Search Results (28)

Microseismic signals induced by mining activities often have low signal-to-noise ratios, and traditional picking methods are easily affected by noise, making accurate identification of P-wave arrivals difficult. To address this problem, this study proposes an adaptive denoising algorithm based on wavelet-threshold-enhanced Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) and develops an automatic P-wave arrival picking method incorporating fractal box dimension features, along with a corresponding accuracy evaluation framework. The raw microseismic signals are decomposed using the improved CEEMDAN method, with high-frequency intrinsic mode functions (IMFs) processed by wavelet-threshold denoising and low- and mid-frequency IMFs retained for reconstruction, effectively suppressing background noise and enhancing signal clarity. Fractal box dimension is applied to characterize waveform complexity over short and long-time windows, and by introducing fractal derivatives and short-long window differences, abrupt changes in local-to-global complexity at P-wave arrivals are revealed. Energy mutation features are extracted using the short-term/long-term average (STA/LTA) energy ratio, and noise segments are standardized via Z-score processing. A multi-feature weighted fusion scoring function is constructed to achieve robust identification of P-wave arrivals. Evaluation metrics, including picking error, mean absolute error, and success rate, are used to comprehensively assess the method’s performance in terms of temporal deviation, statistical consistency, and robustness. Case studies using microseismic data from a mining site show that the proposed method can accurately identify P-wave arrivals under different signal-to-noise conditions, with automatic picking results highly consistent with manual labels, mean errors within the sampling interval (2–4 ms), and a picking success rate exceeding 95%. The method provides a reliable tool for seismic source localization and dynamic hazard prediction in mining microseismic monitoring. Full article

As a key target area in China’s new round of strategic mineral exploration initiatives, Tibet possesses favorable metallogenic conditions shaped by its unique geological evolution and tectonic setting. In this paper, the Saga region of Tibet is the research object, and Level-2A Sentinel-2 imagery is utilized. By applying mixed pixel decomposition, interfering endmembers were identified, and spectral unmixing and reconstruction were performed, effectively avoiding the drawback of traditional methods that tend to remove mineral alteration signals and masking interference. Combined with band ratio analysis and principal component analysis (PCA), various types of remote sensing alteration anomalies in the region were extracted. Furthermore, the fractal box-counting method was employed to quantify the fractal dimensions of the different alteration anomalies, thereby delineating their spatial distribution and fractal structural characteristics. Based on these results, two prospective mineralization zones were identified. The results indicate the following: (1) In areas of Tibet with low vegetation cover, applying spectral mixture analysis (SMA) effectively removes substantial background interference, thereby enabling the extraction of subtle remote sensing alteration anomalies. (2) The fractal dimensions of various remote sensing alteration anomalies were calculated using the fractal box-counting method over a spatial scale range of 0.765 to 6.123 km. These values quantitatively characterize the spatial fractal properties of the anomalies, and the differences in fractal dimensions among alteration types reflect the spatiotemporal heterogeneity of the mineralization system. (3) The high-potential mineralization zones identified in the composite contour map of fractal dimensions of alteration anomalies show strong spatial agreement with known mineralization sites. Additionally, two new prospective mineralization zones were delineated in their periphery, providing theoretical support and exploration targets for future prospecting in the study area. Full article

The identification of unmarked graves is important in archaeology, forensics, and cemetery management, but invasive methods are often restricted due to ethical or cultural concerns. This necessitates the use of non-invasive geophysical techniques. Our study demonstrates the potential of induced polarization (IP) imaging as a non-invasive remote sensing technique specifically suited for detecting and characterizing unmarked graves. IP leverages changes in the electrical properties of soil and pore water, influenced by the accumulation of organic matter from decomposition processes. Measurements were conducted at an inactive cemetery using non-invasive textile electrodes to map a documented grave from the early 1990s, with a survey design optimized for high spatial resolution. The results reveal a distinct polarizable anomaly at a 0.75–1.0 m depth with phase shifts exceeding 12 mrad, attributed to organic carbon from wooden burial boxes, and a plume-shaped conductive anomaly indicating the migration of dissolved organic matter. While electrical conductivity alone yielded diffuse grave boundaries, the polarization response sharply delineated the grave, aligning with photographic documentation. These findings underscore the value of IP imaging as a non-invasive, data-driven approach for the accurate localization and characterization of graves. The methodology presented here offers a promising new tool for archaeological prospection and forensic search operations, expanding the geophysical toolkit available for remote sensing in culturally and legally sensitive contexts. Full article

This article describes the case of the personal items found in common graves dated between 1939 and 1956 after the Spanish Civil War (1936–1939), located in Paterna’s cemetery (Spain). It was important in this study to know the state of the conservation of the objects and to obtain clues about their origin and use just as in a forensic study. This would allow the moral restitution of the historical memory of the victims of the war conflict. The multi-technique strategy has included light and electron microscopy, infrared spectroscopy and X-ray diffraction. Materials of the early 20th century used in pencil sharpeners, glasses, cutlery, lighters, rings, and buttons or medications contained in small bottles and boxes have been identified and have enabled the lives of their owners to be reconstructed during their imprisonment and execution. All these objects exhibited a thin layer of adipocere, a well-known compound in forensic science formed during the decomposition of human and animal corpses. Interestingly, rare corrosion processes have been identified in two of the objects analyzed, which are linked to their proximity to the decomposing corpses of the deceased. Copper sulfides and/or sulfates have been identified in the lighter, and scholzite, a zinc and calcium phosphate, has been identified in the glasses. Full article

Wake effects significantly reduce efficiency and increase structural loads in wind farms. Therefore, accurate and computationally efficient models are crucial for wind farm layout optimization and operational control. High-fidelity computational fluid dynamics (CFD) simulations, while accurate, are too slow for these tasks, whereas faster analytical models often lack dynamic fidelity and 3D detail, particularly under complex conditions. Existing data-driven surrogate models based on neural networks often struggle with the high dimensionality of the flow field and scalability to large wind farms. This paper proposes a novel data-driven surrogate modeling framework to bridge this gap, leveraging Neural Networks (NNs) trained on data from the high-fidelity SOWFA (simulator for wind farm applications) tool. A physics-inspired NN architecture featuring an autoencoder for spatial feature extraction and latent space dynamics for temporal evolution is introduced, motivated by the time–space decoupling structure observed in the Navier–Stokes equations. To address scalability for large wind farms, a “wind box” decomposition strategy is employed. This involves training separate NN models on smaller, canonical domains (with and without turbines) that can be stitched together to represent larger farm layouts, significantly reducing training data requirements compared to monolithic farm simulations. The development of a batch simulation interface for SOWFA to generate the required training data efficiently is detailed. Results demonstrate that the proposed surrogate model accurately predicts the 3D dynamic wake evolution for single-turbine and multi-turbine configurations. Specifically, average velocity errors (quantified as RMSE) are typically below 0.2 m/s (relative error < 2–5%) compared to SOWFA, while achieving computational accelerations of several orders of magnitude (simulation times reduced from hours to seconds). This work presents a promising pathway towards enabling advanced, model-based optimization and control of large wind farms. Full article

The issue of fluid loss in fractured formations presents a significant challenge in petroleum engineering, often leading to increased operational costs and construction risks. To address the limitations of traditional lost circulation materials (LCMs) in oil reservoirs with different fracture sizes, this study developed an acrylic resin gel particle with excellent thermal stability (thermal decomposition temperature up to 314 °C) and compatibility. By employing Box–Behnken design and response surface methodology, the synergistic interaction of calcium hydroxide (Ca(OH)₂), asbestos fibers, and cement was optimized to create a novel gel solidification plugging system that meets the requirements of fluid loss control and compressive strength improvement. Experimental results revealed that the gel-based system demonstrated exceptional performance, achieving rapid fluid loss (total fluid loss time of 18~47 s) and forming a high-strength gelled filter cake (24 h compressive strength up to 17.5 MPa). Under simulated conditions (150 °C), the gel-based system provided efficient fracture sealing, showcasing remarkable adaptability and potential for engineering applications. This study underscores the promise of acrylic resin gel particles in overcoming fluid loss challenges in complex fractured formations. Full article

Electricity price forecasting is of significant practical importance, and improving prediction accuracy has become a key area of focus. Although substantial progress has been made in electricity price forecasting research, the unique characteristics of the electricity market make prices highly sensitive to even minor market changes. This results in prices exhibiting long-term trends while also experiencing sharp fluctuations due to sudden events, often leading to extreme values. Furthermore, most current models are “black-box” models, lacking transparency and interpretability. These unique features make electricity price forecasting particularly complex and challenging. This paper introduces a forecasting framework that incorporates the Seasonal Trend decomposition using Loess (STL), Gated Recurrent Unit (GRU), Light Gradient Boosting Machine (LightGBM), and Shapley Additive Explanations (SHAPs) and applies it to forecasting in the electricity markets of the United States and Australia. The proposed forecasting framework significantly improves prediction accuracy compared to nine other baseline models, especially in terms of RMSE and R² metrics, while also providing clear insights into the factors influencing the forecasts. On the U.S. dataset, the RMSE of this framework is 12.7% lower than that of the second-best model, while, on the Australian dataset, the RMSE of the SLGSEF is 2.58% lower than that of the second-best model. Full article

Wood-decay fungi, including white- and brown-decay fungi, are well known for their ability to degrade lignin and cellulose, respectively. The combined use of these fungi can increase the decomposition of woody substrates. Research has indicated that these fungi also exhibit inhibitory effects against Bursaphelenchus xylophilus, the causative agent of pine wilt disease (PWD). In this study, we investigated a composite microbial formulation that efficiently decomposes pine wood while inhibiting B. xylophilus. We initially established a correlation between the degradation rate of wood blocks and fungal biomass, underscoring the necessity of optimizing biomass for effective treatment. A systematic approach involving a one-way test, a Plackett–Burman design, a steepest ascent experiment, and a Box–Behnken design, was employed to optimize the fermentation process. Validation trials were conducted in a 10-L fermenter. The bioagent’s efficacy and safety were assessed through field applications in a forest, with a focus on wood degradation capacity and B. xylophilus mortality rate. Additionally, the environmental impact of the microbial products was evaluated by analysing soil quality around treated areas to ensure that the formulation did not adversely affect soil health. Full article

Rail corrugation intensifies wheel–rail vibrations, often leading to damage in vehicle–track system components within affected sections. This paper proposes a novel method for identifying rail corrugation, which combines Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN), permutation entropy (PE), and Smoothed Pseudo Wigner–Ville Distribution (SPWVD). Initially, vertical acceleration data from the axle box are decomposed using CEEMDAN to extract intrinsic mode functions (IMFs) with distinct frequencies. PE is used to evaluate the randomness of each IMF component, discarding those with high permutation entropy values. Subsequently, correlation analysis is performed on the retained IMFs to identify the component most strongly correlated with the original signal. The selected component is subjected to SPWVD time–frequency analysis to identify the location and wavelength of the corrugation occurrence. Filtering is applied to the IMF based on the frequency concentration observed in the time–frequency analysis results. Then, frequency–domain integration is performed to estimate the rail’s corrugation depth. Finally, the algorithm is validated and analyzed using both simulated data and measured data. Validation results show that this approach reliably identifies the wavelength and depth characteristics of rail corrugation. Additionally, the time–frequency analysis results reveal variations in the severity of corrugation damage at different locations. Full article

The estuarine exchange flow increases the longitudinal dispersion of passive tracers and trap sinking particles, potentially creating an estuarine turbidity maximum (ETM): a localized maximum of suspended particulate matter concentration in an estuary. The ETM can have many implications: dead zones due to increased turbidity or hypoxia from organic matter decomposition, naval navigation challenges, and other water quality problems. Using timescales, we investigate how the interaction between exchange flow and particle sinking leads to ETMs by modeling a sinking tracer in an idealized box model of the Total Exchange Flow (TEF) first developed by Parker MacCready. Results indicate that the balance of particle sinking and vertical mixing is critical to determining ETM size and location. We then focus on the role of ecology in ETM formation through the use of the Peter–Parker Model, a new biophysical model which combines the TEF box model with a Nutrient–Phytoplankton–Zooplankton–Detritus (NPZD) model, the likes of which were first developed by Peter J.S. Franks. Detritus sinking rates similarly influence detritus peak concentration and location (an ETM), but detritus ETMs occur in a different location than the sinking tracer due to the influence of biological factors, which create a time lag of about 1 day. Lastly, we characterize the flow of the models with a dimensionless parameter that compares timescales and summarizes the dynamics of the sinking tracer in ETM formation and that can be used across systems. Full article

This article introduces an eco-friendly method for the reclamation of carbon fiber-reinforced polymers (CFRP). The research project involved numerous experiments using microwave-assisted pyrolysis (MAP) to explore a range of factors, such as the inert gas flow, the power level, the On/Off frequency of rotation, and the reaction duration. To design the experiments, the three-level Box–Behnken optimization tool was employed. To determine the individual and combined effects of the input parameters on the thermal decomposition of the resin, the data were analyzed using least-squares variance adjustment. The results demonstrate that the models developed in this study were successful in predicting the direct parameters of influence in the microwave-assisted decomposition of CFRPs. An optimal set of operating conditions was found to be the maximum nitrogen flow (2.9 L/min) and the maximum operating experimental power (914 W). In addition, it was observed that the reactor vessel’s On/Off rotation frequency and that increasing the reaction time beyond 6 min had no significant influence on the resin elimination percentage when compared to the two other parameters, i.e., power and carrier gas flow rate. Consequently, the above-mentioned conditions resulted in a maximum resin elimination percentage of 79.6%. Following successful MAP, various post-pyrolysis treatments were employed. These included mechanical abrasion using quartz sand, chemical dissolution, thermal oxidative treatment using a microwave (MW) applicator and thermal oxidative treatment in a conventional furnace. Among these post-treatment techniques, thermal oxidation and chemical dissolution were found to be the most efficient methods, eliminating 100% of the carbon black content on the surface of the recovered carbon fibers. Finally, SEM evaluations and XPS analysis were conducted to compare the surface morphology and elementary constitution of the recovered carbon fibers with virgin carbon fibers. Full article

The balance between accuracy and computational complexity is currently a focal point of research in dynamical system modeling. From the perspective of model reduction, this paper addresses the mode selection strategy in Dynamic Mode Decomposition (DMD) by integrating an embedded fractal theory based on fractal dimension (FD). The existing model selection methods lack interpretability and exhibit arbitrariness in choosing mode dimension truncation levels. To address these issues, this paper analyzes the geometric features of modes for the dimensional characteristics of dynamical systems. By calculating the box counting dimension (BCD) of modes and the correlation dimension (CD) and embedding dimension (ED) of the original dynamical system, it achieves guidance on the importance ranking of modes and the truncation order of modes in DMD. To validate the practicality of this method, it is applied to the reduction applications on the reconstruction of the velocity field of cylinder wake flow and the force field of compressor blades. Theoretical results demonstrate that the proposed selection technique can effectively characterize the primary dynamic features of the original dynamical systems. By employing a loss function to measure the accuracy of the reconstruction models, the computed results show that the overall errors of the reconstruction models are below 5%. These results indicate that this method, based on fractal theory, ensures the model’s accuracy and significantly reduces the complexity of subsequent computations, exhibiting strong interpretability and practicality. Full article

In modern information technology, research on block cipher security is imperative. Concerning the ultra lightweight block cipher PICO, there has been only one study focused on recovering its complete master key, with a large search space of $2^{64}$, and no fault analysis yet. This paper proposes a new fault analysis approach, combining differential fault and algebraic equation techniques. It achieved the recovery of PICO’s entire master key with 40 faults in an average time of 0.57 h. S-box decomposition was utilized to optimize our approach, reducing the time by a remarkable 75.83% under the identical 40-fault condition. Furthermore, PICO’s complete master key could be recovered with 28 faults in an average time of 0.78 h, indicating a significant $2^{37}$ reduction in its search space compared to the previous study. This marks the first fault analysis on PICO. Compared to conventional fault analysis methods DFA (differential fault analysis) and AFA (algebraic fault analysis), our approach outperforms in recovering PICO’s entire master key, highlighting the cruciality of key expansion complexity in block cipher security. Therefore, our approach could serve to recover master keys of block ciphers with comparably complicated key expansions, and production of more secure block ciphers could result. Full article

A semi-analytical method for the S-parameter calculations of an $N\times M$ multimode interference coupler (MMI coupler) is presented. The proposed semi-analytical method is based on the mode decomposition and utilizes an effective index method to approximate the channel waveguide using an equivalent slab waveguide whose modes are described by exact analytic expressions. In comparison to the commonly used beam propagation method (BPM) and finite difference time domain method, which require significant time and computational resources, the proposed method accelerates the design process of photonic integrated circuits and basic building blocks such as an MMI coupler. The simulation results obtained using the developed method and the BPM were compared and showed very similar outcomes for different topologies of the MMI coupler. The key advantage of the proposed semi-analytical method over other analytical models is its ability to accurately simulate MMI couplers with an arbitrary position and number of input and output waveguides. In addition, this method can be extended using the theory of local coupled modes by taking into account the reflections from the end face of the MMI box. Full article

Lightweight block ciphers are normally used in low-power resource-constrained environments, while providing reliable and sufficient security. Therefore, it is important to study the security and reliability of lightweight block ciphers. SKINNY is a new lightweight tweakable block cipher. In this paper, we present an efficient attack scheme for SKINNY-64 based on algebraic fault analysis. The optimal fault injection location is given by analyzing the diffusion of a single-bit fault at different locations during the encryption process. At the same time, by combining the algebraic fault analysis method based on S-box decomposition, the master key can be recovered in an average time of 9 s using one fault. To the best of our knowledge, our proposed attack scheme requires fewer faults, is faster to solve, and has a higher success rate than other existing attack methods. Full article