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The various applications of bearing-only sensor networks for detection and localization are becoming increasingly widespread and important. The array layout of the bearing-only sensor network seriously impacts the detection performance. This paper proposes a multi-strategy fusion improved adaptive mayfly algorithm (MIAMA) in a bearing-only sensor network to perform layout planning on the geometric configuration of the optimal detection. Firstly, the system model of a bearing-only sensor network was constructed, and the observability of the system was analyzed based on the Cramer–Rao Lower Bound and Fisher Information Matrix. Then, in view of the limitations of the traditional mayfly algorithm, which has a single initial population and no adaptability and poor global search capabilities, multi-strategy fusion improvements were carried out by introducing Tent chaos mapping, the adaptive inertia weight factor, and Random Opposition-based Learning. Finally, three simulation experiments were conducted. Through comparison with the Particle Swarm Optimization (PSO) algorithm, Mayfly Algorithm (MA), and Genetic Algorithm (GA), the effectiveness and superiority of the proposed MIAMA were validated.

The problem of state estimation based on bearing-only sensors is increasingly important while existing research on distributed filtering solutions is rather limited. Therefore, this paper proposed the novel distributed cubature information filtering (DCIF) method for addressing the state estimation challenge in bearing-only sensor networks. Firstly, the system model of the bearing-only sensor network was constructed, and the observability of the system was analyzed. The sensor nodes are paired to measure relative angle information. Subsequently, the coordinated consistency theory is employed to achieve a unified state estimation of the maneuvering target. The DCIF method enhances the observability of the system, addressing the issues of large accuracy errors and divergence in traditional nonlinear filtering algorithms. Building upon the theoretical proof of consistency convergence in DCIF, four simulation experiments were conducted for comparison. These experiments validate the effectiveness and superiority of the DCIF method in bearing-only sensor networks.

Height estimation has long been a pivotal topic within measurement and remote sensing disciplines, with monocular height estimation offering wide-ranging data sources and convenient deployment. This paper addresses the existing challenges in monocular height estimation methods, namely the difficulty in simultaneously achieving high-quality instance-level height and edge reconstruction, along with high computational complexity. This paper presents a comprehensive solution for monocular height estimation in remote sensing, termed HeightFormer, combining multilevel interactions and image-adaptive classification–regression. It features the Multilevel Interaction Backbone (MIB) and Image-adaptive Classification–regression Height Generator (ICG). MIB supplements the fixed sample grid in the CNN of the conventional backbone network with tokens of different interaction ranges. It is complemented by a pixel-, patch-, and feature map-level hierarchical interaction mechanism, designed to relay spatial geometry information across different scales and introducing a global receptive field to enhance the quality of instance-level height estimation. The ICG dynamically generates height partition for each image and reframes the traditional regression task, using a refinement from coarse to fine classification–regression that significantly mitigates the innate ill-posedness issue and drastically improves edge sharpness. Finally, the study conducts experimental validations on the Vaihingen and Potsdam datasets, with results demonstrating that our proposed method surpasses existing techniques.

The inter-system-like bias between the regional (BDS-2) and global (BDS-3) constellation of the BeiDou Navigation Satellite System (BDS) has been identified on common B1I pseudo-range observations. In this study, its characteristics are investigated with tracking data from the International GNSS Service (IGS) and International GNSS Monitoring and Assessment System (iGMAS) network. Firstly, the satellite-specific inter-system-like bias is calculated and the dependency on satellite is observed. Clearly noticeable discrepancies on BDS-2 and BDS-3 can be identified. Hence, the constellation-specific inter-system-like bias is estimated. Biases for all receivers are quite stable, with standard derivation (STDev) less than 0.2 m in average. The bias shows clear dependence on the receiver, while the firmware and antenna have limited but not negligible impacts, particularly for Trimble NetR9 and Alloy receivers. The Trimble NetR9 with TRM59800.00 antenna shows noticeable discrepancy up to about 1.5 m with different antenna, and the bias of the Trimble Alloy 5.37 jumps about 2.4 m with respect to later firmware. In addition, clear annual variations are observed for stations ABPO and MIZU with Septentrio POLARX5 5.3.2 and ASTERX4 4.4.2 receivers, respectively. Furthermore, the impacts of the biases on the BDS orbit and clock solutions are analyzed. Once BDS-2 and BDS-3 are treated as two independent systems, the root mean square (RMS) of code and carrier phase residuals can be reduced by around 9.3 cm and 0.23 mm, respectively, while the three-dimensional orbit consistency is improved by 6.8%, mainly in the tracking direction. Satellite laser ranging (SLR) shows marginal impacts on IGSO and MEO satellites. However, the SLR residual of C01 shifts −13.2 cm, resulting in a smaller RMS value. In addition, the RMS of linear clock fitting is reduced from 0.050 ns to 0.038 ns for BDS-3 MEO satellites in average.

Airborne LiDAR (Light Detection and Ranging) is an active Earth observing system, which can directly acquire high-accuracy and dense building roof data. Thus, airborne LiDAR has become one of the mainstream source data for building detection and reconstruction. The emphasis for building reconstruction focuses on the accurate extraction of feature lines. Building roof feature lines generally include the internal and external feature lines. Efficient extraction of these feature lines can provide reliable and accurate information for constructing three-dimensional building models. Most related algorithms adopt intersecting the extracted planes fitted by the corresponding points. However, in these methods, the accuracy of feature lines mostly depends on the results of plane extraction. With the development of airborne LiDAR hardware, the point density is enough for accurate extraction of roof feature lines. Thus, after acquiring the results of building detection, this paper proposed a feature lines extraction strategy based on the geometric characteristics of the original airborne LiDAR data, tracking roof outlines, normal ridge lines, oblique ridge lines and valley lines successively. The final refined feature lines can be obtained by normalization. The experimental results showed that our methods can achieve several promising and reliable results with an accuracy of 0.291 m in the X direction, 0.295 m in the Y direction and 0.091 m in the H direction for outlines extraction. Further, the internal feature lines can be extracted with reliable visual effects using our method.

Accurate identification of road objects is crucial for achieving intelligent traffic systems. However, developing efficient and accurate road object detection methods in complex traffic scenarios has always been a challenging task. The objective of this study was to improve the target detection algorithm for road object detection by enhancing the algorithm’s capability to fuse features of different scales and levels, thereby improving the accurate identification of objects in complex road scenes. We propose an improved method called the Enhanced YOLOv5 algorithm for road object detection. By introducing the Bidirectional Feature Pyramid Network (BiFPN) into the YOLOv5 algorithm, we address the challenges of multi-scale and multi-level feature fusion and enhance the detection capability for objects of different sizes. Additionally, we integrate the Convolutional Block Attention Module (CBAM) into the existing YOLOv5 model to enhance its feature representation capability. Furthermore, we employ a new non-maximum suppression technique called Distance Intersection Over Union (DIOU) to effectively address issues such as misjudgment and duplicate detection when significant overlap occurs between bounding boxes. We use mean Average Precision (mAP) and Precision (P) as evaluation metrics. Finally, experimental results on the BDD100K dataset demonstrate that the improved YOLOv5 algorithm achieves a 1.6% increase in object detection mAP, while the P value increases by 5.3%, effectively improving the accuracy and robustness of road object recognition.

The scale of Internet of Things (IoT) systems has expanded in recent times and, in tandem with this, IoT solutions have developed symbiotic relationships with technologies, such as edge Computing. IoT has leveraged edge computing capabilities to improve the capabilities of IoT solutions, such as facilitating quick data retrieval, low latency response, and advanced computation, among others. However, in contrast with the benefits offered by edge computing capabilities, there are several detractors, such as centralized data storage, data ownership, privacy, data auditability, and security, which concern the IoT community. This study leveraged blockchain’s inherent capabilities, including distributed storage system, non-repudiation, privacy, security, and immutability, to provide a novel, advanced edge computing architecture for IoT systems. Specifically, this blockchain-based edge computing architecture addressed centralized data storage, data auditability, privacy, data ownership, and security. Following implementation, the performance of this solution was evaluated to quantify performance in terms of response time and resource utilization. The results show the viability of the proposed and implemented architecture, characterized by improved privacy, device data ownership, security, and data auditability while implementing decentralized storage.

Airborne light detection and ranging (LiDAR) technology has been widely utilized for collecting three-dimensional (3D) point cloud data on forest scenes, enabling the generation of high-accuracy digital elevation models (DEMs) for the efficient investigation and management of forest resources. Point cloud filtering serves as the crucial initial step in DEM generation, directly influencing the accuracy of the resulting DEM. However, forest filtering presents challenges in dealing with sparse point clouds and selecting appropriate initial ground points. The introduction of full-waveform LiDAR data offers a potential solution to the problem of sparse point clouds. Additionally, advancements in multi-source data integration and machine learning algorithms have created new avenues that can address the issue of initial ground point selection. To tackle these challenges, this paper proposes a novel filtering method for forest scenes utilizing full-waveform LiDAR data and hyperspectral image data. The proposed method consists of two main steps. Firstly, we employ the improved dynamic graph convolutional neural network (IDGCNN) to extract initial ground points. In this step, we utilize three types of low-correlation features: LiDAR features, waveform features, and spectral features. To enhance its accuracy and adaptability, a self-attention module was incorporated into the DGCNN algorithm. Comparative experiments were conducted to evaluate the effectiveness of the algorithm, demonstrating that the IDGCNN algorithm achieves the highest classification accuracy with an overall accuracy (OA) value of 99.38% and a kappa coefficient of 95.95%. The second-best performer was the RandLA-net algorithm, achieving an OA value of 98.73% and a kappa coefficient of 91.68%. The second step involves refining the initial ground points using the cloth simulation filter (CSF) algorithm. By employing the CSF algorithm, non-ground points present in the initial ground points are effectively filtered out. To validate the efficacy of the proposed filtering method, we generated a DEM with a resolution of 0.5 using the ground points extracted in the first step, the refined ground points obtained with the combination of the first and second steps, and the ground points obtained directly using the CSF algorithm. A comparative analysis with 23 reference control points revealed the effectiveness of our proposed method, as evidenced by the median error of 0.41 m, maximum error of 0.75 m, and average error of 0.33 m.

We introduce an approach for the direct measurement of the gravitational potential (GP) along the trajectory of a satellite, with a specific focus on Low-Earth Orbit (LEO) satellites. A LEO satellite communicates with several Geosynchronous Equatorial Orbit (GEO) satellites via frequency signal links. The GP difference can be measured in real-time using the gravitational frequency shift approach by equipping both LEO and GEO satellites with precise atomic clocks. Since the GP at the high orbits of the GEO satellites can be precisely determined by the present gravitational field model EGM2008, the GP along the LEO satellite’s trajectory can be determined. In this study, simulation experiments were conducted, featuring a GRACE-type satellite as the LEO satellite in communication with three equidistant GEO satellites. The results indicated that the accuracy of the GP measurements along the LEO satellite’s trajectory primarily depends on the precision of the onboard atomic clocks. Supposing optical atomic clocks attain an instability level of 1×10−17τ−1/2 (τ in seconds), we determined the GP distribution covered by the LEO satellite’s trajectories with 30-day observations. Then, we determined a gravitational field at the centimeter level based on the GP distribution. The GP data derived from the trajectory of a LEO satellite can be utilized to establish temporal gravitational fields, which have broad applications in different disciplines.

The International Association of Geodesy (IAG) aims to establish the International Height Reference System (IHRS) as one of its primary scientific objectives. Central to the realization of the IHRS is global vertical datum unification, which requires the connection of existing local vertical height reference systems (VHS) robustly and consistently. However, conventional methods are not suitable for estimating the offsets between two distant local height systems. In this paper, we propose a framework for connecting two local VHSs using ultraprecise clock frequency signal links between satellites and ground stations, referred to as the satellite frequency signal transmission (SFST) approach. The SFST approach allows for the direct determination of the geopotential and height differences between two ground datum stations without any location restrictions between the two VHSs. The simulation results show that the VHSs of China and the US can be unified with an accuracy of several centimeters, provided that the stability of atomic clocks used on-board the satellite and at on-ground datum locations reaches 4.8×10−17τ−1/2 for an averaging time τ (in seconds). We conclude that the SFST approach shows promise for achieving centimeter-level accuracy in unifying the global vertical height datum and represents a new paradigm for the realization of the IHRS.

China’s high-tech industrial development zones (HIDZs) is a key “place-based” policy targeting national innovation-driven development. Despite the extensive research on HIDZs, it remains unclear whether and to what extent the policy has fostered cities’ innovation output and quality. Basing our research on panel data of Chinese cities from 2001 to 2019, we employed the PSM-DID model to examine the impact of HIDZs policy on their host cities’ innovation output and quality. The empirical results show that: (1) In general, the establishment of HIDZs has a more significant positive effect on fostering urban innovation output compared to its role in promoting urban innovation quality. However, (2) the effect on urban innovation output and quality varies across different cities. For cities with more advantageous locations and policies, HIDZ policy plays a more instrumental role in promoting the quality of urban innovation, while the establishment of HIDZs in other cities is more conducive to increasing the output of urban innovation. Ultimately, we argue that authorities must recognize the importance of integrated development of HIDZs and their host cities and incorporating HIDZs’ impact on the host cities into the HIDZ evaluation. It is necessary to understand that HIDZs have multiple development modes due to their specific local conditions. Hence, differentiated guidance must be carried out rather than directly replicating the experience from developed regions.

To study the axial compression performance of a steel-reinforced ultra-high-performance concrete-filled square stainless-steel tube (SR-UHPCFSSST), eight specimens were designed with different length-to-diameter ratios and skeleton contents, and axial compression tests and numerical simulations were performed. Damage pattern, ultimate load capacity, and load–displacement curve data of the specimens were obtained. Finite element analysis was performed using ABAQUS software for parameter expansion. The damage mechanisms of the specimens and the influences of various parameters, such as the length-to-diameter ratio, skeleton content, diameter-to-thickness ratio, and concrete strength, on the damage processes and ultimate bearing capacities of the specimens were studied. The results showed that among the components of the test piece under the same axial load, the stress of the built-in steel skeleton was the first to approach its yield stress, and the steel pipe was the first to produce a bulging deformation. The ultimate bearing capacities of the specimens increased with the increase in the skeleton content and concrete strength and decreased with the increase in the length-to-diameter and diameter-to-thickness ratios. Based on the test and numerical simulation results, this paper puts forward a calculation formula of the axial compression bearing capacity of a square-stainless-steel-tube ultra-high-performance-concrete middle-length column with a steel skeleton, which provides a reference for engineering design and for compiling relevant codes.

The ocean is a significant strategic resource, and the insufficient development and use of the ocean, as well as the increase in attention to the ocean, have led to the development of underwater robot technology. The need for in-depth marine exploration and the limitations of one underwater robot has sparked research on the underwater multi-robot system. In the underwater environment, weak communication is caused by the shielding effect of the seawater medium, which makes multi-robot systems difficult to form. Hence, we combine the robot’s vision system with the leader-follower structure to form a vision-based underwater formation method, in which the visual solution serves as the control system’s feedback. By using three small underwater robot platforms, the proposed method is proved to be effective and practicable through underwater formation experiments. Furthermore, the coordination period and error of the control system are analyzed.

In the research on electric vehicle transmission vibration characteristics, the dynamic model involving a multistage gear system is still rare, especially the influences of driving motor excitation and load excitation which are not considered, and which makes the gear system research deviate from the actual situation. In addition, the changing processes of variables are usually simplified or neglected in the study of gear systems, which is not conducive in revealing the mechanism of gear dynamic behavior. In this paper, an improved dynamic model of a motor-gear system is established. The influences of driving motor excitation and load excitation are included, and the changing processes of tangential, axial, and torsional vibration variables of driving gear and driven gear are obtained using the state space method. Furthermore, the transmission housing vibration responses are investigated. By comparing the simulation results with the measurement data, the improved dynamic model, as well as the state space solution method, are verified as reliable and universal. On this basis, the influence of motor excitation on the state change of the gear system is discussed, which provides a theoretical approach for further study of motor drive gear systems.

Developing new coating modification technology of aluminum nitride (AlN) powder for higher hydrolysis resistance is the key to prepare high-performance AlN ceramic substrate with water-based wet process in the future. In the this paper, The poly(vinyl pyrrolidone)-b-poly(Styrene/Itaconic anhydride) (PVP-b-P(St/ITA))block copolymer with PVP as the independent chain segment was designed and synthesized through reversible addition fragmentation chain transfer (RAFT) polymerization, which was used for the study on coating modification, hydrolysis resistance, and dispersion performance of AIN powder. The study results show that, when using PVP macromolecular chain transfer agent (PVP-CTA) for the RAFT chain extension and polymerization in St/ITA binary system, the molecular weight increases linearly and the molecular weight distribution tends to decrease with the monomer conversion rate, which is in line with the activity-controlled characteristics of RAFT polymerization. The copolymer PVP-b-P(St/ITA) was used to for surface modification treatment of submicron AlN powder to generate esterification reaction, which was absorbed and bound to the powder surface. Hydrolysis resistance and dispersion experiments were conducted for modified powder, and the crystal phase and micro structure of modified powder were analyzed and observed through XRD, SEM, and TEM. It was found that copolymer modification had no effect on the powder crystal phase. A 8–21 nm passivation layer was coated on the surface, which can exist stably for 10 h in 60 °C water. Zeta potential and laser particle analyzer tests showed that modified powder featured excellent water-based slurry dispersion performance, and certain self-dispersing characteristics. The highest Zeta potential appeared in pH 6~7, and the particle granularity was distributed uniformly with the median particle diameter of 875 nm. The powder hydrolysis resistance and dispersion performance are significantly improved.

The technique of interferometric synthetic aperture radar (InSAR) is increasingly employed for landslide detection over large areas, even though the limitations of initial InSAR analysis results have been well acknowledged. Steep terrain in mountainous areas may cause geometric distortions of SAR images, which could affect the accuracy of InSAR analysis results. In addition, due to the existence of massive ground deformation points in the initial InSAR analysis results, accurate landslide recognition from the initial results is challenging. To efficiently identify potential landslide areas from the ascending–descending SAR datasets, this paper presents a novel interpretation approach to analyze the initial time-series InSAR analysis results. Within the context of the proposed approach, SAR visibility analysis, conversion analysis of deformation rates obtained from the time-series InSAR analysis, and spatial analysis and statistics tools for cluster extraction are incorporated. The effectiveness of the proposed approach is illustrated through a case study of landslide identification in Danba, a county in Sichuan, China. The potential landslide regions in the study area are identified based on the interpretation of small baseline subset InSAR (SBAS-InSAR) results, obtained with ascending–descending Sentinel-1A datasets. Finally, on the basis of the field survey results, a total of 21 landslides are detected in the potential landslide regions identified, through which the results obtained from the proposed interpretation approach are tested.

Poly(methyl acrylate)-b-poly(N-vinyl pyrrolidone/maleic anhydride/styrene) (PMA-b-P (NVP/MAH/St)) quaternary amphiphilic block copolymer prepared by reversible addition-fragmentation chain transfer (RAFT) was used to improve the anti-hydrolysis and dispersion properties of aluminum nitride (AIN) powders that were modified by copolymers. Its structure was characterized by Fourier transform infrared spectroscopy (FT-IR) and Hydrogen nuclear magnetic spectroscopy (1H-NMR). The results demonstrate that the molecular weight distribution of the quaternary amphiphilic block copolymers is 1.35–1.60, which is characteristic of controlled molecular weight and narrow molecular weight distribution. Through charge transfer complexes, NVP/MAH/St produces a regular alternating arrangement structure. After being treated with micro-crosslinking, AlN powder modified by copolymer PMA-b-P(NVP/MAH/St) exhibits outstanding resistance to hydrolysis and can be stabilized in hot water at 50 °C for more than 14 h, and the agglomeration of powder particles was improved remarkably.

This study was aimed at investigating the effect of astaxanthin on the immune function and its safety in mice. It was administered once daily at low, medium and high doses (4.2, 8.35, 16.70 mg/kg BW) to mice for 30 days. Subsequently, the spleen and thymus index, spleen lymphocyte transformation activity, delayed allergy reaction, amounts of antibody-producing cells, half-hemolytic value HC50, carbon particle clearance rate, macrophage phagocytosis, and natural killer cell (NK) activity were determined. Acute oral toxicity and genotoxicity tests were conducted to evaluate the safety of astaxanthin. Compared with the control group, medium and high doses of astaxanthin significantly increased the proliferation and transformation activities of spleen lymphocytes, activities of antibody-producing cells, serum hemolysin levels, and carbon particle clearance rate in mice (phagocytic index). High doses significantly improved delayed allergy reaction and NK cell activity. Results of acute oral toxicity and genotoxicity tests were negative. Gross anatomical observations and histopathological examination showed no abnormal changes associated with the treatments. In the article, it is confirmed that astaxanthin treatments significantly improve immune functions and show no toxic effects in the experimental doses.

The knowledge concerning the relationship between vegetation restoration and soil microorganisms is limited, especially at high altitudes. In order to evaluate the restoration efficacy of the reforestation on the soil microbial community, we have examined vegetation composition, edaphic properties and structure and function of different soil microbial groups in two different aged (25- and 40-year-old) Picea likiangensis var. rubescens Rehder & E. H. Wilson (P. rubescens) plantations and the primeval coniferous forest (PCF) dominated by Abies squamata Masters by plot-level inventories and sampling in western Sichuan Province, China. Our results suggested that only the fungal samples in 25-year-old P. rubescens plantation could be distinguished from those in the PCF in both structure and function. The structure and function of the fungal community recovered relatively slowly compared with bacterial and archaeal communities. In addition to the soil chemical properties and tree species composition, the shrub composition was also a key factor influencing the soil microbial community. The P. rubescens plantations were conducive to restoring the soil microbial community in both structure and function. However, there were uncertainties in the variations of the bacterial and archaeal communities with increasing the P. rubescens plantation age.

There are very few studies about the effects of relatively higher CO2 concentration (e.g., 1000 μmol·mol−1) or plus N fertilization on woody plants. In this study, Schima superba seedings were exposed to ambient or eCO2 (550, 750, and 1000 μmol·mol−1) and N fertilization (0 and 10 g·m−2·yr−1, hereafter: low N, high N, respectively) for one growth season to explore the potential responses in a subtropical site with low soil N availability. N fertilization strongly increased leaf mass-based N by 118.38%, 116.68%, 106.78%, and 138.95%, respectively, in different CO2 treatments and decreased starch, with a half reduction in leaf C:N ratio. Leaf N was significantly decreased by eCO2 in both low N and high N treatments, and N fertilization stimulated the decrease of leaf N and mitigated the increase of leaf C:N by eCO2. In low N treatments, photosynthetic rate (Pn) was maximized at 733 μmol·mol−1 CO2 in August and September, while, in high N treatments, Pn was continuously increased with elevation of CO2. N fertilization significantly increased plant biomass especially at highly elevated CO2, although no response of biomass to eCO2 alone. These findings indicated that N fertilization would modify the response of S. superba to eCO2.

Building extraction from LiDAR data has been an active research area, but it is difficult to discriminate between buildings and vegetation in complex urban scenes. A building extraction method from LiDAR data based on minimum cut (min-cut) and improved post-processing is proposed. To discriminate building points on the intersecting roof planes from vegetation, a point feature based on the variance of normal vectors estimated via low-rank subspace clustering (LRSC) technique is proposed, and non-ground points are separated into two subsets based on min-cut after filtering. Then, the results of building extraction are refined via improved post-processing using restricted region growing and the constraints of height, the maximum intersection angle and consistency. The maximum intersection angle constraint removes large non-building point clusters with narrow width, such as greenbelt along streets. Contextual information and consistency constraint are both used to eliminate inhomogeneity. Experiments of seven datasets, including five datasets provided by the International Society for Photogrammetry and Remote Sensing (ISPRS), one dataset with high-density point data and one dataset with dense buildings, verify that most buildings, even with curved roofs, are successfully extracted by the proposed method, with over 94.1% completeness and a minimum 89.8% correctness at the per-area level. In addition, the proposed point feature significantly outperforms the comparison alternative and is less sensitive to feature threshold in complex scenes. Hence, the extracted building points can be used in various applications.

Building detection using airborne Light Detection And Ranging (LiDAR) data is the essential prerequisite of many applications, including three-dimensional city modeling. In the paper, we propose a coarse-to-fine building detection method that is based on semi-suppressed fuzzy C-means and restricted region growing. Based on a filtering step, the remaining points can be separated into two groups by semi-suppressed fuzzy C-means. The group contains points that are located on building roofs that form a building candidate set. Subsequently, a restricted region growing algorithm is implemented to search for more building points. The proposed region growing method perfectly ensures the rapid growth of building regions and slow growth of non-building regions, which enlarges the area differences between building and non-building regions. A two-stage strategy is then adopted to remove tiny point clusters with small areas. Finally, a minimum bounding rectangle (MBR) is used to supplement the building points and refine the results of building detection. Experimental results on five datasets, including three datasets that were provided by the International Society for Photogrammetry and Remote Sensing (ISPRS) and two Chinese datasets, verify that most buildings and non-buildings can be well separated during our coarse building detection process. In addition, after refined processing, our proposed method can offer a high success rate for building detection, with over 89.5% completeness and a minimum 91% correctness. Hence, various applications can exploit our proposed method.

The study reports a facile and eco-friendly approach for nanomaterial synthesis and enzyme immobilization. A corresponding glucose biosensor was fabricated by immobilizing the gold nanoparticles (AuNPs) and glucose oxidase (GOD) multilayer films onto the polypyrrole (PPy)/reduced graphene oxide (RGO) modified glassy carbon electrode (GCE) via the electrodeposition and self-assembly. PPy and graphene oxide were first coated on the surface of a bare GCE by the electrodeposition. Then, AuNPs and GOD were alternately immobilized onto PPy-RGO/GCE electrode using the electrodeposition of AuNPs and self-assembly of GOD to obtain AuNPs-GOD multilayer films. The resulting PPy-RGO-(AuNPs-GOD)n/GCE biosensors were used to characterize and assess their electrocatalytic activity toward glucose using cyclic voltammetry and amperometry. The response current increased with the increased number of AuNPs-GOD layers, and the biosensor based on four layers of AuNPs-GOD showed the best performance. The PPy-RGO-(AuNPs-GOD)4/GCE electrode can detect glucose in a linear range from 0.2 mM to 8 mM with a good sensitivity of 0.89 μA/mM, and a detection limit of 5.6 μM (S/N = 3). This study presents a promising eco-friendly biosensor platform with advantages of electrodeposition and self-assembly, and would be helpful for the future design of more complex electrochemical detection systems.

Ochratoxin A (OTA) contamination in grape production is an important problem worldwide. Microbial volatile organic compounds (MVOCs) have been demonstrated as useful tools to identify different toxigenic strains. In this study, Aspergillus carbonarius strains were classified into two groups, moderate toxigenic strains (MT) and high toxigenic strains (HT), according to OTA-forming ability. The MVOCs were analyzed by GC-MS and the data processing was based on untargeted profiling using XCMS Online software. Orthogonal projection to latent structures discriminant analysis (OPLS-DA) was performed using extract ion chromatogram GC-MS datasets. For contrast, quantitative analysis was also performed. Results demonstrated that the performance of the OPLS-DA model of untargeted profiling was better than the quantitative method. Potential markers were successfully discovered by variable importance on projection (VIP) and t-test. (E)-2-octen-1-ol, octanal, 1-octen-3-one, styrene, limonene, methyl-2-phenylacetate and 3 unknown compounds were selected as potential markers for the MT group. Cuparene, (Z)-thujopsene, methyl octanoate and 1 unknown compound were identified as potential markers for the HT groups. Finally, the selected markers were used to construct a supported vector machine classification (SVM-C) model to check classification ability. The models showed good performance with the accuracy of cross-validation and test prediction of 87.93% and 92.00%, respectively.

We describe the novel, multiply gaited, vectored water-jet, hybrid locomotion-capable, amphibious spherical robot III (termed ASR-III) featuring a wheel-legged, water-jet composite driving system incorporating a lifting and supporting wheel mechanism (LSWM) and mechanical legs with a water-jet thruster. The LSWM allows the ASR-III to support the body and slide flexibly on smooth (flat) terrain. The composite driving system facilitates two on-land locomotion modes (sliding and walking) and underwater locomotion mode with vectored thrusters, improving adaptability to the amphibious environment. Sliding locomotion improves the stability and maneuverability of ASR-III on smooth flat terrain, whereas walking locomotion allows ASR-III to conquer rough terrain. We used both forward and reverse kinematic models to evaluate the walking and sliding gait efficiency. The robot can also realize underwater locomotion with four vectored water-jet thrusters, and is capable of forward motion, heading angle control and depth control. We evaluated LSWM efficiency and the sliding velocities associated with varying extensions of the LSWM. To explore gait stability and mobility, we performed on-land experiments on smooth flat terrain to define the optimal stride length and frequency. We also evaluated the efficacy of waypoint tracking when the sliding gait was employed, using a closed-loop proportional-integral-derivative (PID) control mechanism. Moreover, experiments of forward locomotion, heading angle control and depth control were conducted to verify the underwater performance of ASR-III. Comparison of the previous robot and ASR-III demonstrated the ASR-III had better amphibious motion performance.

A new atisane-type diterpene, ent-16α-hydroxy-atisane-3,4-lactone (4) and three known diterpenes, ent-16α-hydroxy-atisane-3-one (1), ent-atisane-3β,16α-diol (2), ent-3,4-seco-16α-hydroxyatis- 4(19)-en -3-oic acid (3) were isolated from the bark of the mangrove plant Excoecaria agallocha. Their structures and relative stereochemistry were elucidated by means of extensive NMR and MS analysis. Compound 3 exhibited significant anti-microfouling activity against the adherence of Pseudomonas pseudoalcaligenes, with an EC50 value of 0.54 ± 0.01 ppm.

The new diterpene methoxy-ent-8(14)-pimarenely-15-one (1) and three knownmetabolites: ent-8(14)-pimarene-15R,16-diol (2), stigmasterol (3) and β-sitosterol (4), wereisolated from the roots of the mangrove plant Ceriops tagal. Their structures and relativestereochemistry were elucidated by means of extensive NMR, IR and MS analysis.Compounds 1, 2, 3 and 4 exhibited significant antifouling activities against cyprid larvaeof the barnacle Balanus albicostatus Pilsbry, with EC50 values of 0.32 ± 0.01, 0.04 ± 0.00,4.05 ± 0.15 and 18.47 ± 0.40 μg/cm2, respectively, whereas their toxicities towards cypridswere very low, with LC50 values all above 10 μg/cm2.

Whole oat milk (WOM) was prepared from germinated oat by an innovatively designed high-energy fluidic microfluidizer (HEFM). The results indicated that germination treatment significantly raised the content of total protein, γ-aminobutyric acid, total phenolics, and reducing sugar, while it decreased the content of total starch and β-glucan. Oat with a germination time of 48 h had the best nutritional quality for producing WOM. The physical stability of the WOM prepared from germinated oat was effectively improved by HEFM treatment. The apparent viscosity increased, the instability index reduced from 0.67 to 0.37, and the precipitate weight ratio decreased from 13.54% to 9.51%. As the pressure of the HEFM increased from 0 to 120 MPa, the particle size decreased from 169.5 to 77.0 µm, which was helpful to improve the physical stability of the WOM. Meanwhile, the color of the WOM became whiter after the HEFM treatment. The content of β-glucan and soluble protein in the WOM significantly increased, which was due to the disruption of cells by the HEFM processing. The optimal HEFM pressure for WOM production is 120 MPa. This study provided a new way to produce whole oat milk with a high nutritional quality and good physical properties.

China ranks first in apple production worldwide, making the assessment of apple quality a critical factor in agriculture. Sucrose concentration (SC) is a key factor influencing the flavor and ripeness of apples, serving as an important quality indicator. Nondestructive SC detection has significant practical value. Currently, SC is mainly measured using handheld refractometers, hydrometers, electronic tongues, and saccharimeter analyses, which are not only time-consuming and labor-intensive but also destructive to the sample. Therefore, a rapid nondestructive method is essential. The fluorescence hyperspectral imaging system (FHIS) is a tool for nondestructive detection. Upon excitation by the fluorescent light source, apples displayed distinct fluorescence characteristics within the 440-530 nm and 680-780 nm wavelength ranges, enabling the FHIS to detect SC. This study used FHIS combined with machine learning (ML) to predict SC at the apple's equatorial position. Primary features were extracted using variable importance projection (VIP), the successive projection algorithm (SPA), and extreme gradient boosting (XGBoost). Secondary feature extraction was also conducted. Models like gradient boosting decision tree (GBDT), random forest (RF), and LightGBM were used to predict SC. VN-SPA + VIP-LightGBM achieved the highest accuracy, with Rp2, RMSEp, and RPD reaching 0.9074, 0.4656, and 3.2877, respectively. These results underscore the efficacy of FHIS in predicting apple SC, highlighting its potential for application in nondestructive quality assessment within the agricultural sector.

Frankliniella intonsa (Trybom) (Thysanoptera: Thripidae) is an important type of thrip and a polyphagous pest, which poses a serious threat to many crops, especially those in tropical regions of China. Its feeding behavior and the damage caused vary among different host plant species and are affected by ambient temperature and plant nutrients as well. The digestion rate is an important index for directly observing the digestion process, but there have been no studies directly measuring the digestion in thrips under the influence of different temperatures and host plants. Here, the digestion rate of F. intonsa was assessed by using a molecular diagnostic tool. We also determined the nutrient content in three host plant (mango, cowpea, and pepper), including soluble proteins, free fatty acids, soluble sugars, and water. The results showed that the high and low temperatures (16 °C and 32 °C) both seemed to accelerate the digestion of F. intonsa compared to the optimal temperature (26 °C) and the protein content of plants played an important role in the digestive response of F. intonsa to temperature changes. The findings can help reveal the feeding damage caused by F. intonsa to different plants and help to better understand its feeding ecology, according to its interaction with the host plant.

Chestnuts become sweetened with better tastes for fried products after cold storage, but the possible mechanism is not clear. The dynamics of sugar components and related physiological responses, as well as the possible molecular mechanism in chestnuts during cold storage, were investigated. Sucrose accumulation and starch degradation contributed to taste improvement. Sucrose content reached the peak after two months of cold storage, along with the accumulation of reducing sugars of maltose, fructose and glucose to a much lesser extent. Meanwhile, alpha-amylase and beta-amylase maintained high levels, and the activities of acid invertase and sucrose synthase increased. Transcriptome data demonstrated that differentially expressed genes (DEGs) were significantly enriched in the process of starch and sucrose metabolism pathway, revealing the conversion promotion of starch to sucrose. Furthermore, DEGs involved in multiple phytohormone biosynthesis and signal transduction, as well as the transcription regulators, indicated that sucrose accumulation might be interconnected with the dormancy release of chestnuts, with over 90% germinated after two months of cold storage. Altogether, the results indicated that cold storage improved the taste of chestnuts mainly due to sucrose accumulation induced by DEGs of starch and sucrose metabolism pathway in this period, and the sweetening process was interconnected with dormancy release.

Cyclobalanopsis gilva , a valuable timber species in China, holds significant importance for understanding the constraints imposed by climate change on the dynamic geographic distribution of tree species. This study utilized the MaxEnt maximum entropy model to reconstruct the migratory dynamics of C. gilva geographical distribution since the Last Glacial Maximum. The objective was to comprehend the restrictive mechanisms of environmental factors on its potential geographical distribution, aiming to provide insights for mid-to-long-term afforestation planning of C. gilva . The optimized MaxEnt model exhibited a significantly high predictive accuracy, with an average AUC value of 0.949 ± 0.004 for the modern suitable habitat model of C. gilva . The total suitable habitat area for C. gilva in contemporary times was 143.05 × 10 4 km 2 , with a highly suitable habitat area of 3.14 × 10 4 km 2 . The contemporary suitable habitat was primarily located in the southeastern regions of China, while the highly suitable habitat was concentrated in eastern Fujian and central-eastern Taiwan. Bioclimatic variables such as mean diurnal range (Bio2), min temperature of coldest month (Bio6), precipitation of driest quarter (Bio17), and precipitation of driest month (Bio14) predominantly influenced the modern geographic distribution pattern of C. gilva , with temperature factors playing a leading role. With global climate warming, there is a risk of fragmentation or even loss of suitable habitat for C. gilva by 2050 and 2090. Therefore, the findings of this study can significantly contribute to initiating a habitat conservation campaign for this species.

To thoroughly understand the profile of phenolic phytochemicals in kidney bean seeds cultivated in a cold region, the extractions, contents, antioxidant activities, compositions of free and bound phenols in the seed coat and cotyledon, and also relevant color attributes, were investigated. The results indicated that ultrasound-assisted extraction was an efficient method for free phenols. The bound phenols in seed coat and cotyledon were released more efficiently by alkali-acid and acid-alkali sequential hydrolysis, respectively. Under the optimized extractions, total phenols (TPC), flavonoids (TFC), and anthocyanins (TAC) ranged in 7.81-32.89 mg GAE/g dw, 3.23-15.65 mg RE/g dw, and 0-0.21 mg CE/g dw in the whole seeds of the five common kidney beans. There was a big difference in phenolic distribution between red and white seeds. From whole seed, the phenols in the four red cultivars mainly existed in free state (78.84%) and seed coat (71.56%), while the phenols in the white 'Sark' divided equally between free (51.18%) and bound (48.82%) states and consisted chiefly in cotyledon (81.58%). The correlation analyses showed that the antioxidant activities were significantly and positively correlated with TPC and TFC. The phenolic attributes were closely associated with the color of the seed coat. Red seeds had higher total contents of phenols than white seeds. TAC had a positively significant correlation with redness. Brightness and yellowness showed a negatively significant correlation with TPC, TFC, and antioxidant capacities, which were necessarily linked with redness degree and spot in red seeds. The spotted red 'Yikeshu' with the most outstanding performance on phenolic attributes was selected to analyze phenolic compounds with UHPLC-QE-MS. Among the 85 identified phenolics, 2 phenolic acids and 10 flavonoids were dominant. The characteristic phenolics in free and bound states were screened in both seed coat and cotyledon, respectively. The available information on the phenolic profile may expand the utilization of kidney beans as a nutritional ingredient in the food industry.

Respiratory illnesses present a significant threat to porcine health, with co-infections involving Porcine Reproductive and Respiratory Syndrome Virus (PRRSV), Streptococcus suis ( SS ), Porcine Circovirus Type 2 (PCV2), and Porcine Circovirus Type 3 (PCV3) acting as the primary causative agents. As a result, the precise diagnosis of PRRSV, PCV2, PCV3 and SS is of paramount importance in the prevention and control of respiratory diseases in swine. Therefore, we conducted a molecular bioinformatical analysis to concurrently detect and differentiate PRRSV, PCV2, PCV3 and SS . We selected the ORF6 gene of PRRSV, the ORF2 gene of PCV2 and PCV3, and the glutamate dehydrogenase (GDH) gene of SS as targets. Specific primers and probes were designed for each pathogen, and following meticulous optimization of reaction conditions, we established a multiple TaqMan fluorescence quantitative PCR detection method. Subsequently, we subjected this method to a comprehensive assessment, evaluating its specificity, sensitivity, and repeatability. The research results demonstrated that the established multiple TaqMan fluorescence quantitative PCR detection method displays displayed exemplary specificity, with no instances of cross-reactivity with other pathogens. The method's minimum detection concentrations for PRRSV, PCV2, PCV3, and SS were 2.80 × 10 1 copies/µL, 1.96 × 10 2 copies/µL, 2.30 × 10 2 copies/µL, and 1.75 × 10 3 copies/µL, respectively. When applied to the analysis of 30 clinical samples, the results closely mirrored those obtained through Chinese standard uniplex real-time qPCR detection method for PRRSV, as well as the general PCR methods for SS , PCV2, and PCV3. This study underscores the robust specificity, high sensitivity, and consistent stability of the multiple TaqMan fluorescence quantitative PCR detection method that we have developed. It is ideally suited to the clinical monitoring of PRRSV, PCV2, PCV3, and SS , and it carries significant importance in ongoing efforts to prevent and manage respiratory diseases in porcine populations.

To cope with the yield loss caused by drought stress, new oat varieties with greater drought tolerance need to be selected. In this study, two oat varieties with different drought tolerances were selected for analysis of their phenotypes and physiological indices under moderate and severe soil drought stress. The results revealed significant differences in the degree of wilting, leaf relative water content (RWC), and SOD and CAT activity between the two oat genotypes under severe soil drought stress; moreover, the drought-tolerant variety exhibited a significant increase in the number of stomata and wax crystals on the surface of both the leaf and guard cells; additionally, the morphology of the guard cells was normal, and there was no significant disruption of the grana lamella membrane or the nuclear envelope. Furthermore, transcriptome analysis revealed that the expression of genes related to the biosynthesis of waxes and cell-wall components, as well as those of the WRKY family, significantly increased in the drought-tolerant variety. These findings suggest that several genes involved in the antioxidant pathway could improve drought tolerance in plants by regulating the increase/decrease in wax and cell-wall constituents and maintaining normal cellular water potential, as well as improving the ability of the antioxidant system to scavenge peroxides in oats.

Warmer temperatures significantly influence crop yields, which are a critical determinant of food supply and human well-being. In this study, a probabilistic approach based on bivariate copula models was used to investigate the dependence (described by joint distribution) between crop yield and growing season temperature (T GS ) in the major producing provinces of China for three staple crops (i.e., rice, wheat, and maize). Based on the outputs of 12 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) under Shared Socioeconomic Pathway 5-8.5, the probability of yield reduction under 1.5 °C and 2 °C global warming was estimated, which has great implications for agricultural risk management. Results showed that yield response to T GS varied with crop and region, with the most vulnerable being rice in Sichuan, wheat in Sichuan and Gansu, and maize in Shandong, Liaoning, Jilin, Nei Mongol, Shanxi, and Hebei. Among the selected five copulas, Archimedean/elliptical copulas were more suitable to describe the joint distribution between T GS and yield in most rice-/maize-producing provinces. The probability of yield reduction was greater in vulnerable provinces than in non-vulnerable provinces, with maize facing a higher risk of warming-driven yield loss than rice and wheat. Compared to the 1.5 °C global warming, an additional 0.5 °C warming would increase the yield loss risk in vulnerable provinces by 2-17%, 1-16%, and 3-17% for rice, wheat, and maize, respectively. The copula-based model proved to be an effective tool to provide probabilistic estimates of yield reduction due to warming and can be applied to other crops and regions. The results of this study demonstrated the importance of keeping global warming within 1.5 °C to mitigate the yield loss risk and optimize agricultural decision-making in vulnerable regions.

Objective: Walnuts have rich nutritional value and are favored by the majority of consumers. As walnuts are shelled nuts, they are prone to suffer from defects such as mildew during storage. The fullness and mildew of the fruit impose effects on the quality of the walnuts. Therefore, it is of great economic significance to carry out a study on the rapid, non-destructive detection of walnut quality., Methods: Terahertz spectroscopy, with wavelengths between infrared and electromagnetic waves, has unique detection advantages. In this paper, the rapid and nondestructive detection of walnut mildew and fullness based on terahertz spectroscopy is carried out using the emerging terahertz transmission spectroscopy imaging technology. First, the normal walnuts and mildewed walnuts are identified and analyzed. At the same time, the image processing is carried out on the physical samples with different kernel sizes to calculate the fullness of the walnut kernels. The THz image of the walnuts is collected to extract the spectral information in different regions of interest. Four kinds of time domain signals in different regions of interest are extracted, and three qualitative discrimination models are established, including the support vector machine (SVM), random forest (RF), and k-nearest neighbor (KNN) algorithms. In addition, in order to realize the visual expression of walnut fullness, the terahertz images of the walnut are segmented with a binarization threshold, and the walnut fullness is calculated by the proportion of the shell and kernel pixels., Results: In the frequency domain signal, the amplitude intensity from high to low is the mildew sample, walnut kernel, and walnut shell, and the distinction between walnut kernel, shell samples, and mildew samples is high. The overall identification accuracy of the aforementioned three models is 90.83%, 97.38%, and 97.87%, respectively. Among them, KNN has the best qualitative discrimination effect. In a single category, the recognition accuracy of the model for the walnut kernel, walnut shell, mildew sample, and reference group (background) reaches 94%, 100%, 97.43%, and 100%, respectively. The terahertz transmission images of the five categories of walnut samples with different kernel sizes are processed to visualize the detection of kernel fullness inside walnuts, and the errors are less than 5% compared to the actual fullness of walnuts., Conclusion: This study illustrates that terahertz spectroscopy detection can achieve the detection of walnut mildew, and terahertz imaging technology can realize the visual expression and fullness calculation of walnut kernels. Terahertz spectroscopy and imaging provides a non-destructive detection method for walnut quality, which can provide a reference for the quality detection of other dried nuts with shells, thus having significant practical value.

This study aimed to examine the association between the psychotic-like experiences (PLEs) and emotion-regulation (ER) strategies of adolescents and their parents' relationship, and we hypothesized that the parents' relationship moderates the link between ER strategies and PLEs. We recruited a total of 2708 first-year college students (1659 males and 1049 females) aged 15-20 years (mean = 17.9). Participants completed assessments of PLEs, their use of ER strategies, and reported their parents' relationship as harmonious, conflicting, or divorced. Regression analyses indicated that the lower the use of the emotion-reappraisal strategy, the greater the use of the emotion-suppression strategy and that parental conflict or divorce predicted the number of PLEs endorsed and the level of distress from the PLEs. The parents' relationship moderated the association between ER strategies and distress from PLEs. Among those who reported parental conflict or divorce, their lower use of the reappraisal strategy predicted their experiencing higher levels of distress from their PLEs. This study suggested the direct and interactive influence of the parents' relationship and ER strategies on the presence of PLEs and PLE-related distress levels among adolescents, which may represent potential intervention targets.

Background: There is a lack of consensus in optimal management of portal vein thrombosis (PVT) in patients with cirrhosis. The purpose of this study is to compare the safety and thrombosis burden change for cirrhotic patients with non-tumoral PVT managed by transjugular intrahepatic portosystemic shunt (TIPS) only, anticoagulation only, or no treatment., Methods: This single-center retrospective study evaluated 52 patients with cirrhosis and non-tumoral PVT managed by TIPS only (14), anticoagulation only (11), or no treatment (27). The demographic, clinical, and imaging data for patients were collected. The portomesenteric thrombosis burden and liver function tests at early follow-up (6-9 months) and late follow-up (9-16 months) were compared to the baseline. Adverse events including bleeding and encephalopathy were recorded., Results: The overall portomesenteric thrombosis burden improved in eight (72%) TIPS patients, three (27%) anticoagulated patients, and two (10%) untreated patients at early follow-up ( p = 0.001) and in seven (78%) TIPS patients, two (29%) anticoagulated patients, and three (17%) untreated patients in late follow-up ( p = 0.007). No bleeding complications attributable to anticoagulation were observed., Conclusion: TIPS decreased portomesenteric thrombus burden compared to anticoagulation or no treatment for cirrhotic patients with PVT. Both TIPS and anticoagulation were safe therapies.

Synthetic rubber produced from nonrenewable fossil fuel requires high energy costs and is dependent on the presumed unstable petroleum price. Natural rubber latex (NRL) is one of the major alternative sustainable rubber sources since it is derived from the plant ' Hevea brasiliensis '. Our study focuses on integrating sustainably processed carboxycellulose nanofibers from untreated jute biomass into NRL to enhance the mechanical strength of the material for various applications. The carboxycellulose nanofibers (NOCNF) having carboxyl content of 0.94 mmol/g was prepared and integrated into its nonionic form (-COONa) for its higher dispersion in water to increase the interfacial interaction between NRL and NOCNF. Transmission electron microscopy (TEM) and atomic force microscopy (AFM) analyses of NOCNF showed the average dimensions of nanofibers were length (L) = 524 ± 203 nm, diameter (D) 7 ± 2 nm and thickness 2.9 nm. Furthermore, fourier transform infra-red spectrometry (FTIR) analysis of NOCNF depicted the presence of carboxyl group. However, the dynamic light scattering (DLS) measurement of NRL demonstrated an effective diameter in the range of 643 nm with polydispersity of 0.005. Tensile mechanical strengths were tested to observe the enhancement effects at various concentrations of NOCNF in the NRL. Mechanical properties of NRL/NOCNF films were determined by tensile testing, where the results showed an increasing trend of enhancement. With the increasing NOCNF concentration, the film modulus was found to increase quite substantially, but the elongation-to-break ratio decreased drastically. The presence of NOCNF changed the NRL film from elastic to brittle. However, at the NOCNF overlap concentration (0.2 wt. %), the film modulus seemed to be the highest.