Search Results (51)

The COVID-19 lockdown had a significant impact on human activities, reducing anthropogenic heat and CO₂ emissions. To effectively assess the impact of the lockdown on the thermal environment, we used the sliding paired t-test, which we improved from the traditional sliding t-test by introducing the paired t-test for sliding statistical tests, to test the abrupt change in the thermal environment. Furthermore, an additive decomposition model and wavelet analysis method were used to analyze the characteristics of trend and irregular change, coherence, and phase difference of the time series data with respect to the thermal environment. We chose the Greenland Sea and the Norwegian Sea, regions highly sensitive to changes in climate and ocean circulation, as case studies and used remote sensing data of the sea surface temperature (SST) and the atmospheric CO₂ concentration data obtained from the Goddard Earth Sciences Data and Information Services Center from January 2015 to December 2021 for the analysis. The results show that although the annual spatial mean SST in 2020 is lower than the mean of all 7 years in most areas of the two seas, there is no evidence of a significant mutation in the decrease in the SST during the lockdown in 2020 compared with the temperatures before, according to the sliding paired t-test. The analysis of the irregular components of the monthly mean SST decomposed by an additive decomposition model also does not show the anomalously low SST during the lockdown in 2020. In addition, the lockdown had almost no impact on the increasing trend of CO₂ concentration. The wavelet analysis also shows that there is no obvious anomaly in coherence or phase difference between the periodic variation of the SST and the CO₂ concentrations in 2020 compared with other years. These results suggest that the direct effect of the COVID-19 lockdown on the thermal environment of the study area could be negligible. Full article

Climate change and human activities affect regional sediment transport and ecological environment construction. Investigating sediment transport and its influencing factors in the Yihe River Basin (YHRB) will provide guidance for regional soil and water conservation and sustainable development. We analyzed the chronological changes, cycles, spatial distribution and influencing factors using Mann–Kendall (M-K) trend analysis, wavelet analysis, and the Pettitt mutation point (PMP) test, then quantified the role of precipitation and human activities in sediment transport changes. The results showed that annual precipitation decreased marginally, whereas sediment load has noticeably declined. Four precipitation cycles were observed: 4–8a, 9–14a, 16–19a, and 20–28a, where 9–14a was dominant; sediment transport cycles were tracked: 3–5a, 9–15a, and 30a, where 30a was dominant with a decreasing trend. The sediment load was higher in the central, northern, and southwestern sub-basins of the YHRB, while it was lower in the southeast. The contribution of human activities and precipitation changes to sediment transport was 73.14% and 26.86% in transitional phase I (1965–1980) and 71.97% and 28.03% in transitional phase II (1981–2020), respectively. Hydraulic engineering construction, water resource development, land-use changes, and soil and water conservation measures intercepted precipitation and sediment, making them the primary factor affecting sediment transport changes in the YHRB. Full article

To enhance fault detection in slewing bearing vibration signals, an advanced noise-reduction model, HRCSA-VMD-WT, is designed for effective signal noise elimination. This model innovates by refining the Chameleon Swarm Algorithm (CSA) into a more potent Hybrid Reinforcement CSA (HRCSA), incorporating strategies from Chaotic Reverse Learning (CRL), the Whale Optimization Algorithm’s (WOA) bubble-net hunting, and the greedy strategy with the Cauchy mutation to diversify the initial population, accelerate convergence, and prevent local optimum entrapment. Furthermore, by optimizing Variate Mode Decomposition (VMD) input parameters with HRCSA, Intrinsic Mode Function (IMF) components are extracted and categorized into noisy and pure signals using cosine similarity. Subsequently, the Wavelet Threshold (WT) denoising targets the noisy IMFs before reconstructing the vibration signal from purified IMFs, achieving significant noise reduction. Comparative experiments demonstrate HRCSA’s superiority over Particle Swarm Optimization (PSO), WOA, and Gray Wolf Optimization (GWO) regarding convergence speed and precision. Notably, HRCSA-VMD-WT increases the Signal-to-Noise Ratio (SNR) by a minimum of 74.9% and reduces the Root Mean Square Error (RMSE) by at least 41.2% when compared to both CSA-VMD-WT and Empirical Mode Decomposition with Wavelet Transform (EMD-WT). This study improves fault detection accuracy and efficiency in vibration signals and offers a dependable and effective diagnostic solution for slewing bearing maintenance. Full article

Based on the data of 2254 daily meteorological stations in China from 1961 to 2021, this study calculated the standardized precipitation evapotranspiration index (SPEI) of the national multi-time scale by using the FAO Penman–Monteith model to quantify the changes in dry and wet conditions. The Mann–Kendall mutation test, wavelet analysis, and other methods were used to study the spatial pattern and temporal evolution of drought. The results showed: (1) In the past 61 years, there were obvious spatial and temporal differences in drought in China, and the interannual variation in drought severity in SPEI-1, SPEI-3, and SPEI-12 gradually decreased at a rate of 0.005/10a, 0.021/10a, and 0.092/10a, respectively. (2) The time point of dry and wet mutation was 1989 according to the MK mutagenicity test. (3) Wavelet analysis showed that the drought cycle on the annual scale and the seasonal scale was consistent, and the main period was about 30 years. (4) In the past 61 years, the drought intensity of different degrees in China has shown a weakening trend, and the drought intensity reached the highest value in 61 years in 1978, at 1836.42. In 2020, the drought intensity was the lowest, at 261.55. (5) The proportion of drought stations has shown a decreasing trend. The proportion of drought-free stations has fluctuated greatly, ranging from 42.12% to 89.25%, with 2020 being the highest. This study provides a scientific basis for further research on the causes and coping strategies of drought and is of great significance for strengthening China’s drought monitoring, early warning ,and adaptation capabilities. Full article

A reformed particle swarm optimization (R_PSO)-based up-conversion mixer circuit is proposed for radar application in this paper. In practice, a non-optimized up-conversion mixer suffers from high power consumption, poor linearity, and conversion gain. Therefore, the R_PSO algorithm is proposed to optimize the up-conversion mixer. The novelty of the proposed R_PSO algorithm is it helps to solve the problem of local optima and premature convergence in traditional particle swarm optimization (T_PSO). Furthermore, in the R_PSO, a velocity position-based convergence (VP_C) and wavelet mutation (W_M) strategy are used to enhance R_PSO’s swarm diversity. Moreover, this work also features novel circuit configurations based on the two-fold transconductance path (T_TP), a technique used to improve linearity. A differential common source (D_CS) amplifier is included in the primary transconductance path (P_TP) of the T_TP. As for the subsidiary transconductance path (S_TP), the enhanced cross-quad transconductor (E_CQT) is implemented within the T_TP. A benchmark function verification is conducted to demonstrate the effectiveness of the R_PSO algorithm. The proposed R_PSO has also been compared with other optimization algorithms such as the genetic algorithm (GA) and the non-dominated sorting genetic algorithm II (NSGA-II). By using R_PSO, the proposed optimized mixer achieves a conversion gain (CG) of 2.5 dB (measured). In this study, the proposed mixer achieves a 1 dB compression point (OP₁dB) of 4.2 dBm with a high linearity. In the proposed mixer, the noise figure (NF) is approximately 3.1 dB. While the power dissipation of the optimized mixer is 3.24 mW. Additionally, the average time for R_PSO to design an up-conversion mixer is 4.535 s. Simulation and measured results demonstrate the excellent performance of the R_PSO optimized up-conversion mixer. Full article

Night-time light data (NTL) have been extensively utilized to map urban fringe areas, but to date, there has not been a comprehensive evaluation of the existing spatial clustering methods for delineating the urban fringe using different types of night-time light data. Therefore, we first selected three popular sources of night-time light data (i.e., NPP/VIIRS, Luojia 1-01, and NASA’s Black Marble) to identify the urban fringe. The recognition of spatial mutations across the urban–rural gradient was conducted based on changes in night light intensity using a spatial continuous wavelet transform model. Then, we employed three representative dual spatial clustering approaches (i.e., MK-Means, DBSC, and DSC) for extracting urban fringe areas using different NTL. By using dual spatial clustering, the spatial patterns of the mutation points were effectively transformed into homogeneous spatially adjacent clusters, enabling the measurement of similarity between mutation points. Taking Nanjing city, one of China’s megacities, as the study area, we found that (1) Compared with the fragmented and concentrated results obtained from the Luojia 1-01, NASA’s Black Marble and NPP/VIIRS data can effectively capture the abrupt change of urban fringes with NTL variations; (2) DSC provided a reliable approach for accurately extracting urban fringe areas using NASA’s Black Marble data. Full article

Drought has a direct impact on regional agricultural production, ecological environment, and economic development. The northwest region of China is an important agricultural production area, but it is also one of the most serious areas of water shortage due to drought and little rain. It is of great significance to make full use of agricultural resources to clarify the temporal and spatial distribution characteristics of the drought regime in Northwest China. Based on the Standardized Precipitation Evapotranspiration Index (SPEI), this paper used the methods of Mann–Kendall non-parameter trend, mutation test, and Morlet wavelet analysis to explore the drought characteristics in Northwest China from 1961 to 2017. The results showed that the spatial distribution of SPEI on annual and seasonal scales differed slightly in different regions, but from northwest to southeast, the distribution was generally wetter to drier. The drought intensity (S_ij) had a step-like distribution with a range of 1.14–1.98. Based on S_ij analysis, the frequency of drought in Northwest China was moderate, followed by extreme drought, severe drought, and light drought. The inter-annual drought station proportion (P_j) ranged from 7.4% to 84.1%. A total of 25, 18, 7, and 5 years of pan-regional drought, regional drought, partial region drought, and local drought occurred, respectively, based on P_j analysis. Moreover, from the whole study period, the regional drought changes tended to cause humidification to different degrees. The results of Morlet wavelet analysis showed that there were multiple time scales of 33–52, 11–19, and 4–7 years of SPEI in the entire time domain, and dry and wet trends occurred. The results of the present research can provide a reference for the efficient utilization of water resources, drought monitoring and early warning, drought prevention, and drought relief in Northwest China. Full article

Atmospheric visibility is an important indicator that reflects the transparency of the atmosphere and characterizes the air quality, so it is of great significance to study the long-term change in visibility. This paper is based on the global surface summary of day data (GSOD) site dataset and other relevant data, using the Mann–Kendall (MK) mutation point test, wavelet transform, and seasonal autoregressive integrated moving average (SARIMA) model forecasting. The time-frequency domain variation characteristics and related influencing factors of regional visibility in China were studied in detail, and the visibility was predicted; the results of the study showed the following: (1) the overall interannual variation of regional visibility in China has a decreasing trend, and the four-season variation has a decreasing trend, except for the rising trend in summer, with abrupt change points in both the overall interannual variation and the four-season variation. (2) There are main cycles of visibility in the Chinese region with time scales of 180 months and 18 months. Under the time scale of 180 months for the main cycle, the variation period of visibility is about 123 months, experiencing two high to low variations; under the time scale of 18 months for the main cycle, the variation period of visibility is 12 months, experiencing 21 high to low variations. (3) The development of the economy indirectly affects changes in visibility. Cities with high economies are densely populated, with concentrations of various particulate emissions and high concentrations of particulate matter, which can directly reduce visibility. (4) Two prediction models, SARIMA and long and the short-term memory (LSTM) neural network, were used to predict the visibility in China, both of which achieved good evaluation indexes, and the visibility in China may show an increasing trend in the future. Full article

Hydrological conditions are strongly regulated by monsoon systems in the Mekong River Basin (MRB), while relevant studies investigating the intensity of the rainy season are still insufficient. This study employed the Mann-Kendall (M-K) test, Sen’s slope estimator, and innovation trend analysis to detect the variation of summer discharge in the MRB. Wavelet analysis is used to investigate the correlation between discharge and two South Asian summer monsoon subsystems (SAMI1 and SAMI2). Results show that the summer discharge in the MRB generally shows significant downward trends during 1970–2016 with a Z value range of −3.59–−1.63, while the high discharge at Vientiane, Mukdahan, and Pakse increases after 1970. The mutation years of the summer discharge series are around 2010 for Chiang Sean and Vientiane, and in 2015 for Luang Prabang, which resulted from the newly built large dams, Xiaowan and Nuozhadu. The wavelet analysis shows that the SAMI1 can be used to predict the summer discharge at Chiang Sean at a ~8-year timescale, while the SAMI2 correlates with the summer discharge well at a 1–8-year scale, especially at Mukdahan and Kratie during 1980–2016. Full article

Ozone is a very important trace gas in the atmosphere, it is like a “double-edged sword”. Because the ozone in the stratosphere can effectively help the earth’s organisms to avoid the sun’s ultraviolet radiation damage, the ozone near the ground causes pollution. Therefore, it is essential to explore the time-frequency domain variation characteristics of total column ozone and have a better understanding of its cyclic variation. In this paper, based on the monthly scale dataset of total column ozone (TCO) (September 2002 to February 2023) from Atmospheric Infrared Sounder (AIRS) carried by NASA’s Aqua satellite, linear regression, coefficient of variation, Mann-Kendall (M-K) mutation tests, wavelet analysis, and empirical orthogonal function decomposition (EOF) analysis were used to analyze the variation characteristics of the TCO in China from the perspectives of time domain, frequency domain, and spatial characteristics. Finally, this study predicted the future of TCO data based on the seasonal autoregressive integrated moving average (SARIMA) model in the time series algorithm. The results showed the following: (1) From 2003 to 2022, the TCO in China showed a slight downward trend, with an average annual change rate of −0.29 DU/a; the coefficient of variation analysis found that TCO had the smallest intra-year fluctuations in 2008 and the largest intra-year fluctuations in 2005. (2) Using the M-K mutation test, it was found that there was a mutation point in the total amount of column ozone in 2016. (3) Using wavelet analysis to analyze the frequency domain characteristics of the TCO, it was observed that TCO variation in China had a combination of 14-year, 6-year, and 4-year main cycles, where 14 years is the first main cycle with a 10-year cycle and 6 years is the second main cycle with a 4-year cycle. (4) The spatial distribution characteristics of the TCO in China were significantly different in each region, showing a distribution characteristic of being high in the northeast and low in the southwest. (5) Based on the EOF analysis of the TCO in China, it was found that the variance contribution rate of the first mode was as high as 52.85%, and its spatial distribution of eigenvectors showed a “-” distribution. Combined with the trend analysis of the time coefficient, this showed that the TCO in China has declined in the past 20 years. (6) The SARIMA model with the best parameters of (1, 1, 2) × (0, 1, 2, 12) based on the training on the TCO data was used for prediction, and the final model error rate was calculated as 1.34% using the mean absolute percentage error (MAPE) index, indicating a good model fit. Full article

The net primary productivity (NPP) of vegetation is an important indicator used to evaluate the quality of terrestrial ecosystems and characterize the carbon balance of ecosystems. In this study, the spatiotemporal distribution and dynamic change in NPP in Africa from 1981 to 2018 were analyzed using the long time series data of NPP. The results of the trend and fluctuation analysis showed that the NPP in the Sahara arid region in northern Africa and the arid region in South Africa exhibited a significant reduction and a high degree of fluctuation; most of the NPP in the tropical rainforests in central Africa and the deciduous broadleaved forests and deciduous needle-leaved forests on the north and south sides of the tropical rainforests increased and showed a low degree of fluctuation; the Congo basin, Gabon, Cameroon, Ghana, Nigeria, Tanzania, and other regions were affected by human activities, while the NPP in these regions exhibited a significant reduction and a high degree of fluctuation. Anomaly analysis showed that the NPP in Africa generally exhibited a slow upward trend during the period from 1981 and 2018. The trend was basically consistent in different seasons, and can be segmented into three phases: (1) a phase of descent from 1981 to 1992, with the NPP below the average value in most years; (2) a phase of steady growth from 1993 to 2000, reaching a peak in 2000; (3) a phase of fluctuations from 2001 to 2018, where the NPP value was above the average value in all years except 2015 and 2016, when the NPP value was low due to abnormally high temperatures and drought. The Mann–Kendall test further showed that the annual and seasonal NPP in Africa exhibited a significant upward trend, and the mutation time points occurred around 1995. The wavelet time series analysis revealed obvious periodic changes in the time series of NPP in Africa. The annual and seasonal NPP showed clear oscillations on time scales of 7, 20, 29, and 55 years. The 55-year period had the strongest signal, and was the first main period. The study can provide a scientific gist for the sustainable development of environmental ecology, agricultural production, and the social economy in Africa. Full article

We present a novel deformation prediction model for super-high arch dams based on the prototype monitoring displacement field. The noise reduction processing of the monitoring data is conducted by a wavelet technique. The performance-improved random forest intelligent regression approach is then established for constructing the arch dam deformation statistical models, whose hyper-parameters are intelligently optimized in terms of the improved salp swarm algorithm. In total, three enhancement strategies are developed into the standard salp swarm algorithm to improve the global searching ability and the phenomenon of convergence precocious, including the elite opposition-based learning strategy, the difference strategy, and the Gaussian mutation strategy. A prediction example for super-high arch dams is presented to confirm the feasibility and applicability of the prediction model based on five evaluation criteria. The prediction results show that the proposed model is superior to other standard models, and exhibits high-prediction accuracy and excellent generalization performance. The stability of the proposed prediction model is investigated by artificially introducing noise strategies, which demonstrates the high-robust prediction features and provides a promising tool for predicting carbon emissions, epidemics, and so forth. Full article

The detection of poultry egg microcracks based on electrical characteristic models is a new and effective method. However, due to the disorder, mutation, nonlinear, time discontinuity, and other factors of the current data, detection algorithms such as support-vector machines (SVM) and random forest (RF) under traditional statistical characteristics cannot identify subtle defects. The detection system voltage is set to 1500 V in the existing method, and higher voltages may cause damage to the hatched eggs; therefore, how to reduce the voltage is also a focus of research. In this paper, to address the problem of the low signal-to-noise ratio of microcracks in current signals, a wavelet scattering transform capable of extracting translation-invariant and small deformation-stable features is proposed to extract multi-scale high-frequency feature vectors. In view of the time series and low feature scale of current signals, various convolutional networks, such as a one-dimensional convolutional neural network (1DCNN), long short-term memory (LSTM), bi-directional long short-term memory (Bi-LSTM), and gated recurrent unit (GRU) are adopted. The detection algorithm of the wavelet scattering convolutional network is implemented for electrical sensing signals. The experimental results show that compared with previous works, the accuracy, precision, recall, F1-score, and Matthews correlation coefficient of the proposed wavelet scattering convolutional network on microcrack datasets smaller than 3 $\mathsf{\mu }$m at a voltage of 1000 V are 99.4393%, 99.2523%, 99.6226%, 99.4357%, and 98.8819%, respectively, with an average increase of 2.0561%. In addition, the promotability and validity of the proposed detection algorithm were verified on a class-imbalanced dataset and a duck egg dataset. Based on the good results of the above experiments, further experiments were conducted with different voltages. The new feature extraction and detection method reduces the sensing voltage from 1500 V to 500 V, which allows for achieving higher detection accuracy with a lower signal-to-noise ratio, significantly reducing the risk of high voltage damage to hatching eggs and meeting the requirements for crack detection. Full article

The precipitation conce ntration degree (PCD) and precipitation concentration period (PCP) in the Liaohe River basin (LRB) from 1960 to 2020 were calculated depending on the daily precipitation data derived from meteorological stations. The mutations of the PCD and PCP were identified by sliding t-test, and spatiotemporal evolution characteristics before and after the mutation point were further analyzed. Cross wavelet transform (CWT) was used to reveal the influence of four low-frequency climate factors (Pacific Decadal Oscillation (PDO), Arctic Oscillation (AO), El Niño -Southern Oscillation (ENSO), and Sunspots (SS)) on precipitation concentration. The results were presented as follows: Mutations occurred in the PCD sequence in 1980 and the PCP sequence in 2005 in the LRB. Spatial distribution of the PCD generally increased from the southeast to the northwest and tended to flatten. Over the past 60 years, the annual PCD tended to decrease, with a variation range of 0.53 to 0.80. The PCP was relatively concentrated in early July to early August, decreasing before and increasing after the mutation. Important climatic factors driving the mutation of PCD included PDO, SS, and AO. However, the resonance between climate factors and the PCD was characterized by complexity and diversity. The PCP was mainly affected by AO and SS before the mutation. ENSO had an important influence on both PCD and PCP, but had no significant correlation with mutation occurrence. Full article

Watershed water cycles undergo profound changes under changing environments. Analyses of runoff evolution characteristics are fundamental to our understanding of the evolution of water cycles under changing environments. In this study, linear regression, moving average, Mann–Kendall, Pettitt, accumulative anomaly, STARS, wavelet analysis, and CEEMDAN methods were used to analyze the trends, mutations, and periodic and intrinsic dynamic patterns of runoff evolution using long-term historical data. The intra-annual distribution of runoff in the Dawen River Basin was uneven, with an overall decreasing trend and mutations in 1975–1976. The main periods of runoff were 1.9 and 2.2 years, and the strongest oscillations in the study period occurred in 1978–1983. The runoff decomposition high-frequency term (intra-annual fluctuation term) had a stronger fluctuation frequency, with a period of 0.51–0.55 years, while the low-frequency term (interannual fluctuation term) had a period of 1.55–2.26 years. The trend term for the runoff decomposition tended to decrease throughout the monitoring period and gradually stabilized at the end of the monitoring period, while the period gradually decreased from upstream to downstream. In summary, we used multiple methods to analyze the evolutionary characteristics of runoff, which are of great relevance to the adaptive management of water resources under changing environments. Full article