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Nanofine zero−valent iron (nZVI) is a new, eco−friendly material with strong reducing and adsorbent properties that can be used to clean up heavy metal−affected soils. Herein, nZVI encapsulated with carboxymethyl cellulose (CMC−nZVI) is synthesized via an aqueous−phase reduction technique and subsequently deployed to evaluate its effectiveness in Cr(VI) soil remediation. The characterization analysis used SEM−EDS, XRD, XPS, and LSV to determine the relevant properties of the material. The results show that at an initial Cr(VI) concentration of 169.5 mg·kg−1, 93.2% of Cr(VI) was removed from the soil after 10 h of treatment with CMC−nZVI at pH 3.3. The kinetic analysis showed that CMC−nZVI had the maximum equilibrium adsorption capacity for removing Cr(VI) from soil at 105.3 mg·g−1. This followed a pseudo−second−order kinetic model. The study shows that CMC−nZVI converts Cr(VI) to Cr(III), which forms complexes with Fe(III) ions in the presence of hydroxide ions (OH−) to form a highly stable compound that eventually adsorbs into the nanomaterial’s surface for efficient removal.

In engineering applications, the accuracy of on-load tap changer (OLTC) mechanical fault identification methods based on vibration signals is constrained by the quantity and quality of the samples. Therefore, a novel small-sample-size OLTC mechanical fault identification method incorporating short-time Fourier transform (STFT), synchrosqueezed wavelet transform (SWT), a dual-stream convolutional neural network (DSCNN), and support vector machine (SVM) is proposed. Firstly, the one-dimensional time-series vibration signals are transformed using STFT and SWT to obtain time–frequency graphs. STFT time–frequency graphs capture the global features of the OLTC vibration signals, while SWT time–frequency graphs capture the local features of the OLTC vibration signals. Secondly, these time–frequency graphs are input into the CNN to extract key features. In the fusion layer, the feature vectors from the STFT and SWT graphs are combined to form a fusion vector that encompasses both global and local time–frequency features. Finally, the softmax classifier of the traditional CNN is replaced with an SVM classifier, and the fusion vector is input into this classifier. Compared to the traditional fault identification methods, the proposed method demonstrates higher identification accuracy and stronger generalization ability under the conditions of small sample sizes and noise interference.

The global demand for sustainable and nutritious food sources has catalyzed interest in legumes, known for their rich repertoire of health-promoting compounds. This review delves into the diverse array of bioactive peptides, protein subunits, isoflavones, antinutritional factors, and saponins found in the primary legume protein sources—soybeans, peas, chickpeas, and mung beans. The current state of research on these compounds is critically evaluated, with an emphasis on the potential health benefits, ranging from antioxidant and anticancer properties to the management of chronic diseases such as diabetes and hypertension. The extensively studied soybean is highlighted and the relatively unexplored potential of other legumes is also included, pointing to a significant, underutilized resource for developing health-enhancing foods. The review advocates for future interdisciplinary research to further unravel the mechanisms of action of these bioactive compounds and to explore their synergistic effects. The ultimate goal is to leverage the full spectrum of benefits offered by legumes, not only to advance human health but also to contribute to the sustainability of food systems. By providing a comprehensive overview of the nutraceutical potential of legumes, this manuscript sets a foundation for future investigations aimed at optimizing the use of legumes in the global pursuit of health and nutritional security.

In order to further clarify the growth-promoting effect of the non-core Metarhizium sp. Marquandomyces marquandii on plants, M. marquandii SGSF043, which was obtained via pre-screening in the laboratory, was selected as a test strain and the seed soaking method was adopted. The effects of a fermentation broth obtained from this strain on the seed germination, seedling growth, and rhizosphere soil enzyme activity of maize were studied. The results were as follows: In seed germination tests, M. marquandii SGSF043 fermentation liquid had a certain inhibitory effect on corn seed germination, and the germination rate was only 15%. When the fermentation solution was diluted 10 times, the germination rate reached 97%. After the germination test, the growth of maize plumules was promoted in the groups treated with 10-times and 1000-times dilutions. In the field community experiment, based on the comprehensive evaluation of seedling biomass indicators, the solution diluted 100 times had the best growth-promoting effect. The aboveground fresh weight was increased by 127.13% compared with the control group. The results show that M. marquandii SGSF043 has the potential to promote the growth of maize and improve the soil environment, which provides a theoretical basis for the research on and the application of M. marquandii in farmland.

This paper investigates a novel reinforcement learning (RL)-based quadratic programming (QP) method for the safety-critical trajectory tracking control of autonomous underwater vehicles (AUVs). The proposed approach addresses the substantial challenge posed by model uncertainty, which may hinder the safety and performance of AUVs operating in complex underwater environments. The RL framework can learn the inherent model uncertainties that affect the constraints in Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). These learned uncertainties are subsequently integrated for formulating a novel RL-CBF-CLF Quadratic Programming (RL-CBF-CLF-QP) controller. Corresponding simulations are demonstrated under diverse trajectory tracking scenarios with high levels of model uncertainties. The simulation results show that the proposed RL-CBF-CLF-QP controller can significantly improve the safety and accuracy of the AUV’s tracking performance.

Complex problem solving (CPS) refers to a set of higher-order capacities that allow an individual to interact with a dynamic environment and solve complex problems. The purpose of this study was to develop and validate Sokoban, a game-based assessment of the planning–execution stage of the CPS framework proposed by PISA 2012. The psychometric properties of this instrument were examined in a large sample of Chinese students (n = 1145) ranging from elementary to tertiary education. The results supported the two-faceted nature of Sokoban, as well as providing preliminary evidence about criterion-related and predictive validity for the planning–execution stages of complex problem solving. These empirical results lend support to the validity of this game-based assessment, as well as its practical implications in educational settings.

In the context of multi-array multi-target bearing-only localization, due to the existence of direction-finding errors, the crossing results of bearing lines cannot accurately determine correspondence with targets. Under conditions that clutter interference and missing of detection in direction-finding, the traditional method will produce false alarm targets and miss some targets. To address this issue, this paper draws on the idea of a spatial likelihood map which calculates the likelihood of target presence at each grid point within the observation area by partitioning the observation area into grids and utilizing bearing data from each array, yielding the distribution of targets in the observation area. Then, a multi-target cyclic peak extraction algorithm based on a statistical dual-threshold is proposed, which eliminates false peaks by cyclic extraction of target positions, so as to reduce false targets. Simulation results demonstrate that the spatial likelihood mapping-based localization exhibits good performance. Furthermore, when the multi-target cyclic peak extraction algorithm based on statistical dual-thresholds is applied, it outperforms direct target extraction from the spatial likelihood map, showcasing enhanced multi-target localization capabilities. Moreover, compared to the position non-linear least squares multi-target localization method, the proposed method has lower optimal sub-pattern assignment distance and lower localization error under the condition of interference and missing detection.

Scientific identification of ecological sources and corridors is crucial in constructing an ecological security pattern (ESP). To develop an ESP tailored to the scale of central urban areas in megacities, this study takes Central Beijing as the research object. It innovatively integrates the integrated valuation of ecosystem services and tradeoffs (InVEST), the morphological spatial pattern analysis (MSPA), and the Conefor software to identify ecological sources. Seven indicators related to topographic, natural conditions, and human disturbance factors are selected to build the ecological resistance surface, which is then combined with circuit theory to construct the ESP. The results show the following: (1) Central Beijing contains 157 ecological sources, primarily distributed in the western, northern, and eastern regions, with woodland as the dominant land type. (2) A total of 439 ecological corridors were extracted, including 317 key ecological corridors and 122 inactive ecological corridors. (3) The identified ecological pinch points are mainly the Jingmi Diversion Canal and the West Moat. (4) The identified ecological barriers are spread throughout the entire study area. The results of this study are highly significant for improving the quality of ecological security and protecting biodiversity in the study area and other urban centers.

Due to the all-time and all-weather characteristics of synthetic aperture radar (SAR) data, they have become an important input for optical image restoration, and various cloud removal datasets based on SAR-optical have been proposed. Currently, the construction of multi-source cloud removal datasets typically employs single-polarization or dual-polarization backscatter SAR feature images, lacking a comprehensive description of target scattering information and polarization characteristics. This paper constructs a high-resolution remote sensing dataset, AIR-POLSAR-CR1.0, based on optical images, backscatter feature images, and polarization feature images using the fully polarimetric synthetic aperture radar (PolSAR) data. The dataset has been manually annotated to provide a foundation for subsequent analyses and processing. Finally, this study performs a performance analysis of typical cloud removal deep learning algorithms based on different categories and cloud coverage on the proposed standard dataset, serving as baseline results for this benchmark. The results of the ablation experiment also demonstrate the effectiveness of the PolSAR data. In summary, AIR-POLSAR-CR1.0 fills the gap in polarization feature images and demonstrates good adaptability for the development of deep learning algorithms.

Existing autonomous driving systems face challenges in accurately capturing drivers’ cognitive states, often resulting in decisions misaligned with drivers’ intentions. To address this limitation, this study introduces a pioneering human-centric spatial cognition detecting system based on drivers’ electroencephalogram (EEG) signals. Unlike conventional EEG-based systems that focus on intention recognition or hazard perception, the proposed system can further extract drivers’ spatial cognition across two dimensions: relative distance and relative orientation. It consists of two components: EEG signal preprocessing and spatial cognition decoding, enabling the autonomous driving system to make more contextually aligned decisions regarding the targets drivers focus on. To enhance the detection accuracy of drivers’ spatial cognition, we designed a novel EEG signal decoding method called a Dual-Time-Feature Network (DTFNet). This approach integrates coarse-grained and fine-grained temporal features of EEG signals across different scales and incorporates a Squeeze-and-Excitation module to evaluate the importance of electrodes. The DTFNet outperforms existing methods, achieving 65.67% and 50.65% accuracy in three-class tasks and 84.46% and 70.50% in binary tasks. Furthermore, we investigated the temporal dynamics of drivers’ spatial cognition and observed that drivers’ perception of relative distance occurs slightly later than their perception of relative orientation, providing valuable insights into the temporal aspects of cognitive processing.

Currently, research on the rotational modulation of dual-axis inertial navigation for isolated carrier motion does not provide sufficient solutions for the compensation of the gyroscope scale factor error caused by the Earth’s rotation. Moreover, it is primarily applied to ships with low maneuverability and has not yet been implemented in the field of pure inertial guidance weapons. A dual-axis inertial isolation rotation modulation method is proposed to address this issue, taking into account the application characteristics of long-endurance guided weapons. An analysis of the system error characteristics under the coupling of multiple error sources acting on IMU was conducted, and it was found that the angular velocity of the inertial isolation carrier can significantly reduce the output error of the IMU. A dual-axis inertial isolation shaft system installation error compensation algorithm was designed, and an improvement was made based on the traditional sixteen-sequence rotation scheme to compensate for the projection components of the Earth’s rotation and carrier motion on the inner and outer frame rotation axes, achieving the inertial isolation rotation modulation function of dual-axis inertial navigation. Based on the attitude changes in long-range guided weapons, Monte Carlo simulation verification was conducted, and the results showed that this scheme can improve inertial navigation accuracy by 10% to 20%.

Due to the short time, high labor intensity and high workload of fruit and vegetable harvesting, robotic harvesting instead of manual operations is the future. The accuracy of object detection and location is directly related to the picking efficiency, quality and speed of fruit-harvesting robots. Because of its low recognition accuracy, slow recognition speed and poor localization accuracy, the traditional algorithm cannot meet the requirements of automatic-harvesting robots. The increasingly evolving and powerful deep learning technology can effectively solve the above problems and has been widely used in the last few years. This work systematically summarizes and analyzes about 120 related literatures on the object detection and three-dimensional positioning algorithms of harvesting robots over the last 10 years, and reviews several significant methods. The difficulties and challenges faced by current fruit detection and localization algorithms are proposed from the aspects of the lack of large-scale high-quality datasets, the high complexity of the agricultural environment, etc. In response to the above challenges, corresponding solutions and future development trends are constructively proposed. Future research and technological development should first solve these current challenges using weakly supervised learning, efficient and lightweight model construction, multisensor fusion and so on.

The calcium requirements of dairy cows increase dramatically soon after calving, and many cows have subclinical hypocalcemia, adversely affecting health and performance. Traditional laboratory tests for calcium are complex and not easily adapted to rapid point-of-care applications. The objectives were to evaluate a portable iCa testing device, Horiba LAQUAtwin Ca-11C, for measuring ionized calcium (iCa) in the whole blood of dairy cows and to investigate the iCa-to-total-calcium (tCa) ratio in blood collected from dairy cows within 9 days after calving. This study was conducted on two large dairies in northern China. First, blood samples were collected from 246 cows within 3 days after calving, and whole-blood iCa concentrations were measured with a Horiba LAQUAtwin Ca-11C and Abbott i-STAT 1. In addition, 885 blood samples were collected from 102 dairy cows between calving and 9 days postpartum, with iCa concentrations measured using a Horiba LAQUAtwin Ca-11C and tCa concentrations determined by a fully automated biochemical analyzer. The Horiba LAQUAtwin Ca-11C and Abbott i-STAT 1 had significant consistency in the measurement of ionized calcium concentration in whole blood (the frequency of differences between the measured values was within ±20% of the average, reaching 95.53%), and the measurement results of the two instruments were correlated (Deming regression analysis R2 = 0.87). This implied the potential application of the Horiba LAQUAtwin Ca-11C as a simplified device for measuring iCa in dairy farms for on-site testing. Within the first 9 days after calving, the ratio of ionized calcium to total calcium fluctuated but overall remained within the range of 44.2 to 47.22%. Furthermore, significant variations in the ratio among individual cows indicated that individual differences and physiological states of cows affected calcium metabolism. This study identified the potential to use the Horiba LAQUAtwin Ca-11C for point-of-care testing on dairy farms. Nevertheless, the impacts of factors such as health status and individual cow differences on the ratio of ionized calcium to total calcium still require further investigation.

Anomalous Sound Detection (ASD) is crucial for ensuring industrial equipment safety and enhancing production efficiency. However, existing methods, while pursuing high detection accuracy, are often associated with high computational complexity, making them unsuitable for resource-constrained environments. This study proposes an efficient self-supervised ASD framework that integrates spectral features, lightweight neural networks, and various anomaly scoring methods. Unlike traditional Log-Mel features, spectral features retain richer frequency domain details, providing high-quality inputs that enhance detection accuracy. The framework includes two network architectures: the lightweight ASDNet, optimized for resource-limited scenarios, and SpecMFN, which combines SpecNet and MobileFaceNet for advanced feature extraction and classification. These architectures employ various anomaly scoring methods, enabling complex decision boundaries to effectively detect diverse anomalous patterns. Experimental results demonstrate that ASDNet achieves an average AUC of 94.42% and a pAUC of 87.18%, outperforming existing methods by 6.75% and 9.34%, respectively, while significantly reducing FLOPs (85.4 M, a 93.81% reduction) and parameters (0.51 M, a 41.38% reduction). SpecMFN achieves AUC and pAUC values of 94.36% and 88.60%, respectively, with FLOPs reduced by 86.6%. These results highlight the framework’s ability to balance performance and computational efficiency, making it a robust and practical solution for ASD tasks in industrial and resource-constrained environments.

Background: Cholesterol gallstone disease (CGS) is often accompanied by gallbladder contraction dysfunction and chronic inflammation, but effective therapeutic options remain limited. This study investigates whether a low-intensity pulsed ultrasound (LIPUS) treatment can improve gallbladder motility and alleviate chronic inflammation while exploring the underlying mechanisms. Methods: Gallbladder motility was assessed through in vitro and in vivo contraction tests, while bile condition was evaluated by observing bile crystal clearance. Tissue analysis and Western blotting were performed to examine the expression of the cholecystokinin A receptor (CCKAR) and α-smooth muscle actin (α-SMA) as markers of gallbladder smooth muscle health and the inflammatory microenvironment. Blood cholesterol levels were measured via biochemical assays. Results: LIPUS treatment obviously enhanced gallbladder contractility in response to CCK-8 stimulation and accelerated bile crystal clearance. It also reduced inflammatory cell infiltration and tissue edema, and promoted new capillary formation in the gallbladder, mitigating the progression of CGS. Furthermore, LIPUS restored CCKAR expression and improved the thickness of the gallbladder smooth muscle layer, providing a structural basis for increased smooth muscle contractility. Conclusion: LIPUS improves gallbladder motility and reduces chronic inflammation in CGS by enhancing CCKAR expression and smooth muscle integrity. These findings highlight the potential of LIPUS as a non-invasive therapeutic approach for managing CGS.

The tumor microenvironment (TME) plays a pivotal role in neoplastic initiation and progression. Epigenetic machinery, governing the expression of core oncogenes and tumor suppressor genes in transformed cells, significantly contributes to tumor development at both primary and distant sites. Recent studies have illuminated how epigenetic mechanisms integrate external cues and downstream signals, altering the phenotype of stromal cells and immune cells. This remolds the area surrounding tumor cells, ultimately fostering an immunosuppressive microenvironment. Therefore, correcting the TME by targeting the epigenetic modifications holds substantial promise for cancer treatment. This review synthesizes recent research that elucidates the impact of specific epigenetic regulations—ranging from DNA methylation to histone modifications and chromatin remodeling—on stromal and immune cells within the TME. Notably, we highlight their functional roles in either promoting or restricting tumor progression. We also discuss the potential applications of epigenetic agents for cancer treatment, envisaging their ability to normalize the ecosystem. This review aims to assist researchers in understanding the dynamic interplay between epigenetics and the TME, paving the way for better epigenetic therapy.

China, like many other countries around the world, faces a rapidly aging population. “Aging in place” is the most popular choice for older people in China, with more than 90% of urban older people living in the same residential areas they did in middle age. Nighttime outdoor activity (NOA) is popular among the elderly and has a positive impact on both their physical and mental health. However, the conditions of nighttime activity places in old gated communities often do not meet the activity needs of the elderly, and there are problems such as low lighting and poor accessibility. This study focuses on typical old gated communities in a large city and analyzes data on three dimensions of independent variables: the lighting of activity places, the built environment of old gated communities, and the attributes of the elderly residents themselves. Taking “satisfaction with the environment for NOAs”, “the biggest environmental problem with NOAs”, and “locations that need improved lighting” as dependent variables, we used machine learning to calculate the contributions of various influencing factors on the dependent variables. It was found that the most important factors that influence satisfaction with the NOA environments are lighting levels, with the built environment or elderly residents’ attributes being less important. The most important factor that influences older people’s judgment of “locations that need improved lighting” is the perception of safety related to lighting in NOAs, followed by the perception of uniform lighting and green areas in their gated community. This study provides a reference for the renovation of age-friendly community environments as well as the overall enhancement of the nighttime activity environments in old gated communities.

To investigate the impact of airfoil angle of attack on the output performance of a valveless piezoelectric pump with airfoil baffles, this study conducted comprehensive performance tests and full-flow field simulations of piezoelectric pumps across a range of angles. At a driving voltage of 100 V and with a Clark Y airfoil set at an angle of 0°, the piezoelectric pump reached a peak output flow rate of 200.7 mL/min. An increase in the angle of attack corresponded to a decline in both the maximum output flow rate and the maximum back pressure of the pump. Flow field simulation results demonstrated that an increased airfoil angle of attack led to a gradual increase in entropy production within the piezoelectric pump. Turbulent dissipation and wall entropy production were found to be more pronounced compared to viscous entropy production. High turbulent dissipation was primarily observed at the pump chamber inlet, the trailing edges of the airfoils in both the inlet and outlet pipes, and the outlet bend. As the angle of attack increased, the complexity of the vortex core structures within the flow field escalated as well. Regions with significant wall entropy production were notably concentrated at the outlet bend.

Bearing-only multi-target localization (BOMTL) determines the positions of multiple targets by intersecting bearing lines from multiple spatial locations. However, non-ideal measurements can result in a large number of ghost targets. A β-S-dimensional assignment (β-SDA) method incorporating waveguide characteristics is proposed to address the BOMTL problem in shallow water with low detection rates. The estimated distance for the warping transformation is derived from the intersection points of the bearing lines, then the autocorrelation function of the broadband beamforming output is transformed using a warping operator to obtain the corresponding characteristic spectrum. The peaks in the characteristic spectrum correspond to the cross-correlation terms of the normal modes, with the frequencies of these peaks related to the ratio of the actual distance to the estimated distance of the sound source. The global target localization results are obtained using the proposed method, which incorporates confidence coefficients derived from the characteristic spectrum and geometric intersection information from the bearing lines. Simulation and sea trial data demonstrate that the β-SDA method effectively overcomes the limitation of pure bearing-only localization in low detection rate scenarios under a given signal-to-noise ratio (SNR), and can localize target positions without requiring precise prior environmental parameters.

The horizontal-transverse coherence of low-frequency (300 Hz) and long-range (10–40 km) acoustic fields near the bottom in deep water is investigated based on experimental data obtained from the South China Sea. The results indicate that the horizontal-transverse coherence length exhibits a strong dependence on the source-receiver distance, with fluctuations consistent with sound intensity trends. In high-intensity regions, the horizontal-transverse coherence is relatively high, with a coherence length exceeding 600 λ, where λ is the acoustic wavelength, whereas in low-intensity regions, the horizontal-transverse coherence decreases significantly, with the coherence length shortening to 10–30 λ. The physical mechanisms underlying the horizontal-transverse coherence are analyzed using the ray theory. In high-intensity regions, the energy of the dominant ray (the ray with the highest energy) accounts for over 70% of the total energy of the rays, exerting a decisive influence on the coherence coefficient and leading to stable horizontal-transverse coherence in the received acoustic field. In contrast, in low-intensity regions, the energy distribution is dispersed, and when amplitude and phase disturbances due to spatial inhomogeneity are introduced, the horizontal coherence deteriorates significantly. The numerical simulations are also performed, and the results are consistent with the experimental observations.

Wheat stripe rust, caused by a biotrophic, obligate fungus Puccinia striiformis f. sp. tritici (Pst), is a destructive wheat fungal disease that exists worldwide and caused huge yield reductions during pandemic years. Low temperatures favor the development of the disease, but the global average temperature has been increasing since 1850, especially in China, which has a higher rising rate than the global average. In the last two decades, Pst isolates have shown increased aggressiveness under high temperatures. However, the effect of rising temperatures on the aggressiveness of Pst has remained unknown in China. Therefore, this study assessed the aggressiveness of 15 representative Pst isolates (6 new isolates collected before 2016 and 9 old isolates collected after 2016) in Gansu under high temperatures by measuring and comparing disease severity, spore germination, and latent period on wheat seedlings at 16 °C, 18 °C, and 22 °C. The results indicated that the six new isolates showed greater disease severity, higher spore germination ratio, and shorter latent period than the nine old isolates, indicating that the new isolates were more aggressive under high temperatures than the old isolates. Some new isolates, such as CYR34, CYR33, and CYR32, which are predominant, were inferred to be associated with high-temperature adaptation in addition to having more susceptible hosts. Our results provided an insight into changes in Pst isolates at warmer temperatures and increasing incidence of wheat stripe rust in China, especially in eastern sporadic epidemiological areas in recent years. Thus, the new isolates are likely to be a potential risk for causing increasing stripe rust incidence.

This paper presents the solar-driven electrocarboxylation of 2-bromopyridine (2-BP) with CO2 into high-value-added chemicals 2-picolinic acid (2-PA) using dye-sensitized photovoltaics under simulated sunlight. Using three series-connected photovoltaic modules and an Ag electrode with excellent catalytic performance, a Faraday efficiency (FE) of 33.3% is obtained for 2-PA under mild conditions. The experimental results show that photovoltaics-driven systems for electrocarboxylation conversion of CO2 with heterocyclic halide to afford value-added heterocyclic carboxylic acid are feasible and effective.

This study innovatively leveraged proximal remote sensing to address the challenge of mineral exploration in vegetation-covered regions. Remote and proximal sensing has proven to be highly effective in pinpointing surface-exposed alteration minerals and detecting potential mining sites in previously unproductive areas. However, in regions where vegetation is abundant, the presence of foliage poses a significant challenge to mineral exploration efforts, creating a natural barrier that hinders the search for valuable minerals. In this study, we explored the linear relationship between the spectral changes induced by metals (specifically Fe and Mo) in wheat plants and the concentrations of these metal elements in different parts of the plant canopy at various growth stages. This investigation was conducted through meticulously designed controlled experiments to understand the interaction between metal elements in the soil and wheat plants. We have established linear models linking wheat biochemistry, vegetation spectroscopy, and soil concentration gradients of Fe and Mo. The analysis of Fe and Mo concentrations in leaves and wheat spikes across varying soil concentration gradients revealed significant positive correlations between the canopy accumulation sites and soil element concentrations (p < 0.05), with a correlation coefficient (R) exceeding 0.85, affirming the representativeness of these two canopy sites for subsequent spectral analysis and modeling. Regarding the wheat spectral analysis, the absorption features at specified wavelengths were identified as significant for creating valid linear models to analyze the effect of Fe and Mo in wheat leaf and spike spectra. Comparing the univariate (URL) and multivariate (MLR) models demonstrated that MLR modeling, incorporating multiple absorption feature parameters, provided more accurate results compared to scenarios with only one absorption feature in the modeling process (MLR: Fe-leaf: R2 = 0.941, RMSE = 1.171; Mo-spike: R2 = 0.934, RMSE = 0.042). To conclude, this study introduces a novel method that exploits the wheat spectral properties observed across different canopy sections during various growth stages of vegetation and under varying concentrations of Fe and Mo gradients. The methodology elucidated in this research provides technical support and lays the theoretical foundation for evaluating mineral resources in vegetated areas.

Path following is crucial for enhancing the autonomy of unmanned surface vehicles (USVs) in water monitoring missions. This paper presents an offline reinforcement learning (RL) controller for USVs. The controller employs the soft actor–critic algorithm with a diversified Q-ensemble to optimize the steering control policy using a pre-collected dataset of USV path-following trials. A Markov decision process (MDP) tailored for path following is formulated. The proposed offline RL steering controller, trained on static datasets, demonstrates improved sample efficiency and asymptotic performance due to an expanded ensemble of Q-networks. The accuracy and adaptive learning capabilities of the RL controller are validated through simulations and free-running tests.

Wastewater treatment (WWT) contributes 2–9% of global greenhouse gas (GHG) emissions. The noticeable uncertainty in emissions estimation is due in large part to the lack of measurement data. Several methods have recently been developed for monitoring fugitive GHG emissions from WWT. However, limited by the short duration of the monitoring, only “snapshot” data can be obtained, necessitating extrapolation of the limited data for estimating annual emissions. Extrapolation introduces substantial errors, as it fails to account for the spatial and temporal variations of fugitive emissions. This research evaluated the feasibility of studying the long-term CH4 emissions from WWT by analyzing high spatial resolution Sentinel-2 data. Satellite images of a WWT plant in Calgary, Canada, taken between 2019 and 2023, were processed to retrieve CH4 column concentration distributions. Digital image processing techniques were developed and used for extracting the time- and space-varying features of CH4 emissions, which revealed daily, monthly, seasonal, and annual variations. Emission hotspots were also identified and corroborated with ground-based measurements. Despite limitations due to atmospheric scattering, cloud cover, and sensor resolution, which affect precise ground-level concentration assessments, the findings reveal the dynamic nature of fugitive GHG emissions from WWT, indicating the need for continuous monitoring. The results also show the potential of utilizing satellite images for cost-effectively evaluating fugitive CH4 emissions.

To address the design and application requirements for USVs (Unmanned Surface Vehicles) to autonomously escape from constrained environments using a minimal number of sensors, we propose a path planning algorithm based on the RRT* (Rapidly Exploring Random Tree*) method, referred to as BN-RRT* (Blind Navigation Rapidly Exploring Random Tree*). This algorithm utilizes the positioning information provided by the GPS onboard the USV and combines collision detection data from collision sensors to navigate out of the trapped space. To mitigate the inherent randomness of the RRT* algorithm, we integrate the Artificial Potential Field (APF) method to enhance directional guidance during the sampling process. Additionally, inspired by blind navigation principles, we propose an active collision mechanism that relies on continuous collisions to identify obstacles and adjust the next movement direction, thereby improving the efficiency of escape path planning. We also implement an obstacle memory mechanism to prevent exploration into erroneous areas during sampling, significantly increasing the success rate of escape and reducing the path length. We validate the proposed algorithm in a dedicated MATLAB environment, comparing its performance with existing RRT, RRT*, and APF-RRT* algorithms. Experimental results indicate that the improved algorithm achieves significant enhancements in both planning speed and path length compared to the other methods.

High-quality grape raw materials are fundamental for producing premium wine. Ensuring the quality of grape raw materials, particularly enhancing their phenolic profiles, significantly improves wine flavor. Therefore, this study focused on ‘Cabernet Franc’ grapes, where a 0.1% chitooligosaccharide (COS) solution was foliar sprayed during the green pea stage, the onset of veraison stage, and the mid-ripening stage to investigate the impact of exogenous COS treatment on the accumulation of phenolic compounds in grape berries. The results revealed that COS treatment during the green pea and the onset of veraison stages significantly increased the levels of total phenolic, total flavonoid, and total anthocyanin in grapes, with distinct effects on flavanols, phenolic acids, flavonols, and stilbenes, respectively. Eight key compounds most significantly influenced by the treatment were identified through orthogonal partial least squares discriminant analysis (OPLS-DA) and machine learning screening. Specifically, treatment during the green pea stage had a significant impact on total soluble solids, proanthocyanidin B1, catechin, and vanillic acid, while veraison treatment notably affected petunidin-3-O-(6″-O-p-coumaryl)-glucoside, cyanidin-3-O-(6″-O-p-coumaryl)-glucoside, cyanidin-3-O-glucoside and isorhamnetin. This study could provide valuable data references and theoretical support for applying COS in wine grapes and regulating high-quality raw materials.

Water consumption forecasting is a critical aspect of the increasingly strained water resources and sustainable water management processes. It is essential to explore the current status of water use patterns and future development directions in Zhangye City. In this study, 17 factors affecting water consumption in Zhangye City were selected to analyze changes in water consumption and to predict values from 2003 to 2022, utilizing the entropy weight–VIKOR model and the grey neural network model. The results indicate that agricultural water consumption and annual rainfall are the factors with the largest weights among the social and natural attribute indicators, respectively, significantly influencing water consumption in Zhangye City. As the proportions of water consumption for forestry, animal husbandry, fishery, livestock, urban public use, and ecological environment increase, while agricultural water consumption continues to decline, the overall water consumption trend in Zhangye City from 2003 to 2022 shows a positive trajectory. Each water consumption factor is tending toward greater balance, and the relationship between water supply and distribution is improving. The multi-year average relative error of the water consumption predictions for Zhangye City from 2003 to 2022 using the grey neural network model was 4.28%. Furthermore, the relative error values for annual predictions ranged from 0.60% to 5.00%, achieving an accuracy rate of 80.00%. This indicates a strong predictive performance. Ultimately, the model was used to predict a water consumption of 20.18 × 108 m3 in Zhangye City in 2027. The model can serve as a theoretical reference for short-term water consumption forecasting and for establishing a basin water resource allocation system in Zhangye City.

Background: Eosinophilic esophagitis (EoE) is a rare, chronic immune-mediated disorder with limited treatment options. Despite the U.S. Food and Drug Administration (FDA) approval of dupilumab for EoE, other monoclonal antibodies remain unapproved and are used off-label with limited evidence on their efficacy and safety. This systematic review rigorously and comprehensively evaluates the evidence for monoclonal antibody therapies used off-label to treat EoE. Methods: We conducted a systematic review across PubMed, EMBASE, Cochrane Central, and ClinicalTrials.gov, assessing the efficacy and safety of off-label monoclonal antibodies in EoE through clinical outcomes and the FDA Adverse Event Reporting System (FAERS) data. Results: Among ten monoclonal antibodies reviewed, mepolizumab that targets IL-5 showed the most promise with a moderate recommendation based on Level 2 evidence. Others like omalizumab (anti-IgE), dectrekumab (anti-IL-13), and reslizumab (anti-IL-5) showed limited utility. Safety evaluations via the FAERS database revealed significant adverse drug reactions, including serious events like asthmatic crises, pneumonia, and adrenal insufficiency for mepolizumab and reslizumab, as well as chronic obstructive pulmonary disease and gastroenteritis for omalizumab. Dectrekumab’s safety profile remains unclear due to a lack of data. Conclusions: While mepolizumab demonstrates potential as an off-label treatment, none of the antibodies reviewed have FDA approval for EoE. Clinicians should consider the balance between local and systemic effects and exercise caution, closely monitoring for adverse effects, particularly in patients with respiratory comorbidities. Continued research is crucial to establish a more robust evidence base for these therapies.

Melanoma represents a formidable challenge in dermatological oncology due to its resistance to conventional treatments. The Celandine Alkali Injection Formula (CAIF) offers benefits on clinical internal medicine treatments, within which chelidonine and tetrandrine are recognized as potential quality markers. However, their synergistic mechanisms facilitating their anti-melanoma action remain unveiled. This study embarked on an exploration of CAIF’s therapeutic potential through a multifaceted research design, integrating system pharmacological predictions with empirical molecular biological evaluations. The dual application of chelidonine and tetrandrine within CAIF exhibited a pronounced inhibitory effect on the proliferation of B16F10 cells, surpassing the effectiveness of individual compound administration. Computational predictions identified the top 50 targets, involved in key signaling pathways including cell cycle regulation, and melanogenesis. RNA sequencing further elucidated that the combinatory treatment modulated a broader spectrum of differentially expressed genes, implicating crucial biological processes including cell differentiation, and tyrosinase metabolism. The combination markedly enhanced melanogenesis and apoptotic indices, arrested cell cycle progression, and fostered cellular differentiation. Notably, chelidonine additionally curtailed the migratory capacity of B16F10 cells. Our findings underscore the therapeutic potential of chelidonine and tetrandrine, key components of CAIF, in effectively combating melanoma by targeting cell proliferation, migration, differentiation, and melanogenesis.

Bromus tectorum, a winter annual plant, produces dimorphic diaspores: complex diaspores with multi-awns and simple diaspores with one awn. However, there is no information available about the role of awns and the germination characteristics of dimorphic diaspores. Dispersal germination and awns hygroscopicity of the dimorphic diaspores were assessed. The complex diaspore with multi-awns can easily be dispersed long distances from the mother plant by mammals. The simple diaspores with one awn are tightly attached to the mother plant. Caryopses from the two types of diaspores exhibited non-deep physiological dormancy at maturity, which can be released by dry storage and GA3 treatment. The awns have hygroscopic activity and can move in response to changes in moisture, moving the complex diaspore (the seed) into the soil. The seedling emergence from complex diaspores was significantly higher than those from simple diaspores at all burial depths. Germination of caryopses on the soil surface was poor. The optimal planting depth for both types of diaspores’ emergence is 1–2 cm. The distinct characteristics of dimorphic diaspores and the beneficial influence of hygroscopic awns on dispersal, germination, and seedling establishment have significant ecological implications for B. tectorum’s successful reproduction in unpredictable cold deserts.

Comprehensive growth index (CGI) more accurately reflects crop growth conditions than single indicators, which is crucial for precision irrigation, fertilization, and yield prediction. However, many current studies overlook the relationships between different growth parameters and their varying contributions to yield, leading to overlapping information and lower accuracy in monitoring crop growth. Therefore, this study focuses on winter wheat and constructs a comprehensive growth monitoring index (CGIac), based on adaptive weight allocation of growth parameters’ contribution to yield, using data such as leaf area index (LAI), soil plant analysis development (SPAD) values, plant height (PH), biomass (BM), and plant water content (PWC). Using UAV data on vegetation indices, feature selection was performed using the Elastic Net. The growth inversion model was then constructed using machine learning methods, including linear regression (LR), random forest (RF), gradient boosting (GB), and support vector regression (SVR). Based on the optimal growth inversion model for winter wheat, spatial distribution of wheat growth in the study area is obtained. The findings demonstrated that CGIac outperforms CGIav (constructed using equal weighting) and CGIcv (built using the coefficient of variation) in yield correlation and prediction accuracy. Specifically, the yield correlation of CGIac improved by up to 0.76 compared to individual indices, while yield prediction accuracy increased by up to 23.14%. Among the evaluated models, the RF model achieved the best performance, with a coefficient of determination (R2) of 0.895 and a root mean square error (RMSE) of 0.0058. A comparison with wheat orthophotos from the same period confirmed that the inversion results were highly consistent with actual growth conditions in the study area. The proposed method significantly improved the accuracy and applicability of winter wheat growth monitoring, overcoming the limitations of single parameters in growth prediction. Additionally, it provided new technological support and innovative solutions for regional crop monitoring and precision farming operations.

The impact of an ice-covered environment on the local flow characteristics of a bridge pier was studied through a series of flume tests, and the dominant factors affecting the scour pattern were found to grasp the change laws of the local hydrodynamic characteristics of the bridge pier under the ice cover. At the same time, because the scour problem of the pier foundation is a technical problem throughout the life-cycle of the bridge, to determine the optimal anti-scour protection effect on the foundation of the bridge pier, active protection scour plate was used to carry out scour protection tests, and its structural shape was optimized to obtain better anti-scour performance. The test results show that the jumping movements of sediment particles in the scour hole around the pier are mainly caused by events Q2 and Q4, which are accompanied by events Q1 and Q3 and cause the particle rolling phenomenon, where Q1 and Q3 events are outward and inward interacting flow regimes, and Q2 and Q4 events are jet and sweeping flow regimes, respectively. The power spectral attenuation rate in front of the upstream pier is high without masking effects, while strong circulation at the remaining locations results in strong vorticity and high spectral density, in particular, when the sampling time series is 60 s (i.e., f = 1/60), the variance loss rates under ice-covered conditions at the front of the upstream pier, between the two piers, and at the tail end of the downstream pier are 0.5%, 4.6%, and 9.8%, respectively, suggesting a smaller contribution of ice cover to the variance loss.

Electric field sensors (EFSs) are widely used in various fields, particularly in accurately assessing atmospheric electric fields and high-voltage power lines. Precisely detecting electric fields enhances the accuracy of weather forecasting and contributes to the safe operation of power grids. This paper comprehensively reviews the development of micro-electromechanical system (MEMS) resonant EFSs, including theoretical analysis, working principles, and applications. MEMS resonant EFSs have developed into various structures over the past decades. They have been reported to measure electric field strength by detecting changes in the induced charge on the electrodes. Significant advancements include diverse driving and sensing structures, along with improved dynamic range, sensitivity, and resolution. Recently, mode localization has gained attention and has been applied to electric field sensing. This paper reviews the performances and structures of MEMS resonant EFSs over recent decades and highlights recent advances in weakly coupled resonant EFSs, offering comprehensive guidance for researchers.

Accurate traffic flow prediction in road networks is essential for intelligent transportation systems (ITS). Since traffic data are collected from the road network with spatial topological and time series sequences, the traffic flow prediction is regarded as a spatial–temporal prediction task. With the powerful ability to model the non-Euclidean data, the graph convolutional network (GCN)-based models have become the mainstream framework for traffic forecasting. However, existing GCN-based models either use the manually predefined graph structure to capture the spatial features, ignoring the heterogeneity of road networks, or simply perform 1-D convolution with fixed kernel to capture the temporal dependencies of traffic data, resulting in insufficient long-term temporal feature extraction. To solve those issues, a spatial–temporal correlation constrained dynamic graph convolutional network (STC-DGCN) is proposed for traffic flow forecasting. In STC-DGCN, a spatial–temporal embedding encoder module (STEM) is first constructed to encode the dynamic spatial relationships for road networks at different time steps. Then, a temporal feature encoder module with heterogeneous time series correlation modeling (TFE-HCM) and a spatial feature encoder module with dynamic multi-graph modeling (SFE-DCM) are designed to generate dynamic graph structures for effectively capturing the dynamic spatial and temporal correlations. Finally, a spatial–temporal feature fusion module based on a gating fusion mechanism (STM-GM) is proposed to effectively learn and leverage the inherent spatial–temporal relationships for traffic flow forecasting. Experimental results from three real-world traffic flow datasets demonstrate the superior performance of the proposed STC-DGCN compared with state-of-the-art traffic flow forecasting models.

The On-Load Tap Changer (OLTC) is the only movable mechanical component in a converter transformer. To ensure the reliable operation of the OLTC and to promptly detect mechanical faults in OLTCs to prevent them from developing into electrical faults, this paper proposes a fault diagnosis method for OLTCs based on a combination of Particle Swarm Optimization (PSO) algorithm and Least Squares Support Vector Machine (LSSVM) with multi-feature fusion. Firstly, a multi-feature extraction method based on time/frequency domain statistics, synchrosqueezed wavelet transform, singular value decomposition, and multi-scale modal decomposition is proposed. Meanwhile, the random forest algorithm is used to screen features to eliminate the influence of redundant features on the accuracy of fault diagnosis. Secondly, the PSO algorithm is introduced to optimize the hyperparameters of LSSVM to obtain optimal parameters, thereby constructing an optimal LSSVM fault diagnosis model. Finally, different types of feature combinations are utilized for fault diagnosis, and the impact of these feature combinations on the fault diagnosis results is compared. Experimental results indicate that features of different types can complement each other, making the OLTC state information carried by multi-dimensional features more comprehensive, which helps to improve the accuracy of fault diagnosis. Compared with four traditional fault diagnosis methods, the proposed method performs better in fault diagnosis accuracy, achieving the highest accuracy of 98.58%, which can help to detect mechanical faults in the OLTC early and reduce the system’s downtime.

In multi-source fusion deformation-monitoring methods that utilize fiber Bragg grating (FBG) data and other data types, the lack of FBG constraint points in edge regions often results in inaccuracies in fusion results, thereby impacting the overall deformation-monitoring accuracy. This study proposes a multi-source fusion deformation-monitoring calibration method and develops a calibration model that integrates vision and FBG multi-source fusion data. The core of this model is a normal distribution transform (NDT)–convolutional neural network (CNN)–self-attention (SA) calibration network. This network enhances continuity between points in point clouds using the NDT module, thereby reducing outliers at the edges of the fusion results. Experimental validation shows that this method reduces the absolute error to below 0.2 mm between multi-source fusion calibration results and high-precision measured point clouds, with a confidence interval of 99%. The NDT-CNN-SA network offers significant advantages, with a performance improvement of 36.57% over the CNN network, 14.39% over the CNN–gated recurrent unit (GRU)–convolutional block attention module (CBAM) network, and 9.54% over the CNN–long short term memory (LSTM)–SA network, thereby demonstrating its superior generalization, accuracy, and robustness. This calibration method provides smoother and accurate structural deformation data, supports real-time deformation monitoring, and reduces the impact of assembly deviation on product quality and performance.

Submerged plants and related disturbances can affect both the phosphorus (P) release and the microbial communities in sediments. In this study, a sediment resuspension system was constructed, and P variability characteristics influenced by Vallisneria natans (V. natans) and the response mechanism of the microbial community were studied. The results indicated that the total phosphorus (TP) content increased from 678.875 to 1019.133 mg/kg and from 1126.017 to 1280.679 mg/kg in sediments and suspended solids (SSs) during the sediment resuspension process, respectively. Organic P (OP) increased by 127.344 mg/kg and 302.448 mg/kg in sediments and SSs after the disturbance, respectively. The microbial communities in the sediments and the leaves of V. natans had higher Chao values after the disturbance, while Shannon values decreased after the disturbance compared to the control in SSs. Proteobacteria had the highest abundance with the value of 51.1% after the disturbance in the sediments and SSs, and the abundance values of Proteobacteria in rhizomes and leaves of V. natans could reach 73.2% on average. Chloroflexi, Acidobacteria, and Firmicutes were also the main phyla in the sediment resuspension system. Sodium hydroxide extractable P (NaOH-P) in sediments could reduce the bioavailability of this P fraction under disturbance conditions. The decrease in the abundance of Bacteroidetes and Nitrospirae indicated that they were more sensitive to the disturbance, and the rotational speed changed the survival conditions for the Bacteroidetes and Nitrospirae. The response mechanism of microbial community during the sediment resuspension process could reflect the influence of the microbial community on the changing characteristics of P and could provide a theoretical foundation for P control at the micro level.

To address the industry’s demand for sustainable oilfield development and safe production, it is crucial to enhance the scientific rigor and accuracy of monitoring ground stability and reservoir parameter inversion. For the above purposes, this paper proposes a technical solution that employs two-dimensional time-series ground deformation monitoring based on ascending and descending Interferometric Synthetic Aperture Radar (InSAR) technique first, and the composite dislocation model (CDM) is utilized to achieve high-precision reservoir parameter inversion. To validate the feasibility of this method, the Liaohe Oilfield is selected as a typical study area, and the Sentinel-1 ascending and descending Synthetic Aperture Radar (SAR) images obtained from January 2020 to December 2023 are utilized to acquire the ground deformation in various line of sight (LOS) directions based on Multitemporal Interferometric Synthetic Aperture Radar (MT-InSAR). Subsequently, by integrating the ascending and descending MT-InSAR observations, we solved for two-dimensional ground deformation, deriving a time series of vertical and east-west deformations. Furthermore, reservoir parameter inversion and modeling in the subsidence trough area were conducted using the CDM and nonlinear Bayesian inversion method. The experimental results indicate the presence of uneven subsidence troughs in the Shuguang and Huanxiling oilfields within the study area, with a continuous subsidence trend observed in recent years. Among them, the subsidence of the Shuguang oilfield is more significant and shows prominent characteristics of single-source center subsidence accompanied by centripetal horizontal displacement, the maximum vertical subsidence rate reaches 221 mm/yr, and the maximum eastward and westward deformation is more than 90 mm/yr. Supported by the two-dimensional deformation field, we conducted a comparative analysis between the Mogi, Ellipsoidal, and Okada models in terms of reservoir parameter inversion, model fitting efficacy, and residual distribution. The results confirmed that the CDM offers the best adaptability and highest accuracy in reservoir parameter inversion. The proposed technical methods and experimental results can provide valuable references for scientific planning and production safety assurance in related oilfields.

An accurate assessment of the spatial–temporal variations in regional net ecosystem productivity (NEP), water use efficiency (WUE), and carbon use efficiency (CUE) are vital for understanding the water–carbon cycle. We analyzed the spatial–temporal patterns of the NEP, WUE, and CUE in the middle reaches of the Yellow River (MRYR) from 2001 to 2022, and the factors that influenced them using remote sensing data, NEP estimation models, and various statistical methods. The results indicate that the recovery of the ecosystem in the MRYR is a result of the combined effects of climate change and human activities. Climate change in the MRYR led to warming and humidification from 2001 to 2022. The NEP, WUE, and CUE were characterized by increasing trends, with average growth rates of 7.75 gC m−2a−1, 0.012 gC m−2 mm−1a−1, and 0.009a−1, respectively. For four vegetation types, the interannual rates of change were, in descending order, grassland, cropland, shrubs, and forest. Spatially, the NEP, WUE, and CUE showed significant regional heterogeneity, increasing from the northwest to the southeast. Based on an analysis of the interannual anomalies, precipitation accumulation contributed to carbon sink accumulation. The correlation of the NEP, WUE, and CUE with the drought severity index (DSI) was high, and their correlation with precipitation showed latitudinal zonality, which suggests that precipitation (PRE) is the main climatic factor influencing the water–carbon cycle in the MRYR rather than temperature (TEM). There were 67,671.27 km2 of land that changed use during 2001–2022, and 15.07 Tg of NEP was added to these areas.

Truck cranes, which are crucial construction equipment, need to maintain good operational performance to ensure safe use. However, the complex and ever-changing working conditions they face often make it challenging to test their performance effectively. To address this issue, a multi-input and multi-output soft sensor technology model is suggested, utilizing a graph convolutional network and random forest to predict key performance indicators of crane operations such as luffing, telescoping, winching, and slewing under varying conditions. This method aims to streamline the process of testing and debugging truck cranes, ultimately reducing time and costs. Initially, the graph convolutional network model is employed to extract relevant feature information linked to the target variable. Subsequently, using this feature information and the RF model, multiple decision trees are constructed for regression prediction of the target variables. An operational dataset reflecting the crane’s actual working conditions is then generated to assess the graph convolutional network and random forest model. The effectiveness of this approach is further confirmed through comparisons with other methods like gradient boosting trees, support vector regression, and multi-layer perceptron.

The control of the injection speed in hydraulic injection molding machines is critical to product quality and production efficiency. This paper analyzes servomotor-driven constant pump hydraulic systems in injection molding machines to achieve optimal tracking control of the injection speed. We propose an efficient reinforcement learning (RL)-based approach to achieve fast tracking control of the injection speed within predefined time constraints. First, we construct a precise Markov decision process model that defines the state space, action space, and reward function. Then, we establish a tracking strategy using the Deep Deterministic Policy Gradient RL method, which allows the controller to learn optimal policies by interacting with the environment. Careful attention is also paid to the network architecture and the definition of states/actions to ensure the effectiveness of the proposed method. Extensive numerical results validate the proposed approach and demonstrate accurate and efficient tracking of the injection velocity. The controller’s ability to learn and adapt in real time provides a significant advantage over the traditional Proportion Integration Differentiation controller. The proposed method provides a practical solution to the challenge of maintaining accurate control of the injection speed in the manufacturing process.

The Phytoseiulus persimilis specialized in preying on Tetranychus species, with particularly strong predation capability against Tetranychus urticae. To investigate the morphology of female reproductive structures and effects of different gravid times on structures of oocytes and embryos in Phytoseiulus persimilis, we employed paraffin sectioning, transmission electron microscopy (TEM), and scanning electron microscopy (SEM) on the model species of predatory mite Phytoseiulus persimilis. The female adult possessed several reproductive organs, including paired solenostomes, major ducts, embolus, calyces, and vesicles within the sperm-access system, as well as lyrate organ and the ovary. Furthermore, the reproductive system also encompassed the uterus, vagina, and genital pore, which were involved in egg development and expulsion. The solenostomes were situated between the third and fourth legs, and they were scarcely discernible in virgin, but they became apparent during mating. The occurrence of mating significantly influenced the nucleus of lyrate organ. In virgin, the nucleus exhibited underdeveloped morphology, whereas in mated individuals, it was well-formed. However, the duration of mating did not impact its development. The cellular structure of the ovary was solely associated with the stage of the surrounding oocyte and was not directly linked to mating occurrences. The uterus was barely visible outside of mating periods but became observable 12 h after mating when eggs were present within the body. At this point, it opened in preparation for egg laying when both the vagina and reproductive opening were open. Positioned in front of the vesicle but behind the ovary was the lyrate organ, with its lower part housing the uterus. The vagina was connected to the genital pore. No significant difference was observed in oocyte morphology between the virgin ovaries and the mated. Oocyte development occurred through four stages: during stage I (4–9 h after mating), yolk accumulation took place; stage II (10 h after mating) involved egg relocation; stage III (12–13 h after mating) was marked by eggshell formation; finally, at stage IV (14–16 h after mating), embryonic development commenced, leading to egg deposition. The fusion of sperm and egg occurred approximately 9–10 h after mating. These findings established a solid foundation for investigating the Phytoseiid reproductive mechanisms.

The average daily throughput of large-scale passenger high-speed railway stations is large, and the design of the inbound space connecting with the underground and other modes of transport affects the passengers’ wayfinding behaviour and time spent, which in turn affects the efficiency of the inbound station. How to optimise the design of station entry space and signage arrangement becomes the key to shortening the station entry time. In this paper, eye tracking, spatial syntax, and semantic difference methods are used to evaluate the passenger’s wayfinding process in the underground hub of a large high-speed railway station and the spatial syntax is used to quantify and analyse the wayfinding path segments, to explore the influence of the spatial attributes of different nodes and the spatial arrangement of the guiding signs on the passenger’s wayfinding behaviour data and the difference in attention, and to find out that the connectivity of the wayfinding nodes, the area of the field of view, and the passengers’ The study concludes that the connectivity and visual field area of wayfinding nodes have a strong positive correlation with the passengers’ route choice time, which has less influence on the correct rate of wayfinding and can be taken into less consideration in the subsequent design. While analysing the spatial density of signs and the correct rate of wayfinding in the sample, it is concluded that the density of guide signs is maintained in the interval of 5–11‰, and at the same time, the number is sufficient to point to the destination is a more appropriate interval, and ultimately, the impact of the correct rate of wayfinding of the weighting of the following: signage focus on the time > density of information > density of key information > diameter of the pupil. The study analyses the influencing factors affecting passengers’ wayfinding behaviour from a human factors perspective and provides feedback on the design of underground entry spaces in large passenger high-speed rail stations.

This paper proposes an evaluating and mapping methodology of terrain traversability for off-road navigation of autonomous vehicles in unstructured environments. Terrain features are extracted from RGB images and 3D point clouds to create a traversal cost map. The cost map is then employed to plan safe trajectories. Bayesian generalized kernel inference is employed to assess unknown grid attributes due to the sparse raw point cloud data. A Kalman filter also creates density local elevation maps in real time by fusing multiframe information. Consequently, the terrain semantic mapping procedure considers the uncertainty of semantic segmentation and the impact of sensor noise. A Bayesian filter is used to update the surface semantic information in a probabilistic manner. Ultimately, the elevation map is utilized to extract geometric characteristics, which are then integrated with the probabilistic semantic map. This combined map is then used in conjunction with the extended motion primitive planner to plan the most effective trajectory. The experimental results demonstrate that the autonomous vehicles obtain a success rate enhancement ranging from 4.4% to 13.6% and a decrease in trajectory roughness ranging from 5.1% to 35.8% when compared with the most developed outdoor navigation algorithms. Additionally, the autonomous vehicles maintain a terrain surface selection accuracy of over 85% during the navigation process.

During the axial modification in helical gear form grinding, the contact line between the grinding wheel and the gear constantly changes, and the additional radial motion can cause a “tooth surface twist” phenomenon. An optimization method for tooth surface twist error was proposed to address this. Based on the gear meshing principles, a mathematical model for axial modification in form grinding was established to solve for the instantaneous contact lines at various positions on the actual modified tooth surface. By analyzing the influence of the grinding wheel installation angle on the axial modification contact line, an optimization model was constructed to reduce the twist of the transverse profile, reduce the twist of the flank profile, reduce the helix deviation, and improve the form grinding efficiency. The practical implications of this research are significant, as the Particle Swarm Optimization (PSO) algorithm was employed to optimize the form grinding parameters, leading to a method that effectively reduced tooth surface twist error and improved the form grinding accuracy of the modified tooth surface.

Heilongjiang, located in the cold region, is China’s largest rice-producing and commercializing province. The variety selection of rice in cold regions (RCR) is indispensable in promoting the rice industry’s development, and technological innovation in breeding theory plays a significant role in breeding breakthrough. Based primarily on long-term research, contemplation, and breeding practices, the 4S (selected topic, selected breeding, selected progenies, and selected promotion) phenotypic-design breeding technical system for RCR, which has revolutionized the theoretical basis and deepened the conceptual model, has been developed through systematic analyses and generalization. The system covers several key aspects such as scientific question formulation, breeding objective optimization, parental taxa hybridization, progeny population selection, and innovation dissemination, aiming to improve the foresight, precision, and efficiency of breeding. Furthermore, the system has demonstrated successful cases of rice variety breeding over the past 20 years, such as for Suijing 3, Suijing 4, Suijing 18, and a series of other rice varieties, providing theoretical and technical supports for cold-region rice breeding.

The accuracy and reliability of soil moisture retrieval based on Global Positioning System (GPS) single-star Signal-to-Noise Ratio (SNR) data is low due to the influence of spatial and temporal differences of different satellites. Therefore, this paper proposes a Random Forest (RF)-based multi-satellite data fusion Global Navigation Satellite System Interferometric Reflectometry (GNSS-IR) soil moisture retrieval method, which utilizes the RF Model’s Mean Decrease Impurity (MDI) algorithm to adaptively assign arc weights to fuse all available satellite data to obtain accurate retrieval results. Subsequently, the effectiveness of the proposed method was validated using GPS data from the Plate Boundary Observatory (PBO) network sites P041 and P037, as well as data collected in Lamasquere, France. A Support Vector Machine model (SVM), Radial Basis Function (RBF) neural network model, and Convolutional Neural Network model (CNN) are introduced for the comparison of accuracy. The results indicated that the proposed method had the best retrieval performance, with Root Mean Square Error (RMSE) values of 0.032, 0.028, and 0.003 cm3/cm3, Mean Absolute Error (MAE) values of 0.025, 0.022, and 0.002 cm3/cm3, and correlation coefficients (R) of 0.94, 0.95, and 0.98, respectively, at the three sites. Therefore, the proposed soil moisture retrieval model demonstrates strong robustness and generalization capabilities, providing a reference for achieving high-precision, real-time monitoring of soil moisture.

Seed geometric parameters are important in yielding trait scorers, quantitative trait loci, and species recognition and classification. A novel method for automatic measurement of three-dimensional seed phenotypes is proposed. First, a handheld three-dimensional (3D) laser scanner is employed to obtain the seed point cloud data in batches. Second, a novel point cloud-based phenotyping method is proposed to obtain a single-seed 3D model and extract 33 phenotypes. It is connected by an automatic pipeline, including single-seed segmentation, pose normalization, point cloud completion by an ellipse fitting method, Poisson surface reconstruction, and automatic trait estimation. Finally, two statistical models (one using 11 size-related phenotypes and the other using 22 shape-related phenotypes) based on the principal component analysis method are built. A total of 3400 samples of eight kinds of seeds with different geometrical shapes are tested. Experiments show: (1) a single-seed 3D model can be automatically obtained with 0.017 mm point cloud completion error; (2) 33 phenotypes can be automatically extracted with high correlation compared with manual measurements (correlation coefficient (R2) above 0.9981 for size-related phenotypes and R2 above 0.8421 for shape-related phenotypes); and (3) two statistical models are successfully built to achieve seed shape description and quantification.

The genus Gymnopus plays a significant role in ecological systems, with certain species holding potential as food or medicinal resources. However, the species diversity of Gymnopus in China remains unclear. In recent years, more than one thousand Gymnopus specimens have been collected across China. Thus, through the integration of ecological evidence, detailed morphological studies, and phylogenetic analysis using a multiloci dataset of ITS + nLSU + tef1-ɑ, four new species—Gymnopus longistipes, Gymnopus striatipileatus, Gymnopus viridiscus, and Gymnopus spadiceus—have been differentiated from known species. Gymnopus similis has been newly documented from Jiangxi Province, China. Detailed descriptions and vivid illustrations have been provided based on the newly collected specimens, along with comparisons to closely related species. Additionally, a key to the reported species of Gymnopus s.l. from East China has been included.