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Recent advancements in nanogenerator-based human body motion energy harvesting systems have paved the way for self-powered wearable and implantable devices. This review explores the principles, materials, fabrication methods, and applications of piezoelectric and triboelectric nanogenerators for converting mechanical energy from human motion into electricity. We discuss the working mechanisms of these nanogenerators and highlight their unique advantages. The review examines a wide range of piezoelectric and triboelectric materials, including ZnO, BaTiO₃, PVDF, and PTFE, along with novel fabrication techniques such as hydrothermal synthesis and electrospinning. Innovative structural designs, including multilayer stacking and 3D architectures, have significantly enhanced energy harvesting efficiency and output power. The integration of nanogenerators into wearable sensors enables continuous, self-powered monitoring of human activities and physiological signals. Additionally, implantable nanogenerators show promise in powering medical devices and stimulating tissue repair, demonstrating excellent biocompatibility. Our analysis reveals that recent improvements in materials and designs have led to remarkable increases in power output, with some devices achieving power densities over 100 W/m². However, challenges remain in further improving energy conversion efficiency and long-term stability. With ongoing advancements, nanogenerator-based systems are poised to revolutionize self-powered wearable and implantable devices, promising significant progress in healthcare, fitness, and human-machine interactions.

Abstract Background Cesarean hysterectomy as a traditional therapeutic maneuver for placenta accreta spectrum (PAS) has been associated with serious morbidity, conservative management has been used in many institutions to treat women with PAS. This systematic review aims to compare maternal outcomes according to conservative management or cesarean hysterectomy in women with placenta accreta spectrum disorders. Methods A systematic literature search was performed in MEDLINE, EMBASE, Cochrane Central Register of Controlled Trials, Web of Science, and four Chinese databases (Chinese Biomedical Literature Database, China National Knowledge Infrastructure, Chinese Wanfang database and VIP database) to May 2024. Included studies were to be retrospective or prospective in design and compare and report relevant maternal outcomes according to conservative management (the placenta left partially or totally in situ) or cesarean hysterectomy in women with PAS. A risk ratio (RR) with 95% confidence interval (95% CI) was calculated for categorical outcomes and weighted mean difference (WMD) with 95% CI for continuous outcomes. The Newcastle-Ottawa Quality Assessment Scale was used to assess the observational studies. All analyses were performed using STATA version 18.0. Results Eight studies were included in the meta-analysis. Compared with cesarean hysterectomy, PAS women undergoing conservative management showed lower estimated blood loss [WMD − 1623.83; 95% CI: -2337.87, -909.79], required fewer units of packed red blood cells [WMD − 2.37; 95% CI: -3.70, -1.04] and units of fresh frozen plasma transfused [WMD − 0.40; 95% CI: -0.62, -0.19], needed a shorter mean operating time [WMD − 73.69; 95% CI: -90.52, -56.86], and presented decreased risks of bladder injury [RR 0.24; 95% CI: 0.11, 0.50], ICU admission [RR 0.24; 95% CI: 0.11, 0.52] and coagulopathy [RR 0.20; 95% CI: 0.06, 0.74], but increased risk for endometritis [RR 10.91; 95% CI: 1.36, 87.59] and readmission [RR 8.99; 95% CI: 4.00, 12.21]. The incidence of primary or delayed hysterectomy rate was 25% (95% CI: 19–32, I 2 = 40.88%) and the use of uterine arterial embolization rate was 78% (95% CI: 65–87, I 2 = 48.79%) in conservative management. Conclusion Conservative management could be an effective alternative to cesarean hysterectomy when women with PAS desire to preserve the uterus and are informed about the limitations of conservative management. Prospero ID CRD42023484578.

Considering agriculture's substantial carbon emissions, it is essential to examine how the digital economy can stimulate green innovation and low-carbon development by impacting these emissions. This article employs panel data from 31 provinces in China, covering the period from 2011 to 2020, to empirically analyze the effects of digital economy development on agricultural carbon emissions. The results indicate that: (1) Agricultural green total factor productivity has a partial mediating effect in the impact of the digital economy on agricultural carbon emissions, and green finance has a negative regulatory effect. (2) Green finance positively regulates the inhibitory effect of agricultural green total factor productivity on agricultural carbon emissions. (3) The development of the digital economy can inhibit agricultural carbon emissions, and this negative inhibitory effect follows a “U” shape. Our research offers a scientific foundation for the promotion of green and low-carbon development within Chinese agriculture, while also providing valuable references and insights for the global integration and advancement of agricultural emissions reduction alongside the digital economy.

Abstract Graphene films grown by the chemical vapor deposition (CVD) method suffer from contamination and damage during transfer. Herein, an innovative ice‐enabled transfer method under an applied electric field and in the presence of Cu2O (or Cu2O‐Electric‐field Ice Transfer, abbreviated as CEIT) is developed. Ice serves as a pollution‐free transfer medium while water molecules under the electric field fully wet the graphene surface for a bolstered adhesion force between the ice and graphene. Cu2O is used to reduce the adhesion force between graphene and copper. The combined methodology in CEIT ensures complete separation and clean transfer of graphene, resulting in successfully transferred graphene to various substrates, including polydimethylsiloxane (PDMS), Teflon, and C4F8 without pollution. The graphene obtained via CEIT is utilized to fabricate field‐effect transistors with electrical performances comparable to that of intrinsic graphene characterized by small Dirac points and high carrier mobility. The carrier mobility of the transferred graphene reaches 9090 cm2 V−1 s−1, demonstrating a superior carrier mobility over that from other dry transfer methods. In a nutshell, the proposed clean and efficient transfer method holds great potential for future applications of graphene.

The rational allocation of carbon emission quotas is crucial for improving the orderly operation of carbon markets. As a major energy-consuming province in China, Jiangsu's cities must allocate allowable carbon emissions effectively to meet China's 2030 peaking target. This study aims to achieve an optimal allocation of carbon emission quotas by considering principles such as responsibility, efficiency, and equity. To accomplish this goal, we developed a three-stage progressive optimization allocation process. This process incorporates the entropy weight TOPSIS allocation model, zero-sum game-DEA model, and Gini coefficient. The initial allocation scheme revealed that cities in southern Jiangsu, which have higher economic development levels, also received higher carbon emission quotas, compromising efficiency and equity. In response, the second stage involves adjusting the allocation to optimize efficiency for each city, aligning the quotas more closely with historical levels. Finally, the third stage uses the Gini coefficient to further refine the allocation scheme, achieving a more balanced distribution that aligns responsibility, efficiency, and fairness. This research highlights the importance of a structured approach to carbon quota allocation, ensuring a balance that supports both environmental goals and equitable development.

Designing and fabricating a highly sensitive non-enzymatic glucose sensor is crucial for the early detection and management of diabetes. Meanwhile, the development of innovative electrode substrates has become a key focus for addressing the growing demand for constructing flexible sensors. Here, a simple one-step laser engraving method is applied for preparing laser-induced graphene (LIG) on polyimide (PI) film, which serves as the sensor substrate. NiCo-layered double hydroxides (NiCo-LDH) are synthesized on LIG as a precursor, utilizing the zeolitic imidazolate framework (ZIF-67), and then reacted with Ni(NO3)2 via solvent-thermal methods. The sensitivity of the non-enzymatic electrochemical glucose sensor is significantly improved by employing NiCo-LDH/LIG as the sensing material. The porous and interconnected structure of NiCo-LDH, derived from ZIF-67, enhances the accessibility of electrochemically active sites, while the incorporation of LIG ensures exceptional conductivity. The combination of NiCo-LDH with LIG enables efficient electron transport, leading to an increased electrochemically active surface area and enhanced catalytic efficiency. The fabricated electrode achieves a low glucose detection limit of 0.437 μM and demonstrates a high sensitivity of 1141.2 and 631.1 μA mM−2 cm−2 within the linear ranges of 0–770 μM and 770–1970 μM, respectively. Furthermore, the NiCo-LDH/LIG glucose sensor demonstrates superior reliability and little impact from other substances. A flexible integrated LIG-based non-enzymatic glucose sensor has been developed, demonstrating high sensitivity and suggesting a promising application for LIG-based chemical sensors.

Reduced graphene oxide (rGO) exhibits mechanical, optoelectronic, and conductive properties comparable to pristine graphene, which has led to its widespread use as a method for producing graphene-like materials in bulk. This paper reviews the characteristics of graphene oxide and the evolution of traditional reduction methods, including chemical and thermal techniques. A comparative analysis reveals that these traditional methods encounter challenges, such as toxicity and high energy consumption, while plasma reduction offers advantages like enhanced controllability, the elimination of additional reducing agents, and reduced costs. However, plasma reduction is complex and significantly influenced by process parameters. This review highlights the latest advancements in plasma technology for reducing graphene oxide, examining its effectiveness across various gas environments. Inert gas plasmas, such as argon (Ar) and helium (He), demonstrate superior reduction efficiency, while mixed gases facilitate simultaneous impurity reduction. Additionally, carbon-based gases can aid in restoring defects in graphene oxide. This paper concludes by discussing the future prospects of plasma-reduced graphene and emphasizes the importance of understanding plasma parameters to manage energy and chemical footprints for effective reduction.

Traditional U-shape segmentation models can achieve excellent performance with an elegant structure. However, the single-layer decoder structure of U-Net or SwinUnet is too “thin” to exploit enough information, resulting in large semantic differences between the encoder and decoder parts. Things get worse in the field of medical image processing, where annotated data are more difficult to obtain than other tasks. Based on this observation, we propose a U-like model named MS-UNet with a plug-and-play adaptive denoising module and ELoss for the medical image segmentation task in this study. Instead of the single-layer U-Net decoder structure used in Swin-UNet and TransUNet, we specifically designed a multi-scale nested decoder based on the Swin Transformer for U-Net. The proposed multi-scale nested decoder structure allows for the feature mapping between the decoder and encoder to be semantically closer, thus enabling the network to learn more detailed features. In addition, ELoss could improve the attention of the model to the segmentation edges, and the plug-and-play adaptive denoising module could prevent the model from learning the wrong features without losing detailed information. The experimental results show that MS-UNet could effectively improve network performance with more efficient feature learning capability and exhibit more advanced performance, especially in the extreme case with a small amount of training data. Furthermore, the proposed ELoss and denoising module not only significantly enhance the segmentation performance of MS-UNet but can also be applied individually to other models.

Three-dimensional (3D) conformal printing of temperature sensors with a wide sensing range and high stability requires the potential printable material with excellent functionality and printability. Herein, a high-performance silver nanoparticles (Ag NPs) decorated reduced graphene oxide (RGO)/Ag (ADR/Ag) ink with low viscosity was synthesized for aerosol jet printing (AJP). Ag ions are electrostatically adsorbed on the graphene oxide (GO) surface, and Ag NPs (∼10 nm) were attached onto the RGO surface by in-situ liquid-phase reduction. The ADR nanoflakes were mixed with commercial Ag NPs ink, high resolution patterns with ink stream widths down to 10 μm are printed, and ADR/Ag-0.8% ink shows excellent low-temperature sintering capability and flexibility, and the resistivity of the printed traces is 9.79 × 10−5 Ω m after 1 h sintering at a relatively low temperature (75 °C). The resistance negligibly increases by only 5% after 1000 bending cycles, which is about 7 times lower compared to the pure Ag traces. Furthermore, we achieve the 3D printing of a conformal temperature sensor with a sensitivity range of (1.162–1.519) × 10−3/°C in the broad sensing range of 0–200 °C. These findings shed light on the additive manufacturing of high-performance conformal electronics using the emerging AJP technology.

Combined hyperlipidemia (CHL) manifests as elevated cholesterol and triglycerides, associated with fatty liver and cardiovascular diseases. Emerging evidence underscores the crucial role of the intestinal microbiota in metabolic disorders. However, the potential therapeutic viability of remodeling the intestinal microbiota in CHL remains uncertain. In this study, CHL was induced in low-density lipoprotein receptor-deficient (LDLR-/-) hamsters through an 8-week high-fat and high-cholesterol (HFHC) diet or a 4-month high-cholesterol (HC) diet. Placebo or antibiotics were administered through separate or cohousing approaches. Analysis through 16S rDNA sequencing revealed that intermittent antibiotic treatment and the cohousing approach effectively modulated the gut microbiota community without impacting its overall abundance in LDLR-/- hamsters exhibiting severe CHL. Antibiotic treatment mitigated HFHC diet-induced obesity, hyperglycemia, and hyperlipidemia, enhancing thermogenesis and alleviating nonalcoholic steatohepatitis (NASH), concurrently reducing atherosclerotic lesions in LDLR-/- hamsters. Metabolomic analysis revealed a favorable liver lipid metabolism profile. Increased levels of microbiota-derived metabolites, notably butyrate and glycylglycine, also ameliorated NASH and atherosclerosis in HFHC diet-fed LDLR-/- hamsters. Notably, antibiotics, butyrate, and glycylglycine treatment exhibited protective effects in LDLR-/- hamsters on an HC diet, aligning with outcomes observed in the HFHC diet scenario. Our findings highlight the efficacy of remodeling gut microbiota through antibiotic treatment and cohousing in improving obesity, NASH, and atherosclerosis associated with refractory CHL. Increased levels of beneficial microbiota-derived metabolites suggest a potential avenue for microbiome-mediated therapies in addressing CHL-associated diseases.

Orthogonal chirp division multiplexing (OCDM) offers a promising modulation technology for shallow water underwater acoustic (UWA) communication systems due to multipath fading resistance and Doppler resistance. To handle the various channel distortions and interferences, obtaining accurate channel state information is vital for robust and efficient shallow water UWA communication. In recent years, deep learning has attracted widespread attention in the communication field, providing a new way to improve the performance of physical layer communication systems. In this paper, the pilot-based channel estimation is transformed into a matrix completion problem, which is mathematically equivalent to the image super-resolution problem arising in the field of image processing. Simulation results show that the deep learning-based method can improve the channel distortion, outperforming the equalization performed by traditional estimator, the performance of Bit Error Rate is improved by 2.5 dB compared to the MMSE method in OCDM system. At the 7.5 to 20 dB region, it achieves better bit error rate performance than OFDM systems, and the bit error rate is reduced by approximately 53% compared to OFDM when the SNR value is 20, which is very useful in shallow water UWA channels with multipath extension and severe time-varying characteristics.

The ocean is a crucial area for future economic development. The marine environment has high energy-efficient and ecological requirements for building construction. Meteorological parameters are the key basis for the analysis and design of building energy efficiency. The lack of meteorological parameters for energy efficiency, particularly hourly data, under oceanic climatic conditions is a universal problem. The appropriate calculation methods of hourly meteorological parameters under oceanic climatic conditions are explored in this study. The impact of the calculation errors of the hourly meteorological parameters on building energy consumption is also analyzed. Three key meteorological parameters are selected: temperature, humidity, and wind speed. Five hourly calculations methods, including linear interpolation, cubic spline interpolation, pieceated three-Hermite interpolation, Akima interpolation, and radial basis function interpolation, are selected to calculate the error of the difference method, with Xiamen, Haikou, and Sanya as the locations of meteorological research. Appropriate interpolation methods are selected for the three parameters, and the seasonal and regional characteristics of the errors of each parameter are compared. Different interpolation methods should be selected for different meteorological parameters in different seasons. The error data of the three parameters of different magnitudes are constructed. A quantitative relationship between the sum of squares due to error of the three meteorological parameters and the rate of change of cooling energy consumption is established. The hourly calculation errors of meteorological parameters have an important impact on the calculation of dynamic energy consumption. The energy consumption differences caused by the errors of different parameters are significant. Obvious regional and seasonal differences also exist. This research strengthens the research foundation of building energy consumption calculation under oceanic climate conditions.

The rational fabrication of composite structures made of mixed components has shown great potential for boosting the energy density of supercapacitors. Herein, an elaborate hierarchical MOF-derived NiCo2S4@Mo-doped Co-LDH arrays hybrid electrode was fabricated through a step-wise method. By leveraging the synergistic effects of a uniform array of NiCo2S4 nanowires as the core and an MOF-derived porous shell, the NiCo2S4@Mo-doped Co-LDH hybrid electrode demonstrates an exceptional specific capacitance of 3049.3 F g−1 at 1 A g−1. Even at a higher current density of 20 A g−1, the capacitance remains high at 2458.8 F g−1. Moreover, the electrode exhibits remarkable cycling stability, with 91% of the initial capacitance maintained after 10,000 cycles at 10 A g−1. Additionally, the as-fabricated asymmetric supercapacitor (ASC) based on the NiCo2S4@Mo-doped Co-LDH electrode achieves an impressive energy density of 97.5 Wh kg−1 at a power density of 835.6 W kg−1. These findings provide a promising approach for the development of hybrid-structured electrodes, enabling the realization of high-energy-density asymmetric supercapacitors.

Converting biowaste into carbon-based supercapacitor materials provides a new solution for high-performance and environmentally friendly energy storage applications. Herein, the hierarchical PAC/NiCo2S4 composite structure was fabricated through the combination of activation and sulfuration treatments. The PAC/NiCo2S4 electrode garnered advantages from its hierarchical structure and hollow architecture, resulting in a notable specific capacitance (1217.2 F g−1 at 1.25 A g−1) and superior cycling stability. Moreover, a novel all-solid-state asymmetric supercapacitor (ASC) was successfully constructed, utilizing PAC/NiCo2S4 as the cathode and PAC as the anode. The resultant device exhibited exceptionally high energy (49.7 Wh kg−1) and power density (4785.5 W kg−1), indicating the potential of this biomass-derived, hierarchical PAC/NiCo2S4 composite structure for employment in high-performance supercapacitors.

Steroid 5α-reductase 2 (SRD5A2), a testosterone metabolism enzyme, is implicated in human disease. Structural and biochemical analyses of PbSRD5A, a bacterial homolog, reveal SRD5A2 substrate binding pocket and provide framework for the design of new drugs targeting this enzyme.

A voltage compensation and energy supplement-based power conversion system (VC-PCS) is proposed in this paper. A VC-PCS is a series connected to a battery that only processes fractional battery power; therefore, it has lower rated voltage and power in comparison with traditional PCSs. Compared with results of previous studies, the VC-PCS proposed herein can output bipolar voltage to meet voltage variation requirements and is a necessary and comprehensive improvement to previous research. First, its topology structure, working principle, and control strategy are comprehensively examined in this paper. Next, results of the simulations and physical experiments carried out are presented; the results show that the proposed VC-PCS is feasible in principle, and can be realized in physical applications.

As a key sphingolipid metabolite, sphingosine-1-phosphate (S1P) plays crucial roles in vascular and immune systems. It regulates angiogenesis, vascular integrity and homeostasis, allergic responses, and lymphocyte trafficking. S1P is interconverted with sphingosine, which is also derived from the deacylation of ceramide. S1P levels and the ratio to ceramide in cells are tightly regulated by its metabolic pathways. Abnormal S1P production causes the occurrence and progression of numerous severe diseases, such as metabolic syndrome, cancers, autoimmune disorders such as multiple sclerosis, and kidney and cardiovascular diseases. In recent years, huge advances on the structure of S1P metabolic pathways have been accomplished. In this review, we have got a glimpse of S1P metabolism through structural and biochemical studies of: sphingosine kinases, S1P transporters and S1P receptors, and the development of therapeutics targeting S1P signaling. The progress we summarize here could provide fresh perspectives to further the exploration of S1P functions and facilitate the development of therapeutic molecules targeting S1P signaling with improved specificity and therapeutic effects.

Air pollution is a major global problem, closely related to economic and social development and ecological environment construction. Air pollution data for most regions of China have a close correlation with time and seasons and are affected by multidimensional factors such as meteorology and air quality. In contrast with classical peaks-over-threshold modeling approaches, we use a deep learning technique and three new dynamic conditional generalized Pareto distribution (DCP) models with weather and air quality factors for fitting the time-dependence of the air pollutant concentration and make statistical inferences about their application in air quality analysis. Specifically, in the proposed three DCP models, a dynamic autoregressive exponential function mechanism is applied for the time-varying scale parameter and tail index of the conditional generalized Pareto distribution, and a sufficiently high threshold is chosen using two threshold selection procedures. The probabilistic properties of the DCP model and the statistical properties of the maximum likelihood estimation (MLE) are investigated, simulating and showing the stability and sensitivity of the MLE estimations. The three proposed models are applied to fit the PM2.5 time series in Beijing from 2015 to 2021. Real data are used to illustrate the advantages of the DCP, especially compared to the estimation volatility of GARCH and AIC or BIC criteria. The DCP model involving both the mixed weather and air quality factors performs better than the other two models with weather factors or air quality factors alone. Finally, a prediction model based on long short-term memory (LSTM) is used to predict PM2.5 concentration, achieving ideal results.

Measures of predictability in physiological signals based on entropy metrics have been widely used in the application domain of medical assessment and clinical diagnosis. In this paper, we propose a new entropy-based pattern learning by a combination of singular spectrum analysis (SSA) and entropy measures for assessment of physiological signals. Physiological signals are first represented as a series of SSA components, and then well-established entropy measures are extracted from the resulting SSA components that can help to facilitate the features extraction from physiological signals. The entropy measures of notable SSA components are used to form input features and fed into pattern classifier. To demonstrate its validity, applicability, and versatility, the proposed entropy-based pattern learning is used to perform medical assessments with three kinds of classical physiological signals, that is, electroencephalogram (EEG), electromyogram (EMG), and RR-interval signals. Experiments demonstrate that in all cases, the proposed entropy-based pattern learning can effectively capture specific biosignal patterns of physiological signals and achieve excellent identification performances for the assessments of EEG, EMG, and RR-interval signals. Besides, through the comparison of the identification performances for entropy-based pattern learning based on the physiological signals themselves and the SSA components, it is concluded that the discriminating power of entropy-based pattern learning based on the SSA components is much stronger than that based on the physiological signals themselves. Since it can be easily extended to any other physiological signal analysis, the proposed entropy-based pattern learning may use as an efficient approach to reveal biosignal patterns for medical assessment of physiological signals.

ABSTRACT Irreversible destruction of bronchi and alveoli can lead to multiple incurable lung diseases. Identifying lung stem/progenitor cells with regenerative capacity and utilizing them to reconstruct functional tissue is one of the biggest hopes to reverse the damage and cure such diseases. Here we showed that a rare population of SOX9+ basal cells (BCs) located at airway epithelium rugae can regenerate adult human lung. Human SOX9+ BCs can be readily isolated by bronchoscopic brushing and indefinitely expanded in feeder-free condition. Expanded human SOX9+ BCs can give rise to alveolar and bronchiolar epithelium after being transplanted into injured mouse lung, with air-blood exchange system reconstructed and recipient’s lung function improved. Manipulation of lung microenvironment with Pirfenidone to suppress TGF-β signaling could further boost the transplantation efficiency. Moreover, we conducted the first autologous SOX9+ BCs transplantation clinical trial in two bronchiectasis patients. Lung tissue repair and pulmonary function enhancement was observed in patients 3–12 months after cell transplantation. Altogether our current work indicated that functional adult human lung structure can be reconstituted by orthotopic transplantation of tissue-specific stem/progenitor cells, which could be translated into a mature regenerative therapeutic strategy in near future.

The analytical model of a permanent magnet eddy current coupler (PMECC) is mainly used for evaluation of its characteristics and the initial optimization of design. Based on the equivalent magnetic circuit method, this paper carries out analytical modeling for four typical PMECCs composed of surface-mounted and interior permanent magnet, slotted and non-slotted conductor rotors, which provides a theoretical basis for the subsequent research in this paper. The basic electromagnetic characteristics of the PMECCs are investigated by the established analytical model. Simultaneously, the analytical results about permeance, flux density, torque and power are verified by FEA simulation. The analysis results show that the slotted CR will obtain a much higher power density, and the iron loss mainly exists in the CRs. In addition, the analytical and FEA results agree well, which proves the reliability of the proposed, nearly unified analytical model.

A Ms6.4 earthquake occurred at Linkou country, Heilongjiang Province (44.8°N, 129.9°E) on January 2, 2016 at a depth of 580 km. Pre-seismic gravity anomalies obtained at a 1 Hz sampling rate from Crustal Movement Observation Network of China (CMONOC) are analyzed after the earthquake. The results show that: (1) different from previous studies, both pre-seismic amplitude perturbation and co-seismic amplitude perturbation are not critical inversely proportional to epicentral distance; (2) unlike shallow earthquake, the pre-seismic and co-seismic amplitude perturbation of gravity illustrate synchronous spatial variation characters with decrease of epicentral distance for Linkou earthquake. This may because Linkou earthquake is a deep earthquake and occurred in Pacific Plate subduction zone; (3) compared to basement and semi-basement, cave can provide a better observation environment for gPhone gravimeter to detect pre-seismic gravity anomalies.

The relationship between gravity variation and the Akto Ms6.7 earthquake on November 11, 2016, was studied by use of mobile gravity observation data from the China continental structural environmental monitoring network. The result revealed that before the Akto earthquake, a high positive gravity variation was observed in the Pamir tectonic knots region (within a maximum magnitude of approximately +80 microgal), which was consistent with the existing knowledge of gravity abnormality and the locations of strong earthquakes. In view of the recent strong seismic activities in the Pamir tectonic knots region, as well as the strong upward crust movement and compressive strain, it is believed that gravity change in the Pamir tectonic knots region reflects the recent strong seismic activities and crust movement.

This research utilized two periods of gravity monitoring results from 2010 to 2013 from the Continental Tectonics Environmental Monitoring Network of China, analyzed the correlation between gravity variation in the Tibet area and the Nepal Ms8.1 earthquake, and investigated the gravity variation mechanism in combination with the crust vertical movement and horizontal strain field observed by Global Positioning System (GPS). The research results indicated that (1) the gravity variation exhibited apparent characteristics of a positive anomaly and high gradient zone in the Himalayan frontier. This observation is consistent with the existing recognition of the gravity anomaly and occurrence regularity of a strong earthquake; (2) the gravity variation exhibited apparent consistence with the spacious distribution of the vertical movement and the horizontal deformation field in that area. The crustal vertical movement was not the direct cause leading to the gravity variation. It is assumed that the crust stress–strain accumulation in the Qinghai–Tibetan Plateau and its adjacent areas is the important factor that resulted in the variation of gravity.

We study the sensor and relay nodes’ power scheduling problem for the remote state estimation in a Wireless Sensor Network (WSN) with relay nodes over a finite period of time given limited communication energy. We also explain why the optimal infinite time and energy case does not exist. Previous work applied a predefined threshold for the error covariance gap of two contiguous nodes in the WSN to adjust the trade-off between energy consumption and estimation accuracy. However, instead of adjusting the trade-off, we employ an algorithm to find the optimal sensor and relay nodes’ scheduling strategy that achieves the smallest estimation error within the given energy limit under our model assumptions. Our core idea is to unify the sensor-to-relay-node way of error covariance update with the relay-node-to-relay-node way by converting the former way of the update into the latter, which enables us to compare the average error covariances of different scheduling sequences with analytical methods and thus finding the strategy with the minimal estimation error. Examples are utilized to demonstrate the feasibility of converting. Meanwhile, we prove the optimality of our scheduling algorithm. Finally, we use MATLAB to run our algorithm and compute the average estimation error covariance of the optimal strategy. By comparing the average error covariance of our strategy with other strategies, we find that the performance of our strategy is better than the others in the simulation.

Possible ionospheric disturbances relating to the May 12, 2008, Ms8.0 Wenchuan earthquake were identified by Global Positioning System (GPS)-derived total electron content (TEC), ionosonde observations, the global ionospheric map (GIM), and electron density profiles detected by the Constellation Observation System for Meteorology Ionosphere and Climate (COSMIC). We applied a statistical test to detect anomalous TEC signals and found that a unique enhancement in TEC, recorded at 16 GPS stations, appeared on May 9, 2008. The critical frequency at F2 peak (f0F2), observed by the Chinese ionosondes, and maximal plasma frequency, derived from COSMIC data, revealed a characteristic similar to GPS TEC variations. The GIM showed that the anomalous variations of May 9 were located southeast of the epicenter. Using GPS data from 13 stations near the epicenter, we analyzed the TEC variations of satellite orbit traces during 04:00–11:00 UT. We found that TEC decreased to the east and increased to the southeast of the epicenter during this period. Results showed that the abnormal disturbance on May 9 was probably an ionospheric precursor of the Wenchuan earthquake of May 12, 2008.

In this study, bromelain was used to break soy protein molecules into polypeptide chains, and triglycidylamine (TGA) was added to develop a bio-adhesive. The viscosity, residual rate, functional groups, thermal behavior, and fracture surface of different adhesives were measured. A three-ply plywood was fabricated and evaluated. The results showed that using 0.1 wt% bromelain improved the soy protein isolate (SPI) content of the adhesive from 12 wt% to 18 wt%, with viscosity remaining constant, but reduced the residual rate by 9.6% and the wet shear strength of the resultant plywood by 69.8%. After the addition of 9 wt% TGA, the residual rate of the SPI/bromelain/TGA adhesive improved by 13.7%, and the wet shear strength of the resultant plywood increased by 681.3% relative to that of the SPI/bromelain adhesive. The wet shear strength was 30.2% higher than that of the SPI/TGA adhesive, which was attributed to the breakage of protein molecules into polypeptide chains. This occurrence led to (1) the formation of more interlocks with the wood surface during the curing process of the adhesive and (2) the exposure and reaction of more hydrophilic groups with TGA to produce a denser cross-linked network in the adhesive. This denser network exhibited enhanced thermal stability and created a ductile fracture surface after the enzymatic hydrolysis process.

Convergence speed and steady-state performance are important factors to estimate the variable tap-length algorithm. The step-size of tap-length (ST) and the error width considerably impact the performance of the fractional variable tap-length algorithm. A mixed fractional variable tap-length algorithm that simultaneously applies time-varying ST and error width strategies is presented in this paper. The ST and the error width are analyzed in detail. This method uses the same variable to control both aspects, so their changes are relatively consistent. The parameters have strong adaptive ability at different stages. The proposed algorithm can solve some defects of the previous algorithms, including slow convergence rate, weak steady-state performance and robustness. A novel mean square deviation (MSD) gradient experiment is performed. Experimental results show good agreement with the theoretical analysis.

Existing research has revealed that auditory attention can be tracked from ongoing electroencephalography (EEG) signals. The aim of this novel study was to investigate the identification of peoples’ attention to a specific auditory object from single-trial EEG signals via entropy measures and machine learning. Approximate entropy (ApEn), sample entropy (SampEn), composite multiscale entropy (CmpMSE) and fuzzy entropy (FuzzyEn) were used to extract the informative features of EEG signals under three kinds of auditory object-specific attention (Rest, Auditory Object1 Attention (AOA1) and Auditory Object2 Attention (AOA2)). The linear discriminant analysis and support vector machine (SVM), were used to construct two auditory attention classifiers. The statistical results of entropy measures indicated that there were significant differences in the values of ApEn, SampEn, CmpMSE and FuzzyEn between Rest, AOA1 and AOA2. For the SVM-based auditory attention classifier, the auditory object-specific attention of Rest, AOA1 and AOA2 could be identified from EEG signals using ApEn, SampEn, CmpMSE and FuzzyEn as features and the identification rates were significantly different from chance level. The optimal identification was achieved by the SVM-based auditory attention classifier using CmpMSE with the scale factor τ = 10. This study demonstrated a novel solution to identify the auditory object-specific attention from single-trial EEG signals without the need to access the auditory stimulus.