach is fuel-intensive and costly. This study investigated whether root-zone heating can mitigate cold injury in coffee trees. In the Control, non-heated treatments, leaf relative water content dropped to approximately 70%, leading to wilting, whereas in the Heat treatment, it remained above 90%. In the Control treatment, defoliation progressed, ultimately resulting in more than 50% leaf loss. In contrast, defoliation was reduced by approximately 20% with the Heat treatment. During the cold-treatment period, photosynthesis declined sharply in both the Control and Heat treatments, with CO₂ assimilation dropping to nearly zero. However, one week after the complete of cold treatment, F_v/F_m recovered to pre-treatment levels, while CO₂ assimilation and electron transport rates improved to more than 50% of pre-treatment levels in the Heat treatment. These findings indicate that root-zone heating helps prevent leaf wilting and defoliation by maintaining high leaf water content. The surviving leaves recovered their photosynthetic function and were crucial in subsequent biomass production. Thus, root-zone heating is a cost-effective and efficient strategy for cultivating tropical trees in temperate regions. Full article

Accurate trend prediction of valve internal leakage is crucial for the safe and economical operation of thermal power units. To address the issues of prediction lag and insufficient accuracy in existing methods when dealing with the dynamic changes in internal leakage, this paper proposed an Improved Autoregressive Integrated Moving Average–Generalized Autoregressive Conditional Heteroskedasticity (IARIMA-GARCH) method that integrated Multi-Time-Scale Decomposition, an Improved ARIMA (IARIMA) model, and an Improved GARCH (IGARCH) model for accurate prediction of drain valve internal leakage. First, using a Multi-Time-Scale Decomposition method based on sampling at different time intervals, the original valve internal leakage time series were reconstructed into three characteristic subsequences—short-term, medium-term, and long-term—to capture the evolutionary features at various time scales. Then, an IARIMA model, employing the Huber loss function for robust parameter estimation, was constructed as the leakage prediction model to effectively suppress the interference of outliers. Simultaneously, an IGARCH model was built as the leakage volatility prediction model by introducing the previous moment’s volatility to correct the current residual, establishing a feedback mechanism between the mean and volatility equations, thereby enhancing the characterization of volatility clustering. Finally, using a weight coefficient dynamic calculation method based on RMSE, the Multi-Time-Scale prediction results of each subsequence were fused to obtain the final predicted valve internal leakage. Taking the main steam drain valve of a thermal power plant as the research object, and using Mean Absolute Error (MAE), Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and symmetric Mean Absolute Percentage Error (sMAPE) as evaluation metrics, a case study on trend prediction of drain valve internal leakage was conducted, comparing the proposed method with ARIMA, Long Short-Term Memory networks (LSTM) and eXtreme Gradient Boosting (XGBoost) methods. The results showed that compared to ARIMA, LSTM and XGBoost, the proposed IARIMA-GARCH method achieved the lowest values on error metrics such as Mean Absolute Error (MAE), Root-Mean-Squared Error (RMSE), Mean Absolute Percentage Error (MAPE), and symmetric Mean Absolute Percentage Error (sMAPE), and its Coefficient of Determination (R²) is closest to 1. The standardized residual sequence most closely resembled a white noise sequence with zero mean and unit variance, and its distribution was the closest to a normal distribution. This proved that the IARIMA-GARCH method possessed higher prediction accuracy, stronger dynamic adaptability, and superior statistical robustness, providing an effective solution for valve condition prediction and predictive maintenance. Full article

Municipal solid waste (MSW) is a rapidly escalating global challenge, with Thailand exemplifying the persistence of a policy–practice gap in zero-waste transitions. Despite national initiatives such as Zero Waste Thailand, household segregation and recycling rates remain modest, particularly in semi-rural municipalities. This study addresses this gap by introducing and validating the ARUN Model, a competency-based governance framework for community-level zero-waste management. Using a mixed-methods sequential explanatory design, quantitative data from 300 households were analyzed using exploratory factor analysis and regression modeling, complemented by focus group interviews with local leaders to interpret behavioral mechanisms. The findings revealed that Responsibility and Nurturing competencies exert the strongest positive effects on household zero-waste behavior, confirming the model’s reliability and construct validity. These results empirically demonstrate how community competencies shape sustainable waste practices and bridge the structural–behavioral divide in waste governance. This research provides the first empirical validation of a competency-based governance framework in a semi-rural Thai context, extending beyond participatory and capacity-based models. By integrating statistical rigor with community insight, the ARUN Model advances theoretical and practical understanding of competency-driven sustainability transitions. This study provides actionable insights for policymakers and supports the achievement of SDGs 11, 12, and 13, offering a locally rooted yet globally relevant pathway toward circular economy governance. Full article

To enhance the low-frequency vibration suppression capability of cantilever beams, a magnetically tunable piezoelectric cantilever beam structure (MTPCBS) is proposed in this paper. A magnetic spring with negative stiffness (NSMS) is fixed at the free end of a cantilever beam, forming a quasi-zero-stiffness structure. Meanwhile, a macro-fiber composite (MFC) patch is bonded near the root of the beam to implement active skyhook damping control for active vibration control. A theoretical model of the cantilever beam, NSMS, and MFC is established, and the displacement transmissibility of the MTPCBS is derived. The influences of the magnet distance of the NSMS and the control gain of the controller are investigated via simulation. Experimental results indicate that compared to the single beam, the effective vibration isolation frequency of the proposed MTPCBS shifts from 15.3 Hz to 4.6 Hz. When subjected to random vibration excitation ranging from 1 to 80 Hz, the root mean square (RMS) value of vibration decreases from 0.03 g to 1.77 × 10⁻³ g, with the vibration attenuation rate improving from −50% to 91%. The proposed MTPCBS and active–passive vibration control method for cantilever beams significantly enhances low-frequency vibration suppression capabilities, providing a feasible strategy for achieving broadband vibration suppression. Full article

Many authors analyze the prediction of chaos in a Josephson junction with quadratic damping by the Melnikov technique. Due to the lack of an explicit presentation of the Melnikov integral, the researchers apply numerical methods and illustrative examples to verify a good agreement between the numerical method and the analytical one. The reader has difficulty navigating and touching upon Melnikov’s elegant theory and, in particular, the formulation of the Melnikov criterion for the possible occurrence of chaos in a dynamical system, based solely on the provided illustrations of dependencies between the main parameters of the model under consideration. The statements in a number of publications devoted to this interesting topic, such as “It is easy to see that Melnikov’s integrals are finite and not zero. It is possible to see that the transverse zeros of the Melnikov function”, do not shed enough light on the origin of the “horseshoe”-type chaos. In this paper we will try to shed additional light on this important problem. A new planar system corresponding to the N-generalized Josephson junction with quadratic damping with many free parameters is considered, which may be of interest to specialists in the field of engineering sciences. Prediction of chaos in the proposed model by the Melnikov technique is closely related to the problem of approximately simultaneously finding all roots (simple or multiple) of generalized trigonometric polynomials. Several simulations are composed. We also demonstrate some specialized modules for investigating the dynamics of the model. One application about generating stochastic construction for possible control over oscillations is also discussed. Full article

Ensuring food security in Pakistan, particularly for rice production, is a critical challenge due to increasing population demands and the growing impact of climate change variability. Accurate estimation of rice crop yields is essential for optimizing resource allocation, managing supply chains, and forecasting economic growth while minimizing agricultural losses. This study utilizes a Deep Neural Network (DNN) to predict rice yields in Pakistan by analyzing the effects of maximum temperature and precipitation trends under high-emission scenarios (SSP5-8.5) derived from CMIP6 climate models. Historical (1980–2014) and future (2015–2100) climate projections were evaluated using key variables, including precipitation, meteorological conditions, cultivated area, and crop yields. Results from CMIP6 SSP5-8.5 indicate a significant rise in maximum temperatures and increased precipitation variability, exacerbating risks to rice crop yields. DNN demonstrated superior accuracy in forecasting these trends, achieving high R-squared values and low error metrics, including Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE). The findings reveal that Pakistan, particularly Eastern South Asia, is highly vulnerable to climate extremes, with severe implications for rice production and agricultural sustainability. These results highlight the urgent need for policymakers to adopt climate adaptation strategies, including advanced predictive modeling and resilient agricultural practices, to safeguard rice production and ensure long-term food security in Pakistan’s monsoon-dependent regions. This study aligns with Sustainable Development Goal 2 (Zero Hunger) by contributing to food security and sustainable agricultural development, and with Sustainable Development Goal 13 (Climate Action) by addressing climate change impacts on agriculture and promoting resilience in rice production systems. Full article

The permanent magnet levitation (PML) transportation system utilizes Halbach arrays to achieve zero-power levitation. However, the system’s lateral negative stiffness characteristic leads to a significant increase in lateral force during operation, exacerbating lateral vibration and compromising system stability. Taking the Xingguo Line PML system as the research object, this study systematically analyzes the nonlinear characteristics of the levitation force and lateral force in a single-point levitation system through theoretical modeling, finite element simulation, and experimental validation. The concept of a ‘Magnetic Wheelset’ coupling the left and right levitation points of the bogie is proposed. The influence of five mounting forms—Aligned, X-type, Different center distance, Double V-type, and Single V-type—on the levitation performance of the Magnetic Wheelset is investigated. The coefficient of variation (CV) method is employed to evaluate force stability, and an optimal case is subsequently screened out using a dual-objective constraint approach that incorporates mean levitation force and lateral force thresholds. Results indicate that the X-type mounting at 25° is the optimal case. At 40 km/h, compared to the baseline Aligned configuration, the root mean square (RMS) values of the bogie’s vertical and lateral vibration accelerations are reduced by 14.7% and 23.8%, respectively. The vehicle’s vertical and lateral ride comfort indices decrease by 0.33 and 0.27, respectively, and the track beam’s vertical and lateral vibration accelerations are reduced by 19.4% and 13.3%. The methodology presented in this study provides a valuable reference for vibration suppression in PML systems. Full article

Peter Lombard’s First Book of the Sentences presents formidable questions concerning the principle of the Son’s generation. Addressing a gap in contemporary scholarship, this article examines Lombard’s foundational exposition of the Father’s power and will to generate. Placing Lombard in dialogue with Thomas Aquinas, this study traces the development of this doctrine across Aquinas’s career, from his commentary on the Sentences to De potentia and the Summa theologiae. Our analysis adopts Aquinas’s own framework to investigate a series of questions: whether generation is an act of nature or will; whether the power to generate is part of omnipotence; whether it is essential or relational; and whether the Son possesses this power. This study finds that Aquinas’s conclusions often converge with Lombard’s intuitions. Both affirm that generation is by nature while simultaneously accompanied by a concomitant will, and that the generative power is rooted in the divine essence. Aquinas’s analysis, however, represents a significant metaphysical development. A key evolution is traced in Aquinas’s understanding of the power to generate, which shifts from being a quasi-natural power distinct from omnipotence to a form of paternal omnipotence. His characterization of this power also matures from being a middle ground between the essential and the relational to being principally essential, signifying the relation of paternity only obliquely. This trajectory toward a firmer grounding in the divine essence is supported by an increasingly refined set of arguments for the Son’s unicity, with principles like the determination of nature and divine simplicity becoming more prominent in his later works. By charting these developments, this article demonstrates how Aquinas builds upon Lombard’s foundational intuitions to construct a more systematic and robust Trinitarian theology. Ultimately, our analysis illuminates the intellectual journey from sound doctrinal intuition to profound metaphysical articulation, where the tenets of faith are secured by a cogent intellectual framework. Our analysis also offers a counter-narrative to contemporary assumptions, challenging modern conceptions of power as a zero-sum game and of freedom as mere arbitrary choice. Full article