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This paper explores the significance of digital governance for sustainable rural development in China, emphasizing the collaborative efforts of village administrative organizations, new agricultural business entities, and peasant households. Utilizing an evolutionary game approach, we examine the decision-making behaviors and stability points of these three entities within the context of rural digital governance. Our analysis is grounded in a mechanism of interest linkage among the stakeholders, with numerical simulations used to assess the impact of key variables and parameters on their evolutionary outcomes. The paper reveals that village administrative organizations are highly sensitive to changes in performance gains, special subsidies, penalty losses, and benefit distribution coefficients. Enhancing these variables can significantly motivate these organizations to engage in digital governance. In contrast, new agricultural business entities and peasant households demonstrate a stronger and more consistent willingness to collaborate, minimally affected by variable changes, which suggests a solid economic and social foundation for rural digital governance in China. Our paper underscores the need for positive incentives and a robust fault-tolerance mechanism to foster collaboration among village administrative organizations. It also highlights the importance of integrating new agricultural business entities into the digital governance framework to promote sustainable rural development. These insights provide valuable theoretical and practical implications for policymakers aiming to enhance the efficacy and inclusivity of digital governance in rural China.

To improve the signal-to-noise ratio (SNR) of vibration signals in a phase-sensitive optical time-domain reflectometer (Φ-OTDR) system, a principal component analysis variable step-size normalized least mean square (PCA-VSS-NLMS) denoising method was proposed in this study. First, the mathematical principle of the PCA-VSS-NLMS algorithm was constructed. This algorithm can adjust the input signal to achieve the best filter effect. Second, the effectiveness of the algorithm was verified via simulation, and the simulation results show that compared with the wavelet denoising (WD), Wiener filtering, variational mode decomposition (VMD), and variable step-size normalized least mean square (VSS-NLMS) algorithms, the PCA-VSS-NLMS algorithm can improve the SNR to 30.68 dB when the initial SNR is −1.23 dB. Finally, the PCA-VSS-NLMS algorithm was embedded into the built Φ-OTDR system, an 11.22 km fiber was measured, and PZT was added at 10.19–10.24 km to impose multiple sets of fixed-frequency disturbances. The experimental results show that the SNR of the vibration signal is 8.77 dB at 100 Hz and 0.07 s, and the SNR is improved to 26.17 dB after PCA-VSS-NLMS filtering; thus, the SNR is improved by 17.40 dB. This method can improve the SNR of the system’s position information without the need to change the existing hardware conditions, and it provides a new scheme for the detection and recognition of long-distance vibration signals.

Satellite-based remote sensing enables the quantification of tropospheric NO2 concentrations, offering insights into their environmental and health impacts. However, remote sensing measurements are often impeded by extensive cloud cover and precipitation. The scarcity of valid NO2 observations in such meteorological conditions increases data gaps and thus hinders accurate characterization and variability of concentration across geographical regions. This study utilizes the Empirical Orthogonal Function interpolation in conjunction with the Extreme Gradient Boosting (XGBoost) algorithm and dense urban atmospheric observed station data to reconstruct continuous daily tropospheric NO2 column concentration data in cloudy and rainy areas and thereby improve the accuracy of NO2 concentration mapping in meteorologically obscured regions. Using Chengdu City as a case study, multiple datasets from satellite observations (TROPOspheric Monitoring Instrument, TROPOMI), near-surface NO2 measurements, meteorology, and ancillary data are leveraged to train models. The results showed that the integration of reconstructed satellite observations with provincial and municipal control surface measurements enables the XGBoost model to achieve heightened predictive accuracy (R2 = 0.87) and precision (RMSE = 5.36 μg/m3). Spatially, this approach effectively mitigates the problem of missing values in estimation results due to absent satellite data while simultaneously ensuring increased consistency with ground monitoring station data, yielding images with more continuous and refined details. These results underscore the potential for reconstructing satellite remote sensing information and combining it with dense ground observations to greatly improve NO2 mapping in cloudy and rainy areas.

Gardenia is both a food and medicine plant. It is widely used for cardiovascular protection, and its main bioactive ingredient is crocetin. This study aims to observe the therapeutic effects of crocetin on chronic heart failure in rats induced by various etiologies. It further compares the efficacy differences between preventative and treatment administration, varying dosages, and treatment durations, to provide improved guidance for medication in heart failure rats and determine which categories of chronic heart failure rats might benefit most from crocetin. Chronic heart failure models induced by abdominal aorta constriction, renal hypertension, and coronary artery ligation were constructed. By examining cardiac function, blood biochemistry, and histopathology, the study assessed the preventive and therapeutic effects of crocetin on load-induced and myocardial ischemia-induced heart failure. The results showed that in all three models, both treatment and preventative administration of crocetin significantly improved chronic heart failure in rats, especially in preventative administration. The results indicate crocetin may be beneficial for improving symptoms and functional capacity in rats with heart failure. Furthermore, long-term administration was more effective than short-term administration across all three rat models, with therapeutic onset observed over 6 weeks.

Compared with traditional two-level inverters, multilevel inverters have many solid-state switches and complex composition methods. Therefore, diagnosing and treating inverter faults is a prerequisite for the reliable and efficient operation of the inverter. Based on the idea of intelligent complementary fusion, this paper combines the genetic algorithm–binary granulation matrix knowledge-reduction method with the extreme learning machine network to propose a fault-diagnosis method for multi-tube open-circuit faults in T-type three-level inverters. First, the fault characteristics of power devices at different locations of T-type three-level inverters are analyzed, and the inverter output power and its harmonic components are extracted as the basis for power device fault diagnosis. Second, the genetic algorithm–binary granularity matrix knowledge-reduction method is used for optimization to obtain the minimum attribute set required to distinguish the state transitions in various fault cases. Finally, the kernel attribute set is utilized to construct extreme learning machine subclassifiers with corresponding granularity. The experimental results show that the classification accuracy after attribute reduction is higher than that of all subclassifiers under different attribute sets, reflecting the advantages of attribute reduction and the complementarity of different intelligent diagnosis methods, which have stronger fault-diagnosis accuracy and generalization ability compared with the existing methods and provides a new way for hybrid intelligent diagnosis.

Porous NiTi shape memory alloys (SMAs) possess compatible mechanical properties with human bones and can effectively reduce the risk of stress shielding and stress concentration; therefore, they have been termed promising candidates for orthopedic implants. However, microstructure characteristics of porous NiTi SMAs during plastic deformation have rarely been investigated. The present study aims to specifically investigate microstructure characteristics and the corresponding underlying mechanisms of fabricated porous NiTi SMAs via a conventional sintering (CS) process with NaCl space holder during compressive deformation at room temperature. To realize the aforementioned target, X-ray diffraction (XRD), scanning electron microscope (SEM), electron backscattered diffraction (EBSD), transmission electron microscopy (TEM), and high-resolution transmission electron microscopy (HRTEM) are applied in the present study. The results show that the fabricated porous NiTi SMA is 51.8% for porosity, 181.65 μm for the average pore size, and 0.78 μm for the average grain size. Many Ni4Ti3 and NiTi2 phases are formed in the mixed matrix with dominant B2 (NiTi) and some B19′ (NiTi). Severe inhomogeneous deformation happens within compressed specimens, leading to the occurrence of tangled dislocation and shear bands. Microcracks occur within fabricated porous NiTi SMAs at a deformation degree of 9.2%; then, they extend quickly to form macrocracks, which finally results in the failure of regions between pores. The observed nanocrystallization and amorphization around microcrack tips within the 12.5%-deformed sample can be attributed to the relatively small grain size and the grain segmentation effect via statistically stored dislocation (SSD) and geometrically necessary dislocation (GND).

Epilepsy does great harm to the human body, and even threatens human life when it is serious. Therefore, research focused on the diagnosis and treatment of epilepsy holds paramount clinical significance. In this paper, we utilized variational modal decomposition (VMD) and an enhanced grey wolf algorithm to detect epileptic electroencephalogram (EEG) signals. Data were extracted from each patient’s preseizure period and seizure period of 200 s each, with every 2 s as a segment, meaning 100 data points could be obtained for each patient’s health period as well as 100 data points for each patient’s epilepsy period. Variational modal decomposition (VMD) was used to obtain the corresponding intrinsic modal function (VMF) of the data. Then, the differential entropy (DE) and high frequency detection (HFD) of each VMF were extracted as features. The improved grey wolf algorithm is adopted for a selected channel to improve the maximum value of the channel. Finally, the EEG signal samples were classified using a support vector machine (SVM) classifier to achieve the accurate detection of epilepsy EEG signals. Experimental results show that the accuracy, sensitivity and specificity of the proposed method can reach 98.3%, 98.9% and 98.5%, respectively. The proposed algorithm in this paper can be used as an index to detect epileptic seizures and has certain guiding significance for the early diagnosis and effective treatment of epileptic patients.

Brillouin optical time domain reflectometry (BOTDR) detects fiber temperature and strain data and represents one of the most critical ways of identifying abnormal conditions such as ice coverage and lightning strikes on optical fiber composite overhead ground wire (OPGW) cable. Existing BOTDR extracts brillouin frequency shift (BFS) features with cumulative averaging and curve fitting. BFS feature extraction is slow for long-distance measurements, making realizing real-time measurements on fiber optic cables challenging. We propose a fast feature extraction method for block matching and 3D filtering (BM3D) + Sobel brillouin scattering spectroscopy (BGS). BM3D takes the advantage of non-local means (NLM) and wavelet denoising (WD) and utilizes the spatial-domain non-local principle to enhance the denoising in the transform domain. The global filtering capability of BM3D is utilized to filter out the low cumulative average BGS noise and the BFS feature extraction is completed using Sobel edge detection. Simulation verifies the feasibility of the algorithm, and the proposed method is embedded in BOTDR to measure 30 km of actual OPGW line. The experimental results show that under the same conditions, the processing time of this method is reduced by 37 times compared to that with the 50,000-time cumulative averaging + levenberg marquardt (LM) algorithm without severe distortion of the reference resolution. The method improves the sensor demodulation speed by using image processing technology without changing the existing hardware equipment, which is expected to be widely used in the new generation of BOTDR.

In light of external bias potential separating charge carriers on the photocatalyst surface, piezo materials’ built-in electric field plays a comparable role in enhancing photocatalyst performance. The synergistic effect provided by combining piezo materials assures the future of photocatalysis in practical applications. This paper discusses the principles and mechanisms of piezo-photocatalysis and various materials and structures used for piezo-photocatalytic processes. In piezo-photocatalyst composites, the built-in electric field introduced by the piezo component provides bias potential and extracts photocatalytically generated charge carriers for their subsequent reaction to form reactive oxygen species, which crucially affects the catalytic performance. In the composites, the shape and structure of substrate materials particularly matter. The potential of this technology in other applications, such as energy generation and environmental remediation, are discussed. To shed light on the practical application and future direction of the technique, this review gives opinions on moving the technique forward in terms of material development, process optimization, pilot-scale studies, comprehensive assessment of the technology, and regulatory frameworks to advance practical applications, and by analyzing its principles, applications, and challenges, we hope to inspire further research and development in this field and promote the adoption of piezo-photocatalysis as a viable treatment method for treating emerging pollutants in wastewater.

The controlled synthesis of hollow structure transition metal compounds has long been a very interesting and significant research topic in the energy storage and conversion fields. Herein, an ultrasound-assisted chemical etching strategy is proposed for fabricating concave Ni(OH)2 nanocubes. The morphology and composition evolution of the concave Ni(OH)2 nanocubes suggest a possible formation mechanism. The as-synthesized Ni(OH)2 nanostructures used as supercapacitor electrode materials exhibit high specific capacitance (1624 F g−1 at 2 A g−1) and excellent cycling stability (77% retention after 4000 cycles) due to their large specific surface area and open pathway. In addition, the corresponding hybrid capacitor (Ni(OH)2//graphene) demonstrates high energy density (42.9 Wh kg−1 at a power density of 800 W kg−1) and long cycle life (78% retention after 4000 cycles at 5 A g−1). This work offers a simple and economic approach for obtaining concave Ni(OH)2 nanocubes for energy storage and conversion.

Micro-credit for poverty alleviation is an important financial measure of targeted poverty reduction and rural revitalization in China. This paper employs the OLS model and Logit model to empirically test the effect of micro-credit for poverty alleviation on the income level and stability of income growth of farmers based on the field survey data of 458 registered poverty-stricken farmer households in Fuping County and Quyang County of Hebei Province. The results suggest that micro-credit for poverty alleviation can increase farmers’ income, stabilize the growth of their income, and exert significant short-term and long-term effects on income growth and poverty alleviation. The specialized farmer cooperatives, the scale of production and operation, the proportion of family labor force, and the education level of the head of the farmer’s household exert a significantly positive effect on the farmers’ income and the stable growth of their income. There is a significant interaction between micro-credit for poverty alleviation and specialized farmer cooperatives. The physical conditions of family members exert a negative effect on the stable growth of their income, and other financing channels have no significant effect.

Fingerprints are the most widely used of all biological characteristics in public safety and forensic identification. However, fingerprint images extracted from the crime scene are incomplete. On the one hand, due to the lack of effective area in partial fingerprint images, the extracted features are insufficient. On the other hand, a broken ridge may lead to a large number of false feature points, which affect the accuracy of fingerprint recognition. Existing fingerprint identification methods are not ideal for partial fingerprint identification. To overcome these problems, this paper proposes an attention-based partial fingerprint identification model named APFI. Firstly, the algorithm utilizes the residual network (ResNet) for feature descriptor extraction, which generates a representation of spatial information on fingerprint expression. Secondly, the channel attention module is inserted into the proposed model to obtain more accurate fingerprint feature information from the residual block. Then, to improve the identification accuracy of partial fingerprints, the angular distance between features is used to calculate the similarity of fingerprints. Finally, the proposed model is trained and validated on a home-made partial fingerprint image dataset. Experiments on the home-made fingerprint datasets and the NIST-SD4 datasets show that the partial fingerprint identification method proposed in this paper has higher identification accuracy than other state-of-the-art methods.

Background: Ginseng is widely used as herb or food. Different parts of ginseng have diverse usages. However, the comprehensive analysis on the ginsenosides in different parts of ginseng root is scarce. Methods: An ultra-high-performance liquid chromatography-quadrupole time-of-flight mass spectrometry (UHPLC-Q-TOF/MS) combined with UNIFI informatics platform and ultra-high-performance liquid chromatography-charged aerosol detection (UHPLC-CAD) were employed to evaluate the different parts of cultivated ginseng root. Results: 105 ginsenosides including 16 new compounds were identified or tentatively characterized. 22 potential chemical markers were identified, 20, 17, and 19 for main root (MR) and fibrous root (FR), main root (MR) and branch root (BR), and main root (MR) and rhizome (RH), respectively. The relative contents of Re, Rb1, 20(R)-Rh1, Rd, and Rf were highest in FR. The relative content of Rg1 was highest in RH. The total relative content of pharmacopoeia indicators Rg1, Re, and Rb1 was highest in FR. Conclusion: The differences among these parts were the compositions and relative contents of ginsenosides. Under our research conditions, the peak area ratio of Rg1 and Re could distinguish the MR and FR samples. Fibrous roots showed rich ingredients and high ginsenosides contents which should be further utilized.

It is not easy to find learning materials of interest quickly in the vast amount of online learning materials. The purpose of this study is to find students’ interests according to their learning behaviors in the network and to recommend related video learning materials. For the students who do not leave an evaluation record in the learning platform, the association rule algorithm in data mining is used to find out the videos that students are interested in and recommend them. For the students who have evaluation records in the platform, we use the collaborative filtering algorithm based on items in machine learning, and use the Pearson correlation coefficient method to find highly similar video materials, and then recommend the learning materials they are interested in. The two methods are used in different situations, and all students in the learning platform can get recommendation. Through the application, our methods can reduce the data search time, improve the stickiness of the platform, solve the problem of information overload, and meet the personalized needs of the learners.

Thermo-vacuum stability of the aerospace gyroscopes is one of the crucial issues in the harsh and remote environment of space. This paper reports a bias drift compensation algorithm for the MEMS (microelectromechanical systems) gyroscope with atmosphere package. This approach takes advantage of linear frequency–temperature dependence and linear amplitude–pressure dependence for self-compensation of the gyroscope bias drifts in real-time. The dependences were analyzed and evaluated by subjecting the gyroscope to a thermo-vacuum condition. The real-time self-compensation yielded a total bias error of 0.01°/s over a temperature range of 7–45 °C. A MEMS rate sensor was flown in space and the on-orbit data also verify the effectiveness of the approach.

The security of cryptographic protocols has always been an important issue. Although there are various verification schemes of protocols in the literature, efficiently and accurately verifying cryptographic protocols is still a challenging research task. In this work, we develop a formal method based on dynamic epistemic logic to analyze and describe cryptographic protocols. In particular, we adopt the action model to depict the execution process of the protocol. To verify the security, the intruder’s actions are analyzed. We model exactly the protocol applying our formal language and give the verification models according to the security requirements of this cryptographic protocol. With analysis and proof on a selected example, we show the usefulness of our method. The result indicates that the selected protocol meets the security requirements.

Controlling the synthesis of metallic nanostructures for high quality surface-enhanced Raman scattering (SERS) materials has long been a central task of nanoscience and nanotechnology. In this work, silver aggregates with different surface morphologies were controllably synthesized on a glass−solution interface via a facile laser-induced reduction method. By correlating the surface morphologies with their SERS abilities, optimal parameters (laser power and irradiation time) for SERS aggregates synthesis were obtained. Importantly, the characteristics for largest near-field enhancement were identified, which are closely packed nanorice and flake structures with abundant surface roughness. These can generate numerous hot spots with huge enhancement in nanogaps and rough surface. These results provide an understanding of the correlation between morphologies and SERS performance, and could be helpful for developing optimal and applicable SERS materials.

CuBi2O4/Ag3PO4 was synthesized through a combination of hydrothermal synthesis and an in situ deposition method with sodium stearate as additives, and their textures were characterized with XRD, XPS, SEM/HRTEM, EDS, UV-Vis, and PL. Then, the photodegradation performance of CuBi2O4/Ag3PO4 toward the degradation of diclofenac sodium (DS) was investigated, and the results indicate that the degradation rate of DS in a CuBi2O4/Ag3PO4 (1:1) system is 0.0143 min−1, which is 3.6 times that in the blank irradiation system. Finally, the photocatalytic mechanism of CuBi2O4/Ag3PO4 was discussed, which follows the Z-Scheme theory, and the performance enhancement of CuBi2O4/Ag3PO4 was attributed to the improved separation efficiency of photogenerated electron−hole pairs.

Let S 3 be the symmetric group on three elements. Let k be a field and M 3 ( k ) be the full matrix algebra of 3 × 3 -matrices over k. In this paper, the k S 3 -module algebra structures on M 3 ( k ) are described, and classified up to isomorphism.

The effects of nano-TiO2 mediated photocatalysis on Microcystis aeruginosa, a common species that causes algal bloom, were studied. The metabolic activity of the M. aeruginosa cells was inhibited by nano-TiO2 mediated photocatalysis, as demonstrated by the significant decrease in metabolic heat with the increase in the time of photocatalysis. SEM images also showed that photocatalysis significantly altered the surface morphology of the cells, and the cell disruption was observed by treatment for 6 h. The nano-TiO2 mediated photocatalysis decreased the negative charge on the cell surface because the hydrophilic carboxylic acid groups and ammonium groups in the proteins were modified by free radicals. Metal cations of different valence and charge density could neutralize the negative charges on the cell surface to varying degrees. The adsorption heat of metal cations on the cell surface was higher for the control cells than the cells by photocatalysis, because the surface of the latter was modified and had lower charge density.

Novel Z-Scheme CuBi2O4/β-Bi2O3 composite photocatalysts with different mass ratios and calcination temperatures were firstly synthesized by the hydrothermal method following a temperature-programmed process. The morphology, crystal structure, and light absorption properties of the as-prepared samples were systematically characterized, and the composites exhibited enhanced photocatalytic activity toward diclofenac sodium (DS) degradation compared with CuBi2O4 and β-Bi2O3 under visible light irradiation. The optimal photocatalytic efficiency of the composite, achieved at the mass ratio of CuBi2O4 and β-Bi2O3 of 1:2.25 and the calcination temperature of 600 °C is 92.17%. After the seventh recycling of the composite, the degradation of DS can still reach 82.95%. The enhanced photocatalytic activity of CuBi2O4/β-Bi2O3 is closely related to OH•, h+ and O2•−, and the photocatalytic mechanism of CuBi2O4/β-Bi2O3 can be explained by the Z-Scheme theory.

The Chang 6 sandstone reservoir of the Upper Triassic Yanchang Formation in the Ordos Basin is one of the tight-oil-rich intervals in the basin. Owing to the strong heterogeneity and complex lithology of the Chang 6 reservoir, lithology and fluid identification have become more challenging, hindering exploration and development. This study focused on the Chang 6 member in the Qingcheng area of the Ordos Basin to systematically analyze the lithology, physical properties, and oil-bearing properties of the Chang 6 reservoir. We adopted the method of normalized superposition of neutron and acoustic time-difference curves, the method of induced conductivity–porosity–density intersection analysis, the method of superposition of difference curves (Δφ), and the induced conductivity curve. Our results indicated that the method of normalized superposition of neutron and acoustic wave time-difference curves could quickly and effectively identify the lithologies of tight fine sandstone, silty mudstone, mudstone, and carbonaceous mudstone. The induced conductivity–porosity–density cross-plot could be used to effectively identify oil and water layers, wherein the conductivity of tight oil layers ranged from 18 to 28.1 mS/m, the density ranged from 2.42 to 2.56 g/cm3, the porosity was more than 9.5%, and the oil saturation was more than 65%. Based on the identification of tight fine sandstone using the dual-curve normalized superposition method, the oil layer thickness within the tight fine sandstone could be effectively identified using the superposition of difference curves (Δφ) and induced conductivity curves. Verified by oil-bearing reservoir data from the field test, the overall recognition accuracy of the plots exceeded 90%, effectively enabling the identification of reservoir lithology and fluid types and the determination of the actual thickness of oil layers. Our results provide a reference for predicting favorable areas in the study area and other tight reservoirs.

Mutations of the leucine-rich repeat kinase 2 (LRRK2) gene are associated with pronounced sleep disorders or cognitive dysfunction in neurodegenerative diseases. However, the effects of LRRK2 deficiency on sleep rhythms and sleep deprivation-related cognitive changes, and the relevant underlying mechanism, remain unrevealed. In this study, Lrrk2-/- and Lrrk2+/+ mice were subjected to normal sleep (S) or sleep deprivation (SD). Sleep recording, behavioral testing, Golgi-cox staining, immunofluorescence, and real-time PCR were employed to evaluate the impacts of LRRK2 deficiency on sleep behaviors and to investigate the underlying mechanisms. The results showed that after SD, LRRK2-deficient mice displayed lengthened NREM and shortened REM, and reported decreased dendritic spines, increased microglial activation, and synaptic endocytosis in the prefrontal cortex. Meanwhile, after SD, LRRK2 deficiency aggravated cognitive impairments, especially in the recall memory cued by fear conditioning test. Our findings evidence that LRRK2 modulates REM/NREM sleep and its deficiency may exacerbate sleep deprivation-related cognitive disorders by perturbing synaptic plasticity and microglial synaptic pruning in mice.

Species diversity is high in the Himalaya-Hengduan Mountains, particularly at the edges characterized by deep ravines and “sky islands”. Studies focused on sky-island species are sparse and the patterns observed in response to both geographic and climatic factors are inconsistent. Here phylogeographic and phylogenetic analyses of Gaultheria nummularioides, a species originating in the late Pliocene with its main distribution in the Himalaya-Hengduan Mountains, were conducted to reveal the pattern of genetic dynamics in response to physical geography, glacial fluctuations, and monsoons. We found that in this species genetic variation is higher among populations than within populations, with a significant phylogeographic boundary between the central Himalaya and the eastern Himalaya and the Hengduan Mountains. We also found a high incidence of private alleles, possibly associated with strong habitat isolation. The phylogeographic pattern recovered is consistent with populations in glacial refugia that have experienced expansion after glaciation. The divergence times of most haplotypes coincide with the time of the weakening of the Asian monsoon in these regions. Models of geographic range size showed a significant decrease from the Last Interglacial through the Last Glacial Maximum to the Current, and a predicted increase from the Current to the year 2070. Our study provides insights for understanding speciation among sky islands in this region.