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Blade tip-timing (BTT) is a direct blade vibration monitoring technique and how to use under-sampled BTT signals for blade damage detection is still challenging under variable conditions. Compressed sensing (CS) has been introduced for reconstructing BTT vibration signals, but classical CS reconstruction algorithms are limited by the sparsity and slow optimization process. In order to overcome this issue, this paper presents an iteratively learning reconstruction method by introducing the vector approximate message passing (VAMP) algorithm, called VampNet. Firstly, a discrete Multi-coset sampling (MCS)-based CS model is built for BTT vibration signals in order domain and an improved Hanning-Possion window is integrated to reduce the order spectrum leakage in discretization. Then the VampNet model is proposed to reconstruct blade vibration engine orders (EOs) and the sensing matrix is discussed. Based on reconstructed vibration EOs, a cointegration-based method is proposed for blade damage detection, so that the influences of variable conditions can be reduced. Finally, the feasibility of the proposed method is testified by Matlab simulations and experimental dataset. The results show that blade vibration EOs can be accurately reconstructed by the VampNet and then small cracks can be detected by using the cointegrating residual.

Simultaneous vibration isolation and energy harvesting (SVIEH) are becoming increasingly attractive in suspension systems. However, there is a conflict between low natural frequency and large loading capacity. In addition, the energy harvesting performance is always limited by maintaining the vibration isolation performance in most existing studies. Therefore, how to enhance vibration energy harvesting while satisfying vibration isolation is still a challenge. In this paper, a pair of magnetic rings is used to generate quasi-zero stiffness (QZS) for dealing with the conflict between large loading capacity and low natural frequency, and a Halbach magnetic ring is presented to enhance the energy harvesting performance. Based on them, a novel low-frequency QZS-based SVIEH (QZS-SVIEH) structure is proposed. Firstly, the structural condition of generating QZS is derived. Next the electro-mechanical coupling equations of the QZS-SVIEH system are built and solved by using the harmonic balance method (HBM), which is also verified by the Runge-Kutta method. Then the displacement transmissibility and output power are defined as the two performance indicators for vibration isolation and energy harvesting, respectively. The effects of electrical and mechanical parameters on the two performance indicators are studied in detail. Finally, the prototype of the proposed QZS-SVIEH structure is constructed for experimental testing. The experimental results show that the QZS-SVIEH can effectively suppress vibrations with frequencies above 7.2Hz and generate the peak power of 0.8W under the excitation of acceleration 1.5g and frequency 10Hz.

Abstract Background The purpose of this study was to investigate the role of hsa_circRNA_102051 in colorectal cancer (CRC) and its effect on the stemness of tumor cells. Methods CircRNA microarray was under analysis to screen differentially expressed novel circRNAs in the pathology of CRC. Quantitative real-time PCR was used to detect the relative RNA expression in CRC cells and samples. The effects of hsa_circRNA_102051 on biological functions in CRC cells were accessed both in vitro and in vivo. FISH, RIP and luciferase reporter assay were conducted to confirm the regulatory correlations between hsa_circRNA_102051 and miR-203a, as well as miR-203a and BPTF. Xenograft models were applied to further verify the impacts and fluctuations of hsa_circRNA_102051/miR-203a/BPTF. Moreover, the mechanism how hsa_circRNA_102051 affected the Notch signals was also elucidated. Results Hsa_circRNA_102051 was up-regulated in CRC tissues and cell lines, capable to promote the growth and invasion of CRC. In addition, hsa_circRNA_102051 could enhance stemness of CRC cells. BPTF was identified as downstream factors of hsa_circRNA_102051, and miR-203a was determined directly targeting both hsa_circRNA_102051 and BPTF as an intermediate regulator. Hsa_circRNA_102051 in CRC could block miR-203a expression, and subsequently activated BPTF. Hsa_circRNA_102051/miR-203a/BPTF axis modulated stemness of CRC cells by affecting Notch pathway. Conclusions Our findings provided new clues that hsa_circRNA_102051 might be a potential predictive or prognostic factor in CRC, which induced the fluctuation of downstream miR-203a/BPTF, and subsequently influenced tumor growth, activities and stemness. Thereinto, the Notch signals were also involved. Hence, the hsa_circRNA_102051/miR-203a/BPTF axis could be further explored as a therapeutic target for anti-metastatic therapy in CRC patients.

Objective To explore the risk of Alzheimer′s disease (AD) transmitted by blood transfusion. Methods There were 10 APP/PS1 mice of 3, 6 and 9 months old, half female and half male, and the cognitive and behavioral abilities of C57 mice of the same age were measured, and the blood of the oldest APP/PS1 mice with no behavioral changes were collected to detect the contents of Aβ40 and Aβ42. The polymers Aβ40 and Aβ42 were prepared and Western blotting analysis was conducted. Kunming mice aged from 6 to 7 months were randomly divided into 6 groups (10 mice/ group, half male and half female). The blood of APP/PS1 mice was injected intravenously in experimental group 1-2(100 μL/mouse) with high frequency injection (3 times/week) and low frequency injection (1 time/week), respectively. In experimental group 3-4, Aβ40 and Aβ42 polymerized mixture (100 μL/mouse) were injected in high frequency and low frequency, respectively. The control group 1-2 was injected with the same amount of normal saline, with high frequency and low frequency, respectively. The above groups were injected for 4 weeks, and the cognitive and behavioral abilities were tested and analyzed one week after injection. Finally, the contents of Aβ40 and Aβ42 in blood of Kunming mice were detected. Results Change in cognitive and behavioral ability showed in 9 months old APP/PS1 mice, but not in 3 and 6 months old APP/PS1 mice. The contents of Aβ40 and Aβ42 (pg/mL) in blood of 6-7 months old APP/PS1 mice were 418.40±2.18 and 15.68±0.20, respectively. Except for monomers, most of the polymerized mixtures of Aβ40 and Aβ42 were dimers and trimers. In both high frequency and low frequency, Kunming mice transfused with blood of APP/PS1 mice (experimental group 1-2) showed a certain degree of anxiety-like behavior and short-term memory shortening in open-field test and conditioned fear test, but without significant difference. There was no significant difference in open field test, new object recognition, Barnes maze and cognitive behavior analysis of conditioned fear between experimental group 3-4 and the control group. The levels of blood Aβ40 and Aβ42(pg/mL) of Kunming mice detected by ELISA were 10.30±0.08 and 3.360±0.005, respectively, and there was no significant difference between the two groups. Conclusion Blood transfusion of APP/PS1 mice and the mixture of Aβ40 and Aβ42 have no significant effect on the cognitive function of healthy Kunming mice in a short time, and the risk of AD transmission is relatively low.

Blade tip-timing (BTT) signals are always seriously under sampled, so reconstruction is much needed for vibration analysis. Blade vibration responses are sparse in order domain and classical compressed sensing (CS) algorithms are difficult to reconstruct vibration orders due to lack of prior sparse information under variable speeds. In order to address this issue, this paper introduces deep learning (DL) into BTT vibration reconstruction and proposes an end-to-end deep compressed sensing (DCS) method. Firstly, a multi-coset BTT measurement model is built under variable speeds and the DCS model is derived in order domain, where a specific convolutional neural network (CNN) is designed. Next, a Simulink model is built to generate training and testing samples. The simulation results show that the convolution layer with the rectified linear unit (ReLU) layer placed after the batch normalization (BN) layer can improve the reconstruction performance and the proposed method has better reconstruction accuracy and efficiency than classical CS algorithms. Finally, experiments are done and the results demonstrate that blade vibration orders can be recovered accurately by the proposed method, which will provide a novel way of BTT signal analysis.

Abstract Background Blood derivatives therapy is a conventional clinical treatment, while the treatment for Alzheimer’s disease (AD) is relatively novel. To provide clinical references for treating AD, this meta-analysis was performed to evaluate the efficacy and safety of blood derivatives therapy on the patients with AD. Methods A systematic articles search was performed for eligible studies published up to December 6, 2021 through the PubMed, Embase, Cochrane library, ClinicalTrials.gov , Chinese National Knowledge Infrastructure database, and Wanfang databases. The included articles were screened by using rigorous inclusion and exclusion criteria. Study selection and data-extraction were performed by two authors independently. Random effects model or fixed effects model was used. Quality of studies and risk of bias were evaluated according to the Cochrane risk of bias tool. All analyses were conducted using Review Manager 5.4. The study was designed and conducted according to the Preferring Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) reporting guideline. Results A total of three plasma administrations (two plasma exchange and one young plasma infusion) and five intravenous immunoglobulin (IVIG) randomized controlled trials with a sample size of 1148 subjects diagnosed with AD were included. There was no significant difference in cognitive improvement and all-cause discontinuation between intervention and placebo groups (RR 1.10, 95% CI 0.79–1.54). And Intervention groups showed not a statistically significant improvement in cognition of included subjects measured by the ADAS-Cog (MD 0.36, 95% CI 0.87–1.59), ADCS-ADL (MD −1.34, 95% CI − 5.01–2.32) and NPI (MD 2.20, 95% CI 0.07–4.32) score compared to the control groups. IVIG is well tolerated for AD patients even under the maximum dose (0.4 g/kg), but it is inferior to placebo in Neuropsychiatric Inventory scale in AD patients (MD 2.19, 95% CI 0.02–4.37). Conclusions The benefits of blood derivatives therapy for AD are limited. It is necessary to perform well-designed randomized controlled trials with large sample sizes focusing on the appropriate blood derivatives for the specific AD sub-populations in the future. Systematic review registration PROSPERO CRD42021233886

Objective To investigate the effect of intravenous immunoglobulin(IVIG) with different Aβ antibody content on the cognitive function of Alzheimer′s disease model mice. Methods IVIG from 8 domestic blood products companies were selected. Enzyme-linked immunosorbent assay(ELISA) was used to detect the content of Aβ40/42 antibody. Three kinds of IVIG with high, middle and low Aβ42/40 antibody levels were selected to treat 3xTg-AD mice. Forty 3-month-old 3xTg-AD mice were randomly divided into 4 groups with 10 mice in each group(half male and half female). Three treat groups were intraperitoneally injected with three kinds of IVIG with 1g·kg-1 for 12 weeks(twice a week). The controls were injected with the same volume of saline. Behavioral tests were performed immediately by using the mouse behavior analysis system after a total of 24 injections. Results The concentrations of antibodies(μg/mL) against Aβ40 monomer in IVIG ranged 0.7±0.05 to 3.1±0.05, concentrations of antibodies against Aβ40 oligomer ranged 11.7±0.7 to 32.0±2.2, concentrations of antibodies against Aβ42 monomer ranged 1.8±0.1 to 27.9±0.3, and concentrations of antibodies against Aβ42 oligomeric ranged 2.3±0.1 to 49.4±2. High(IVIG-1), medium(IVIG-8) and low(IVIG-6) IVIG were selected for mice study. In the open field test, the time of four groups of mice entering the central area(s) was 0.5±0.9, 23.4±6.1(P

With the deepening of urbanization in China, the coordinated development of cities in different regions is an important part of the sustainable development of the country, and the reasonable quantification of the unbalanced development of cities in different regions is an important issue facing the society nowadays. Previous studies usually use population data to analyze the power-law distribution law to quantify the imbalance of urban development in different regions, but China’s population data span a large number of years and numerous division criteria, and the results obtained from different population data are widely disparate and have obvious limitations. The paper starts from a fractal perspective and utilizes OpenStreetMap (OSM) data to extract national road intersections from 2015 to 2022, calculates critical distance thresholds for eight years using urban expansion curves, generates urban agglomerations in China, and quantifies the imbalance of urban development in different regions by calculating the urban agglomeration power-law index. The results indicate that (1) the critical distance threshold of urban expansion curves exhibits a slight overall increase and stabilizes within the range of 120–130 m, (2) the number of urban agglomerations in China has been increasing significantly year by year, but the power-law index has been decreasing from 1.49 in 2015 to 1.36 in 2022, and (3) the number of urban agglomerations and the power–law index of the Beijing–Tianjin–Hebei, Yangtze River Delta, Pearl River Delta, and Chengdu–Chongqing regions, which is consistent with the national scale trend, indicates that the scale distribution of urban agglomerations in China at this stage does not conform to Zipf’s law, and there is a certain Matthew effect among cities in different geographic areas with a large unevenness. The results of the study can provide new ideas for assessing the coordinated development of cities in different regions. It compensates for the instability of population and economic data in traditional studies.

Energy is an important resource for human survival and development. China has become the world’s second largest energy consumer, and its energy consumption growth rate ranks first in the world. With the rapid growth of China’s economy, such a way of energy consumption will inevitably hinder the rapid development of China’s economy in the long run, so it is necessary to improve China’s energy consumption way. In order to explore the impact of energy consumption on regional agricultural economic growth, improve the negative impact of unscientific energy consumption, and improve the level of regional economic development, this paper takes three regions as the research object to study the relationship between energy consumption and economic development. The research results show that there is no one-way or two-way causality in Type I areas with high economic development and high energy consumption, no matter in the short-term or long-term; in Type II areas with high economic development and low consumption, there is a bidirectional causal relationship between energy consumption and economic development in the short term. In the long run, economic growth is a one-way causal relationship. In Type III regions with slow economic growth and low energy consumption, only the single causal relationship between long-term energy consumption and economic development is less than 10% significant. This suggests that the long- and short-term causality between energy consumption and economic development varies across regions. Therefore, in order to coordinate regional economic development and narrow regional gaps, it is necessary to formulate appropriate regional energy consumption policies and strategies.

It is necessary to alleviate the high temperatures and heat wave disasters in cities in southwest China that are beginning to occur because of global warming. During this study, the spatial and temporal characteristics of heat waves in Nanchong from 1961 to 2022 are analyzed by using the signal smooth method and mutation test. Based on the meteorological data and socioeconomic statistics, the entropy value method is used to obtain the indicator weights to construct a heat wave social vulnerability evaluation index system and conduct vulnerability assessments and classifications. The results show that: ① The heat wave indicators in Nanchong show an increasing trend, although there is a low period of heat waves from 1980 to 1995. Additionally, there are significant mutations in the number of days, frequency, and intensity of high-temperature heat waves from 2009 to 2011, which may be caused by the abnormal high-pressure belt in the mid-latitude. ② The distribution of exposure, sensitivity, and adaptability in Nanchong City, under high temperatures, is uneven in space. Generally, the indicators in the north are lower than those in the south. ③ The high-vulnerability counties are mainly distributed in the east and west of Nanchong, the proportion of the medium social vulnerability index areas are more than a half, while the dominant factor in the distribution pattern is natural factors. ④ The Western Pacific Subtropical High (WPSH) anomaly directly led to the extremely high temperature in Nanchong in the summer of 2022, and the urbanization process index shows a significant positive correlation with the trend of high temperatures and heat waves in Nanchong.

It is necessary to detect changes in birds’ behaviors promptly to realize their health and habitat status. Moreover, promptly provide appropriate medical treatment and environmental remediation. Automating bird behavior recognition can solve this problem and assist in breeding and protecting birds. This paper proposes the transposed non-local module based on a time pyramid network to establish a dual feature-rates deep fusion net (DF2-Net) for bird behavior recognition. The time pyramid network uses spatial alignment and time pooling operations to extract features with different rates from features of different depths and then fuse features containing the information. On this basis, the transposed non-local (TNL) module uses features with different rates to calculate the relationship matrix of each time slice and spatial position. Then TNL uses the transpose operation to make the original feature take advantage of the corresponding relationship when it multiplies the original feature by the matrix. The module further deeply integrates the relational information of behaviors at different rates to enhance features of different rates, respectively, which can improve the recognition effect of the model on dynamic behaviors. Our study has three contributions: (1) The TNL module takes features with different rates as inputs and uses a transpose mechanism to get two relationships in different directions that match their corresponding input. (2) Dual feature-rates deep fusion net (DF2-Net) transforms a single rate feature into two rate features and iteratively fuses them to obtain information and behavior relationships at various rates. (3) We collect the unique video dataset of birds’ behaviors to fill in the blank of the video dataset and support the study of birds’ behaviors. The experiments compare the DF2-Net with the well-known video-based recognition model on the self-collected birds’ behavior dataset, containing eight behaviors. DF2-Net has the best classification accuracy and achieves 80.87%, 81.35%, 80.70%, and 81.35% in precision, recall, f1-score, and OA metrics. They are 1.81%, 2.43%, 2.20%, and 2.43% higher than the second-best approach (TPN) with 8 frames, respectively. The experimental results show that DF2-Net outperforms state-of-the-art methods in various frames, in which 16 frames are the most suitable for bird behavior recognition. Moreover, we execute the various ablation experiments to demonstrate the efficiency of the TNL module, the optimal location and internal operations of TNL, and the most suitable parameters of DF2-Net. The ablation experiments demonstrate that the TNL module dramatically improves the recognition accuracy of the dynamic behavior and proves the validity and rationality of the TNL module. Therefore, the proposed model is practical and feasible for automatically recognizing bird behavior.

Blade tip timing (BTT) vibration measurement is a promising on-line blade monitoring method, but various uncertainties bring great challenge in engineering applications. Most existing works are based on the assumption of constant rotating speeds. However, rotating speed is hardly fixed under variable conditions. In this case, these uncertainties always become more serious. To deal with this problem, this paper proposes to investigate BTT measurement derivations in angular domain, instead of time domain. Firstly, this paper systematically analyzes the effects of variable rotating speed, static angle errors and translational blade motions on the accuracy of BTT vibration measurement. Then the corresponding calibration methods are presented by only using times of arrival (TOAs). In the end, Matlab/Simulink simulations are done to validate the proposed method under linear and quadratic variations of rotating speeds. The results demonstrate that BTT measurement deviation under low rotating speeds is more than those under high rotating speeds and BTT measurement deviation due to static angle errors is independent of rotating speeds. And the proposed TOAs-based calibration method can reduce BTT measurement deviation greatly under variable rotating speeds, compared with traditional methods. BTT measurement deviation due to static angle errors can be calibrated by using low rotating speeds and those due to translational blade motions are difficult to be calibrated under variable rotating speeds. Thus simulation results indicate great potential of the proposed method for practical applications of the BTT method.

The classical SURF algorithm has many disadvantages, such as high dimension of feature descriptor, large amount of computation, and low matching accuracy when the angle of rotation and angle of view is too large. To solve the above problems, an improved algorithm is proposed. Firstly, image preprocessing is carried out by image binarization, feature points are extracted by Hes-sian matrix, and then feature description is carried out by using circular neighborhood of feature points. Har wavelet response is used to establish descriptors for each feature point, and the normalized gray-level difference and second-order gradient in the neighborhood are calculated simultaneously to form a new feature descriptor. Finally, the RANSAC algorithm is used to eliminate the mismatch points. The algorithm does not Compared with the classical SURF algorithm, it has the advantage of speed, and makes full use of the gray level information and the detail information, so it has higher accuracy. Experimental results show that the algorithm has good robustness and stability to image blur, illumination difference, angle rotation, field of view transformation and so on. The algorithm is applied to remote sensing image stitching to obtain the stitched image with no obvious geometric shift and good edge connection. This algorithm is a kind of image registration algorithm with short time and high precision, which can meet the registration requirements of remote sensing image stitching.

Although some methods have identified the physical urban area to a certain extent, the driving factors for the identification threshold have not been studied deeply. In this paper, vector building data and road intersection data are used for comparative validation based on the urban expansion curve method to identify the physical urban area using the meso-city scale. The geographical detector technique is used to detect how and to what extent the urban spatial structure factors, geographical environment factors and social economic factors affect the optimal distance threshold of 22 administrative districts in the Chongqing municipality. The results based on the vector buildings are more precise and show the characteristics of the physical urban area of core-periphery distribution and the distribution along the water corridor. From the results of quantitative attribution, it was found that the road network density, building density, urbanization rate and urban population density, and their interaction with regional GDP, play a critical role in the optimal distance threshold, with the index value of influence degree ≥0.79. Under the influence of different factors, the optimal distance thresholds of 22 administrative districts show adaptive characteristics. Looking forward to the future, this study provides ideas for further research on the morphological characteristics and distribution laws of multi-spatial scale cities.

In recent years, the advantages of nonlinearity in vibration isolation and energy harvesting have become increasingly apparent. The quasi-zero stiffness (QZS) of the nonlinear term provided by the negative stiffness element can achieve vibration isolation under low-frequency environments while improving the efficiency of energy harvesting. The QZS provides a new research idea for simultaneous vibration isolation and energy harvesting. The main purpose of this paper is to review past research results, summarize possible problems, and discuss trends. After briefly analyzing the basic principle of QZS vibration isolation, the progress of QZS in vibration isolation and energy harvesting in recent years is reviewed. At the same time, main challenges of QZS in realizing synchronous vibration isolation and energy harvesting are also discussed. Finally, according to the existing QZS challenges, the future development trend of QZS is proposed. This paper would provide a quick guide for future newcomers to this field.

To detect the leucine-rich repeats and immunoglobulin 1 (LRIG1) ameliorated liver fibrosis and hepatic stellate cell (HSC) activation via inhibiting sphingosine kinase 1 (SphK1)/Sphingosine-1-Phosphate (S1P) pathway. C57BL/6 male mice (eight weeks old) were intraperitoneal injection with 10% carbon tetrachloride (CCl4) as an in vivo model. The LX-2 cells were induced as amodel for in vitro study by TGF-β (10 ng/mL). The Hematoxylin-eosin (HE) staining, Masson staining, and Sirius red staining results showed that CCl4 caused serious fibrosis and injury in liver tissue, high expression of type I collagen α1 chain (Col1α1) and α-smooth muscle actin (α-SMA) in liver tissue, while the LRIG1 expression level was significantly decreased in LX-2 cell lines. The LRIG1 ameliorated CCl4-induced liver fibrosis, indicated by the fibronectin, α-SMA, LRIG1, SphK1, Col1α1, fibrin Connexin 1 (Fn1), tissue inhibitor of metalloproteinase-1 (TIMP1), sphingosine-1-phosphate (S1P), transforming growth factor-beta 1 (TGF-β1) expression level changes. Similar results were observed in TGF-β1 treated of LX-2 cells. However, the effects were attenuated by treatment with LRIG1. Moreover, SphK1 inhibitors abrogated the effect of LRIG1 on fibrosis. These results demonstrated that LRIG1 improved liver fibrosis in vitro and in vivo via suppressing the SphK1/S1P pathway, indicating its potential use in the treatment of liver fibrosis.

This study evaluates the applicability of the Tropical Rain Measurement Mission (TRMM) 3B43V7 product for use throughout mainland China. Four statistical metrics were used based on the observations made by rain gauges; these metrics were the correlation coefficient (R), the relative bias (RB), the root mean square error (RMSE) and Nash-Sutcliffe efficiency (NSE), and they were chosen to evaluate the performance of the 3B43V7 product at temporal and spatial scales. The results revealed that 3B43V7 performed satisfactorily on all timescales (R > 0.9 and NSE > 0.86); however, it overestimated the results when compared with the rain gauge observations in certain circumstances (RB = 9.7%). Monthly estimates from 3B43V7 were in agreement with rain gauge observations. 3B43V7 can effectively capture the seasonal patterns of precipitation characteristics over mainland China. However, 3B43V7 tends to register a greater overestimation of precipitation in the winter (RB = 14%) than in other seasons while showing greater consistency with the observations made by rain gauges during dry periods. The 3B43V7 product performs well in the eastern part of mainland China, while its performance is poor in the western part of mainland China. In terms of altitude, 3B43V7 performs satisfactorily in areas with moderate to low altitudes (when altitude < 3,500 m, R > 0.9, NSE > 0.8 and RB < 10.2%) but RB values increase with altitude. Overall, 3B43V7 had a favorable performance throughout mainland China.

Piezoelectric metamaterial plate (PMP) is being investigated for structural vibration energy harvesting (SVEH), in which an interface circuit is often used. Thus, it is a challenge to perform bandgap optimization of such an elastic–electro–mechanical coupling system. This paper presents a binary-like topology optimization scheme by dividing the unit cell into identical pieces, where a {0, 1} matrix is optimized to indicate material distribution. Firstly, a unified motion equation is derived for the elastic plate and the piezoelectric patch, and an electromechanical coupling model is built for a self-powered synchronized charge extraction circuit. Then, an extended plane wave expansion method is presented to model the bandgap character of the PMP with interface circuits (PMPICs), and the numerical solution of the dispersion curves is derived based on the Bloch theorem. Next, an extended genetic algorithm is applied for the topology optimization of the PMPIC. In the end, numerical and finite element simulations are performed to validate the proposed method. The results demonstrate that both the structure and the circuit can be optimized simultaneously to obtain the maximum first-order bandgap at a given central frequency. Therefore, the proposed method should provide an effective solution for the topology optimization of a PMPIC for broadband SVEH.

Blade tip-timing (BTT) is a promising method of online monitoring rotating blade vibrations. Since BTT-based vibration signals are typically undersampled, how to reconstruct characteristic vibrations from BTT signals is a big challenge. Existing reconstruction methods are mainly based on the assumption of constant rotation speeds. However, rotating speed fluctuation is inevitable in many engineering applications. In this case, the BTT sampling process should be nonuniform, which will cause existing reconstruction methods to be unavailable. In order to solve this problem, this paper proposes a new reconstruction method based on nonlinear time transformation (NTT). Firstly, the effects of rotating speed fluctuation on BTT vibration reconstruction are analyzed. Next, the NTT of BTT sampling times under rotating speed fluctuation is presented. Then, two NTT-based reconstruction algorithms are derived for uniform and nonuniform BTT sensor configurations, respectively. Also several evaluation metrics of BTT vibration reconstruction under rotating speed fluctuation are defined. Finally, numerical simulations are done to verify the proposed algorithms. The results testify that the proposed NTT-based reconstruction method can reduce effectively the influence of rotating speed fluctuation and decrease the reconstruction error. In addition, rotating speed fluctuation has more bad effects on the reconstruction method under nonuniform sensor configuration than under uniform sensor configuration. For nonuniform BTT signal reconstruction under rotating speed fluctuation, more attentions should be paid on selecting proper angles between BTT sensors. In summary, the proposed method will benefit for detecting early blade damages by reducing frequency aliasing.

Embedded wireless sensing networks (WSNs) provide effective solutions for structural health monitoring (SHM), where how to provide long-term electric power is a bottle-neck problem. Piezoelectric vibration energy harvesting (PVEH) has been widely studied to realize self-powered WSNs due to piezoelectric effect. Structural vibrations are usually variable and exist in the form of elastic waves, so cantilever-like harvesters are not appropriate. In this paper, one kind of two-dimensional (2D) piezoelectric metamaterial plates with local resonators (PMP-LR) is investigated for structural vibration energy harvesting. In order to achieve low-frequency and broadband PVEH in SHM, it is highly necessary to study dynamic characteristics of PMP-LR, particularly bandgaps. Firstly, an analytical model is developed based on the Kirchhoff plate theory, and modal analysis is done by using the Rayleigh–Ritz method. Then, effects of geometric and material parameters on vibration bandgaps are analyzed by finite element-based simulations. In the end, experiments are carried out to validate the simulated results. The results demonstrate that the location of bandgaps can be easily adjusted by the design of local resonators. Therefore, the proposed method will provide an effective tool for optimizing local resonators in PMP-LR.

To measure and present urban size urban spatial forms, in solving problems in the rapid urbanization of China, urban territorial scope identification is essential. Although current commonly used methods can quantitatively identify urban territorial scopes to a certain extent, the results are displayed using a continuous and closed curve with medium- and low-resolution images. This makes the acquisition and interpretation of data challenging. In this paper, by extracting discretely distributed urban settlements, road intersections in OpenStreetMap (OSM), electronic maps, and urban expansion curve based on fractal thoughts have been used to present urban territorial scope and spatial form. Guangzhou, Chengdu, Nanjing, and Shijiazhuang cities were chosen as the identification targets. The results showed that the distance threshold corresponding to the principal curvature point of the urban expansion curve plays a vital role in the extraction of urban settlements. Moreover, from the analysis, the optimal distance thresholds of urban settlements in Guangzhou, Chengdu, Nanjing, and Shijiazhuang were 132 m, 204 m, 157 m, and 124 m, respectively, and the corresponding areas of urban territorial scopes were 1099.36 km2, 1076.78 km2, 803.07 km2, and 353.62 km2, respectively. These metrics are consistent with those for the built-up areas.

Rotated blades are key mechanical components in turbine and high cycle fatigues often induce blade cracks. Meanwhile, mistuning is inevitable in rotated blades, which often makes it much difficult to detect cracks. In order to solve this problem, it is important and necessary to study effects of crack on vibration characteristics of mistuned rotated blades (MRBs). Firstly, a lumped-parameter model is established based on coupled multiple blades, where mistuned stiffness with normal distribution is introduced. Next, a breathing crack model is adopted and eigenvalue analysis is used in coupled lumped-parameter model. Then, numerical analysis is done and effects of depths and positions of a crack on natural frequency, vibration amplitude, and vibration localization parameters are studied. The results show that a crack causes natural frequency decease and vibration amplitude increase of cracked blade. Bifurcations will occur due to a breathing crack. Furthermore, based on natural frequencies and vibration amplitudes, variational factors are defined to detect a crack in MRBs, which are validated by numerical simulations. Thus, the proposed method provides theoretical guidance for crack detection in MRBs.

Drought monitoring is crucial to water resource management and strategic planning. Thus, the objective of this study is to identify the space-time variability of hydrological drought across the broad arid region of northwestern China. Seven distributions were applied to fitting monthly streamflow records of 16 gauging stations from 10 rivers. Finally, the general logistic distribution was selected as the most appropriate one to compute the Standardized Streamflow Index (SSI). The severity and duration of hydrological droughts were also captured from the SSI series. Moreover, we investigate the relationship between hydrological drought (SSI) and meteorological drought (Standardized Precipitation-Evapotranspiration Index (SPEI)) at different time scales. The results show that drought duration and severity decreased over time in the Aibihu, Irtysh, Kaidu, Aksu, Yarkand, Hoton, Shule, Heihe (upstream), and Shiyang Rivers. However, the Tarim (upstream) and Heihe (middle stream) Rivers showed increasing drought duration and severity and this can be attributed to recent decades human activities. Furthermore, two correlation coefficient patterns between SSI and SPEI were found for the rivers of interest, an “increasing-decreasing” pattern for the Irtysh, Heihe, and Shiyang Rivers, where the precipitation is the main runoff supply, and an “increasing-stable” pattern for Aibihu and the Kaidu, Aksu, Yarkand, Hotan, and Shule Rivers, where glacier melt water provided a relatively high supply of runoff. Our findings are a contribution towards implementing effective water resources evaluation and planning in this arid region.

Electrostatic sensors are key components of electrostatic monitoring systems. Their sensitivity characteristics have a direct influence on monitoring accuracy. In previous studies, spatial sensitivity, which is called static sensitivity here, was used to describe the sensitivity characteristics. However, it only reflects a basic relationship between static charged particles and induced charges on an electrostatic sensor’s probe. Besides, as a three-dimensional defined parameter, it is difficult to build a unified model if actual boundary conditions are considered. Thus, it is not quite proper for applications that detect moving particles. To solve this problem, dynamic sensitivity is proposed in this article. As for a hemisphere-shaped electrostatic sensor, first, a more accurate model of static sensitivity is built. Based on it, dynamic sensitivity is defined and modeled analytically. Then, a calibration method is proposed to improve the model’s accuracy under actual boundary conditions. In the end, finite element method simulations are done for validations. The results demonstrate that dynamic sensitivity reflects a relationship between moving charged particles and the actual output signals of a sensor, thus it is direct and practical for moving particles. And the theoretical results are highly consistent with the simulated ones. Moreover, the dynamic sensitivity indicates localized sensing characteristics of hemisphere-shaped electrostatic sensors.

Nonlinear magnetic forces are always used to enlarge resonant bandwidth of vibration energy harvesting systems with piezoelectric cantilever beams. However, how to determine properly the distance between two magnets is one of the key engineering problems. In this paper, the Melnikov theory is introduced to overcome it. Firstly, the Melnikov state-space model of the nonlinear piezoelectric vibration energy harvesting (PVEH) system is built. Based on it, chaotic dynamics mechanisms of achieving broadband PVEH by nonlinearity are exposed by potential function of the unperturbed nonlinear PVEH system. Then the corresponding Melnikov function of the nonlinear PVEH system is defined, based on which two Melnikov necessary conditions of determining the distance are obtained. Finally, numerical simulations are done to testify the theoretic results. The results demonstrate that the distance is closely related to the excitation amplitude and frequency once geometric and material parameters are fixed. Under a single-frequency excitation, the nonlinear PVEH system can generate a periodic vibration around a stable point, a large-amplitude vibration around two stable points, or a chaotic vibration. The proposed method is very valuable for optimally designing and utilizing nonlinear broadband PVEH devices in engineering applications.

Rotated blades are key mechanical components in turbomachinery and high cycle fatigues often induce blade cracks. Accurate detection of small cracks in rotated blades is very significant for safety, reliability, and availability. In nature, a breathing crack model is fit for a small crack in a rotated blade rather than other models. However, traditional vibration displacements-based methods are less sensitive to nonlinear characteristics due to small breathing cracks. In order to solve this problem, vibration power flow analysis (VPFA) is proposed to analyze nonlinear dynamic behaviors of rotated blades with small breathing cracks in this paper. Firstly, local flexibility due to a crack is derived and then time-varying dynamic model of the rotated blade with a small breathing crack is built. Based on it, the corresponding vibration power flow model is presented. Finally, VPFA-based numerical simulations are done to validate nonlinear behaviors of the cracked blade. The results demonstrate that nonlinear behaviors of a crack can be enhanced by power flow analysis and VPFA is more sensitive to a small breathing crack than displacements-based vibration analysis. Bifurcations will occur due to breathing cracks and subharmonic resonance factors can be defined to identify breathing cracks. Thus the proposed method can provide a promising way for detecting and predicting small breathing cracks in rotated blades.

A practical piezoelectric vibration energy harvesting (PVEH) system is usually composed of two coupled parts: a harvesting structure and an interface circuit. Thus, it is much necessary to build system-level coupled models for analyzing PVEH systems, so that the whole PVEH system can be optimized to obtain a high overall efficiency. In this paper, two classes of coupled models are proposed by joint finite element and circuit analysis. The first one is to integrate the equivalent circuit model of the harvesting structure with the interface circuit and the second one is to integrate the equivalent electrical impedance of the interface circuit into the finite element model of the harvesting structure. Then equivalent circuit model parameters of the harvesting structure are estimated by finite element analysis and the equivalent electrical impedance of the interface circuit is derived by circuit analysis. In the end, simulations are done to validate and compare the proposed two classes of system-level coupled models. The results demonstrate that harvested powers from the two classes of coupled models approximate to theoretic values. Thus, the proposed coupled models can be used for system-level optimizations in engineering applications.

Considerable attention has recently been devoted to the linear trend of drought at the decadal to inter-decadal time scale; however, the nonlinear variation of drought at multi-decadal scales and its relation to atmospheric circulation need to be further studied. The linear and nonlinear variations of the Palmer drought severity index (PDSI) in Shandong from 1900 to 2012 and its relations to the Pacific decadal oscillation (PDO), El Niño-Southern Oscillation (ENSO), Siberian high (SH) and Southern Oscillation (SO) phase changes from multi-scale are detected using linear regression, the Mann–Kendall test, ensemble empirical mode decomposition (EEMD) and the Pearson correlation analysis method. The results indicate that the PDSI shows no statistically significant linear change trend from 1900 to 2012; however, before (after) the late 1950s, PDSI shows a significant upward (downward) trend (P < 0.01) with a linear rate of 0.28/decade (−0.48/decade). From 1900 to 2012, the PDSI also exhibits a nonlinear variation trend at the inter-annual scale (quasi-3 and quasi-7-year), inter-decadal scale (quasi-14-year) and multi-decadal scale (quasi-46 and quasi-65-year). The variance contribution rate of components from the inter-annual scale is the largest, reaching 38.7%, and that from the inter-decadal scale and multi-decadal scale are 18.9% and 19.0%, respectively, indicating that the inter-annual change exerts a huge influence on the overall PDSI change. The results also imply that the effect of the four atmospheric circulations (PDO, ENSO, SH, SO) on PDSI at the multi-decadal variability scale are more important than that at the other scales. Consequently, we state that PDSI variation at the inter-annual scale has more instability, while that at the inter-decadal and multi-decadal scale is more strongly influenced by natural factors.

On-line vibration monitoring is significant for high-speed rotating blades, and blade tip-timing (BTT) is generally regarded as a promising solution. BTT methods must assume that rotating speeds are constant. This assumption is impractical, and blade damages are always formed and accumulated during variable operational conditions. Thus, how to carry out BTT vibration monitoring under variable rotation speed (VRS) is a big challenge. Angular sampling-based order analyses have been widely used for vibration signals in rotating machinery with variable speeds. However, BTT vibration signals are well under-sampled, and Shannon’s sampling theorem is not satisfied so that existing order analysis methods will not work well. To overcome this problem, a reconstructed order analysis-based BTT vibration monitoring method is proposed in this paper. First, the effects of VRS on BTT vibration monitoring are analyzed, and the basic structure of angular sampling-based BTT vibration monitoring under VRS is presented. Then a band-pass sampling-based engine order (EO) reconstruction algorithm is proposed for uniform BTT sensor configuration so that few BTT sensors can be used to extract high EOs. In addition, a periodically non-uniform sampling-based EO reconstruction algorithm is proposed for non-uniform BTT sensor configuration. Next, numerical simulations are done to validate the two reconstruction algorithms. In the end, an experimental set-up is built. Both uniform and non-uniform BTT vibration signals are collected, and reconstructed order analysis are carried out. Simulation and experimental results testify that the proposed algorithms can accurately capture characteristic high EOs of synchronous and asynchronous vibrations under VRS by using few BTT sensors. The significance of this paper is to overcome the limitation of conventional BTT methods of dealing with variable blade rotating speeds.

The temperature data from 3 meteorological stations (Kashi, Ruoqiang, and Hotan) in the South of Tarim River Basin (STRB) during 1964–2011 were analyzed by Mann-Kendall test and correlation analysis. The results from Mann-Kendall test show that the surface temperature (ST), 850 hPa temperature (T850), and 700 hPa temperature (T700) exhibited upward trends, while 300 hPa temperature (T300) revealed a downward trend. On the whole, the change rate of ST, T850, T700, and T300 was 0.26~0.46°C/10a, 0.15~0.40°C/10a, 0.03~0.10°C/10a, and −0.38~−0.13°C/10a, respectively. For the periods, ST and T850 declined during 1964–1997 and then rose during 1998–2011. T700 declined during 1964–2005 and then rose during 2006–2011, while T300 rose from 1964 to 1970s and then declined. The results from correlation analysis show that T850 and T700 positively correlated with ST (P