Search Results (29)

Can supply chain strategies, ESG practices, and digital innovations be the game-changers the planet needs for a sustainable future? Motivated by this question, this study investigates the drivers of CO₂ emissions, focusing on supply chain management (GSC), ESG sustainability practices, and Information and Communication Technology (ICT) in China from 2002Q4 to 2024Q4. Utilizing a series of wavelet tools—including wavelet coherence (WTC), partial wavelet coherence (PWC), and multiple wavelet coherence (MWC)—the study uncovers associations across time and frequency domains. To the best of the authors’ knowledge, this is the first study to examine these dynamics within the Chinese context using advanced wavelet techniques. The WTC results reveal that GSC, ICT, and patents are positively associated with CO₂ emissions, particularly during 2008–2016 and 2018–2024, while ESG practices reduced emissions before 2016 but became positively linked to emissions afterward. MWC and PWC analyses confirm that these drivers influence CO₂ within 1–4-year bands, while wavelet Granger causality tests indicate weak short-term but strong medium- to long-term causal relationships among ESG, GSC, PAT, ICT, and CO₂ emissions. Based on these results, policy recommendations are formulated. Full article

Climate change intensifies hydrological cycles, leading to an increased variability in terrestrial water storage anomalies (TWSAs) and a heightened drought risk. Understanding the spatiotemporal dynamics of TWSAs and their driving factors is crucial for sustainable water management. While previous studies have primarily attributed TWSAs to regional factors, this study employs wavelet coherence, partial correlation analysis, and multiple linear regression to comprehensively analyze TWSA dynamics and their drivers in the Hengduan Mountains (HDM) region from 2003 to 2022, incorporating both regional and global influences. Additionally, dry–wet variations were quantified using the GRACE-based Drought Severity Index (GRACE-DSI). Key findings include the following: The annual mean TWSA showed a non-significant decreasing trend (−2.83 mm/y, p > 0.05), accompanied by increased interannual variability. Notably, approximately 36.22% of the pixels in the western HDM region exhibited a significantly decreasing trend. The Nujiang River Basin (NRB) (−17.17 mm/y, p < 0.01) and the Lancang (−17.17 mm/y, p < 0.01) River Basin experienced the most pronounced declines. Regional factors—particularly precipitation (PRE)—drove TWSA in 59% of the HDM region, followed by potential evapotranspiration (PET, 28%) and vegetation dynamics (13%). Among global factors, the North Atlantic Oscillation showed a weak correlation with TWSAs (r = −0.19), indirectly affecting it via winter PET (r = −0.56, p < 0.05). The decline in TWSAs corresponds to an elevated drought risk, notably in the NRB, which recorded the largest GRACE-DSI decline (slope = −0.011, p < 0.05). This study links TWSAs to climate drivers and drought risk, offering a framework for improving water resource management and drought preparedness in climate-sensitive mountain regions. Full article

The objective of this study was to analyse the influence of two large-scale climate indices on precipitation in the Danube basin, both separately and in combination. The evolution of the hydroclimatic regime in this area is of particular importance but has received limited attention. One of the indices for these data is the well-known the North Atlantic Oscillation (NAOI) climate index, which has been used in numerous investigations; the aim of using this index is to determine its influence on various hydroclimatic variables in many regions of the globe. The other index, the Greenland–Balkan Oscillation index (GBOI), has been demonstrated to have a greater influence on various hydroclimatic variables in Southeastern Europe compared to the NAOI. First, through different bivariate methods, such as estimating wavelet total coherence (WTC) in the time–frequency domain and applying partial wavelet coherence (PWC), the performance of the GBOI contributing to precipitation in the Danube basin was compared with that of the NAOI in the winter season. Then, by using relatively simple multivariate methods such as multiple linear regression (MLR) and a variant thereof called ridge regression (RR), notable results were obtained regarding the prediction of overall precipitation in the Danube basin in the winter season. The training period was 90 years (1901–1990), and the testing period was 30 years (1991–2020). The used Nash–Sutcliffe (NS) performance criterion varied between 0.65 and 0.94, depending on the preprocessing approach applied for the input data, proving that statistical modelling for the winter season is both simple and powerful compared to modern deep learning methods. Full article

Around the world, a large number of stone artifacts have been exposed to air for long periods of time, showing multiple types of deterioration that have attracted widespread attention. Among them, there is an often overlooked deterioration of stone artifacts, i.e., black stains on the surface of the calcareous stone, which are tightly bonded to the substrate as a result of the long-term deposition of air pollution. However, due to the current lack of a clear understanding of the black stains, people often tend to use the wrong cleaning and conservation methods, which is not conducive to sustainable conservation. Therefore, there is an urgent need to comprehensively recognize the black stains in terms of material properties and environmental sustainability to guide scientific sustainable conservation methods. To this end, in this paper, we take the black stains observed on marble buildings in the Xianling Tomb, China, as an example, and for the first time, we aim to create a comprehensive understanding of black deposition from the aspects of material properties and environmental characteristics. Multi-analytical approaches, including polarized light microscopy, X-ray fluorescence (XRF), and scanning electron microscopy with energy dispersive X-ray spectrometry (SEM-EDS), were employed to discern the differences between the substrate and black stains. The results revealed that the formation of black stains was attributed to prolonged exposure to various air pollutants (PM, SO₂, NO₂, CO, and O₃). Subsequently, observational data from 2015 to 2023 were utilized to investigate the temporal evolution of local air pollutants and their coupled resonances. Multi-scale variations (annual, seasonal, monthly, weekly, and daily) of pollutant concentration sequences were identified, which helps us to have a clearer perception and to proactively control air pollutants in the region from different cycles. In addition, wavelet coherence (WTC) demonstrated significant time-scale dependency in correlation with air pollutants, which provides effective data support for the coordinated control of air pollutants. This study reveals the mechanism of black stain deterioration on stone artifact surfaces, provides data support for the control and prediction of air pollutants oriented to the sustainable conservation of stone artifacts, and provides a novel and comprehensive approach to the scientific knowledge and sustainable conservation of stone artifacts. Full article

This paper examines Bitcoin’s viability as money through the lens of its risk profile, with a particular focus on its store of value function. We employ a suite of wavelet techniques, including Wavelet Transform (WT), Wavelet Transform Coherence (WTC), Multiple Wavelet Coherence (MWC), and Partial Wavelet Coherence (PWC), to decompose the risk structure of Bitcoin and analyze its relationship with various systematic risk factors. Our dataset spans from 13 August 2015 to 29 June 2024, and includes Bitcoin, major commodities, global and US equities, Shari’ah-compliant equities, Ethereum, and the Secured Overnight Financing Rate (SOFR). We find that Bitcoin’s risk profile is increasingly aligned with traditional financial assets, indicating growing market integration. While Bitcoin exhibits high volatility, a significant portion of this volatility can be attributed to systematic rather than idiosyncratic factors. This suggests that Bitcoin’s risk may be more diversifiable than previously thought. Our findings have important implications for monetary policy and financial regulation, challenging the notion that Bitcoin’s volatility precludes its use as money and suggesting that regulatory approaches should consider Bitcoin’s evolving risk characteristics and increasing integration with broader financial markets. Full article

Power transformers are fundamental components in electrical grids, requiring robust insulation to operate reliably under various abnormal conditions, including overvoltages caused by lightning or switching. As defined by existing standards, the Basic Insulation Level (BIL) or Switching Insulation Level (SIL) of a transformer validates its reliability through impulse testing. These tests presume linearity in the overall system and equipment being tested. They compare waveforms at reduced and full impulse levels to detect or enhance insulation failures. Traditionally, this relies on visual inspection due to subjective acceptance criteria. This article presents a historical background review of the practices involving the use of analogue instruments evolved into digital oscilloscopes and digitizers, and the ways in which they enhance waveform acquisition and analysis capabilities. Despite advances in digital processing, including analyses on the frequency domain rather than only on time, such as transfer function analysis and coherence functions, and other signal transformations, such as wavelet calculation, interpreting differences in waveform records remains subjective. This article presents the development of a tool designed to emulate traditional photographic methods for waveform comparison. Moreover, the TRIMP software used enables multiple comparisons using various similarity and dissimilarity metrics in both the time and frequency domains, providing a robust system for identifying significant differences. The developed methodology and implemented metrics can form the basis for future machine learning or artificial intelligence (AI) applications. While digital tools offer significant advantages in impulse testing, improve reliability, reduce subjectivity, and provide robust decision-making metrics, their test approval remains based on visual comparisons due to consolidated engineering practices. Regardless of the metrics or indications obtained, the developed tool is a powerful graphic visualizer. Full article

Significant in various industrial applications, centrifugal pumps (CPs) play an important role in ensuring operational efficiency, yet they are susceptible to faults that can disrupt production and increase maintenance costs. This study proposes a robust hybrid model for accurate fault detection and classification in CPs, integrating Wavelet Coherence Analysis (WCA) with deep learning architectures VGG16 and ResNet50. WCA is initially applied to vibration signals, creating time–frequency representations that capture both temporal and frequency information, essential for identifying subtle fault characteristics. These enhanced signals are processed by VGG16 and ResNet50, each contributing unique and complementary features that enhance feature representation. The hybrid approach fuses the extracted features, resulting in a more discriminative feature set that optimizes class separation. The proposed model achieved a test accuracy of 96.39%, demonstrating minimal class overlap in t-SNE plots and a precise confusion matrix. When compared to the ResNet50-based and VGG16-based models from previous studies, which reached 91.57% and 92.77% accuracy, respectively, the hybrid model displayed better classification performance, particularly in distinguishing closely related fault classes. High F1-scores across all fault categories further validate its effectiveness. This work underscores the value of combining multiple CNN architectures with advanced signal processing for reliable fault diagnosis, improving accuracy in real-world CP applications. Full article

Video fusion aims to synthesize video footage from different sources into a unified, coherent output. It plays a key role in areas such as video editing and special effects production. The challenge is to ensure the quality and naturalness of synthetic video, especially when dealing with footage of different sources and qualities. Researchers continue to strive to optimize algorithms to adapt to a variety of complex application scenarios and improve the effectiveness and applicability of video fusion. We introduce an algorithm based on a convolution pyramid and propose a 3D video fusion algorithm that looks for the potential function closest to the gradient field in the least square sense. The 3D Poisson equation is solved to realize seamless video editing. This algorithm uses a multi-scale method and wavelet transform to approximate linear time. Through numerical optimization, a small core is designed to deal with large target filters, and multi-scale transformation analysis and synthesis are realized. In terms of seamless video fusion, it shows better performance than existing algorithms. Compared with editing multiple 2D images into video after Poisson fusion, the video quality produced by this method is very close, and the computing speed of the video fusion is improved to a certain extent. Full article

Gaining a comprehensive understanding of the characteristics and propagation of precipitation-based meteorological drought to terrestrial water storage (TWS)-derived hydrological drought is of the utmost importance. This study aims to disentangle the frequency–time relationship between precipitation-derived meteorological and TWS-based hydrological drought from June 2002 to June 2017 based on the Standardized Precipitation Index (SPI) and Standardized Terrestrial Water Storage Index (STI) by employing wavelet coherence rather than a traditional correlation coefficient. The possible influencing factors on drought propagation in 28 regions across the world are examined. The results show that the number of drought months detected by the STI is higher than that detected by the SPI worldwide, especially for slight and moderate drought. Generally, TWS-derived hydrological drought is triggered by and occurs later than precipitation-based meteorological drought. The propagation characteristics between meteorological and hydrological droughts vary by region across the globe. Apparent intra-annual and interannual scales are detected by wavelet analysis in most regions, but not in the polar climate region. Drought propagation differs in phase lags in different regions. The phase lag between hydrological and meteorological drought ranges from 0.5 to 4 months on the intra-annual scale and from 1 to 16 months on the interannual scale. Drought propagation is influenced by multiple factors, among which the El Niño–Southern Oscillation, North Atlantic Oscillation, and potential evapotranspiration are the most influential when considering one, two, or three factors, respectively. The findings of this study improve scientific understanding of drought propagation mechanisms over a global scale and provide support for water management in different subregions. Full article

In the past several decades, drought events have occurred frequently around the world. However, research on the propagation of drought events has not been adequately explored. This study investigated the drought propagation process from meteorological drought to agricultural drought (PMAD) and from meteorological drought to hydrological drought (PMHD) using a 72-year reanalysis dataset in the tropical Lancang–Mekong River Basin. Firstly, we used a new method—Standardized Drought Analysis Toolbox—to construct drought indices. Then, a linear method (Pearson correlation analysis) and a nonlinear method (mutual information) were used to investigate the drought propagation process. Cross-wavelet analysis and wavelet coherence analysis were employed to explore the statistical relationship among the three drought types. Finally, the random forest method was applied to quantify the major factors in drought response time (DRT). The results revealed the following: (1) both linear and nonlinear methods exhibited strong temporal and spatial consistency for both PMAD and PMHD, with linear relationships being stronger than nonlinear ones. (2) The DRTs of PMAD and PMHD were around 1–2 months and 3–5 months, respectively. Significant differences existed in the DRT between the dry season and the rainy season. (3) A divergent spatial pattern of the proportion of DRT was observed between PMAD and PMHD. (4) Significant statistical correlations between meteorological drought and agricultural drought and between meteorological drought and hydrological drought were observed in specific periods for each sub-region; (5) Hydrometeorological factors contributed the most to DRT, followed by terrain factors and the land cover types. The findings of this study deepened our understanding of the spatial–temporal relationship of multiple drought propagation types in this transboundary river basin. Full article

In order to improve the signal-to-noise ratio (SNR) of vibration sensing in the phase-sensitive optical time-domain reflectometer (φ-OTDR) system, a fiber sensing signal processing method based on the FFDNet convolutional neural network is proposed in this paper. In the network, the concept of residual learning is introduced, which involves constructing a residual mapping and utilizing multi-layer convolutional neural networks to learn the noise distribution present in the original image. The denoised result can be obtained by subtracting the learned noise from the original image. We have built a φ-OTDR system based on coherent detection, using three PZTs as simulated vibration sources and a series of experiments at 200 Hz, with each experiment simulating a single vibration event or multiple vibration events by setting different intensities. The experimental results demonstrate that the FFDNet based fiber optic sensing signal processing method enhances the SNR to 37.84 dB, 37.11 dB, and 37.31 dB, respectively, while preserving vibration signal details more effectively than wavelet denoising and Gaussian filtering techniques. The trained FFDNet model has great potential for improving the performance of the φ-OTDR system and has some practical application value. Full article

The Yellow Sea Cold Water Mass (YSCWM) is an important component of the hydrodynamic system and it significantly impacts the primary production of the Yellow Sea. This study investigated the difference in the interannual variability and long-term trends between the northern YSCWM (NYSCWM) and southern YSCWM (SYSCWM), and explored the main physical environmental factors that led to their inconsistency using multiple wavelet coherence. On the interannual scale, the intensities of the NYSCWM and SYSCWM exhibited consistent variability, but the intensity of the SYSCWM had a larger standard deviation and longer periodic signal than that of the NYSCWM. The two-factor combination of surface air temperature (SAT)–Niño 3.4 in the NYSCWM and sea surface temperature (SST)–northward seawater velocity (Vgos) in the SYSCWM controlled the interannual variability, which meant the influencing intensity variability differed in the NYSCWM and SYSCWM. In the long-term trend, the intensities of the NYSCWM and SYSCWM both showed decreasing trends during the study period. However, the enhanced circulation provided more horizontal heat input into the SYSCWM, and the relatively higher increase in SST and decrease in the amplitude of variation in the thermocline depth promoted vertical heat exchange in the SYSCWM, thereby making the intensity of the SYSCWM decrease more quickly than that of the NYSCWM. These findings provide important references that facilitate a deeper understanding of the influence of hydrological processes on marine ecosystems in marginal seas. Full article

Gas is the transaction-fee metering system of the Ethereum network. Users of the network are required to select a gas price for submission with their transaction, creating a risk of overpaying or delayed/unprocessed transactions involved in this selection. In this work, we investigate data in the aftermath of the London Hard Fork and shed insight into the transaction dynamics of the network after this major fork. As such, this paper provides an update on work previous to 2019 on the link between EthUSD/BitUSD and gas price. For forecasting, we compare a novel combination of machine learning methods such as Direct-Recursive Hybrid LSTM, CNN-LSTM, and Attention-LSTM. These are combined with wavelet threshold denoising and matrix profile data processing toward the forecasting of block minimum gas price, on a 5-min timescale, over multiple lookaheads. As the first application of the matrix profile being applied to gas price data and forecasting that we are aware of, this study demonstrates that matrix profile data can enhance attention-based models; however, given the hardware constraints, hybrid models outperformed attention and CNN-LSTM models. The wavelet coherence of inputs demonstrates correlation in multiple variables on a 1-day timescale, which is a deviation of base free from gas price. A Direct-Recursive Hybrid LSTM strategy is found to outperform other models, with an average RMSE of 26.08 and R² of 0.54 over a 50-min lookahead window compared to an RMSE of 26.78 and R² of 0.452 in the best-performing attention model. Hybrid models are shown to have favorable performance up to a 20-min lookahead with performance being comparable to attention models when forecasting 25–50-min ahead. Forecasts over a range of lookaheads allow users to make an informed decision on gas price selection and the optimal window to submit their transaction in without fear of their transaction being rejected. This, in turn, gives more detailed insight into gas price dynamics than existing recommenders, oracles and forecasting approaches, which provide simple heuristics or limited lookahead horizons. Full article

Ground-level ozone pollution causes adverse health effects, and the detailed influences of meteorological factors and precursors on ozone at an hourly scale need to be further understood. We conducted an in-depth analysis of the phase relationships and periods of ground-level ozone in Shunyi station, Beijing, and contributing factors using wavelet analysis and geographic detectors in 2019. The combined effects of different factors on ozone were also calculated. We found that temperature had the strongest influence on ozone, and they were in phase over time. NO₂ had the greatest explanatory power for the temporal variations in ozone among precursors. The wavelet power spectrum indicated that ozone had a periodic effect on multiple time scales, the most significant being the 22–26 h period. The wavelet coherence spectrum showed that in January–March and October–December, NO₂ and ozone had an antiphase relationship, largely complementary to the in-phase relationship of temperature and ozone. Thus, the main influencing factors varied during the year. The interactions of temperature with NO₂ significantly affected the temporal variations in ozone, and explanatory power surpassed 70%. The findings can deepen understanding of the effects of meteorological factors and precursors on ozone and provide suggestions for mitigating ozone pollution. Full article

The accumulation of nutrients in rivers is a major cause of eutrophication, and the change in nutrient content is affected by a variety of factors. Taking the River Yi as an example, this study used wavelet analysis tools to examine the periodic changes in nutrients and environmental factors, as well as the relationship between nutrients and environmental factors. The results revealed that total phosphorus (TP), total nitrogen (TN), and ammonia nitrogen (${\mathrm{NH}}_4^{+}$–N) exhibit multiscale oscillation features, with the dominating periods of 16–17, 26, and 57–60 months. The continuous wavelet transform revealed periodic fluctuation laws on multiple scales between nutrients and several environmental factors. Wavelet transform coherence (WTC) was performed on nutrients and environmental factors, and the results showed that temperature and dissolved oxygen (DO) have a strong influence on nutrient concentration fluctuation. The WTC revealed a weak correlation between pH and TP. On a longer period, however, pH was positively correlated with TN. The flow was found to be positively correct with N and P, while N and P were found to be negatively correct with DO and electrical conductance (EC) at different scales. In most cases, TP was negatively correlated with 5-day biochemical oxygen demand (BOD₅) and permanganate index (COD_Mn). The correlation between TN and COD_Mn and BOD₅ was limited, and no clear dominant phase emerged. In a nutshell, wavelet analysis revealed that water temperature, pH, DO, flow, EC, COD_Mn, and BOD₅ had a pronounced influence on nutrient concentration in the River Yi at different time scales. In the case of the combination of environmental factors, pH and DO play the largest role in determining nutrient concentration. Full article