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Current authentication schemes based on zero-knowledge proof (ZKP) still face issues such as high computation costs, low efficiency, and security assurance difficulty. Therefore, we propose a secure and efficient authentication scheme (SEAS) for large-scale IoT devices based on ZKP. In the initialization phase, the trusted authority creates prerequisites for device traceability and system security. Then, we propose a new registration method to ensure device anonymity. In the identity tracing and revocation phase, we revoke the real identity of abnormal devices by decrypting and updating group public keys, avoiding their access and reducing revocation costs. In the authentication phase, we check the arithmetic relationship between blind certificates, proofs, and other random data. We propose a new anonymous batch authentication method to effectively reduce computation costs, enhance authentication efficiency, and guarantee device authentication security. Security analysis and experimental results show that an SEAS can ensure security and effectively reduce verification time and energy costs. Its security and performance exceed existing schemes. [ABSTRACT FROM AUTHOR]

Currently, there are various types of microscales and the conventional line detection system usually has only one detection method, which is difficult to adapt to the diverse calibration needs of microscales. This article investigates the high-precision measurement method of a microscale based on optoelectronics and the image integration method to solve the diversified calibration needs of microscales. The automatic measurement and processing system integrates two methods: the photoelectric signal measurement method and the photoelectric image measurement method. This article studies the smooth motion method based on ordinary linear guides, investigates the method of reducing the cosine error of a small-range interference length measurement, proposes an image-based line positioning method, and studies the edge and center recognition algorithms of the line. According to the experimental data, the system's measurement accuracy was analyzed using the photoelectric signal measurement method to measure the 1 mm microscale, the maximum difference from the reference value was 0.105 μm, the standard uncertainty was 0.068 μm, and the absolute value of normalized error was less than 1. The accuracy of the image measurement method to measure the 1 mm microscale was consistent with that of the photoelectric signal method. The results show good consistency in the measurement results between the two methods of the integrated measurement system. The photoelectric signal method has the technical characteristics of high measurement efficiency and high accuracy, while the pixel-based measurement of the image method has two-dimensional measurement characteristics, which can realize measurements that cannot be realized by the photoelectric signal method; therefore, the measurement system of optoelectronics and image integration is characterized by high precision and a wide range of measurement adaptations. [ABSTRACT FROM AUTHOR]

Liaoning Province, a crucial agricultural region in Northeast China, has endured frequent drought disasters in recent years, significantly affecting both agricultural production and the ecological environment. Conducting drought research is of paramount importance for formulating scientific drought monitoring and prevention strategies, ensuring agricultural production and ecological safety. This study developed a Comprehensive Joint Drought Index (CJDI) using the empirical Copula function to systematically analyze drought events in Liaoning Province from 1981 to 2020. Through the application of MK trend tests, Morlet wavelet analysis, and run theory, the spatiotemporal variation patterns and recurrence characteristics of drought in Liaoning Province were thoroughly investigated. The results show that, compared to the three classic drought indices, Standardized Precipitation Index (SPI), Evaporative Demand Drought Index (EDDI), and Standardized Precipitation Evapotranspiration Index (SPEI), CJDI has the highest accuracy in monitoring actual drought events. From 1981 to 2020, drought intensity in all regions of Liaoning Province (east, west, south, and north) exhibited an upward trend, with the western region experiencing the most significant increase, as evidenced by an MK test Z-value of −4.53. Drought events in Liaoning Province show clear seasonality, with the most significant periodic fluctuations in spring (main cycles of 5–20 years, longer cycles of 40–57 years), while the frequency and variability of drought events in autumn and winter are lower. Mild droughts frequently occur in Liaoning Province, with joint and co-occurrence recurrence periods ranging from 1.0 to 1.8 years. Moderate droughts have shorter joint recurrence periods in the eastern region (1.2–1.4 years) and longer in the western and southern regions (1.4–2.2 years), with the longest co-occurrence recurrence period in the southern region (3.0–4.0 years). Severe and extreme droughts are less frequent in Liaoning Province. This study provides a scientific foundation for drought monitoring and prevention in Liaoning Province and serves as a valuable reference for developing agricultural production strategies to adapt to climate change. [ABSTRACT FROM AUTHOR]

Ni-based coating, as an excellent anti-corrosion material, is widely used in the petroleum field and has become a research hotspot in recent years. With the continuous progress of the petrochemical industry, especially in the carbon capture, utilization, and storage and enhanced oil recovery (CCUS-EOR) systems, Ni-based coatings face significant challenges. Based on the latest research status, this paper describes the influence factors of Ni-based coatings and the applicability of different Ni-based coatings. Research shows that CO2 and H2S are the key factors affecting corrosion. With the increase in CO2 and H2S content in the environment, the corrosion rate of carbon steel will be accelerated. When the CO2 content reaches a certain critical value, further increasing the CO2 content will reduce the corrosion rate. The corrosion mechanism of carbon steel under the combined action of CO2 and H2S was also analyzed. At a high CO2/H2S partial pressure ratio, the corrosion process is dominated by CO2 corrosion, and with the increase in the H2S/CO2 partial pressure ratio, the corrosion process shifts to H2S corrosion control. The application of Ni-P protective coatings in this corrosive environment has been investigated. A surface Ni-P coating is extremely hard, wear-resistant, and corrosion-resistant. It can withstand the corrosion of CO2, high salinity, and other media while preventing electrolyte penetration. It exhibits strong corrosion resistance in NaCl solution and can help prevent scaling. However, in the presence of CO2-H2s, it has been discovered that due to inherent faults in Ni-P, the corrosion phenomenon may be exacerbated by the action of H2S, and its protection ability has to be further investigated. The mechanical properties and corrosion resistance of the coating can be enhanced by adding particles to the coating, and four new coating systems, Ni-Fe-P, Ni-Cu-P, Ni-W-P, and Ni-W-P-nSiO2, were introduced. Finally, the future development focus and prospect of CO2 corrosion protection coatings under harsh conditions are prospected. [ABSTRACT FROM AUTHOR]

Waste cooking oil's (WCO's) potential as a rejuvenator of aged asphalt has received attention in recent years, with the acid value of WCO affecting its rejuvenation effect. This study explored the rejuvenation effect of WCO with a high acid value on aged asphalt by using molecular dynamics simulation. First, the representative molecules of WCO with a high acid value and asphalt were determined. The rejuvenation effect of WCO on aged asphalt was analyzed by adding different contents of WCO to an aged asphalt model. The effect of WCO on the thermodynamic properties of the aged asphalt was analyzed. The results show that WCO can restore the thermodynamic properties of aged asphalt binder to a certain extent. Regarding the microstructure of rejuvenated asphalt, WCO molecules dispersed around asphaltenes weakened the latter's aggregation and improved the colloidal structure of the aged asphalt. In terms of interface adhesion properties, WCO can improve the adhesion properties between asphalt binder and SiO2, but it has limited influence on water sensitivity. The results allowed us to comprehensively evaluate the rejuvenation effect of WCO with a high acid value on aged asphalt and to explore its rejuvenation mechanism. [ABSTRACT FROM AUTHOR]

Data information security on wearable devices has emerged as a significant concern among users, so it becomes urgent to explore authentication methods based on wearable devices. Using PhotoPlethysmoGraphy (PPG) signals for identity authentication has been proven effective in biometric authentication. This paper proposes a convolutional neural network authentication method based on 2D PPG signals applied to wearable devices. This method uses Markov Transition Field technology to convert one-dimensional PPG signal data into two-dimensional image data, which not only retains the characteristics of the signal but also enriches the spatial information. Afterward, considering that wearable devices usually have limited resources, a lightweight convolutional neural network model is also designed in this method, which reduces resource consumption and computational complexity while ensuring high performance. It is proved experimentally that this method achieves 98.62% and 96.17% accuracy on the training set and test set, respectively, an undeniable advantage compared to the traditional one-dimensional deep learning method and the classical two-dimensional deep learning method. [ABSTRACT FROM AUTHOR]

As the main organic materials, straw and manure play a critical role in soil organic carbon (SOC) sequestration and crop yield in China. This meta-analysis evaluated the impact of straw and manure amendments, both individually and combined, on crop yield, SOC, and soil nutrients in China by collecting 173 studies. The findings of this study revealed that straw return and manure application increased crop yields by 14.4% and 70.4%, respectively, overall. Combined straw and manure application gained a better improvement effect than straw alone but was less effective than manure alone. Regarding the straw return results, rice straw and a 3000–6000 kg ha−1 returning quantity improved crop yield, SOC, available phosphorus (AP), available potassium (AK), and total nitrogen (TN) the most; regarding the straw return form, straw incorporated into soil and biochar increased crop yield and SOC more, respectively; and <5 years and ≥5 years of straw return treatment increased crop yield and TN more, respectively. Regarding manure application, pig and chicken manure increased crop yield and TN more, respectively; a 50–80% substitution ratio and 10–20 years of duration were best for improving crop yield, SOC, AP, AK, and TN. This study highlights the importance of optimal organic amendment through straw or manure applications to achieve a win–win between crop yield and soil fertility under the requirement of sustainable agriculture. [ABSTRACT FROM AUTHOR]

Modernization and industrialization have significantly increased energy consumption, causing environmental problems. Given that China is the largest energy user, the rise in building energy consumption necessitates clean energy alternatives. The purpose of this study is to summarize typical building models for primary and secondary schools in Hainan Province, and to use software to simulate and calculate the photovoltaic utilization potential of primary and secondary school buildings. In China, the government is usually the manager of primary and secondary schools, and due to their architectural characteristics, these buildings can be used to assess photovoltaic applications. The aim is to drive the application of photovoltaic systems in all types of buildings and promote urban energy reform. This study summarizes the types of primary and secondary school buildings in Hainan Province and analyzes them. It evaluates rooftop photovoltaic projects at the Second Middle School and the Siyuan School in Wanning City, Hainan Province, and uses PVsyst 7.2 software to assess the photovoltaic utilization potential. The results show that the optimal orientation in Hainan Province is south-facing, and the optimal inclination angle is 10° to 20°. The most favorable orientations of facade photovoltaic systems are 20° southeast or southwest. The longest dynamic investment payback period is approximately 15 years, and the environmental benefits are $0.012/kWh. The findings indicate significant potential for photovoltaic applications in primary and secondary school buildings. A combination of facade and rooftop photovoltaics can result in the zero-energy consumption of these buildings, reducing the pressure on urban power grids and achieving sustainable utilization. [ABSTRACT FROM AUTHOR]

The existence of structural strength in undisturbed soil results in its distinct characteristics compared to remolded soil. Under the influence of freeze–thaw cycles, this difference may easily cause geotechnical disasters in cold regions. This study aimed to analyze and discuss the expression degree and influencing factors of the structural strength of expansive soil. The unconfined compressive strength (UCS) test, high-pressure consolidation test, and microscopic test were performed on expansive soil retrieved from a seasonally frozen region. Moreover, sensitivity parameters, including stress sensitivity (St.qu, St.σk) and strain sensitivity (St.ɛu, St.Cc), were applied to explore the expression degree and influencing factors of structural strength in a seasonally frozen region. The results reveal that the undisturbed samples have better structural connection and particle arrangement than the remolded samples. However, the primary fractures have a certain degrading effect on the strength of the undisturbed soil as influenced by a seasonally frozen region. With the increase in water content and the decrease in density, the expression degree of the structural strength in terms of compressive strength and the ability to resist deformation enhances under the unconfined condition. By contrast, the expression degree increases in strength and decreases in ability under the confined condition. Furthermore, the effect mechanisms of the basic property, particle composition, structural linkage, lateral confinement, and historical role on the structural expression were analyzed. [ABSTRACT FROM AUTHOR]

Background: It is adverse for the safety of a tailings dam to use fine-grained tailings as the materials for a high tailings dam because of the low penetration coefficient, the slow consolidating velocity, and the bad physical mechanical property. Furthermore, with the influence of complicated geography conditions, the phreatic line will be increased enormously when encountering special conditions, which directly affect the safe operation of the tailings dam. Methods: In this study, based on the engineering, geological, and hydrogeological conditions and survey results of a tailings dam, a 210 m fine-grained tailings dam located in three gullies was selected and used to simulate the three-dimensional seepage field of a tailings dam under a steady saturated state by using the finite element software MIDAS GTS. The permeability coefficient was inverted, the seepage field of the project under different working conditions was simulated, and the position of the phreatic line was obtained. The controlled position of phreatic lines was determined by combining the seepage field with the stability requirements. Results: Back analysis could accurately reflect the actual permeability coefficient of each partition of tailings dams. Due to the multiple areas of seepage accumulation, large valley corners, and narrowing of the dam axis, the phreatic line of the shoulder region was elevated by 2~3 m compared to the surrounding area and was thereby the most critical region of the tailings dam seepage control. The stability requirements and minimum controlled position of the phreatic line requirements could be met when the controlled position of the phreatic line was 23 m. Conclusion: This study revealed the key areas and reasons why the tailings dam's phreatic line is prone to be uplifted under complicated geography conditions. It was very critical to control the local phreatic line by adopting local horizontal seepage drainage measures or radiation wells in the key areas of the tailings dam to ensure the safety of the tailings dam. In addition to strengthening the daily monitoring of the key areas and the exfiltration facilities of the tailings dam, it is recommended to carry out determination tests of the permeability coefficient and particle size at regular intervals. The findings could provide countermeasures for seepage control. [ABSTRACT FROM AUTHOR]

CeO2 was prepared using a hydrothermal method, modified by radio-frequency plasma in the form of glow discharge, and then the solid superacid S 2 O 8 2 − /CeO2 was prepared by the impregnation method. A series of properties such as pore structure was characterized by N2 adsorption–desorption experiments, surface morphology was characterized by TEM, crystal phase was characterized by XRD, and surface acidity of the catalyst was characterized by Py-IR and Hammett titration. The methyl esterification reaction of tryptophan was used to evaluate the activity of the solid superacid. The results showed that the catalyst modified by radio-frequency plasma had a larger specific surface area, more surface oxygen vacancies, smaller particle size, and higher total acid content. The yield of tryptophan methyl ester reached a higher level of 94.5% (150 °C, 1 MPa, 2 h), catalyzed by the modified S 2 O 8 2 − /CeO2. This work verified the feasibility of plasma technology in the field of catalytic activity enhancement of solid superacid. [ABSTRACT FROM AUTHOR]

Soil moisture (SM), as one of the crucial environmental factors, has traditionally been estimated using global navigation satellite system interferometric reflectometry (GNSS-IR) microwave remote sensing technology. This approach relies on the signal-to-noise ratio (SNR) reflection component, and its accuracy hinges on the successful separation of the reflection component from the direct component. In contrast, the presence of carrier phase and pseudorange multipath errors enables soil moisture retrieval without the requirement for separating the direct component of the signal. To acquire high-quality combined multipath errors and diversify GNSS-IR data sources, this study establishes the dual-frequency pseudorange combination (DFPC) and dual-frequency carrier phase combination (L4) that exclude geometrical factors, ionospheric delay, and tropospheric delay. Simultaneously, we propose two methods for estimating soil moisture: the DFPC method and the L4 method. Initially, the equal-weight least squares method is employed to calculate the initial delay phase. Subsequently, anomalous delay phases are detected and corrected through a combination of the minimum covariance determinant robust estimation (MCD) and the moving average filter (MAF). Finally, we utilize the multivariate linear regression (MLR) and extreme learning machine (ELM) to construct multi-satellite linear regression models (MSLRs) and multi-satellite nonlinear regression models (MSNRs) for soil moisture prediction, and compare the accuracy of each model. To validate the feasibility of these methods, data from site P031 of the Plate Boundary Observatory (PBO) H2O project are utilized. Experimental results demonstrate that combining MCD and MAF can effectively detect and correct outliers, yielding single-satellite delay phase sequences with a high quality. This improvement contributes to varying degrees of enhanced correlation between the single-satellite delay phase and soil moisture. When fusing the corrected delay phases from multiple satellite orbits using the DFPC method for soil moisture estimation, the correlations between the true soil moisture values and the predicted values obtained through MLR and ELM reach 0.81 and 0.88, respectively, while the correlations of the L4 method can reach 0.84 and 0.90, respectively. These findings indicate a substantial achievement in high-precision soil moisture estimation within a small satellite-elevation angle range. [ABSTRACT FROM AUTHOR]

With the increasing demand for intelligent custom clothing, the development of highly accurate human body dimension prediction tools using artificial neural network technology has become essential to ensuring high-quality, fashionable, and personalized clothing. Although support vector regression (SVR) networks have demonstrated state-of-the-art (SOTA) performances, they still fall short on prediction accuracy and computation efficiency. We propose a novel generalized regression forecasting network (GRFN) that incorporates kernel ridge regression (KRR) within a multi-strategy multi-subswarm particle swarm optimizer (MMPSO)-SVR nonlinear regression model that applies a residual correction prediction mechanism to enhance prediction accuracy for body dimensions. Importantly, the predictions are generated using only a few basic body size parameters from small-batch samples. The KRR regression model is employed for preliminary residual sequence prediction, and the MMPSO component optimizes the SVR parameters to ensure superior correction of nonlinear relations and noise data, thereby yielding more accurate residual correction value predictions. The GRFN hybrid model is superior to SOTA SVR models and increases the root mean square performance by 91.73–97.12% with a remarkably low mean square error of 0.0054 ± 0.07. This outstanding advancement sets the stage for marketable intelligent apparel design tools for the fast fashion industry. [ABSTRACT FROM AUTHOR]

In this study, we address the limitations of current deep learning models in road extraction tasks from remote sensing imagery. We introduce MixerNet-SAGA, a novel deep learning model that incorporates the strengths of U-Net, integrates a ConvMixer block for enhanced feature extraction, and includes a Scaled Attention Gate (SAG) for augmented spatial attention. Experimental validation on the Massachusetts road dataset and the DeepGlobe road dataset demonstrates that MixerNet-SAGA achieves a 10% improvement in precision, 8% in recall, and 12% in IoU compared to leading models such as U-Net, ResNet, and SDUNet. Furthermore, our model excels in computational efficiency, being 20% faster, and has a smaller model size. Notably, MixerNet-SAGA shows exceptional robustness against challenges such as same-spectrum–different-object and different-spectrum–same-object phenomena. Ablation studies further reveal the critical roles of the ConvMixer block and SAG. Despite its strengths, the model's scalability to extremely large datasets remains an area for future investigation. Collectively, MixerNet-SAGA offers an efficient and accurate solution for road extraction in remote sensing imagery and presents significant potential for broader applications. [ABSTRACT FROM AUTHOR]

Mapping tree species distributions in urban areas is significant for managing afforestation plans and pest infestations but can be challenging over large areas. This research compared the classification accuracy of three data sources and three machine learning algorithm combinations. It evaluated the cost benefit of various combinations by mapping the species distribution of the Beijing Plain Afforestation Project with a three-level hierarchical approach. First, vegetation and non-vegetation were mapped. Then, tree crowns were extracted from the vegetation mask. Finally, Decision Tree (DT), Support Vector Machines (SVM), and Random Forest (RF) were applied to the three data sources: Pléiades-1B, WorldView-2, and Sentinel-2. The tree species classification was based on the original bands and spectral and texture indices. Sentinel-2 performed well at the stand level, with an overall accuracy of 89.29%. WorldView-2 was significantly better than Pléiades-1 at the single-tree identification level. The combination of WorldView-2 and SVM achieved the best classification result, with an overall accuracy of 90.91%. This research concludes that the low-resolution Sentinel-2 sensor can accurately map tree areas while performing satisfactorily in classifying pure forests. For mixed forests, on the other hand, WorldView-2 and Pléiades-1, which have higher resolutions, are needed for single-tree scale classification. Compared to Pléiades-1, WorldView-2 produced higher classification accuracy. In addition, this study combines algorithm comparison to provide further reference and guidance for plantation forest classification. [ABSTRACT FROM AUTHOR]

Large nitrogen (N) losses during fertilization in agricultural production may result in energy wastage, soil and water contamination, and potentially influence crop development. Thus, with the help of a 15N-labeled tracer, we carried out a field monitoring analysis of NH3 emissions in a long-term (9-year) conservation tillage agroecosystem of Mollisols in northeast China, in order to determine whether a no-tillage regime and four levels of stover mulching (0%, 33%, 67%, and 100%), combined with urease and nitrification inhibitors, could improve fertilizer utilization efficiency in agricultural systems by reducing ammonia volatilization. Our results showed that in comparison with ridge tillage, no-tillage with stover mulching levels of 33%, 67%, and 100% significantly reduced NH3 emission rates and cumulative volatilization from 159.67 to 130.42 g N ha−1 and 15N-NH3 cumulative volatilization emission by 26% (on average). Furthermore, the application of urease and nitrification inhibitors significantly reduced 15N-NH3 volatilization levels from 1.19 to 0.98 g N ha−1. Our research results demonstrate that a long-term no-tillage regime and straw mulching can significantly reduce NH3 volatilization in fertilizers. Furthermore, when combined with the use of urease and nitrification inhibitors, these practices further enhance the reduction in NH3 volatilization. Although the volatilization of 15N-NH3 is minimally studied in Mollisols, these findings provide a solid foundation for improving fertilizer utilization efficiency, reducing crop production costs and mitigating subsequent environmental pollution. [ABSTRACT FROM AUTHOR]

Paris polyphylla var. yunnanensis is an endangered medicinal plant endemic to China with great economic importance for the pharmaceutical industry. Two significant barriers to its commercial development are the long duration of its seed germination and the frequency of interspecific hybridization. We developed a method for clonal propagation of Paris polyphylla var. yunnanensis and successfully applied it to selected elite wild plants, which could become cultivar candidates based on their biomass production and saponin content. In comparison to the traditional method, somatic embryogenesis produced an average of 63 somatic embryos per gram of callus in just six weeks, saving 12 to 15 months in plantlet production. The produced in vitro plantlets were strong and healthy and 94% survived transplanting to soil. Using this method, four candidate cultivars with diverse morphologies and geographic origins were clonally reproduced from selected elite wild accessions. In comparison to those obtained with the traditional P. polyphylla propagation technique, they accumulated higher biomass and polyphyllin levels in rhizomes plus adventitious roots during a five-year period. In conclusion, somatic embryogenesis-based methods offer an alternate approach for the rapid and scaled-up production of P. polyphylla, as well as opening up species conservation options. [ABSTRACT FROM AUTHOR]

State-of-charge (SoC) estimation is one of the core functions of battery energy management systems. An accurate SoC estimation can guarantee the safe and reliable operation of the batteries system. In order to overcome the practical problems of low accuracy, noise uncertainty, poor robustness, and adaptability in parameter identification and SoC estimation of lithium-ion batteries, this paper proposes a joint estimation method based on the adaptive extended Kalman filter (AEKF) algorithm and the adaptive unscented Kalman filter (AUKF) algorithm in multiple time scales for 18,650 ternary lithium-ion batteries. Based on the slowly varying characteristics of lithium-ion batteries' parameters and the quickly varying characteristics of the SoC parameter, firstly, the AEKF algorithm was used to online identify the parameters of the model of batteries with a macroscopic time scale. Secondly, the identified parameters were applied to the AUKF algorithm for SoC estimation of lithium-ion batteries with a microscopic time scale. Finally, the comparative simulation experiments were implemented, and the experimental results show the proposed joint algorithm has higher accuracy, adaptivity, robustness, and self-correction capability compared with the conventional algorithm. [ABSTRACT FROM AUTHOR]

Road extraction from high-resolution satellite images has become a significant focus in the field of remote sensing image analysis. However, factors such as shadow occlusion and spectral confusion hinder the accuracy and consistency of road extraction in satellite images. To overcome these challenges, this paper presents a multi-scale fusion-based road extraction framework, HRU-Net, which exploits the various scales and resolutions of image features generated during the encoding and decoding processes. First, during the encoding phase, we develop a multi-scale feature fusion module with upsampling capabilities (UMR module) to capture fine details, enhancing shadowed areas and road boundaries. Next, in the decoding phase, we design a multi-feature fusion module (MPF module) to obtain multi-scale spatial information, enabling better differentiation between roads and objects with similar spectral characteristics. The network simultaneously integrates multi-scale feature information during the downsampling process, producing high-resolution feature maps through progressive cross-layer connections, thereby enabling more effective high-resolution prediction tasks. We conduct comparative experiments and quantitative evaluations of the proposed HRU-Net framework against existing algorithms (U-Net, ResNet, DeepLabV3, ResUnet, HRNet) using the Massachusetts Road Dataset. On this basis, this paper selects three network models (U-Net, HRNet, and HRU-Net) to conduct comparative experiments and quantitative evaluations on the DeepGlobe Road Dataset. The experimental results demonstrate that the HRU-Net framework outperforms its counterparts in terms of accuracy and mean intersection over union. In summary, the HRU-Net model proposed in this paper skillfully exploits information from different resolution feature maps, effectively addressing the challenges of discontinuous road extraction and reduced accuracy caused by shadow occlusion and spectral confusion factors. In complex satellite image scenarios, the model accurately extracts comprehensive road regions. [ABSTRACT FROM AUTHOR]

This paper presents a novel unsupervised learning framework for estimating scene depth and camera pose from video sequences, fundamental to many high-level tasks such as 3D reconstruction, visual navigation, and augmented reality. Although existing unsupervised methods have achieved promising results, their performance suffers in challenging scenes such as those with dynamic objects and occluded regions. As a result, multiple mask technologies and geometric consistency constraints are adopted in this research to mitigate their negative impacts. Firstly, multiple mask technologies are used to identify numerous outliers in the scene, which are excluded from the loss computation. In addition, the identified outliers are employed as a supervised signal to train a mask estimation network. The estimated mask is then utilized to preprocess the input to the pose estimation network, mitigating the potential adverse effects of challenging scenes on pose estimation. Furthermore, we propose geometric consistency constraints to reduce the sensitivity of illumination changes, which act as additional supervised signals to train the network. Experimental results on the KITTI dataset demonstrate that our proposed strategies can effectively enhance the model's performance, outperforming other unsupervised methods. [ABSTRACT FROM AUTHOR]

Normalized difference vegetation index (NDVI) time series data, derived from optical images, play a crucial role for crop mapping and growth monitoring. Nevertheless, optical images frequently exhibit spatial and temporal discontinuities due to cloudy and rainy weather conditions. Existing algorithms for reconstructing NDVI time series using multi-source remote sensing data still face several challenges. In this study, we proposed a novel method, an enhanced gap-filling and Whittaker smoothing (EGF-WS), to reconstruct NDVI time series (EGF-NDVI) using Google Earth Engine. In EGF-WS, NDVI calculated from MODIS, Landsat-8, and Sentinel-2 satellites were combined to generate high-resolution and continuous NDVI time series data. The MODIS NDVI was employed as reference data to fill missing pixels in the Sentinel–Landsat NDVI (SL-NDVI) using the gap-filling method. Subsequently, the filled NDVI was smoothed using a Whittaker smoothing filter to reduce residual noise in the SL-NDVI time series. With reference to the all-round performance assessment (APA) metrics, the performance of EGF-WS was compared with the conventional gap-filling and Savitzky–Golay filter approach (GF-SG) in Fusui County of Guangxi Zhuang Autonomous Region. The experimental results have demonstrated that the EGF-WS can capture more accurate spatial details compared with GF-SG. Moreover, EGF-NDVI of Fusui County exhibited a low root mean square error (RMSE) and a high coefficient of determination (R2). In conclusion, EGF-WS holds significant promise in providing NDVI time series images with a spatial resolution of 10 m and a temporal resolution of 8 days, thereby benefiting crop mapping, land use change monitoring, and various ecosystems, among other applications. [ABSTRACT FROM AUTHOR]

Polyurethane microcellular elastomers (PUME) are good at impact protection and energy absorption, and belong to rate sensitive- and strain history-dependent materials. In this study, PUME with different densities of 800 kg/m3, 600 kg/m3 and 400 kg/m3 were prepared, then the compressive responses of PUME in the strain rate range of 0.001 s−1 to 3400 s−1 were systemically investigated. By studying the energy absorption and efficiency diagram of PUME, the compressive properties of materials with different densities under compressive impact load were described, which showed that PUME with a density of 600 kg/m3 had better performance. A visco–hyperelasticity–air constitutive model was established to describe the large deformation response of PUME at high strain rates. The model included three components: hyperelastic part, viscoelastic part and gas pressure part. Quasi-static and dynamic compression tests were used to determine the constitutive relations of seven parameters. The samples with a density of 600 kg/m3 at different strain rates were fitted by MATLAB software, and the constitutive model parameters were obtained. The comparison between the constitutive equation and the experimental results showed that there was a good consistency. The constitutive model can provide data support for simulation analysis and application of PUME as energy absorbing protective facilities. [ABSTRACT FROM AUTHOR]

The promotion of community governance by digital means is an important research topic in developing smart cities. Currently, community governance is mostly based on reactive response, which lacks timely and proactive technical means for emergency monitoring. The easiest way for residents to contact their properties is to call the property call center, and the call centers of many properties store many speech data. However, text sentiment classification in community scenes still faces challenges such as small corpus size, one-sided sentiment feature extraction, and insufficient sentiment classification accuracy. To address such problems, we propose a novel community speech text sentiment classification algorithm combining two-channel features and attention mechanisms to obtain effective emotional information and provide decision support for the emergency management of public emergencies. Firstly, text vectorization based on word position information is proposed, and a SKEP-based community speech–text enhancement model is constructed to obtain the corresponding corpus. Secondly, a dual-channel emotional text feature extraction method that integrates spatial and temporal sequences is proposed to extract diverse emotional features effectively. Finally, an improved cross-entropy loss function suitable for community speech text is proposed for model training, which can achieve sentiment analysis and obtain all aspects of community conditions. The proposed method is conducive to improving community residents' sense of happiness, satisfaction, and fulfillment, enhancing the effectiveness and resilience of urban community governance. [ABSTRACT FROM AUTHOR]

Currently, a significant focus has been established on the privacy protection of multi-dimensional data publishing in various application scenarios, such as scientific research and policy-making. The K-anonymity mechanism based on clustering is the main method of shared-data desensitization, but it will cause problems of inconsistent clustering results and low clustering accuracy. It also cannot defend against several common attacks, such as skewness and similarity attacks at the same time. To defend against these attacks, we propose a K-anonymity privacy protection algorithm for multi-dimensional data against skewness and similarity attacks (KAPP) combined with t-closeness. Firstly, we propose a multi-dimensional sensitive data clustering algorithm based on improved African vultures optimization. More specifically, we improve the initialization, fitness calculation, and solution update strategy of the clustering center. The improved African vultures optimization can provide the optimal solution with various dimensions and achieve highly accurate clustering of the multi-dimensional dataset based on multiple sensitive attributes. It ensures that multi-dimensional data of different clusters are different in sensitive data. After the dataset anonymization, similar sensitive data of the same equivalence class will become less, and it eventually does not satisfy the premise of being theft by skewness and similarity attacks. We also propose an equivalence class partition method based on the sensitive data distribution difference value measurement and t-closeness. Namely, we calculate the sensitive data distribution's difference value of each equivalence class and then combine the equivalence classes with larger difference values. Each equivalence class satisfies t-closeness. This method can ensure that multi-dimensional data of the same equivalence class are different in multiple sensitive attributes, and thus can effectively defend against skewness and similarity attacks. Moreover, we generalize sensitive attributes with significant weight and all quasi-identifier attributes to achieve anonymous protection of the dataset. The experimental results show that KAPP improves clustering accuracy, diversity, and anonymity compared to other similar methods under skewness and similarity attacks. [ABSTRACT FROM AUTHOR]

This paper presents the design and modeling procedure of a wireless power transfer (WPT) system applied to In-wheel-motor (IWM). The system is designed to transmit over 10 kW of power following the physical constraints faced by the IWM applications. The issues of coil misalignment and load change are discussed as particular scenarios in IWM. The finite element model is built for circular, rectangular, and double-D coils, finding that the rectangular coil has the best performance considering the transmission interval and misalignment resistance. The circuit design procedure is presented, and the analysis of the influence of load and mutual inductance change on the WPT system is addressed. Finally, the performance of the design is verified with experiments on a full-scale prototype. It is proved that the WPT system successfully transmits 10 kW of power with a DC–DC efficiency of over 90% under a transmission interval of 140 mm. The output voltage is stable under 40 mm coil misalignment scenarios and over 50% load change. [ABSTRACT FROM AUTHOR]

Pine wilt disease (PWD) has caused huge economic and environmental losses since it invaded China. Although early monitoring is an effective way to control this hazard, the monitoring window for the early stage is hard to identify, and varies in different hosts and environments. We used UAV-based multispectral images of Pinus thunbergii forest in East China to identify the change in the number of infected trees in each month of the growing season. We built classification models to detect different PWD infection stages by testing three machine learning algorithms—random forest, support vector machine, and linear discriminant analysis—and identified the best monitoring period for each infection stage (namely, green attack, early, middle, and late). From the obtained results, the early monitoring window period was determined to be in late July, whereas the monitoring window for middle and late PWD stages ranged from mid-August to early September. We also identified four important vegetation indices to monitor each infection stage. In conclusion, this study demonstrated the effectiveness of using machine learning algorithms to analyze multitemporal multispectral data to establish a window for early monitoring of pine wilt disease infestation. The results could provide a reference for future research and guidance for the control of pine wilt disease. [ABSTRACT FROM AUTHOR]

As a key part of the forest ecosystem, soil microorganisms play extremely important roles in maintaining the ecological environment and the security of water quality in reservoir areas. However, it is not clear whether there are differences in the functional diversity of soil microorganisms in different types of water-conservation forests in reservoir areas, and which factors affect the functional diversity of soil microorganisms. In our study, the Biolog-Eco microplate technique was used to analyze the carbon source metabolic characteristics of soil microbial communities in three typical water-conservation forests and a non-forest land: Pinus massoniana-Quercus variabilis mixed forest (MF), Pinus massoniana forest (PF), Quercus variabilis forest (QF) and non-forest land (CK). The results showed that the average well color development (AWCD), the Shannon diversity index (SDI) and the richness index (S) of the three forest lands was significantly greater than that of the non-forest land (p < 0.05). The mean values of AWCD, SDI and S of the three forests had the same order (QF > PF > MF), but there was no significant difference among different types of forests. The microbial biomass carbon (MBC) and microbial biomass nitrogen (MBN) of QF and PF were higher than those of MF and CK, but the microbial biomass C/N ratio (MBC/MBN) was lower. The variance partitioning analysis (VPA) showed that 86.4% of the variation was explained by plant (community) diversity, soil physical and chemical properties and soil microbial biomass, which independently explained 10.0%, 28.9%, and 14.9% of the variation, respectively. The redundancy analysis (RDA) showed that total phosphorus (TP), microbial biomass carbon (MBC), total nitrogen (TN), number of plant species (Num) and alkali-hydro nitrogen (Wn) were the key factors affecting the functional diversity of soil microorganisms. This study confirmed that forest ecosystem is better than non-forest land in maintaining soil microbial function diversity. Moreover, Quercus variabilis forest may be a better stand type in maintaining the diversity of soil microbial functions in the study area. [ABSTRACT FROM AUTHOR]

The power series polynomial constraining method is proposed in this paper. The dynamical model of the cantilever plate can be established by applying the constraint, which is different from the traditional polynomial. Firstly, the characteristic orthogonal polynomial was used to describe the displacement field of the rectangular plate of which all edges are free. Then the four-sided free plate was equivalent to cantilever plate by power series multiplier constraint method. The characteristic equation of the constructed cantilever plate was obtained by the Rayleigh–Ritz method. Natural frequencies and modal shapes of the plate were obtained by solving the characteristic equation. Next, the proposed method was adopted to establish dynamical model of a pair of solar panels clamped on the central platform symmetrically. The convergence of the proposed method was verified by comparing the calculated results of the cantilever plate with that of the finite element software ANSYS 15.0. The optimum order the power series polynomial was obtained by comparing different results. The analysis of the dynamical characteristics of the cantilever plate and the spacecraft demonstrates the validation of the proposed method. This method can provide a new idea for the plate with local edge constrained. [ABSTRACT FROM AUTHOR]

The mechanical hysteresis loop behavior always limits the applicability of shape memory alloys (SMAs) in mechanical devices requiring high sensitivity, durability and energy conversion efficiency. In this study, through experiments and finite element simulations, we systematically investigated the effects of porosity and pore distribution on the mechanical hysteresis behavior of porous Ti49.2Ni50.8 SMAs. Inspired by atomic crystal structures, some porous SMAs with ordered void distributions were investigated to compare them with SMAs with random pore distributions. Our results show that the hysteresis reduces with increasing porosity in porous SMAs. The designed BCC-type ordered porous SMAs possess a narrower hysteresis loop with less energy dissipation at the same porosity. The gradual and homogenous martensitic-phase transformations are responsible for this characteristic. The present work provides an effective way to design porous SMAs with narrow hysteresis, which is promising in applications for mechanical sensors or actuators. [ABSTRACT FROM AUTHOR]

Flame retardants (FRs) often reduce the mechanical properties of polymer materials, and FR/microcellular polyurethane elastomer (MPUE) composite materials have not been systemically studied. Hence, we conducted this study on FR/MPUE composites by using multiple liquid FRs and/or expandable graphite (EG). Compared with liquid flame retardants, the LOI of an expandable graphite/dimethyl methylphosphonate (EG/DMMP) (3:1) combination was significantly increased (~36.1%), and the vertical combustion grade reached V-0 without a dripping phenomenon. However, the corresponding tensile strength was decreased by 17.5%. With the incorporation of EG alone, although the corresponding LOI was not a match with that of DMMP/EG, there was no droplet phenomenon. In addition, even with 15 wt% of EG, there was no significant decline in the tensile strength. Cone calorimeter test results showed that PHRR, THR, PSPR, and TSR were significantly reduced, compared to the neat MPUE, when the EG content surpassed 10 wt%. The combustion process became more stable and thus the fire risk was highly reduced. It was found that flame retardancy and mechanical properties could be well balanced by adding EG alone. Our proposed strategy for synthesizing FR/MPUE composites with excellent flame retardancy and mechanical properties was easy, effective, low-cost and universal, which could have great practical significance in expanding the potential application fields of MPUEs. [ABSTRACT FROM AUTHOR]

Neuroinflammation plays a significant role in the aging process and the pathophysiology of neurodegenerative diseases, such as Alzheimer's disease. Accordingly, possible therapeutic strategies aimed at anti-inflammatory effects may be beneficial to brain health. Walnut kernels contain large quantities of unsaturated fatty acids, peptides, and phenolic compounds that have anti-inflammatory effects. The long-term intake of walnuts has been found to improve cognitive function and memory in rats and humans. However, the modulatory effect of walnuts on neuroinflammation has received much less attention. This review focuses on the potential influence and main regulating mechanisms of walnuts and their active ingredients on neuroinflammation, including the regulation of microglia activation induced by amyloid β or lipopolysaccharides, inhibition of peripheral inflammation mediated by macrophages, reduction in oxidative stress by decreasing free radical levels and boosting antioxidant defenses, and control of gut microbes to maintain homeostasis. However, the majority of evidence of the beneficial effects of walnuts or their components on neuroinflammation and neurodegeneration comes from experimental work, whereas evidence from clinical studies on the beneficial effects is scarcer and less conclusive. This review aims to provide new insights into the neuroinflammation-regulating mechanisms and natural active ingredients of walnuts and the development of walnut-based functional foods for the alleviation of neurodegenerative diseases. [ABSTRACT FROM AUTHOR]

The point clouds registration is a key step in data processing for the 3D laser scanner to obtain complete information of the object surface, and there are many algorithms. In order to overcome the disadvantages of slow calculation speed and low accuracy of existing point clouds registration algorithms, a fast point clouds registration algorithm based on the improved voxel filter and ISS-USC feature is proposed. Firstly, the improved voxel filter is used for down-sampling to reduce the size of the original point clouds data. Secondly, the intrinsic shape signature (ISS) feature point detection algorithm is used to extra feature points from the down-sampled point clouds data, and then the unique shape context (USC) descriptor is calculated to describe the extracted feature points. Next, the improved random sampling consensus (RANSAC) algorithm is used for coarse registration to obtain the initial position. Finally, the iterative closest point (ICP) algorithm based on KD tree is used for fine registration, which realizes the transform from the point clouds scanned by the 3D laser scanner at different angles to the same coordinate system. Through comparing with other algorithms and the registration experiment of the VGA connector for monitor, the experimental results verify the effectiveness and feasibility of the proposed algorithm, and it has fastest registration speed while maintaining high registration accuracy. [ABSTRACT FROM AUTHOR]

To reduce carbon dioxide emissions, wind power generation is receiving more attention. The conversion of wind energy into electricity requires frequent use of insulated-gate bipolar transistors (IGBTs). Therefore, it is important to improve their reliability. This study proposed a method to predict the junction temperature of IGBTs, which helps to improve their reliability. Limited by the bad working environment, the physical temperature measurement method proposed by previous research is difficult to apply. Therefore, a junction temperature prediction method based on an extreme learning machine optimized by an improved honey badger algorithm was proposed in this study. First, the data of junction temperature were obtained by the electro-heat coupling model method. Then, the accuracy of the proposed method was verified with the data. The results show that the average absolute error of the proposed method is 0.0303 °C, which is 10.62%, 11.14%, 91.67%, and 95.54% lower than that of the extreme learning machine optimized by a honey badger algorithm, extreme learning machine optimized by a seagull optimization algorithm, extreme learning machine, and back propagation neural network model. Therefore, compared with other models, the proposed method in this paper has higher prediction accuracy. [ABSTRACT FROM AUTHOR]

The power grid has the characteristics of a weak grid with an increase in new energy penetration in the power system. Power electronic-based renewable generation brings issues that weaken the entire system, including the loss of inertia. Low inertia and weak damping cause system oscillation. Virtual synchronous generator (VSG) technology gives power electronic equipment inertia-support capability but increases the complexity of control system parameter design. Many researchers have studied the design method of VSG considering the frequency disturbance of the power grid. The traditional VSG parameter design method ignores the influence of source-side and grid-side fluctuations on the transient stability of the system, resulting in the difference between the actual dynamic and static indicators and the design indicators of VSG in a weak grid. This paper analyzes a small-signal model based on power fluctuations and frequency fluctuations and proposes a control parameter design method that combines the influence of grid impedance to ensure the dynamic stability of the system under a weak power grid. The simulation and experimental results verify the correctness and feasibility of the control method. [ABSTRACT FROM AUTHOR]

In order to improve robust operating performance and enhance bus voltage stability, a learning observer-based fault-tolerant control strategy is proposed for the distributed generation in islanded microgrid with sensor faults and uncertain disturbances. Firstly, the output feedback control theory and the linear matrix inequality method are used to design closed-loop controller for the voltage source inverter of distributed generation; secondly, a fault-tolerant model and control structure of the distributed generation in an islanded microgrid with sensor faults is analyzed. By employing the fault output signal conversion filter and proportional derivative type learning observer, the online estimation and real-time compensation of the sensor fault signal are realized. Thirdly, the system synthesis of output feedback control and fault-tolerant control is completed. Finally, the multi-scenario sensor fault scheme simulation experiment verifies that the proposed control strategy has strong sensor fault tolerance and adaptability. [ABSTRACT FROM AUTHOR]

In view of the problems of rapid construction and deformation control of large cross-section tunnels in complex urban environments, the concept of "timely high-strength support" was put forward to form strong support over time and bear the load of surrounding rock in real time, and innovatively developed the spatial reticulated grid bolt-shotcrete support structure (SRGB). The research in this paper focuses on the supporting principle, bearing mechanical properties, snatch simulation test, construction process, and engineering application effects. The components of the SRGB and their respective functions were expounded in detail. The failure modes and deformation characteristics of the tested components were revealed through the indoor loading test, and the performance characteristics of the spatial reticulated grid concrete components with high strength and high rigidity were displayed. With the help of the multi-functional operation trolley, the assembly process test of the spatial reticulated grid was carried out, which verified the feasibility of mechanized construction and the rationality of structural design. Relying on the underground tunnel project of Guangzhou Metro, the spatial reticulated grid structure was successfully used as the initial support of the tunnel for the first time, forming a complete set of mechanized rapid construction technology for a large cross-section tunnel, and constructing a timely high-strength support system suitable for the complex urban environment. The research results provide a new idea and new technology for the mechanized rapid construction and efficient support of large cross-section tunnels. [ABSTRACT FROM AUTHOR]

Poyang Lake is the largest freshwater lake in China. As an important tributary of Poyang Lake, Xinjiang River has an important influence on the water ecology and water resources of the Poyang Lake basin. Based on the hydrological simulation of the SWAT (Soil and Water Assessment Tool) model, the spatiotemporal distribution and evaluation of the blue and green water during the period (1982–2016) in the basin were explored by the Mann–Kendall test, precipitation anomaly percentage, and scenario simulation. It is found that the SWAT model presents a satisfactory performance in runoff simulation of the basin. The multi-year average blue water in the Xinjiang River basin is 1138 mm, and the green water is 829 mm, with a green water coefficient of 0.42. The amount of blue water in wet years is about 1.5 times that in normal years and 2.4 times that in dry years. Compared with the green water, the blue water of the basin is more sensitive to the variations in precipitation. In spatial distribution, the blue and green water in the middle of the basin is obviously more than those in other parts of the basin. During the study period, the blue water in the basin shows a slight decreasing trend, and the green water shows a significant decreasing trend. It is also found that climatic factors have a greater influence on the trend of blue and green water than land use, and the decrease in precipitation is the dominant cause for the trend of blue and green water. [ABSTRACT FROM AUTHOR]

The increased port outages caused by events such as war and public health emergencies have motivated the study of container port shipping network (CPSN) resilience. This paper proposes a resilience framework, which includes prevention, resistance, restoration, adaption, and optimization. The framework is used to analyze the resilience of the CPSN by detecting changing performance of the network indicators before and after the random attack or one of the deliberate attacks. The indicators include the network resilience index, degree distribution, independent path, cluster coefficient, network efficiency and connectivity. The comparative analysis is based on the statistics of China's cases in 2005 and 2017. The results indicate that, first, the resilience of the structure of China's container port shipping network (CCPSN) in 2017 has improved when comparing the 2015 situation. Second, the performance of indicators under betweenness attack (BA) decreases faster than other attacks; the resilience index of deliberate attacks is poorer, when compared with the random attack (RA). Third, network resilience can be improved by protecting and adding hub port nodes. Priority should be given to restoring the hub port nodes during the recovery process. The same network indicator recovers similarly after facing different attacks, while different indicator shows various recovery process. Thus, it is necessary to consider the different recovery performances of network indicators when the damaged CPSN selects recovery mode. [ABSTRACT FROM AUTHOR]

Fault probability rises with an increase in the number of current sensors in multi-phase permanent synchronous motor (PMSM) drives. This paper proposes an improved axis rotation method for fault diagnosis and tolerant control to make the multi-phase PMSM drives against current sensor loss signal and gain faults. This method can effectively diagnose and distinguish faults without selecting a threshold value, and the degree of fault can be further estimated. The proposed method makes current sensor fault diagnosis and tolerant control become an integrated module. The validity and accuracy of the proposed method is verified by different fault diagnoses and tolerant control experiments of a 9 kw nine-phase PMSM drive. [ABSTRACT FROM AUTHOR]

The Haihe Plain is the largest component of the agriculturally vital North China Plain, and it is characterized by serious water shortage and frequent droughts, which lead to crop reduction and have adverse effects on agriculture and ecology. We used daily precipitation data from 1955–2017; the region's spatiotemporal characteristics of drought were analyzed by using the standardized precipitation index (SPI), drought probability, and Mann–Kendall test for seasonal scale including two main crops growth seasons for the region's main crops. Furthermore, a cloud algorithm model was established to analyze the dispersion and instability of the SPI. The annual drought frequency is 28.57%; the SPI for spring has an increasing tendency, while summer shows a significant decreasing trend (p < 0.05); the Haihe Plain has had a tendency towards drought over the last 63 years. The SPI in northwest is the smallest and increases gradually toward the south; the severity of drought in dry years increased from southeast to northwest. The cloud model shows that the SPI randomness of each site decreased significantly and tended to be stable and uniform. The deterministic and stable SPI of each station is stronger in dry years, and the randomness and instability are stronger in wet years. The inter-annual differences of the characteristic values of the SPI cloud model are bigger than the differences among sites, and the inter-annual randomness and inhomogeneity of the SPI are higher. [ABSTRACT FROM AUTHOR]

Beijing's One Million Mu Plain Afforestation Project involves planting large areas with the exotic North American tree species Fraxinus pennsylvanica Marsh (ash). As an exotic tree species, ash is very vulnerable to infestations by the emerald ash borer (EAB), a native Chinese wood borer pest. In the early stage of an EAB infestation, attacked trees show no obvious sign. Once the stand has reached the late damage stage, death occurs rapidly. Therefore, there is a need for efficient early detection methods of EAB stress over large areas. The combination of unmanned aerial vehicle (UAV)-based hyperspectral imaging (HI) with light detection and ranging (LiDAR) is a promising practical approach for monitoring insect disturbance. In this study, we identified the most useful narrow-band spectral HI data and 3D LiDAR data for the early detection of EAB stress in ash. UAV-HI data of different infested stages (healthy, light, moderate and severe) of EAB in the 400–1000 nm range were collected from ash canopies and were processed by Partial Least Squares–Variable Importance in Projection (PLS-VIP) to identify the maximally sensitive bands. Band R678 nm had the highest PLS-VIP scores and the most robust classification ability. We combined this band with band R776 nm to develop an innovative normalized difference vegetation index (NDVI(776,678)) to estimate EAB stress. LiDAR data were used to segment individual trees and supplement the HI data. The new NDVI(776,678) identified different stages of EAB stress, with a producer's accuracy of 90% for healthy trees, 76.25% for light infestation, 58.33% for moderate infestation, and 100% for severe infestation, with an overall accuracy of 82.90% when combined with UAV-HI and LiDAR. [ABSTRACT FROM AUTHOR]

To solve the technical problems of slow construction progress, low mechanization and high risk of shallow buried large cross-section tunnels in a complex urban environment, a series of spatial reticulated shell (SRS) support structures are developed in this paper. Moreover, the equipment of a multifunctional operation trolley is developed to install the SRS arch, and the construction technology system of the prefabricated SRS structure is proposed for a large cross-section tunnel. Therefore, the deformation characteristics of the end-plate joint component and jointless component are clarified by laboratory experiments. The construction mechanics' simulation of the SRS arch is performed to obtain the tunnel deformation and structure stress based on the tunnel project of Panyu Square Station of Guangzhou Metro. A field application of the prefabricated SRS arch is carried out to realize the mechanized construction operation. The obtained results reveal that the end-plate joint component has better ductility, and its ultimate bearing capacity is basically consistent with the jointless component. The SRS arch can effectively control the deformation of the surrounding rock, improve the stress state of the structure, and reduce the plastic zone of shotcrete by numerical simulations. The overall stress of the SRS arch by field measurement represents the characteristics of "bigger on the upside and smaller on the downside" and "uneven symmetry". Additionally, the successful application of the prefabricated SRS arch provides a scientific reference for mechanized construction of large cross-section tunnels. [ABSTRACT FROM AUTHOR]

This paper develops the model of the joint phantom track deception and the joint techniques of time-difference of arrival (TDOA) and frequency-difference of arrival (FDOA) localization to deceive air defense radar networks under the condition that an unmanned aerial vehicle (UAV) swarm has no prior knowledge of the radars' precise locations, and related performance experiment and analysis are presented to demonstrate the effectiveness of the proposed method and to clarify the influence factors of phantom track deception. The main contributions of this paper are as follows. Firstly, the model of phantom track deception against a radar network by UAV swarm without prior knowledge of the radars' positions are established. Secondly, TDOA/FDOA are adapted to locate networked enemy radars using UAV swarm, where the Fisher information matrix (FIM) is derived to evaluate the estimation accuracy. Thirdly, the uncertainty analysis consisting of radar location error and UAV position error is deduced. With these efforts, the integrated capability of sensing and jamming is realized. Moreover, the same source testing using space resolution cell (SRC) from the perspective of a radar network is executed to provide guidance for phantom track design. Finally, performance experiment and analysis are given to verify the theoretical analysis with simulation results. [ABSTRACT FROM AUTHOR]

Cancer prognosis is an essential goal for early diagnosis, biomarker selection, and medical therapy. In the past decade, deep learning has successfully solved a variety of biomedical problems. However, due to the high dimensional limitation of human cancer transcriptome data and the small number of training samples, there is still no mature deep learning-based survival analysis model that can completely solve problems in the training process like overfitting and accurate prognosis. Given these problems, we introduced a novel framework called SAVAE-Cox for survival analysis of high-dimensional transcriptome data. This model adopts a novel attention mechanism and takes full advantage of the adversarial transfer learning strategy. We trained the model on 16 types of TCGA cancer RNA-seq data sets. Experiments show that our module outperformed state-of-the-art survival analysis models such as the Cox proportional hazard model (Cox-ph), Cox-lasso, Cox-ridge, Cox-nnet, and VAECox on the concordance index. In addition, we carry out some feature analysis experiments. Based on the experimental results, we concluded that our model is helpful for revealing cancer-related genes and biological functions. [ABSTRACT FROM AUTHOR]

Prediction on drug–target interaction has always been a crucial link for drug discovery and repositioning, which have witnessed tremendous progress in recent years. Despite many efforts made, the existing representation learning or feature generation approaches of both drugs and proteins remain complicated as well as in high dimension. In addition, it is difficult for current methods to extract local important residues from sequence information while remaining focused on global structure. At the same time, massive data is not always easily accessible, which makes model learning from small datasets imminent. As a result, we propose an end-to-end learning model with SUPD and SUDD methods to encode drugs and proteins, which not only leave out the complicated feature extraction process but also greatly reduce the dimension of the embedding matrix. Meanwhile, we use a multi-view strategy with a transformer to extract local important residues of proteins for better representation learning. Finally, we evaluate our model on the BindingDB dataset in comparisons with different state-of-the-art models from comprehensive indicators. In results of 100% BindingDB, our AUC, AUPR, ACC, and F1-score reached 90.9%, 89.8%, 84.2%, and 84.3% respectively, which successively exceed the average values of other models by 2.2%, 2.3%, 2.6%, and 2.6%. Moreover, our model also generally surpasses their performance on 30% and 50% BindingDB datasets. [ABSTRACT FROM AUTHOR]

Featured Application: This work presents a supervised reinforcement learning (SRL)-based framework for longitudinal vehicle dynamics’ control of the cooperative adaptive cruise control (CACC) system. By incorporating a supervised network trained by real driving data into the actor-critic framework, the training success rate is improved, and the driver characteristics can be learned by the actor to achieve a human-like CACC controller. This paper presents a supervised reinforcement learning (SRL)-based framework for longitudinal vehicle dynamics control of cooperative adaptive cruise control (CACC) system. A supervisor network trained by real driving data is incorporated into the actor-critic reinforcement learning approach. In the SRL training process, the actor and critic network are updated under the guidance of the supervisor and the gain scheduler. As a result, the training success rate is improved, and the driver characteristics can be learned by the actor to achieve a human-like CACC controller. The SRL-based control policy is compared with a linear controller in typical driving situations through simulation, and the control policies trained by drivers with different driving styles are compared using a real driving cycle. Furthermore, the proposed control strategy is demonstrated by a real vehicle-following experiment with different time headways. The simulation and experimental results not only validate the effectiveness and adaptability of the SRL-based CACC system, but also show that it can provide natural following performance like human driving. [ABSTRACT FROM AUTHOR]

TiC-reinforced AZ91D magnesium alloy composites were synthesized through the in situ reaction between an AZ91D melt and Ti-C-Al preforms. The microstructural evolution characteristics and phase transformation were investigated at different melt reaction temperatures (1013, 1033, and 1053 K), with the aim of understanding the in situ formation mechanism of TiC particles from thermodynamic and kinetic perspectives. The results showed that the temperature played a critical role in determining the formation and morphology of TiC. Initially, only the Al3Ti phase was formed through the reaction between Ti and Al when the temperature was 1013 K. With the increase in the melt temperature, the A13Ti's thermodynamic stability decreased, and dissolution and precipitation reactions occurred at higher temperatures (1033 and 1053 K, respectively), contributing to the formation of TiC particles. The formation of the TiC phase was attributed to two factors: Firstly, A13Ti as an intermediate product reacted with carbon and formed TiC with increasing temperature. Secondly, the in situ TiC reaction was promoted due to the increased reaction-driving force provided by the increasing temperature. [ABSTRACT FROM AUTHOR]

With the expansion of pure forest planting area and the increase in the number of rotations used, soil activity and plant productivity have significantly reduced. The functional diversity of soil microorganisms plays a vital role in forest health and the long-term maintenance of productivity. Though the optimization of forest cutting and regeneration methodologies is necessary to improve the functional diversity of soil microorganisms, the effects of harvest residual treatment on the functional diversity of soil microorganisms remain unclear. During the period 2018–2020, we designed four harvest residual treatments—reference (RF), residual burning (RB), crushing and mulching (MT), and no residuals (NR)—to determine soil physical and chemical properties. We also used microbial biomass (MB) to evaluate the diversity in carbon source metabolism of soil microorganisms through Biolog microplate technology, and discussed the response mechanism of microbial functional diversity to the different forest cutting and regeneration methodologies used in Chinese fir plantations. The results indicated that RB significantly increased the carbon metabolic capacity of the microbial community, the community richness, and its dominance compared to RF, MT, and NR; however, they also showed that it decreased the uniformity of the soil microbial community. NR showed a poor carbon utilization capacity for microorganisms compared to RF and MT, while MT significantly increased the utilization capacity of carbohydrate and amino acid carbon compared with RF. Soil nutrients were the main driving factors of soil microbial carbon metabolic activity, and the different responses of microbial functional diversity to various forest cutting and regeneration methodologies were mainly due to the variation in the nutrient inputs of harvest residues. This study provides a practical basis for enhancing the functional diversity of soil microorganisms in plantations through the management of harvest residues. [ABSTRACT FROM AUTHOR]

Scientific evaluation of crop water use efficiency is of great significance for ascertaining water-saving potential and realizing efficient utilization of water resources. In this paper, we calculated the water footprint of crop growth, pollution water footprint and production water footprint of 14 cities in Liaoning Province, China, by using the water footprint theory, established the crop water use efficiency model of stochastic frontier distance function, and analyzed the spatial-temporal variation characteristics of crop water use efficiency (WUE), ecological WUE and production WUE. Results show that: (1) the average water footprint of crop growth was 1.714 × 109 m3, the ecological water footprint of crop was 6.26 × 108 m3, and the water footprint of crop production was 2.34 × 109 m3 from 2001 to 2017 for the whole province. (2) the WUE of crop growth was 0.821, the crop ecological WUE was 0.845 and crop production was 0.865, respectively. We concluded that Liaoning province can save 17.9% of crop consumption water, equivalent to 8.38 × 108 m3, 15.5% of ecological water, equivalent to 7.25 × 108 m3 and 13.5% of production water, equivalent to 6.32 × 108 m3 by strengthening the popularization of agricultural high-efficiency water use technology and improving the level of policy management. This research provides a basic support for the evaluation of crop water-saving potential with the stochastic frontier approach in other regions. [ABSTRACT FROM AUTHOR]