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Ion-adsorbing rare earth tailings (IRETs) contain a large amount of clay minerals, which are a potential source of silicon and aluminum for the preparation of zeolite materials. The complexity of the tailings’ composition and the impurity composition are the main difficulties in the controllable preparation of zeolite. Herein, IRETs were treated by classification activation technology for the preparation of IRET-ZEO, which was used for the removal of heavy metal Pb2+ in water. A new method of resource utilization of ion-type rare earth tailings is realized by “treating waste with waste”. The results showed that the IRETs were classified and then thermally activated, and the optimal activation parameter was calcination at 850 °C for 1 h. The optimal NaOH concentration used in the crystallization process was 5 mol/L, with a crystallization time of 3 h and a crystallization temperature of 85 °C, and the crystallization product was zeolite A. The removal rate of the Pb2+ solution with an initial concentration of 100 mg/L was as high as 96.7% in an acidic solution with a pH value from 2 to 5.5. In particular, when the solution pH was higher than 4.2, the adsorption rate of Pb2+ was close to 100%. The IRET-ZEO showed a fast adsorption rate (5 min to reach adsorption equilibrium), a large adsorption capacity (378.35 mg/g), excellent acid resistance, and selectivity and regenerability for Pb2+. This work provides a new strategy for the green resource utilization of IRETs and the treatment of lead-containing wastewater.

Developing environmentally friendly bulk materials capable of easily and thoroughly removing trace amounts of dye pollutants from water to rapidly obtain clean water has always been a goal pursued by researchers. Herein, a green material with a 3D architecture and with strong underwater rebounding and fatigue resistance ability was prepared by means of the assembly of biopolymer chitosan (CS) and natural caraganate fibers (CKFs) under freezing conditions. The CKFs can randomly and uniformly distribute in the lamellar structure formed during the freezing process of CS and CKFs, playing a role similar to that of “steel bars” in concrete, thus providing longitudinal support for the 3D-architecture material. The 2D layers formed by CS and CKFs as the main basic units can provide the material with a higher strength. The 3D-architecture material can bear the compressive force of a weight underwater for multiple cycles, meeting the requirements for water purification. The underwater compression test shows that the 3D-architecture material can quickly rebound to its original shape after removing the stress. This 3D-architecture material can be used to purify dye-containing water. When its dosage is 3 g/L, the material can remove 99.65% of the Congo Red (CR) in a 50 mg/L dye solution. The adsorption performance of the 3D architecture adsorbent for CR removal in actual water samples (i.e., tap water, seawater) is superior than that of commercial activated carbon. Due to its porous block characteristics, this material can be used for the continuous and efficient treatment of wastewater containing trace amounts of CR dye to obtain pure clean water, meaning that it has great potential for the effective purification of dye wastewater.

To meet the challenges of energy sustainability, the integrated energy system (IES) has become a key component in promoting the development of innovative energy systems. Accurate and reliable multivariate load prediction is a prerequisite for IES optimal scheduling and steady running, but the uncertainty of load fluctuation and many influencing factors increase the difficulty of forecasting. Therefore, this article puts forward a multi-energy load prediction approach of the IES, which combines the fennec fox optimization algorithm (FFA) and hybrid kernel extreme learning machine. Firstly, the comprehensive weight method is used to combine the entropy weight method and Pearson correlation coefficient, fully considering the information content and correlation, selecting the key factors affecting the prediction, and ensuring that the input features can effectively modify the prediction results. Secondly, the coupling relationship between the multi-energy load is learned and predicted using the hybrid kernel extreme learning machine. At the same time, the FFA is used for parameter optimization, which reduces the randomness of parameter setting. Finally, the approach is utilized for the measured data at Arizona State University to verify its effectiveness in multi-energy load forecasting. The results indicate that the mean absolute error (MAE) of the proposed method is 0.0959, 0.3103 and 0.0443, respectively. The root mean square error (RMSE) is 0.1378, 0.3848 and 0.0578, respectively. The weighted mean absolute percentage error (WMAPE) is only 1.915%. Compared to other models, this model has a higher accuracy, with the maximum reductions on MAE, RMSE and WMAPE of 0.3833, 0.491 and 2.8138%, respectively.

Connected and automated vehicles (CAVs) represent a significant development in the transport industry owing to their intelligent and interconnected features. Potential field theory has been extensively used to model CAV driving behaviour owing to its objectivity, universality, and measurability. However, existing car-following models do not consider the impact of time delays and the influence of information from multiple vehicles ahead and behind. This paper focuses on the driving-safety risks associated with CAVs, aiming to enhance vehicle safety and reliability during travelling. We developed a multi-vehicle car-following model based on safety potential fields (MIDM-SPF), taking into account the characteristics of multi-vehicle connected information and time delays. To enhance the model’s precision, real-world data from urban roads were employed, alongside an improved optimisation algorithm to fine-tune the car-following model. The simulation experiment revealed that MIDM-SPF significantly reduces stop-and-go traffic, thereby improving traffic flow stability in urban areas. Additionally, we validated the stability of our model under varying market penetration rates in large-scale mixed traffic. Our findings indicate that increasing the CAV proportion improves the stability of mixed traffic flows, which has important implications for alleviating traffic congestion and guiding the large-scale implementation of autonomous driving in the future.

Power system control is commonly based on linear controllers, where linear controllers are designed using a linearized model of the system at a specific operating point. However, when the system’s operating point is changed, the dynamic characteristics of the system shift significantly. At this point, linear controllers often fail to meet system stability requirements. Furthermore, the range of state variables in the power system is limited by the objective conditions. In addition, the power system has high-precision constraints on the deviation of the load frequency and so on. Therefore, it is worth designing a finite-time controller that satisfies the prescribed performance and full-state constraints based on the nonlinear model of the power systems. Firstly, the prescribed performance is incorporated into the barrier Lyapunov function to ensure that the tracking error is within the desired accuracy. Then, the tracking strategy is designed based on backstepping and incorporating a first-order filter to ensure that the controlled system’s signals and tracking errors remain bounded in finite time. Finally, two simulations are given to illustrate the effectiveness of the proposed control scheme, confirming that all states keep within the predefined range.

With the rapid development of new-generation cyber–physical system (CPS) technologies, the smart operation and management of the broad bean harvesting system are enabled. This paper proposed a smart broad bean harvesting system (SBHS) and a self-adaptive control method based on CPS technologies. Firstly, the overall architecture of the SBHS is designed, which consists of four main components, namely optimal intelligent perception environment configuration, digital twin model construction, virtual simulation and real-time optimization, self-adaptive adjustment and control. Then, three key enabling technologies are discussed in detail, namely, intelligent perception environment configuration for the SBHS, digital twin model construction for the SBHS, colored Petri net (CPN)-based self-adaptive analysis and control of the harvesting system. Lastly, a proof-of-concept experiment based on a cooperative company is presented to illustrate the main work logic and advantage of the proposed SBHS. After the edge–cloud cooperative intelligent harvesting environment is configured, the CPN model for the workflow of the SBHS is created to analyze and optimize the harvesting processes. In addition, a management and control platform are developed to further illustrate the implementation of the proposed SBHS and the self-adaptive control method.

Borehole strain gauges play a crucial role in geophysical, seismological, and crustal dynamics studies. While existing borehole strain gauges are proficient in measuring horizontal strains within vertical boreholes, their effectiveness in capturing vertical and oblique strains is limited due to technical constraints arising from the cylindrical probe’s characteristics. However, the accurate measurement of three-dimensional strain is essential for a comprehensive understanding of crustal tectonics, dynamics, and geophysics, particularly considering the diverse geological structures and force sources within the crustal medium. In this study, we present a novel approach to address this challenge by enhancing an existing horizontal-component borehole strain gauge with a bellows structure and line strain measurement technology to enable vertical and borehole oblique strain measurements. Integrating these enhancements with horizontal strain measurement capabilities enables comprehensive three-dimensional borehole strain measurements within the same hole section. The system was deployed and tested at the Gongxian seismic station in Sichuan Province. Clear observations of solid tides were recorded across horizontal, oblique, and vertical measurement units, with the tidal morphology and amplitude being consistent with the theoretical calculations. The achieved measurement sensitivity of 10-10 meets the requirements for borehole strain measurement, enabling the characterization of three-dimensional strain states within boreholes through association methods.

The pigeon robot has attracted significant attention in the field of animal robotics thanks to its outstanding mobility and adaptive capability in complex environments. However, research on pigeon robots is currently facing bottlenecks, and achieving fine control over the motion behavior of pigeon robots through brain–machine interfaces remains challenging. Here, we systematically quantify the relationship between electrical stimulation and stimulus-induced motion behaviors, and provide an analytical method to demonstrate the effectiveness of pigeon robots based on electrical stimulation. In this study, we investigated the influence of gradient voltage intensity (1.2–3.0 V) on the indoor steering motion control of pigeon robots. Additionally, we discussed the response time of electrical stimulation and the effective period of the brain–machine interface. The results indicate that pigeon robots typically exhibit noticeable behavioral responses at a 2.0 V voltage stimulus. Increasing the stimulation intensity significantly controls the steering angle and turning radius (p < 0.05), enabling precise control of pigeon robot steering motion through stimulation intensity regulation. When the threshold voltage is reached, the average response time of a pigeon robot to the electrical stimulation is 220 ms. This study quantifies the role of each stimulation parameter in controlling pigeon robot steering behavior, providing valuable reference information for the precise steering control of pigeon robots. Based on these findings, we offer a solution for achieving precise control of pigeon robot steering motion and contribute to solving the problem of encoding complex trajectory motion in pigeon robots.

Renewable, green, and safe natural biopolymer-derived materials are highly desired for the purification of pollutants, but significantly improving their performance without the introduction of additional harmful chemicals remains a huge challenge. Based on the concept of “structure optimization design”, environment-friendly composite beads (named SA/PASP/RE) with excellent adsorption performance and recyclability were rationally constructed through a green ionic crosslinking route, using the completely green biopolymer sodium alginate (SA), sodium salt of polyaspartic acid (PASP), and the natural nanoclay rectorite (RE) as starting materials. The nano-layered RE was embedded in the polymer matrix to prevent the polymer chain from becoming over-entangled so that more adsorption sites inside the polymer network were exposed, which effectively improved the mass transfer efficiency of the adsorbent and the removal rate of contaminants. The composite beads embedded with 0.6% RE showed high adsorption capacities of 211.78, 197.13, and 195.69 mg/g for Pb(II) and 643.00, 577.80, and 567.10 mg/g for methylene blue (MB) in Yellow River water, Yangtze River water, and tap water, respectively. And the beads embedded with 43% RE could efficiently adsorb Pb(II) and MB with high capacities of 187.78 mg/g and 586.46 mg/g, respectively. This study provides a new route to design and develop a green, cost-effective, and efficient adsorbent for the decontamination of wastewater.

In this article, we consider the following fractional (p,q)-Laplacian problem (−Δ)ps1u+(−Δ)qs2u+V(x)(|u|p−2u+|u|q−2u)=f(u)+λ|u|r−2u, where x∈RN, (−Δ)ps1 is the fractional p-Laplacian operator ((−Δ)qs2 is similar), 00 is small.

Cefotaxime (CTX) is an easily detectable antibiotic pollutant in the water environment, but little is known about its toxic effects on aquatic invertebrates, especially on the intestine. Here, we determined the oxidative stress conditions of A. sinica under CTX exposure with five concentrations (0, 0.001, 0.01, 0.1 and 1 mg/L) for 14 days. After that, we focused on changes in intestinal tissue morphology and gut microbiota in A. sinica caused by CTX exposure at 0.01 mg/L. We found malondialdehyde (MDA) was elevated in CTX treatment groups, suggesting the obvious antibiotic-induced oxidative stress. We also found CTX exposure at 0.01 mg/L decreased the villus height and muscularis thickness in gut tissue. The 16S rRNA gene analysis indicated that CTX exposure reshaped the gut microbiota diversity and community composition. Proteobacteria, Actinobacteriota and Bacteroidota were the most widely represented phyla in A. sinica gut. The exposure to CTX led to the absence of Verrucomicrobia in dominant phyla and an increase in Bacteroidota abundance. At the genus level, eleven genera with an abundance greater than 0.1% exhibited statistically significant differences among groups. Furthermore, changes in gut microbiota composition were accompanied by modifications in gut microbiota functions, with an up-regulation in amino acid and drug metabolism functions and a down-regulation in xenobiotic biodegradation and lipid metabolism-related functions under CTX exposure. Overall, our study enhances our understanding of the intestinal damage and microbiota disorder caused by the cefotaxime pollutant in aquatic invertebrates, which would provide guidance for healthy aquaculture.

The solution of equilibrium positions is a critical component in the calculation of the dynamic characteristic coefficients of aerostatic bearings. The movement of the rotor in one direction leads to bidirectional variations in the air film force, resulting in low efficiency when using conventional calculation methods. It can even lead to iterative divergence if the initial value is improperly selected. This study concentrates on the orifice throttling aerostatic bearings and proposes a novel method called the bivariate interpolation method (BIM) to calculate the equilibrium position. The equilibrium equation for the rotor under the combined influence of air film forces, gravity, and external loads is established. A calculation program based on the finite difference method is developed to determine the equilibrium position. The process of solving the equilibrium position and the convergence is compared with the secant method and the search method. Furthermore, the variation trend of the equilibrium position and stiffness when the external loads changes are studied based on the BIM. Finally, the correctness of the BIM to solve the equilibrium position is proved by comparing it with the experiment results. The calculation results indicate that the BIM successfully resolves the problem of initial value selection and exhibits superior computational efficiency and accuracy. The equilibrium position initially moves away from the direction of the external load as the load increases, and then this gradually approaches the load direction. The main stiffness increases with increases in the external load, while the variation in cross stiffness depends on the direction of the external load.

Artemia is a genus of halophilic zooplanktons comprising bisexual and parthenogenetic forms, which is an important model for investigating adaption to hypersaline ecosystems. The genus Artemia in China comprises four species: A. sinica, A. tibetiana, A. franciscana and A. parthenogenetica. To investigate the evolutionary relationship of bisexual and parthenogenetic Artemia in China, we analyzed the morphometrics and phylogenetics among twenty-two geographical populations in China. We found significant morphological differentiation across different species and strains of Artemia in China, which exhibited a high level of intra-population variation. We also found overlaps in morphological characteristics between populations, which may raise challenges for the classification of Artemia species using traditional morphological methods. A. franciscana, which originated from various regions in America, was generally distributed along the Chinese coastlines through multiple human introductions. Additionally, native Asian clades split into Western and Eastern Lineages during the late Miocene due to the Himalayan orogeny. Within the Western Lineage, A. tibetiana can be grouped into three taxon units: A. tibeitiana, A. sorgeloosi and A. urmiana. We also found that the distribution and genetic structure of A. sinica were influenced by climate oscillations during the Pleistocene, which might play a pivotal role in driving the formation of parthenogenetic strains in the Eastern Lineage. Overall, our study provides new insight into invertebrate evolution under geographical and climatic impacts in hypersaline environments.

Flexible turning behavior endows Homing Pigeons (Columba livia domestica) with high adaptability and intelligence in long-distance flight, foraging, hazard avoidance, and social interactions. The present study recorded the activity pattern of their local field potential (LFP) oscillations and explored the relationship between different bands of oscillations and turning behaviors in the formatio reticularis medialis mesencephali (FRM). The results showed that the C (13–60 Hz) and D (61–130 Hz) bands derived from FRM nuclei oscillated significantly in active turning, while the D and E (131–200 Hz) bands oscillated significantly in passive turning. Additionally, compared with lower-frequency stimulation (40 Hz and 60 Hz), 80 Hz stimulation can effectively activate the turning function of FRM nuclei. Electrical stimulation elicited stronger oscillations of neural activity, which strengthened the pigeons’ turning locomotion willingness, showing an enhanced neural activation effect. These findings suggest that different band oscillations play different roles in the turning behavior; in particular, higher-frequency oscillations (D and E bands) enhance the turning behavior. These findings will help us decode the complex relationship between bird brains and behaviors and are expected to facilitate the development of neuromodulation techniques for animal robotics.

Nano-bubble irrigation, as a new irrigation technology, can deliver fertilizer-mixed oxygen-enriched water to the root zone of crops, representing a new means for increasing crop yield and carbon sequestration and emission reduction. To systematically analyze the effects of nano-bubble irrigation on crop yield, soil aeration, and soil greenhouse gas (GHG) emissions, as well as evaluating its contribution to the net greenhouse warming potential (NGWP) in greenhouse agriculture, this study was conducted in greenhouse facilities in Zhengzhou, China and focused on tomato plants. A 2-factor, 2-level, completely randomized trial of nitrogen application (low N1: 120 kg/hm2 and normal N2: 180 kg/hm2), conventional irrigation, and nano-bubble irrigation (C: 5 ppm and A: 15 ppm) was conducted. Compared with conventional irrigation, crop yield increased by 18.94% and 16.36% (p < 0.05), CO2 emission by 10.72% and 5.71% (p < 0.05), N2O emission by 29.76% and 35.74% (p < 0.05), and CH4 uptake by 300.67% and 327.67% (p < 0.05) under nano-bubble irrigation. The nano-bubble irrigation increased the crop yield, thus significantly improving the NGWP sink for greenhouse gases. The low-nitrogen and regular-nitrogen treatments increased NGWP by 22.69% and 14.52%, respectively (p < 0.05). This suggests that nano-bubble irrigation can significantly improve soil aeration, increase tomato yield and biomass, and significantly improve crop carbon sequestration. In the future, nano-bubble irrigation can be used along with soil amendments to achieve a more efficient increase in yield and enhance the ability of farmland to sequester carbon and reduce emissions.

Microsoft has implemented several measures to defend against macro viruses, including the use of the Antimalware Scan Interface (AMSI) and automatic macro blocking. Nevertheless, evidence shows that threat actors have found ways to bypass these mechanisms. As a result, phishing emails continue to utilize malicious macros as their primary attack method. In this paper, we analyze 77 obfuscation features from the attacker’s perspective and extract 46 suspicious keywords in macros. We first combine the aforementioned two types of features to train machine learning models on a public dataset. Then, we conduct the same experiment on a self-constructed dataset consisting of newly discovered samples, in order to verify if our proposed method can identify previously unseen malicious macros. Experimental results demonstrate that, compared to existing methods, our proposed method has a higher detection rate and better consistency. Furthermore, ensemble multi-classifiers with distinct feature selection can further enhance the detection performance.

To solve the problems of path planning and dynamic obstacle avoidance for an unmanned surface vehicle (USV) in a locally observable non-dynamic ocean environment, a visual perception and decision-making method based on deep reinforcement learning is proposed. This method replaces the full connection layer in the Proximal Policy Optimization (PPO) neural network structure with a convolutional neural network (CNN). In this way, the degree of memorization and forgetting of sample information is controlled. Moreover, this method accumulates reward models faster by preferentially learning samples with high reward values. From the USV-centered radar perception input of the local environment, the output of the action is realized through an end-to-end learning model, and the environment perception and decision are formed as a closed loop. Thus, the proposed algorithm has good adaptability in different marine environments. The simulation results show that, compared with the PPO algorithm, Soft Actor–Critic (SAC) algorithm, and Deep Q Network (DQN) algorithm, the proposed algorithm can accelerate the model convergence speed and improve the path planning performances in partly or fully unknown ocean fields.

Due to the early formation of rolling bearing fault characteristics in an environment with strong background noise, the single use of the time-varying filtering empirical mode decomposition (TVFEMD) method is not effective for the extraction of fault characteristics. To solve this problem, a new method for early fault detection of rolling bearings is proposed, which combines multipoint optimal minimum entropy deconvolution adjusted (MOMEDA) with parameter optimization and TVFEMD. Firstly, a new weighted envelope spectrum kurtosis index is constructed using the correlation coefficient and envelope spectrum kurtosis, which is used to identify the effective component and noise component of the bearing fault signal decomposed by TVFEMD, and the intrinsic mode function (IMF) containing rich fault information is selected for reconstruction. Then, a new synthetic impact index (SII) is constructed by combining the maximum value of the autocorrelation function and the kurtosis of the envelope spectrum. The SII index is used as the fitness function of the gray wolf optimization algorithm to optimize the fault period, T, and the filter length, L, of MOMDEA. The signal reconstructed by TVF-EMD undergoes adaptive filtering using the MOMEDA method after parameter optimization. Finally, an envelope spectrum analysis is performed on the signal filtered by the adaptive MOMEDA method to extract fault feature information. The experimental results of the simulated and measured signals indicate that this method can effectively extract early fault features of rolling bearings and has good reliability. Compared to the classical FSK, MCKD, and TVFEMD-MOMEDA methods, the first-order correlated kurtosis (FCK) and fault feature coefficient (FFC) of the filtered signal obtained using the proposed method are the largest, while the sample entropy (SE) and envelope spectrum entropy (ESE) are the smallest.

The complete removal of low concentration organic pollutants from wastewater to obtain clean water has always been a highly desired but challenging issue. In response to this, we proposed a new strategy to fabricate a carbon-in-silicate nanohybrid composite by recycling dye-loaded layered clay adsorbent and converting them to new heterogeneous carbon-in-silicate nanocomposite through an associated calcination-hydrothermal activation process. It has been confirmed that most of the dye molecules were present in waste rectorite adsorbent using an intercalation mode, which can be in situ converted to carbon in the confined interlayer spacing of rectorite. The further hydrothermal activation process may further improve the pore structure and increase surface active sites. As expected, the optimal composite shows extremely high removal rates of 99.6% and 99.5% for Methylene blue (MB) and Basic Red 14 (BR) at low concentrations (25 mg/L), respectively. In addition, the composite adsorbent also shows high removal capacity for single-component and two-component dyes in deionized water and actual water (i.e., Yellow River water, Yangtze River water, and seawater) with a removal rate higher than 99%. The adsorbent has good reusability, and the adsorption efficiency is still above 93% after five regeneration cycles. The waste clay adsorbent-derived composite adsorbent can be used as an inexpensive material for the decontamination of dyed wastewater.

This paper presents a high-precision component-type vertical pendulum tiltmeter based on an FPGA (Field Programmable Gate Array) that improves the utility and reliability of geophysical field tilt observation instruments. The system is designed for rapid deployment and offers flexible and efficient adaptability. It comprises a pendulum body, a triangular platform, a locking motor and sealing cover, a ratiometric measurement bridge, a high-speed ADC, and an FPGA embedded system. The pendulum body is a plumb-bob-type single-suspension wire vertical pendulum capable of measuring ground tilt in two orthogonal directions simultaneously. It is installed on a triangular platform, sealed as a whole, and equipped with a locking motor to withstand a free-fall impact of 2 m. The system utilizes a differential capacitance ratio bridge in the measurement circuit, replacing analog circuits with high-speed AD sampling and FPGA digital signal processing technology. This approach reduces hardware expenses and interferences from active devices. The system also features online compilation functionality for flexible measurement parameter settings, high reliability, ease of use, and rapid deployment without the need for professional technical personnel. The proposed tiltmeter holds significant importance for further research in geophysics.

Auto-focus technology plays an important role in the Micro-LED wafer defects detection system. How to accurately measure the defocus amount and the defocus direction of the Micro-LED wafer sample in a large linear range is one of the keys to realizing wafer defects detection. In this paper, a large range and high-precision auto-focus method based on a rectangular amplitude mask is proposed. A rectangular amplitude mask without a long edge is used to modulate the shape of the incident laser beams so that the spot shape distribution of the reflected laser beam on the sensor changes with the defocus amount of the wafer sample. By calculating the shape of the light spots, the defocus amount and the defocus direction can be obtained at the same time. The experimental results show that under the 20× microscopy objective, the linear range of the auto-focus system is 480 μm and the accuracy can reach 1 μm. It can be seen that the automatic focusing method proposed in this paper has the advantages of large linear range, high accuracy, and compact structure, which can meet the requirements of the Micro-LED wafer defects detection equipment.

In this study, an FPGA(Field Programmable Gate Array)-based borehole strain measurement system was designed that makes extensive use of digital signal processing operations to replace analog circuits. Through the formidable operational capability of FPGA, the sampled data were filtered and denoised to improve the signal-to-noise ratios. Then, with the goal of not reducing observational accuracy, the signal amplification circuit was removed, the excitation voltage was reduced, and the dynamic range of the primary adjustments was expanded to 130 dB. The system’s online compilation function made it more flexible to changes in measurement parameters, allowing it to adapt to various needs. In addition, the efficiency of the equipment use was enhanced. The actual observational results showed that this study’s FPGA-based borehole strain measurement system had a voltage resolution higher than 1 μV. Clear solid tides were successfully recorded in low-frequency bands, and seismic wave strain was accurately recorded in high-frequency bands. The arrival times and seismic phases of the seismic waves S and P were clearly recorded, which met the requirements for geophysical field deformation observations. Therefore, the system proposed in this study is of major significance for future analyses of geophysical and crust deformation observations.

An escalator is an essential large-scale public transport equipment; once it fails, this inevitably affects the operation of the escalator and even leads to safety concerns, or perhaps accidents. As an important structural part of the escalator, the foundation of the main engine can cause the operation of the escalator to become abnormal when its fixing bolts become loose. Aiming to reduce the difficulty of extracting the fault features of the footing bolt when it loosens, a fault feature extraction method is proposed in this paper based on empirical wavelet transform (EWT) and the gray-gradient co-occurrence matrix (GGCM). Firstly, the Teager energy operator and multi-scale peak determination are used to improve the spectral partitioning ability of EWT, and the improved EWT is used to decompose the original foundation vibration signal into a series of empirical mode functions (EMFs). Then, the gray-gradient co-occurrence matrix of each EMF is constructed, and six texture features of the gray-gradient co-occurrence matrix are calculated as the fault feature vectors of this EMF. Finally, the fault features of all EMFs are fused, and the degree of the loosening of the escalator foundation bolt is identified using the fused multi-scale feature vector and BiLSTM. The experimental results show that the proposed method based on EWT and GGCM feature extraction can diagnose the loosening degree of foundation bolts more effectively and has a certain engineering application value.

This paper presents a comfort-oriented semi-active suspension system composed of a network-synthesized passive section and a controllable section based on a semi-active inerter. Firstly, the semi-active suspension system is divided into a passive part and a controllable part. For the passive part, first-order and second-order robust positive real controllers are designed. The problem with H2 cost is considered, and the bilinear matrix inequalities (BMI) are solved using an iterative method to obtain two admittance functions. The admittance functions are physically realized as two mechanical networks composed of mechanical passive elements such as inerter, spring, and damper (ISD). Then, the parameters of these mechanical elements in those networks are optimized by Particle Swarm Optimization (PSO). Secondly, a semi-active inerter based on Sky-hook control is introduced for the semi-active part of the suspension system. Finally, the semi-active ISD suspension structure is verified by a quarter vehicle model. The simulation results show that the first-order and second-order suspension systems optimize the RMS of the spring mass acceleration by 14.2% and 23.9%, respectively, as compared to traditional suspension systems. Furthermore, frequency-domain analysis also shows that both suspension systems effectively reduce the value of spring mass acceleration in the low-frequency band.

The vision-based fruit recognition and localization system is the basis for the automatic operation of agricultural harvesting robots. Existing detection models are often constrained by high complexity and slow inference speed, which do not meet the real-time requirements of harvesting robots. Here, a method for apple object detection and localization is proposed to address the above problems. First, an improved YOLOX network is designed to detect the target region, with a multi-branch topology in the training phase and a single-branch structure in the inference phase. The spatial pyramid pooling layer (SPP) with serial structure is used to expand the receptive field of the backbone network and ensure a fixed output. Second, the RGB-D camera is used to obtain the aligned depth image and to calculate the depth value of the desired point. Finally, the three-dimensional coordinates of apple-picking points are obtained by combining two-dimensional coordinates in the RGB image and depth value. Experimental results show that the proposed method has high accuracy and real-time performance: F1 is 93%, mean average precision (mAP) is 94.09%, detection speed can reach 167.43 F/s, and the positioning errors in X, Y, and Z directions are less than 7 mm, 7 mm, and 5 mm, respectively.

Traditional PDF document detection technology usually builds a rule or feature library for specific vulnerabilities and therefore is only fit for single detection targets and lacks anti-detection ability. To address these shortcomings, we build a double-layer detection model for malicious PDF documents based on an entropy method with multiple features. First, we address the single detection target problem with the fusion of 222 multiple features, including 130 basic features (such as objects, structure, content stream, metadata, etc.) and 82 dangerous features (such as suspicious and encoding function, etc.), which can effectively resist obfuscation and encryption. Second, we generate the best set of features (a total of 153) by creatively applying an entropy method based on RReliefF and MIC (EMBORAM) to PDF samples with 37 typical document vulnerabilities, which can effectively resist anti-detection methods, such as filling data and imitation attacks. Finally, we build a double-layer processing framework to detect samples efficiently through the AdaBoost-optimized random forest algorithm and the robustness-optimized support vector machine algorithm. Compared to the traditional static detection method, this model performs better for various evaluation criteria. The average time of document detection is 1.3 ms, while the accuracy rate reaches 95.9%.

Electric freight vehicles have become an important means of transportation in connected and automated environments owing to their numerous advantages. However, the generally short driving range of connected and automated electric freight vehicles (CAEFVs) does not satisfy the growing transport demand. In this study, wireless charging technology is employed to construct a complex driving scenario including urban roads and dynamic wireless charging facilities. A combination of variable-scale elements consisting of vehicles, roads, and the environment is analysed hierarchically to develop a wireless charging scheme for urban transport systems. Using passage efficiency, energy consumption, and passenger comfort as the joint optimisation objectives, an optimal driving model for CAEFVs in wireless charging scenarios at signalised intersections combining scenario boundaries and vehicle dynamic constraints is proposed. Considering the differentiated charging needs of vehicles, this model is divided into a time priority strategy (TPS), balance priority strategy (BPS), and charging priority strategy (CPS). The obtained results reveal that the CPS is superior to the TPS in terms of the charging benefits but requires a longer travel time. Meanwhile, the BPS increases the charging benefits and passing efficiency. This study provides guidance for the deployment of wireless charging lanes with a high application value.

Overfitting often occurs in neural network training, and neural networks with higher generalization ability are less prone to this phenomenon. Aiming at the problem that the generalization ability of photovoltaic (PV) power prediction model is insufficient, a PV power time-sharing prediction (TSP) model combining variational mode decomposition (VMD) and Bayesian regularization neural network (BRNN) is proposed. Firstly, the meteorological sequences related to the output power are selected by mutual information (MI) analysis. Secondly, VMD processing is performed on the filtered sequences, which is aimed at reducing the non-stationarity of the data; then, normalized cross-correlation (NCC) and signal-to-noise ratio (SNR) between the components obtained by signal decomposition and the original data are calculated, after which the key influencing factors are screened out to eliminate the correlation and redundancy of the data. Finally, the filtered meteorological sequences are divided into two datasets based on whether the irradiance of the day is zero or not. Meanwhile, the predictions are performed using BRNN for each of the two datasets. Then, the results are reordered in chronological order, and the prediction of PV power is realized conclusively. It was experimentally verified that the mean absolute value error (MAE) of the method proposed in this paper is 0.1281, which is reduced by 40.28% compared with the back propagation neural network (BPNN) model on the same dataset, the mean squared error (MSE) is 0.0962, and the coefficient of determination (R2) is 0.9907. Other error indicators also confirm that VMD is of much significance and TSP is contributive.

To solve the problems of the impact of carbon emission reduction and low-carbon advertising on the supply chain of fresh agricultural products, a three-level low-carbon supply chain system composed of a manufacturer, a retailer and a third-party logistics service provider is taken as the research object. The profit functions of each party under the three contracts of the manufacturer bearing, the retailer bearing and the two parties jointly bearing the advertising cost are, respectively, established to solve the optimal pricing, advertising level preservation efforts, service levels and carbon emission reduction decisions. The numerical analysis shows that, with the increase in wholesale price and the decrease in fresh-keeping price coefficient and low-carbon cost coefficient, manufacturers will choose better fresh-keeping effort level and low-carbon service level. When the proportion of advertising cost borne by the supplier increases, the benefits of all parties in the supply chain will decrease; however, when the retailer bears the advertising cost alone, the profit of the supply chain system is the largest.

In the working process of the gas bearings, the unbalanced force of the rotor will increase nonlinearly with the increase in the rotating speed, resulting in an increase in the rotor’s vibration amplitude. On the other hand, with the increase in the rotating speed, the hydrodynamic effect will increase, and the nonlinear increase in the gas film force and stiffness will inhibit the increase in the vibration amplitude. In order to deeply study the influence of the unbalanced force and nonlinear gas film force on the vibration amplitude of the ultra-precision aerostatic motorized spindle, taking the double slit throttling gas bearing as an example, according to the equilibrium equation of the rotor under the combined action of gravity, the gas film force, and the unbalanced force, a calculation program based on the finite difference method for solving the rotor’s equilibrium position is completed. The calculation results show that: the hydrodynamic effect can significantly increase the bearing capacity and cause the change of the static equilibrium position of the rotor, but the offset amplitude of the static equilibrium position of the rotor gradually slows down with the increase in the rotating speed. The hydrodynamic effect improves the stiffness near the static equilibrium position of the rotor, making the rotor vibration track tend to be more “round”. Although the unbalanced force of the rotor increases nonlinearly as the rotating speed increases, the maximum offset between the dynamic equilibrium position and the static equilibrium position of the rotor under the action of the rotating unbalanced force is approximately linear with the rotating speed. Compared with the air supply pressure, the rotor unbalance and rotating speed are the main factors affecting the rotor dynamic equilibrium position offset. This study has a reference role in the in-depth study of the influence of rotating speed and rotor unbalance on the rotor static equilibrium position and dynamic equilibrium position offset, as well as in the design of gas bearings and the prediction of rotor vibration amplitude.

The ambient temperature and charging rate are the two most important factors that influence the capacity deterioration of lithium-ion batteries. Differences in temperature for charge–discharge conditions significantly impact the battery capacity, particularly under high-stress conditions, such as ultrafast charging. The combined negative effects of the ambient temperature and a high charging rate on the capacity of a lithium-ion battery require further research. Here, multiple scenarios of different temperatures and charging rates were considered to examine their influence on battery capacity deterioration, focusing on the effect of high charging rates above 2 C. Three test temperatures and three charging rates were selected, and experiments were performed to evaluate the battery capacity over several charge–discharge cycles. A comparative analysis was performed on the capacity, impedance, and probability density function (PDF). The results showed that increasing the charging rate delayed the response of the phase change reaction to the voltage, which accelerated the corresponding capacity deterioration. At high charging rates, the main causes of capacity deterioration were the loss of active lithium in the battery and the loss of active material from the negative electrode. Most of the product from the side reaction between the lithium coating and electrolyte remained in the electrolyte and had no evident effect on impedance. Therefore, high charging rates significantly increase the temperature of the battery, and a high charging rate and temperature exert a coupled negative effect on the battery capacity. Thermal management strategies for lithium-ion batteries must comprehensively optimize the temperature and charging rate in real time.

Cymbal transducers are often used in arrays for underwater communication and detection systems. The working environment of a cymbal array is underwater; therefore, waterproofing, salt-corrosion prevention, and impact resistance are necessary for stable operation of the array. Hence, we simulated potting a cymbal array with 15 different rubber and epoxy materials available in the market, using the finite element method, and analyzed their effect on the transmitting voltage response spectrum of the array. From the analysis results, we selected the material that would achieve the widest frequency bandwidth, while preserving the structural stability of the array. A potting method corresponding to the selected material was suggested as well. This study provides guidelines for the selection of a potting material for use in underwater transducer arrays.

Broussonetia papyrifera has a high lignocellulose content leading to poor palatability and low digestion rate of ruminants. Thus, dynamic profiles of fermentation lignocellulose characteristics, microbial community structure, potential function, and interspecific relationships of B. papyrifera mixing with wheat bran in different ratios: 100:0 (BP100), 90:10 (BP90), 80:20 (BP80), and 65:35 (BP65) were investigated on ensiling days 5, 15, 30, and 50. The results showed that adding bran increased the degradation rate of hemicellulose, neutral detergent fiber, and the activities of filter paper cellulase, endoglucanase, acid protease, and neutral protease, especially in the ratio of 65:35. Lactobacillus, Pediococcus, and Weissella genus bacteria were the dominant genera in silage fermentation, and Pediococcus and Weissella genus bacteria regulated the process of silage fermentation. Compared with monospecific B. papyrifera silage, adding bran significantly increased the abundance of Weissella sp., and improved bacterial fermentation potential in BP65 (p < 0.05). Distance-based redundancy analysis showed that lactic acid bacteria (LAB) were significantly positive correlated with most lignocellulose content and degrading enzymes activities, while Monascus sp. and Syncephalastrum sp. were opposite (p < 0.05). Co-occurrence network analysis indicated that there were significant differences in microbial networks among different mixing ratios of B. papyrifera silage prepared with bran. There was a more complex, highly diverse and less competitive co-occurrence network in BP65, which was helpful to silage fermentation. In conclusion, B. papyrifera ensiled with bran improved the microbial community structure and the interspecific relationship and reduced the content of lignocellulose.

Medical optical imaging, with the aid of the “terahertz tomography”, is a novel medical imaging technique based on the electromagnetic waves. Such advanced imaging techniques strive for the detailed theoretical and computational analysis for better verification and validation. Two important aspects, the analytic approach for the understanding of the Schrodinger transforms and machine learning approaches for the understanding of the medical images segmentation, are presented in this manuscript. While developing an AI algorithm for complex datasets, the computational speed and accuracy cannot be overlooked. With the passage of time, machine learning approaches have been further modified using the Bayesian, genetic and quantum approaches. These strategies have boosted the efficiency of the machine learning, and specifically the deep learning tools, by taking into account the probabilistic, evolutionary and quantum qubits hypothesis and operations, respectively. The current research encompasses the detailed analysis of image segmentation algorithms based on the evolutionary approach. The image segmentation algorithm that converts the color model from RGB to HSI and the image segmentation algorithm that uses the clustering technique are discussed in detail, and further extensions of these genetic algorithms to quantum algorithms are proposed. Based on the genetic algorithm, the optimal selection of parameters is realized so as to achieve a better segmentation effect.

Ellagic acid is a natural polyphenol found in various fruits and vegetables. Numerous studies have shown that ellagic acid has beneficial effects on human health. In this study, we investigated the stress resistant action of ellagic acid in Caenorhabditis elegans (C. elegans). Notably, 50 μM ellagic acid prolonged the lifespan of C. elegans by 36.25%, 36.22%, 155.1%, and 79.07% under ultraviolet radiation stress, heat stress, oxidative stress, and Pseudomonas aeruginosa infection stress, respectively. Furthermore, the mechanism by which ellagic acid reduces the damage caused by ultraviolet radiation in C. elegans was explored. Ellagic acid could significantly induce the nucleus translocation of DAF-16 and, thereby, activate a series of target genes to resist ultraviolet radiation stress. Moreover, ellagic acid also significantly increased the expression of SOD-3 by 3.61 times and the activity of superoxide dismutase by 3.70 times to clean out harmful reactive oxygen species in C. elegans exposed to ultraviolet radiation stress. In both daf-16 mutant and daf-2; daf-16 double-mutant worms exposed to ultraviolet radiation, ellagic acid could no longer prolong their lifespan. These results indicate that ellagic acid plays an important role in resisting ultraviolet radiation stress in C. elegans, probably in an insulin/IGF-1 signaling pathway-dependent way.

Flying ad hoc networks (FANETs), which are composed of autonomous flying vehicles, constitute an important supplement to satellite networks and terrestrial networks, and they are indispensable for many scenarios including emergency communication. Unfortunately, the routing therein is largely affected by rapid topology changes, frequent disconnection of links, and a high vehicle mobility. In this paper, an intelligent routing strategy based on deep reinforcement learning (DRL) is proposed, which is decentralized and takes into account the status of symmetrical nodes in two hops. In order to perceive the local dynamics of the network as comprehensively as possible, the location, moving speed, load degree, and link quality of the nodes are considered into the setting process of state elements in the method. The nodes can select the neighboring node adaptively according to the Q values calculated by the model obtained through the training of Deep Q-Networks. The simulation and analysis show that the proposed method possesses good convergence characteristics and has obviously better performance compared with several common methods.

Dynamic wireless power transfer (DWPT) has attracted widespread attention for its charging flexibility; short-segmented DWPT systems are more suitable for EV charging scenarios because of their higher charging efficiency and lower electromagnetic radiation, compared to long-track DWPT systems. For short-segmented DWPT systems, the structural design of the ground-side coil affects the coupling characteristics of the system, while simultaneously the electric vehicle driving speed and coil arrangement also cause coupling variations, and this will inevitably have an impact on the system’s performance. Therefore, this paper demonstrates the coupler design of a short-segmented system for electric vehicles, focusing on the optimization of ground-side coil. The coupling variations causing by driving speed of EV and coil arrangement are taken into account. Considering the tradeoffs and restrictions, a multi-objective optimization process of coils in DWPT systems is proposed based on the Pareto optimizing method, with three objectives: transfer power, high efficiency and low cost. A reasonable optimal solution is selected from the Pareto front to verify the optimizing method through a constructed prototype.

Soluble sulfur (S8) and insoluble sulfur (IS) have different application fields, and molecular dynamics simulation can reveal their differences in solubility in solvents. It is found that in the simulated carbon disulfide (CS2) solvent, soluble sulfur in the form of clusters mainly promotes the dissolution of clusters through van der Waals interaction between solvent molecules (CS2) and S8, and the solubility gradually increases with the increase in temperature. However, the strong interaction between polymer chains of insoluble sulfur in the form of polymer hinders the diffusion of IS into CS2 solvent, which is not conducive to high-temperature dissolution. The simulated solubility parameter shows that the solubility parameter of soluble sulfur is closer to that of the solvent, which is consistent with the above explanation that soluble sulfur is easy to dissolve.

Objective: the role of the supraspinal system in the neural control mechanisms of adhesion locomotor pattern formation was studied in lizard Gekko gecko. Methods: the locomotor performance and adaptation of the chronically lesioned Gekko gecko was documented before and after either partial or complete spinal lesions. They were filmed moving on a flat and smooth platform that was inclined at 0°, ±45°, and ±90°, as well as the horizontal mats and the vertical oak background board in the terraria, to evaluate locomotor functional recovery. The geckos were also tested on the platform by two half and nose-up or -down rotations in steps of 15° throughout 180° to investigate the recovery of the ability to respond dynamically to external perturbations. Results: after relatively small lesions of a hemisection, the locomotor performance was largely indistinguishable from that before and after a sham operation. During the initial period of recovery after the largest lesions of a dorsal or a ventral hemisection within 1 wk, the geckos behaved essentially as the complete spinal geckos, while permanent deficits in locomotor performance remained and did not decrease afterwards for ≥6 mth. Conclusions: by analyzing the correlation among locomotor performances, and between locomotor performances and spinal cord lesions, we suggest that the dorsal spinal pathways and ventral spinal pathways participate, respectively, in the control of the limb coupling, and in the deployment and the detachment of the adhesive apparatus. The present study will provide certain neurobiological guidance for the design of bio-robots, as well as sprawling robots inspired by the geckos.

Double emulsions (W/O/W) with compartmentalized structures have attracted a lot of research interests due to their diverse applications in the food industry. Herein, oil phase of double emulsions was gelled with beeswax (BW), and the effects of BW mass ratios (0–8.0%) on the stability, oral sensation, and flavor release profile of the emulsions were investigated. Rheological tests revealed that the mechanical properties of double emulsions were dependent on the mass ratio of BW. With the increase in BW content, double emulsions showed a higher resistance against deformation, and lower friction coefficient with a smoother mouthfeel. Turbiscan analysis showed that the addition of BW improved the stability of double emulsions during a 14 days’ storage, under freeze–thawed, and osmotic pressure conditions, but it did not improve the heating stability of double emulsions. The addition of BW contributed to lower air-emulsion partition coefficients of flavor (2,3-diacetyl) compared to those without the addition of BW at 20 °C and 37 °C, respectively. Furthermore, the addition of BW and its mass ratio significantly altered the flavor release behavior during the open-bottle storage of double emulsions. The response value of 0% BW dropped sharply on the first day of opening storage, showing a burst release behavior. While a slow and sustained release behavior was observed in double emulsions with 8.0% BW. In conclusion, gelation of the intermediate oil phase of double emulsions significantly enhanced the stability of double emulsions with tunable oral sensation and flavor release by varying the mass ratio of beeswax.

This paper studies the problem of distributed spectrum/channel access for cognitive radio-enabled unmanned aerial vehicles (CUAVs) that overlay upon primary channels. Under the framework of cooperative spectrum sensing and opportunistic transmission, a one-shot optimization problem for channel allocation, aiming to maximize the expected cumulative weighted reward of multiple CUAVs, is formulated. To handle the uncertainty due to the lack of prior knowledge about the primary user activities as well as the lack of the channel-access coordinator, the original problem is cast into a competition and cooperation hybrid multi-agent reinforcement learning (CCH-MARL) problem in the framework of Markov game (MG). Then, a value-iteration-based RL algorithm, which features upper confidence bound-Hoeffding (UCB-H) strategy searching, is proposed by treating each CUAV as an independent learner (IL). To address the curse of dimensionality, the UCB-H strategy is further extended with a double deep Q-network (DDQN). Numerical simulations show that the proposed algorithms are able to efficiently converge to stable strategies, and significantly improve the network performance when compared with the benchmark algorithms such as the vanilla Q-learning and DDQN algorithms.

The evolution of textures, the degree of recrystallization and the mechanical properties of 7A65 hot rolled plates during re-dissolution were studied with different thicknesses (25 mm, 65 mm, 120 mm) and different degrees of deformation. It was found that different plates exhibited different trends of re-dissolution because the degrees of deformation increased and the degrees of recrystallization were different during the solution treatment. With the increase of deformation and static recrystallization degrees, texture types changed from Cube, R-Cube to Brass, R, Cube and Copper during the re-dissolution process. The value of the Schmid factor (µ¯) was calculated and the value along the rolling direction was significantly larger than along the transverse direction, which led to a lower yield strength along the rolling direction. In terms of the average contribution of the yield strength, the strengthening of the grain boundary including LAGBs (low-angle grain boundaries) was found to play a more significant role than the effect of solid atoms and dislocation densities. Therefore, the 25 mm plate exhibits the best mechanical properties, with a yield strength of 565.7 MPa along the rolling direction.

This study was conducted to evaluate the effect of lactic acid bacteria (LAB) on fermentation quality, mycotoxin concentrations, and microbial communities of whole-crop corn silages infested with mycotoxigenic fungi. Cultured spores (106 cfu/mL) of mycotoxigenic Aspergillus flavus and Fusarium graminearum were sprayed (5 mL) on corn forage on 27 July and 10 August 2018. On 21 August 2018, sprayed (FI; 3 plots) and unsprayed (NFI; 3 plots) corn forage were harvested at the 1/2 kernel milk line stage, followed by chopping and ensiling without inoculants (CON), or with Lactobacillus buchneri (LB, 1 × 106 cfu/g FW), Lactobacillus plantarum (LT, 1 × 106 cfu/g FW), or L. buchneri + L. plantarum (BT: both L. buchneri and L. plantarum applied at 0.5 × 106 cfu/g FW). After 90 d of ensiling, FI silages had a higher (p < 0.05) pH value and higher acetic acid (ACA), ethanol, and ammonia nitrogen (ammonia N) concentrations, but lower (p < 0.05) lactic acid (LA) concentrations than NFI silage. The inoculants decreased pH and increased LA concentration and LA/ACA compared with CON. The aflatoxin B1 (AFB1) was only detected in FI fresh corn and silages; ensiling decreased (p < 0.05) AFB1 concentration compared with fresh corn, and LB and BT decreased AFB1 concentration compared with CON. The zearalenone (ZEN), deoxynivalenol (DON), and fumonisin B1 (FB1) concentrations were similar (p < 0.05) for NFI silages, while ZEN concentration in BT was the lowest (p < 0.05) among all FI silages; DON and FB1 concentrations in LB, LT, and BT silages were significantly lower (p < 0.05) than those of CON in FI silages. The fungal infestation increased the bacterial and fungal diversity of silages compared with NFI silages. The FI silages had a higher relative abundance (RA) of Lactobacillus, Weissella, Wickerhamomyces, Pichia, and Epicoccum than the corresponding NFI silages. The RA of Aspergillus and Fusarium markedly decreased after 90 d of ensiling, and the inoculation expanded this trend irrespective of fungal infestation. The Penicillium in FI silages survived after 90 d of ensiling, while the inoculants decreased the RA of Penicillium. Inoculants mitigate the adverse effects of fungal infestation on corn silage quality by changing the bacterial and fungal communities.

Radio frequency fingerprinting (RFF) methods are becoming more and more popular in the context of identifying genuine transmitters and distinguishing them from malicious or non-authorized transmitters, such as spoofers and jammers. RFF approaches have been studied to a moderate-to-great extent in the context of non-GNSS transmitters, such as WiFi, IoT, or cellular transmitters, but they have not yet been addressed much in the context of GNSS transmitters. In addition, the few RFF-related works in GNSS context are based on post-correlation or navigation data and no author has yet addressed the RFF problem in GNSS with pre-correlation data. Moreover, RFF methods in any of the three domains (pre-correlation, post-correlation, or navigation) are still hard to be found in the context of GNSS. The goal of this paper was two-fold: first, to provide a comprehensive survey of the RFF methods applicable in the GNSS context; and secondly, to propose a novel RFF methodology for spoofing detection, with a focus on GNSS pre-correlation data, but also applicable in a wider context. In order to support our proposed methodology, we qualitatively investigated the capability of different methods to be used in the context of pre-correlation sampled GNSS data, and we present a simulation-based example, under ideal noise conditions, of how the feature down selection can be done. We are also pointing out which of the transmitter features are likely to play the biggest roles in the RFF in GNSS, and which features are likely to fail in helping RFF-based spoofing detection.

A modified convolutional neural network (CNN) is proposed to enhance the reliability of source ranging based on acoustic field data received by a vertical array. Compared to the traditional method, the output layer is modified by outputting Gauss regression sequences, expressed using a Gaussian probability distribution form centered on the actual distance. The processed results of deep-sea experimental data confirmed that the ranging performance of the CNN with a Gauss regression output was better than that using single regression and classification outputs. The mean relative error between the predicted distance and the actual value was ~2.77%, and the positioning accuracy with 10% and 5% error was 99.56% and 90.14%, respectively.

The 5G network is considered as the essential underpinning infrastructure of manned and unmanned autonomous machines, such as drones and vehicles. Besides aiming to achieve reliable and low-latency wireless connectivity, positioning is another function provided by the 5G network to support the autonomous machines as the coexistence with the Global Navigation Satellite System (GNSS) is typically supported on smart 5G devices. This paper is a pilot study of using 5G uplink physical layer channel sounding reference signals (SRSs) for 3D user equipment (UE) positioning. The 3D positioning capability is backed by the uniform rectangular array (URA) on the base station and by the multiple subcarrier nature of the SRS. In this work, the subspace-based joint angle-time estimation and statistics-based expectation-maximization (EM) algorithms are investigated with the 3D signal manifold to prove the feasibility of using SRSs for 3D positioning. The positioning performance of both algorithms is evaluated by estimation of the root mean squared error (RMSE) versus the varying signal-to-noise-ratio (SNR), the bandwidth, the antenna array configuration, and multipath scenarios. The simulation results show that the uplink SRS works well for 3D UE positioning with a single base station, by providing a flexible resolution and accuracy for diverse application scenarios with the support of the phased array and signal estimation algorithms at the base station.

In the non-free-field, with the effect of reflection sounds from the reflection boundary, the vibration character of a submerged structure often changes, which may have significant influences on the measurement system configurations. To reduce the engineering cost in low-frequency sound prediction of a submerged structure with finite depth, two methods based on the theory of acoustic radiation mode (ARM) are proposed. One is called the vibration reconstruction equivalent source method (VR-ESM), which utilizes the ARM to reconstruct the total vibration of the structure, and the sound prediction is completed with the equivalent source method (ESM); the other is called the compressed modal equivalent source method (CMESM), which utilizes the theory of compressive sensing (CS) and the ARM to reinforce the sparsity of source strengths. The sound field separation (SFS) technology is combined with the above two methods for constructing the ARMs accurately in the non-free field. Simulations show that both methods are efficient. Compared with the traditional method based on the structural modal analysis, the methods based on the ARM could efficiently reduce the scale of the measurement system. However, the measurement point arrangement should be optimized to keep the prediction results accurate. In this paper, the optimization process is completed with the efficient independence (EFI) method. In addition, some factors that may affect the prediction accuracy are also analyzed in this paper. When the submerge depth is large enough, the process of contrasting ARMs could be further simplified. The results of the paper could help in saving engineering costs to predict the low-frequency sound radiation of submerged structures in the future.

The residual-based (RB) receiver autonomous integrity monitoring (RAIM) detector is a widely used receiver integrity enhancement technology that has the ability to rapidly respond to outliers. However, the sensitivity and vulnerability of the residuals to the outliers are the weaknesses of the method especially in the case of multi-outlier modes. It is an effective method for enhancing the validity of residuals by robust estimation instead of least squares (LS) estimation. In this paper, a modified RB RAIM detector based on a robust MM estimation with a higher detection performance under multi-outlier modes is presented. A fast subset selection method based on the characteristic slope that could reduce the number of subsets to be calculated is also presented. The experimental results show that the proposed algorithm maintains a more robust performance than the RB RAIM detector based on the LS estimator and M estimator with an IGG III function especially with the increase in the number of outliers. The proposed fast subset selection method can reduce the calculation time by at least 80%, demonstrating the practical application value of the algorithm.

By taking a cylindrical LiFePO4 power battery as the research object, the cycle performance test was conducted under different charging current aging paths in a preset low-temperature environment and combined with EIS results to analyze the dynamic characteristics of the battery during the aging process, using the PDF (Probability Density Function) curve to analyze the change of battery energy storage characteristics, and analyze the aging mechanism of the power battery by analyzing the change in the lithium precipitation energy difference. The experimental results showed that under a low-temperature environment, the effect of increasing the charge rate is mainly reflected in slowing down the phase transformation reaction. From the analysis of lithium precipitation of the battery, it can be seen that the main mechanism of the aging of the battery is the loss of active lithium under the conditions of low-rate cycling at sub-zero temperature. The products from the side reaction between the lithium plating and the electrolyte build up on the SEI (Solid Electrolyte Interphase) film, which significantly increases the battery impedance late in the cycle. The work in this paper complements the mechanistic studies of lithium-ion batteries under different aging paths and is also useful for capacity estimation models and research on battery health.

As part of a comprehensive environmental assessment of the Dun-Gel railway project located in Dunhuang city, Gansu Province, China, a wind tunnel experiment was proposed to predict surface shear stress changes on a sand dune when a bridge was built upstream it. The results show that the length of the wall shear stress shelter region of a bridge is about 10 times of the bridge height (H). In the cases that the interval of the bridge and sand dune (S) is less than 5 H, normalized wall shear stress on the windward crest is decreased from 1.75 (isolated dune) to 1.0 (S = 5.0 H, measured downwind bridge pier) and 1.5 (S = 5.0 H, measured in the middle line of two adjacent bridge piers). In addition, the mean surface shear stress in the downstream zone of the sand dune model is reduced by the bridge pier and is increased by the bridge desk. As for the fluctuation of surface shear stress ( ζ ) on the windward crest, ζ decreases from 1.3 (in the isolated dune case) to 1.2 (in the case S = 5.0 H, measured just downwind the pier) and increases from 1.3 (in the isolated dune case) to 1.6 (in the cases S = 5.0 H, in the middle of two adjacent piers). Taking the mean and fluctuation of surface shear stress into consideration together, we introduce a parameter ψ ranging from 0 to 1. A low value indicates deposition and a high value indicates erosion. On the windward slope, the value of ψ increases with height (from 0 at toe to 0.98 at crest). However, in the cases of S = 1.5 H, ψ is decreased by the bridge in the lower part of the sand dune at y = 0 and is increased at y = L/2 compared with the isolated dune case. In other cases, the change of ψ on the windward slope is not as prominent as in the case of S = 1.5 H. Downstream the sand dune, erosion starts in a point that exists between x = 10 H and 15 H in all cases.