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This study aims to investigate the long-term prognostic utility of circulating tumour DNA (ctDNA) KRAS mutations in colorectal cancer (CRC) patients and compare this with KRAS mutations in matched tissue samples. Tumour tissue (n = 107) and ctDNA (n = 80) were obtained from patients undergoing CRC resection and were analysed for KRAS mutations. The associations between KRAS mutation and overall survival (OS), cancer-specific survival (CSS), and recurrence-free survival (RFS) were analysed. All outcomes were measured in years (y). A total of 28.8% of patients had KRAS mutations in ctDNA and 72.9% in tumour tissue DNA. The high frequency of KRAS mutations in tissue samples was due to 51.4% of these being a detectable low mutation allele frequency (KRAS mutant (KRASmut) to KRAS wild-type (KRASwt) in ctDNA, there was no association found with OS (mean 4.67 y vs. 4.34 y, p = 0.832), CSS (mean 4.72 y vs. 4.49 y, p = 0.747), or RFS (mean 3.89 y vs. 4.26 y, p = 0.616). Similarly, comparing KRASmut to KRASwt in tissue DNA there was no association found with OS (mean 4.23 y vs. 4.61 y, p = 0.193), CSS (mean 4.41 y vs. 4.71 y, p = 0.312), or RFS (mean 4.16 y vs. 4.41 y, p = 0.443). There was no significant association found between KRAS mutations in either tissue or ctDNA and OS, CSS, or RFS.

The Jingbai pear is one of the best pear species in China with high quality and nutrition values which are closely linked to its geographical origin. With the purpose of discriminating the PGI Mentougou Jingbai pear from three other producing regions, the stable isotope ratios and elemental profiles of the pears (n = 52) and the corresponding soils and groundwater were determined using isotope ratio mass spectrometry (IRMS) and inductively coupled plasma mass spectrometry (ICP-MS), respectively. The results revealed that δ15N, δ18OJ, and Li were significantly different (p < 0.05) in samples from different regions, which indicated their potential to be used in the geographical origin classification of the Jingbai pear. The nitrogen isotopic values of the pear pulp were positively correlated with the δ15N value and nitrogen content of the corresponding soils, whilst the B, Na, K, Cr, and Cd contents of the pear pulps were positively correlated with their corresponding soils. Orthogonal partial least squares discriminant analysis (OPLS-DA) was performed in combination with analysis of the stable isotopes and elemental profiles, making it possible to distinguish the cultivation regions from each other with a high prediction accuracy (a correct classification rate of 92.3%). The results of this study highlight the potential of stable isotope ratios and elemental profiles to trace the geographical origin of pears at a small spatial scale.

Eurasian dryland ecosystems consist mainly of cropland and grassland, and their changes are driven by both natural factors and human activities. This study utilized the normalized difference vegetation index (NDVI), gross primary productivity (GPP) and solar-induced chlorophyll fluorescence (SIF) to analyze the changing characteristics of vegetation activity in Eurasia over the past two decades. Additionally, we integrated the mean annual temperature (MAT), the mean annual precipitation (MAP), the soil moisture (SM), the vapor pressure deficit (VPD) and the terrestrial water storage (TWS) to analyze natural factors’ influence on the vegetation activity from 2003 to 2022. Through partial correlation and residual analysis, we quantitatively described the contributions of both natural and human factors to changes in vegetation activity. The results indicated an overall increasing trend in vegetation activity in Eurasia; the growth rates of vegetation greenness, productivity and photosynthetic capacity were 1.00 × 10−3 yr−1 (p < 0.01), 1.30 g C m−2 yr−2 (p < 0.01) and 1.00 × 10−3 Wm−2μm−1sr−1yr−1 (p < 0.01), respectively. Furthermore, we found that soil moisture was the most important natural factor influencing vegetation activity. Human activities were identified as the main driving factors of vegetation activity in the Eurasian drylands. The relative contributions of human-induced changes to NDVI, GPP and SIF were 52.45%, 55.81% and 74.18%, respectively. These findings can deepen our understanding of the impacts of current natural change and intensified human activities on dryland vegetation coverage change in Eurasia.

The virulence analysis of Puccinia stiiformis f. sp. tritici (Pst), the cause of wheat stripe rust, is essential for predicting and managing the disease epidemic in Southwest China, where the wheat cultivation has significantly reduced in the past few decades due to the impact of this disease. From 2020 to 2021, 196 Pst isolates were collected from Guizhou, Yunnan, and Sichuan. The virulence and race assessments were conducted using Chinese differential genotypes. Additionally, the resistance expression of 102 wheat lines was evaluated in 2021 in two disease nurseries located in Ningnan and Jiangyou. All the 45 Pst isolates from Guizhou and Yunnan belonged to pathogroup Hybrid 46, with 36 identified as race CYR32. Among the 69 isolates from the Liangshan Prefecture, 67 belonged to the Hybrid 46 group, while the remaining two were identified as race CYR34 in the G-22 group. Furthermore, all 79 isolates from the western Sichuan Basin belonged to the G-22 group, with 54 identified as race CYR34. The diversity indices of the Pst populations from Guizhou, Sichuan, and Yunnan exhibited a sequential decline. Virulence variation among the Pst populations from Yunnan, Guizhou, and the Ganzi-Liangshan region was minimal; however, significant virulence differences were observed when these populations were compared to those from the western Sichuan Basin. Results from disease nurseries indicated that Pst virulence was notably stronger in Ningnan compared to that in Jiangyou. The Sichuan Basin exhibits a notable diversity in Pst virulence, coupled with a more frequent genetic exchange occurring between the Liangshan Prefecture and the Yunnan-Guizhou Plateau. This information is essential for developing effective management strategies to mitigate the impact of wheat stripe rust in this region.

As climate change increasingly affects global ecosystems, understanding plant responses to drought stress has become essential for both conservation and agricultural productivity [...]

Increasingly massive image data is restricted by conditions such as information transmission and reconstruction, and it is increasingly difficult to meet the requirements of speed and integrity in the information age. To solve the urgent problems faced by massive image data in information transmission, this paper proposes a high-quality image compression algorithm based on unsupervised learning. Among them, a content-weighted autoencoder network is proposed to achieve image compression coding on the basis of a smaller bit rate to solve the entropy rate optimization problem. Binary quantizers are used for coding quantization, and importance maps are used to achieve better bit allocation. The compression rate is further controlled and optimized. A multi-scale discriminator suitable for the generative adversarial network image compression framework is designed to solve the problem that the generated compressed image is prone to blurring and distortion. Finally, through training with different weights, the distortion of each scale is minimized, so that the image compression can achieve a higher quality compression and reconstruction effect. The experimental results show that the algorithm model can save the details of the image and greatly compress the memory of the image. Its advantage is that it can expand and compress a large number of images quickly and efficiently and realize the efficient processing of image compression.

Inspired by prior research on fractional calculus, we introduce new fractional integral and derivative operators: the (ρ1,ρ2,k1,k2,φ)-proportional integral and the (ρ1,ρ2,k1,k2,φ)-proportional Hilfer fractional derivative. Numerous previous studied fractional integrals and derivatives can be considered as particular instances of the novel operators introduced above. Some properties of the (ρ1,ρ2,k1,k2,φ)-proportional integral are discussed, including mapping properties, the generalized Laplace transform of the (ρ1,ρ2,k1,k2,φ)-proportional integral and (ρ1,ρ2,k1,k2,φ)-proportional Hilfer fractional derivative. The results obtained suggest that the most comprehensive formulation of this fractional calculus has been achieved. Under the guidance of the findings from earlier sections, we investigate the existence of mild solutions for the (ρ1,ρ2,k1,k2,φ)-proportional Hilfer fractional Cauchy problem. An illustrative example is provided to demonstrate the main results.

Recently, the Internet of Things (IoT) has witnessed rapid development. However, the scarcity of computing resources on the ground has constrained the application scenarios of IoT. Low Earth Orbit (LEO) satellites have drawn people’s attention due to their broader coverage and shorter transmission delay. They are capable of offloading more IoT computing tasks to mobile edge computing (MEC) servers with lower latency in order to address the issue of scarce computing resources on the ground. Nevertheless, it is highly challenging to share bandwidth and power resources among multiple IoT devices and LEO satellites. In this paper, we explore the efficient data offloading mechanism in the LEO satellite-based IoT (LEO-IoT), where LEO satellites forward data from the terrestrial to the MEC servers. Specifically, by optimally selecting the forwarding LEO satellite for each IoT task and allocating communication resources, we aim to minimize the data offloading latency and energy consumption. Particularly, we employ the state-of-the-art Decision Transformer (DT) to solve this optimization problem. We initially obtain a pre-trained DT through training on a specific task. Subsequently, the pre-trained DT is fine-tuned by acquiring a small quantity of data under the new task, enabling it to converge rapidly, with less training time and superior performance. Numerical simulation results demonstrate that in contrast to the classical reinforcement learning approach (Proximal Policy Optimization), the convergence speed of DT can be increased by up to three times, and the performance can be improved by up to 30%.

Crawling robots are the focus of intelligent inspection research, and the main feature of this type of robot is the flexibility of in-plane attitude adjustment. The crawling robot HIT_Spibot is a new type of steam generator heat transfer tube inspection robot with a unique mobility capability different from traditional quadrupedal robots. This paper introduces a hierarchical motion planning approach for HIT_Spibot, aiming to achieve efficient and agile maneuverability. The proposed method integrates three distinct planners to handle complex motion tasks: a nonlinear optimization-based base motion planner, a TOPSIS-based base orientation planner, and a Mask-D3QN (MD3QN) algorithm-based gait motion planner. Initially, the robot’s base and foot workspace were delineated through envelope analysis, followed by trajectory computation using Larangian methods. Subsequently, the TOPSIS algorithm was employed to establish an evaluation framework conducive to foundational turning planning. Finally, the MD3QN algorithm trained foot-points to facilitate robot movement along predefined paths. Experimental results demonstrated the method’s adaptability across diverse tube structures, showcasing robust performance even in environments with random obstacles. Compared to the D3QN algorithm, MD3QN achieved a 100% success rate, enhanced average overall scores by 6.27%, reduced average stride lengths by 39.04%, and attained a stability rate of 58.02%. These results not only validate the effectiveness and practicality of the method but also showcase the significant potential of HIT_Spibot in the field of industrial inspection.

The extensive implementation of the ‘Grain for Green’ project over the Loess Plateau has improved environmental quality. However, it has resulted in a greater consumption of soil water, and its overall hydrological effects remain highly controversial. Our study utilized a coupled land-atmosphere model to evaluate the effects of vegetation changes resulting from revegetation or reclamation on the hydrology of the Loess Plateau. Revegetation was found to stimulate an increase in precipitation, evapotranspiration, and atmospheric water content. However, the increase in precipitation was insufficient to compensate for soil water loss driven by intensified evapotranspiration, resulting in a decrease in both runoff and soil water content. In contrast to revegetation, reclamation would reduce precipitation, although the reduction was less than the decrease in evapotranspiration. This could lead to an increase in both runoff and soil water content. The results provide an important scientific basis for the hydrological effects of vegetation changes on the Loess Plateau, which is particularly important for guiding current and future revegetation activities toward sustainable ecosystem development and water resources management.

Diabetic cardiomyopathy (DCM), one of the most serious long-term consequences of diabetes, is closely associated with myocardial fatty acid metabolism. Carnitine palmitoyltransferase-1β (CPT-1β) is the rate-limiting enzyme responsible for β-oxidation of long-chain fatty acids. Intermedin (IMD) is a pivotal bioactive small molecule peptide, participating in the protection of various cardiovascular diseases. However, the role and underlying mechanisms of IMD in DCM are still unclear. In this study, we investigated whether IMD alleviates DCM via regulating CPT-1β. A rat DCM model was established by having rats to drink fructose water for 12 weeks. A mouse DCM model was induced by feeding mice a high-fat diet for 16 weeks. We showed that IMD and its receptor complexes levels were significantly down-regulated in the cardiac tissues of DCM rats and mice. Reduced expression of IMD was also observed in neonatal rat cardiomyocytes treated with palmitic acid (PA, 300 μM) in vitro. Exogenous and endogenous IMD mitigated cardiac hypertrophy, fibrosis, dysfunction, and lipid accumulation in DCM rats and IMD-transgenic DCM mice, whereas knockout of IMD worsened these pathological processes in IMD-knockout DCM mice. In vitro, IMD alleviated PA-induced cardiomyocyte hypertrophy and cardiac fibroblast activation. We found that CPT-1β enzyme activity, mRNA and protein levels, and acetyl-CoA content were increased in T2DM patients, rats and mice. IMD up-regulated the CPT-1β levels and acetyl-CoA content in T2DM rats and mice. Knockdown of CPT-1β blocked the effects of IMD on increasing acetyl-CoA content and on inhibiting cardiomyocyte hypertrophy and cardiac fibroblast activation. IMD receptor antagonist IMD17–47 and the phosphatidyl inositol 3 kinase (PI3K)/protein kinase B (Akt) inhibitor LY294002 reversed the effects of IMD on up-regulating CPT-1β and acetyl-CoA expression and on inhibiting cardiomyocyte hypertrophy and cardiac fibroblast activation. We revealed that IMD alleviates DCM by up-regulating CPT-1β via calcitonin receptor-like receptor/receptor activity-modifying protein (CRLR/RAMP) receptor complexes and PI3K/Akt signaling. IMD may serve as a potent therapeutic target for the treatment of DCM.

To investigate the genetic basis of processing quality- and yield-related traits in bread wheat (Triticum aestivum L., AABBDD), a systematic analysis of wheat processing quality- and yield-related traits based on genome-wide association studies (GWASs) of 285 regional test lines of wheat from Hebei province, China, was conducted. A total of 87 quantitative trait loci (QTL), including twenty-one for water absorption (WA), four for wet gluten content, eight for grain protein content, seventeen for dough stability time (DST), thirteen for extension area (EA), twelve for maximum resistance (MR), five for thousand-grain weight (TGW), one for grain length, and six for grain width were identified. These QTL harbored 188 significant single-nucleotide polymorphisms (SNPs). Twenty-five SNPs were simultaneously associated with multiple traits. Notably, the SNP AX-111015470 on chromosome 1A was associated with DST, EA, and MR. SNPs AX-111917292 and AX-109124553 on chromosome 5D were associated with wheat WA and TGW. Most processing quality-related QTL and seven grain yield-related QTL identified in this study were newly discovered. Among the surveyed accessions, 18 rare superior alleles were identified. This study identified significant QTL associated with quality-related and yield-related traits in wheat, and some of them showed pleiotropic effects. This study will facilitate molecular designs that seek to achieve synergistic improvements of wheat quality and yield.

Currently, multi-layer deep convolutional networks are mostly used for field weed recognition to extract and identify target features. However, in practical application scenarios, they still face challenges such as insufficient recognition accuracy, a large number of model parameters, and slow detection speed. In response to the above problems, using chickweed as the identification object, a weed identification model based on improved YOLOv5s was proposed. Firstly, the Squeeze-and-Excitation Module (SE) and Convolutional Block Attention Module (CBAM) were added to the model’s feature extraction network to improve the model’s recognition accuracy; secondly, the Ghost convolution lightweight feature fusion network was introduced to effectively identify the volume, parameter amount, and calculation amount of the model, and make the model lightweight; finally, we replaced the loss function in the original target bounding box with the Efficient Intersection over Union (EloU) loss function to further improve the detection performance of the improved YOLOv5s model. After testing, the accuracy of the improved YOLOv5s model was 96.80%, the recall rate was 94.00%, the average precision was 93.20%, and the frame rate was 14.01 fps, which were improved by 6.6%, 4.4%, 1.0%, and 6.1%, respectively, compared to the original YOLOv5s model. The model volume was 9.6 MB, the calculation amount was 13.6 GB, and the parameter amount was 5.9 MB, which decreased by 29.4%, 14.5%, and 13.2% compared with the original YOLOv5s model, respectively. This model can effectively distinguish chickweed between crops. This research can provide theoretical and technical support for efficient identification of weeds in complex field environments.

Based on the lining structure of an operational tunnel, the AHP and Fuzzy mathematical models were used to determine the weight of the evaluation index and solve the membership matrix. The weighted-average Fuzzy comprehensive function was used to combine the two, and the Fuzzy–AHP evaluation model was built and programmed. According to the self-developed Fuzzy–AHP evaluation-programmed model, a visualized structure safety evaluation system for operational tunnels was developed by using MATLAB. The system’s functional design, program development, and computational visualized interface were implemented, and key codes were provided. The system can be divided into four modules: data management, fuzzy computation, predictive analysis and key disease indexes to focus on. In addition, the system can easily edit and modify the evaluation function, which includes not only the Fuzzy evaluation but also other types of evaluation functions applicable to other practical engineering projects, improving the applicability of the system. After that, the system was applied to the structure safety evaluation of a mountain tunnel, which provided the evaluation results and key indexes to focus on in the tunnel. Finally, the rationality of the system design was verified by constructing the corresponding BP–RBF combined neural network. This study provides a reference for the establishment of an intelligent structure safety warning system for operational tunnels.

Soybean (Glycine max (L.) Merr) is one of the most important crops worldwide, but its yield is vulnerable to abiotic stresses. In Arabidopsis, the AlkB homologue (ALKBH) family genes plays a crucial role in plant development and stress response. However, the identification and functions of its homologous genes in soybean remain obscured. Here, we identified a total of 22 ALKBH genes in soybean and classified them into seven subfamilies according to phylogenetic analysis. Gene duplication events among the family members and gene structure, conserved domains, and motifs of all candidate genes were analyzed. By comparing the changes in the m6A levels on mRNA from hair roots between soybean seedlings harboring the empty vector and those harboring the GmALKBH10B protein, we demonstrated that all four GmALKBH10B proteins are bona fide m6A RNA demethylases in vivo. Subcellular localization and expression patterns of the GmALKBH10B revealed that they might be functionally redundant. Furthermore, an analysis of cis-elements coupled with gene expression data demonstrated that GmALKBH10B subfamily genes, including GmALKBH10B1, GmALKBH10B2, GmALKBH10B3, and GmALKBH10B4, are likely involved in the cis-elements’ response to various environmental stimuli. In summary, our study is the first to report the genome-wide identification of GmALKBH family genes in soybean and to determine the function of GmALKBH10B proteins as m6A RNA demethylases, providing insights into GmALKBH10B genes in response to abiotic stresses.

This paper proposes a method for localized damage detection in tomato, with the objective of enabling the detection of bruises prior to sorting. Bioimpedance spectroscopy technology is employed to assess the extent of localized damage in tomato. An equivalent circuit model is constructed, and the impedance spectroscopy data are obtained by developing a local damage measurement platform for tomatoes using a self-designed circular four-electrode BIS sensor. The electrical parameters are then extracted by fitting the constructed equivalent circuit model to the tomato data. Subsequently, we analyze the variation rules of the electrical parameters in different damage levels. To reduce the dimensionality of the features, including biological variables, fitted electrical parameters, and tomato ripeness, we employ Spearman feature selection. We then classify the reduced features by combining the advantages of the support vector machine and the artificial neural network. The results demonstrate that the designed circular four-electrode BIS sensor can non-destructively measure localized damage conditions in tomato. A localized damage measurement platform for tomatoes has been constructed using this sensor. A comparison of the impedance measurements obtained using the designed circular four-electrode BIS sensor with those obtained using a needle sensor proposed by previous scholars revealed that both sensors exhibited a decrease in impedance with increasing damage degree. This finding indicates that the designed circular four-electrode BIS sensor is an effective tool for characterizing damage conditions in tomatoes. The design of the tomato circular four-electrode BIS sensor is an effective means of characterizing tomato damage. The Spearman-SVM-ANN damage classification algorithm, based on the Spearman feature selection, effectively classified tomato damage with a 98.765% accuracy rate. The findings of this study provide a reference for the grading and transportation of tomatoes after harvest.

The stability of scaffolding structures is crucial for quality management in construction. Currently, scaffolding assembly quality monitoring relies on visual inspections performed by designated on-site personnel, which are highly subjective, inaccurate, and inefficient, hindering the advancement of intelligent construction practices. This study proposes an automated method for scaffolding assembly quality inspection using point cloud data and feature selection algorithms. High-precision point cloud data of the scaffolding are captured by a Trimble X7 3D laser scanner. After registration with the forward design model, a 2D slicing comparison method is developed to measure geometric dimensions with an accuracy controlled within 0.1 mm. The collected data are used to build an SVM model for automated assembly quality inspection. To combat the curse of dimensionality associated with high-dimensional data, an optimized genetic algorithm is employed for the dimensionality reduction in the raw sample data, effectively eliminating data redundancy and significantly enhancing convergence speed and classification accuracy of the detection model. Case studies indicate that the proposed method can reduce feature dimensionality by 70% while simultaneously improving classification accuracy by 13.9%. The proposed method enables high-precision automated inspection of scaffolding assembly quality. By identifying the optimal feature subset, the method differentiates the priority of various structural parameters during inspection, providing insights for optimizing the quality inspection process.

The “The Use of Lactobacillus in Forage Storage and Processing” Special Issue (SI) in Fermentation has brought together groundbreaking research and innovative methodologies in the realm of forage preservation [...]

This paper presents a complete planner and controller scheme to achieve balance and walking for a biped robot, which does not need to distinguish the robot’s dynamic model parameters. The high-level planner utilizes model predictive control to optimize both the foothold location and step duration based on the Divergent Component of Motion (DCM) model to enhance the robustness of generated gaits. For low-level control, we use quadratic programming (QP) to optimize the contact force distribution under the contact constraints to achieve the virtual wrench exerted on the base of the robot. Then, the joint torques sent to the robot are derived from three parts: first, the torques mapped from the contact force; second, the swing leg tracking; and third, the stance foot stabilization. The simulation and experiment on BRUCE, a miniature bipedal robot from Westwood Robotics (Los Angeles, CA, USA), testify to the performance of the control scheme, including push recovery, Center of Mass (CoM) tracking, and omnidirectional walking.

The safety-grade water-intake immersed tunnel plays a vital role in the nuclear power cooling system, and its seismic safety is crucial. This paper employs the response displacement method and dynamic time-history analysis using the finite element software ANSYS to construct a beam–spring model and a 3D finite element model of a shield tunnel and foundation. It also develops equivalent linear dynamic constitutive and viscoelastic boundary element subprograms. This study focuses on the weak joint sections of immersed tunnels, conducting a seismic performance analysis under extreme safety earthquake conditions (SL-2). The results indicate that the joint stiffness of immersed tunnels and the increase in seismic peak values do not affect the trend of joint opening variation with longitudinal position. The change in joint opening is primarily located where the thickness of the cover layer changes abruptly or where the soil hardness is unevenly distributed. The joint opening is mainly influenced by seismic forces when considering static and dynamic superposition. When the stiffness of the joint GINA water stop exceeds a certain value, the correlation between stiffness change and joint compression–tension variation gradually weakens. This research can provide a reference for the seismic design of similar projects.

In this paper, we investigate a cell-free massive multiple-input multiple-output (CF-mMIMO) system with a reconfigurable intelligent surface (RIS) carried by an unmanned aerial vehicle (UAV), called the UAV-RIS. Compared with the RIS located on the ground, the UAV-RIS has a wider coverage that can reflect all signals from access points (APs) and user equipment (UE). By correlating the UAV location with the Rician K-factor, we derive a closed-form approximation of the UE achievable downlink rate. Based on this, we obtain the optimal UAV location and RIS phase shift that can maximize the UE sum rate through an alternating optimization method. Simulation results have verified the accuracy of the derived approximation and shown that the UE sum rate can be significantly improved with the obtained optimal UAV location and RIS phase shift. Moreover, we find that with a uniform UE distribution, the UAV-RIS should fly to the center of the system, while with an uneven UE distribution, the UAV-RIS should fly above the area where UEs are gathered. In addition, we also design the best trajectory for the UAV-RIS to fly from its initial location to the optimal destination while maintaining the maximum UE sum rate per time slot during the flight.

Background: Our previous studies have shown that scorpion venom heat-resistant synthesized peptide (SVHRSP) induces a significant extension in lifespan and improvements in age-related physiological functions in worms. However, the mechanism underlying the potential anti-aging effects of SVHRSP in mammals remains elusive. Methods: Following SVHRSP treatment in senescence-accelerated mouse resistant 1 (SAMR1) or senescence-accelerated mouse prone 8 (SAMP8) mice, behavioral tests were conducted and brain tissues were collected for morphological analysis, electrophysiology experiments, flow cytometry, and protein or gene expression. The human neuroblastoma cell line (SH-SY5Y) was subjected to H2O2 treatment in cell experiments, aiming to establish a cytotoxic model that mimics cellular senescence. This model was utilized to investigate the regulatory mechanisms underlying oxidative stress and neuroinflammation associated with age-related cognitive impairment mediated by SVHRSP. Results: SVHRSP significantly ameliorated age-related cognitive decline, enhanced long-term potentiation, restored synaptic loss, and upregulated the expression of synaptic proteins, therefore indicating an improvement in synaptic plasticity. Moreover, SVHRSP demonstrated a decline in senescent markers, including SA-β-gal enzyme activity, P16, P21, SIRT1, and cell cycle arrest. The underlying mechanisms involve an upregulation of antioxidant enzyme activity and a reduction in oxidative stress-induced damage. Furthermore, SVHRSP regulated the nucleoplasmic distribution of NRF2 through the SIRT1-P53 pathway. Further investigation indicated a reduction in the expression of proinflammatory factors in the brain after SVHRSP treatment. SVHRSP attenuated neuroinflammation by regulating the NF-κB nucleoplasmic distribution and inhibiting microglial and astrocytic activation through the SIRT1-NF-κB pathway. Additionally, SVHRSP significantly augmented Nissl body count while suppressing neuronal loss. Conclusion: SVHRSP could remarkably improve cognitive deficiency by inhibiting oxidative stress and neuroinflammation, thus representing an effective strategy to improve brain health.

Identifying ecosystem service bundles and their long-term evolutionary characteristics is essential for the overall enhancement of regional ecosystem services, as well as the division and management of functional areas, providing a basis for decision-making in formulating ecological and environmental protection policies, as well as regional development planning. Based on land use, remote sensing, and meteorological data obtained from Anhui Province, this study assessed six important ecosystem service functions, including food production (FP), water yield (WY), carbon sequestration (CS), soil conservation (SC), habitat quality (HQ), and landscape aesthetics (LA), at the township scale in 2000, 2010, and 2020. On this basis, the k-means clustering method was used to identify ecosystem service bundles, analyze the spatio-temporal evolution trajectory of service bundles, and explore the driving factors of the spatio-temporal evolution of ecosystem service bundles using GeoDetector 2015 The results indicate the following: (1) At the spatial level, diverse ecosystem services demonstrate pronounced spatial differentiation. The distribution pattern of HQ, carbon fixation, and SC services is generally lower in the north and higher in the south, with areas of high value predominantly located in the western Dabie Mountains and the mountains of Southern Anhui. Conversely, FP services exhibit the reverse pattern, and WY services display a gradual increase from north to south, while cultural services are more dispersed, with areas of high value primarily located in the western Dabie Mountains, the Yangtze River Basin, and other locations. On the temporal scale, WY, SC, and FP services mainly exhibit an increasing trend, marked by a significant increase, whereas other services tend to present a decreasing trend. (2) Anhui Province can be categorized into four distinct types of service bundles: the grain production bundle (GPB), mountain ecological conservation bundle (MECB), urban living bundle (ULB), and core protection bundle (CPB). Ecosystem service bundles exhibit clear spatial differentiation, and identical service bundles demonstrate substantial clustering in space. Between 2000 and 2020, ecosystem service bundles displayed a marked spatio-temporal evolution, with the prevalence of GPBs diminishing, whereas the share of ULBs progressively increased, and the number of MECBs and CPBs remained largely stable. (3) In the spatio-temporal evolution process, the average annual precipitation, the proportion of forest land, and slope constitute the principal natural factors influencing the spatio-temporal evolution of ecosystem service bundles, while the proportion of construction land represents the primary socio-economic factor, with natural factors exerting a more significant influence than socio-economic factors.

Control design for the nonlinear cascaded system is challenging due to its complicated system dynamics and system uncertainty, both of which can be considered some kind of system nonlinearity. In this paper, we propose a novel nonlinearity approximation scheme with a simplified structure, where the system nonlinearity is approximated by a steady component and an alternating component using only local tracking errors. The nonlinearity of each subsystem is estimated independently. On this basis, a model-free adaptive control for a class of nonlinear cascaded systems is proposed. A squared-error correction procedure is introduced to regulate the weight coefficients of the approximation components, which makes the whole adaptive system stable even with the unmodeled uncertainties. The effectiveness of the proposed controller is validated on a flexible joint system through numerical simulations and experiments. Simulation and experimental results show that the proposed controller can achieve better control performance than the radial basis function network control. Due to its simplicity and robustness, this method is suitable for engineering applications.

The Wearable Robotic Limb (WRL) is a type of robotic arm worn on the human body, aiming to enhance the wearer’s operational capabilities. However, proposing additional methods to control and perceive the WRL when human limbs are heavily occupied with primary tasks presents a challenge. Existing interactive methods, such as voice, gaze, and electromyography (EMG), have limitations in control precision and convenience. To address this, we have developed an interactive device that utilizes the mouth and tongue. This device is lightweight and compact, allowing wearers to achieve continuous motion and contact force control of the WRL. By using a tongue controller and mouth gas pressure sensor, wearers can control the WRL while also receiving sensitive contact feedback through changes in mouth pressure. To facilitate bidirectional interaction between the wearer and the WRL, we have devised an algorithm that divides WRL control into motion and force-position hybrid modes. In order to evaluate the performance of the device, we conducted an experiment with ten participants tasked with completing a pin-hole assembly task with the assistance of the WRL system. The results show that the device enables continuous control of the position and contact force of the WRL, with users perceiving feedback through mouth airflow resistance. However, the experiment also revealed some shortcomings of the device, including user fatigue and its impact on breathing. After experimental investigation, it was observed that fatigue levels can decrease with training. Experimental studies have revealed that fatigue levels can decrease with training. Furthermore, the limitations of the device have shown potential for improvement through structural enhancements. Overall, our mouth and tongue interactive device shows promising potential in controlling the WRL during tasks where human limbs are occupied.

During the period preceding the vegetation growing season (GS), temperature emerges as the pivotal factor determining phenology in northern terrestrial ecosystems. Despite extensive research on the impact of daily mean temperature (Tmean) during the preseason period, the influence of diurnal temperature range (DTR) on vegetation photosynthetic phenology (i.e., the impact of the plant photosynthetic cycle on seasonal time scale) has largely been neglected. Using a long-term vegetation photosynthetic phenology dataset and historical climate data, we examine vegetation photosynthetic phenology dynamics and responses to climate change across the mid–high latitudes of the Northern Hemisphere from 2001 to 2020. Our data reveal an advancing trend in the start of the GS (SOS) by −0.15 days per year (days yr−1), affecting 72.1% of the studied area. This is particularly pronounced in western Canada, Alaska, eastern Asia, and latitudes north of 60°N. Conversely, the end of the GS (EOS) displays a delaying trend of 0.17 days yr−1, impacting 62.4% of the studied area, especially northern North America and northern Eurasia. The collective influence of an earlier SOS and a delayed EOS has resulted in the notably prolonged length of the GS (LOS) by 0.32 days yr−1 in the last two decades, affecting 70.9% of the studied area, with Eurasia and western North America being particularly noteworthy. Partial correlation coefficients of the SOS with preseason Tmean, DTR, and accumulated precipitation exhibited negative values in 98.4%, 93.0%, and 39.2% of the study area, respectively. However, there were distinct regional variations in the influence of climate factors on the EOS. The partial correlation coefficients of the EOS with preseason Tmean, DTR, and precipitation were positive in 58.6%, 50.1%, and 36.3% of the region, respectively. Our findings unveil the intricate mechanisms influencing vegetation photosynthetic phenology, holding crucial significance in understanding the dynamics of carbon sequestration within terrestrial ecosystems amidst climate change.

In the human–robot collaboration system, the high-precision distortion correction of the camera as an important sensor is a crucial prerequisite for accomplishing the task. The traditional correction process is to calculate the lens distortion with the camera model parameters or separately from the camera model. However, in the optimization process calculate with the camera model parameters, the mutual compensation between the parameters may lead to numerical instability, and the existing distortion correction methods separated from the camera model are difficult to ensure the accuracy of the correction. To address this problem, this study proposes a model-independent lens distortion correction method based on the image center area from the perspective of the actual camera lens distortion principle. The proposed method is based on the idea that the structured image preserves its ratios through perspective transformation, and uses the local image information in the central area of the image to correct the overall image. The experiments are verified from two cases of low distortion and high distortion under simulation and actual experiments. The experimental results show that the accuracy and stability of this method are better than other methods in training and testing results.

As the depth of mines continues to increase, the problems of high temperature and potential heat damage become more prominent. In this study, the characteristics of natural and industrial heat sources in mines were reviewed, and then mainstream heat hazard governance technologies and corresponding utilization methods were discussed and compared. The first category of technologies comprises the optimization of ventilation systems, the insulation of roc heat, and artificial refrigeration. These cooling approaches are limited because the heat resources cannot be recovered. The second category is the utilization of waste industrial heat in mines, including the use of waste heat from the air compressors, drainage water, and foul airflow, but the current applications of these approaches have limited effectiveness in cooling the underground space. The third category is the application of geo-structures to recover natural heat in mines. Based on the principles of the chiller/heat pump cycle and the characteristics of heat sources and sinks in mines, the potential and constraints of each technology were discussed and summarized. This study provides a scientific reference for the selection of suitable heat governance and utilization technologies.

Arc faults are the main cause of electrical fires according to national fire data statistics. Intensive studies of artificial intelligence-based arc fault detection methods have been carried out and achieved a high detection accuracy. However, the computational complexity of the artificial intelligence-based methods hinders their application for arc fault detection devices. This paper proposes a lightweight arc fault detection method based on the discrimination of a novel feature for lower current distortion conditions and the Adam-optimized BP neural network for higher distortion conditions. The novel feature is the pulse signal number per unit cycle, reflecting the zero-off phenomena of the arc current. Six features, containing the novel feature, are chosen as the inputs of the neural network, reducing the computational complexity. The model achieves a high detection accuracy of 99.27% under various load types recommended by the IEC 62606 standard. Finally, the proposed lightweight method is implemented on hardware based on the STM32 series microcontroller unit. The experimental results show that the average detection accuracy is 98.33%, while the average detection time is 45 ms and the average tripping time is 72–201 ms under six types of loads, which can fulfill the requirements of real-time detection for commercial arc fault detection devices.

The modulation of a terahertz (THz) wave on amplitude, phase and polarization is important for the application of THz technology, especially in the field of imaging, and is one of the current research hotspots. Silicon-based, optically excited THz modulator is a wavefront modulation technique with a simple, compact and reconfigurable optical path. It can realize the dynamic modulation of THz wavefronts by only changing the projected two-dimensional pattern, but it still suffers from the problems of lower modulation efficiency and slower modulation rates. In this article, the Drude model in combination with the multiple thin layers structure model and Fresnel matrix method is used to compare the modulation efficiencies of three modulation modes and more factors. The method is more accurate than the popular proposed method, especially when the thickness of the excited photoconductive layers reaches a few hundred microns. In comparing the three modes, namely transmission, ordinary reflection and total internal reflection, it is found the total internal reflection modulation mode has the best modulation efficiency. Further, under this mode, the effects of three factors, including the lifetime of photo-excited carriers, the wavelength of pump light and the frequency of THz wave, on the performance of THz modulator are analyzed. The simulation results show that the realization of total internal reflection using silicon prisms is a simple and effective method to improve the modulation efficiency of a silicon-based optically excited THz modulator, which provides references for the design of a photo-induced THz modulator.

In this work, a fully discrete mixed finite element (MFE) scheme is designed to solve the multi-term time-fractional reaction–diffusion equations with variable coefficients by using the well-known L1 formula and the Raviart–Thomas MFE space. The existence and uniqueness of the discrete solution is proved by using the matrix theory, and the unconditional stability is also discussed in detail. By introducing the mixed elliptic projection, the error estimates for the unknown variable u in the discrete L∞(L2(Ω)) norm and for the auxiliary variable λ in the discrete L∞((L2(Ω))2) and L∞(H(div,Ω)) norms are obtained. Finally, three numerical examples are given to demonstrate the theoretical results.

The vegetation ecosystem in the southern hilly region of China (SHRC) plays a crucial role in the country’s carbon reservoir. Clarifying the dynamics of net primary productivity (NPP) in this area and its response to climate factors in the context of climate change is important for national forest ecology, management, and carbon neutrality efforts. This study, based on remote sensing and meteorological data spanning the period 2001 to 2021, aims to unveil the spatiotemporal patterns of vegetation productivity and climate factors in the southern hilly region, explore interannual variation characteristics of vegetation productivity with altitude, and investigate the response characteristics of NPP to various climate factors. The results indicate that from 2001 to 2021, the annual average NPP in the southern hilly region had a significant increasing trend of 2.13 ± 0.78 g m−2 a−1. The trend of NPP varies significantly with altitude. Despite a general substantial upward trend in vegetation NPP, regions at lower elevations exhibit a faster rate of increase, suggesting a diminishing difference in the NPP of different elevation ranges. The overall rise in average temperature has positive implications for the southern hilly region, while the impact of precipitation on vegetation NPP demonstrates noticeable spatial heterogeneity. Regions in which vegetation NPP is significantly negatively correlated with precipitation are mainly concentrated in the southern areas of Guangdong, Fujian, and Jiangxi provinces. In contrast, other regions further away from the southeastern coast tend to exhibit a positive correlation. Over the past two decades, there has been an asymmetry in the diurnal temperature variation in the SHRC, with the nighttime warming rate being 1.8 times that of the daytime warming rate. The positive impact of daytime warming on NPP of vegetation is more pronounced than the impact of nighttime temperature changes. Understanding the spatiotemporal patterns of NPP in the SHRC and the characteristics of its response to climate factors contributes to enhancing our ability to protect and manage vegetation resources amidst the challenges of global climate change.

Federated learning (FL) is a promising technique to provide intelligent services for the internet of things (IoT). By transmitting the model parameters instead of user data between the client and central server, FL greatly improves the user privacy and reduces transmission latency. However, due to the fading effects of the wireless channel, the outage of wireless transmission degenerates the learning efficiency when FL is applied in wireless IoT networks. In order to address this issue, we investigate the joint optimization of client selection and wireless resource allocation in FL-aided cellular IoT networks. By taking both the amount of training data and wireless resource consumption into consideration, we formulate the problem as a mixed integer non-linear programming to maximize the utility of the network. To solve the problem effectively, an alternative direction-based algorithm is proposed by decomposing the original problem into two sub problems. The simulation results indicate that the proposed algorithm substantially improves the FL learning performance and reduces the consumption of wireless resources compared with existing methods.

Ensiling density has significant importance for the quality and preservation of silage. Appropriate ensiling density could improve the nutritional value, extend the storage time of silage and reduce the risk of mold and spoilage. This work aimed to evaluate the effect of ensiling densities on the fermentation quality and aerobic stability of rice straw. The rice straw was obtained after threshing, then chopped and ensiled into a 10 L laboratory silo with three ensiling densities (high density at 700 g/L, medium density at 600 g/L and low density at 500 g/L). Five silos per density were opened after 3, 5, 7, 14, 30 and 60 days of ensiling, and then, the fermentation quality and aerobic stability were analyzed. During ensiling, high density had the highest lactic acid content, and the lowest pH and ammonia nitrogen. There was no difference (p > 0.05) in the propionic acid, butyric acid and ethanol contents among all silage, and the contents of propionic acid and butyric acid were trace amounts. On day 60 of ensiling, the Flieg’s point of high density and medium density were higher than the low density. During aerobic exposure, the continuous lactic acid decrease and pH increase were observed in all silage. The aerobic bacteria and yeasts count in the high density and medium density were lower than that in the low density. The aerobic stability of the high density (26 h) and the medium density (24 h) were higher than that of the low density (13 h). It was suggested that if the ensiling density is higher than 600 g/L, it could effectively improve the fermentation quality and aerobic stability of rice straw.

GmSNAP18 and GmSHMT08 are two major genes conferring soybean cyst nematode (SCN) resistance in soybean. Overexpression of either of these two soybean genes would enhance the susceptibility of Arabidopsis to beet cyst nematode (BCN), while overexpression of either of their corresponding orthologs in Arabidopsis, AtSNAP2 and AtSHMT4, would suppress it. However, the mechanism by which these two pairs of orthologous genes boost or inhibit BCN susceptibility of Arabidopsis still remains elusive. In this study, Arabidopsis with simultaneously overexpressed GmSNAP18 and GmSHMT0 suppressed the growth of underground as well as above-ground parts of plants. Furthermore, Arabidopsis that simultaneously overexpressed GmSNAP18 and GmSHMT08 substantially stimulated BCN susceptibility and remarkably suppressed expression of AtPR1 in the salicylic acid signaling pathway. However, simultaneous overexpression of GmSNAP18 and GmSHMT08 did not impact the expression of AtJAR1 and AtHEL1 in the jasmonic acid and ethylene signaling pathways. GmSNAP18, GmSHMT08, and a pathogenesis-related (PR) protein, GmPR08-Bet VI, in soybean, and AtSNAP2, AtSHMT4, and AtPR1 in Arabidopsis could interact pair-wisely for mediating SCN and BCN resistance in soybean and Arabidopsis, respectively. Both AtSNAP2 and AtPR1 were localized on the plasma membrane, and AtSHMT4 was localized both on the plasma membrane and in the nucleus of cells. Nevertheless, after interactions, AtSNAP2 and AtPR1 could partially translocate into the cell nucleus. GmSNAP18 interacted with AtSHMT4, and GmSHMT4 interacted with AtSNAP2. However, neither GmSNAP18 nor GmSHMT08 interacted with AtPR1. Thus, no pairwise interactions among α-SNAPs, SHMTs, and AtPR1 occurred in Arabidopsis overexpressing either GmSNAP18 or GmSHMT08, or both of them. Transgenic Arabidopsis overexpressing either GmSNAP18 or GmSHMT08 substantially suppressed AtPR1 expression, while transgenic Arabidopsis overexpressing either AtSNAP2 or AtSHMT4 remarkably enhanced it. Taken together, no pairwise interactions of GmSNAP18, GmSHMT08, and AtPR1 with suppressed expression of AtPR1 enhanced BCN susceptibility in Arabidopsis. This study may provide a clue that nematode-resistant or -susceptible functions of plant genes likely depend on both hosts and nematode species.

Estimating the contact forces and moments (CFMs) between exoskeletons’ feet and the ground is a prerequisite for calculating exoskeletons’ joint moments. However, comfortable, portable, and high-precision force sensors for CFM detection are difficult to design and manufacture. In addition, there are many unknown CFM components (six force components and six moment components in the double-support phase). These reasons make it challenging to estimate CFMs precisely. In this paper, we propose a novel method for estimating these CFMs based on a proposed dynamic decoupled coordinate system (DDCS) and the minimum energy hypothesis. By decomposing these CFMs into a DDCS, the number of unknowns can be significantly reduced from twelve to two. Meanwhile, the minimum energy hypothesis provides a relatively reliable target for optimizing the remaining two unknown variables. We verify the accuracy of this method using a public data set about human walking. The validation shows that the proposed method is capable of estimating CFMs. This study provides a practical way to estimate the CFMs under the soles, which contributes to reducing the research and development costs of exoskeletons by avoiding the need for expensive plantar sensors. The sensor-free approach also reduces the dependence on high-precision, portable, and comfortable CFM detection sensors, which are usually difficult to design.

Mixed reality technology can give humans an intuitive visual experience, and combined with the multi-source information of the human body, it can provide a comfortable human–robot interaction experience. This paper applies a mixed reality device (Hololens2) to provide interactive communication between the wearer and the wearable robotic limb (supernumerary robotic limb, SRL). Hololens2 can obtain human body information, including eye gaze, hand gestures, voice input, etc. It can also provide feedback information to the wearer through augmented reality and audio output, which is the communication bridge needed in human–robot interaction. Implementing a wearable robotic arm integrated with HoloLens2 is proposed to augment the wearer’s capabilities. Taking two typical practical tasks of cable installation and electrical connector soldering in aircraft manufacturing as examples, the task models and interaction scheme are designed. Finally, human augmentation is evaluated in terms of task completion time statistics.

Robots gradually have the ability to plan grasping actions in unknown scenes by learning the manipulation of typical scenes. The grasping pose estimation method, as a kind of end-to-end method, has rapidly developed in recent years because of its good generalization. In this paper, we present a grasping pose estimation method for robots based on convolutional neural networks. In this method, a convolutional neural network model was employed, which can output the grasping success rate, approach angle, and gripper opening width for the input voxel. The grasping dataset was produced, and the model was trained in the physical simulator. A position optimization of the robotic grasping was proposed according to the distribution of the object centroid to improve the grasping success rate. An experimental platform for robot grasping was established, and 11 common everyday objects were selected for the experiments. Grasping experiments involving the eleven objects individually, multiple objects, as well as a dark environment without illumination, were performed. The results show that the method has the adaptability to grasp different geometric objects, including irregular shapes, and it is not influenced by lighting conditions. The total grasping success rate was 88.2% for the individual objects and 81.1% for the cluttered scene.

Prior to dispatch from manufacturing facilities, seeders require rigorous performance evaluations for their seeding capabilities. Conventional manual inspection methods are notably less efficient. This study introduces a wheat seeding detection approach anchored in an enhanced YOLOv5s image-processing technique. Building upon the YOLOv5s framework, we integrated four CBAM attention mechanism modules into its model. Furthermore, the traditional upsampling technique in the neck layer was superseded by the CARAFE upsampling method. The augmented model achieved an mAP of 97.14%, illustrating its ability to elevate both the recognition precision and processing speed for wheat seeds while ensuring that the model remains lightweight. Leveraging this advanced model, we can effectively count and locate seed images, enabling the precise calculation and assessment of sowing uniformity, accuracy, and dispersion. We established a sowing test bench and conducted experiments to validate our model. The results showed that after the model was improved, the average accuracy of wheat recognition was above 97.55% under different sowing rates and travel speeds. This indicates that this method has high precision for the total number of seed particles. The sowing rate and sowing travel speed were consistent with manual measurements and did not significantly affect uniformity, accuracy, or dispersion.

The self-reconfigurable modular robotic system is a class of robots that can alter its configuration by rearranging the connectivity of their component modular units. The reconfiguration deformation planning problem is to find a sequence of reconfiguration actions to transform one reconfiguration into another. In this paper, a hybrid reconfiguration deformation planning algorithm for modular robots is presented to enable reconfiguration between initial and goal configurations. A hybrid algorithm is developed to decompose the configuration into subconfigurations with maximum commonality and implement distributed dynamic mapping of free vertices. The module mapping relationship between the initial and target configurations is then utilized to generate reconfiguration actions. Simulation and experiment results verify the effectiveness of the proposed algorithm.

In vitro biomechanical experiments utilizing cadaveric specimens are one of the most effective methods for rehearsing surgical procedures, testing implants, and guiding postoperative rehabilitation. Applying dynamic physiological muscle force to the specimens is a challenge to reconstructing the environment of bionic mechanics in vivo, which is often ignored in the in vitro experiment. The current work aims to establish a hardware platform and numerical computation methods to reproduce dynamic muscle forces that can be applied to mechanical testing on in vitro specimens. Dynamic muscle loading is simulated through numerical computation, and the inputs of the platform will be derived. Then, the accuracy and robustness of the platform will be evaluated through actual muscle loading tests in vitro. The tests were run on three muscles (gastrocnemius lateralis, the rectus femoris, and the semitendinosus) around the knee joint and the results showed that the platform can accurately reproduce the magnitude of muscle strength (errors range from −6.2% to 1.81%) and changing pattern (goodness-of-fit range coefficient ranges from 0.00 to 0.06) of target muscle forces. The robustness of the platform is mainly manifested in that the platform can still accurately reproduce muscle force after changing the hardware combination. Additionally, the standard deviation of repeated test results is very small (standard ranges of hardware combination 1: 0.34 N~2.79 N vs. hardware combination 2: 0.68 N~2.93 N). Thus, the platform can stably and accurately reproduce muscle forces in vitro, and it has great potential to be applied in the future musculoskeletal loading system.

In nature, some animals, such as snakes and octopuses, use their limited body structure to conduct various complicated tasks not only for locomotion but also for hunting. Their body segments seem to possess the intelligence to adapt to environments and tasks. Inspired by nature, a modular soft robot with integrated locomotion and manipulation abilities is presented in this paper. A soft modular robot is assembled using several homogeneous cubic pneumatic soft actuator units made of silicone rubber. Both a mathematical model and backpropagation neural network are established to describe the nonlinear deformation of the soft actuator unit. The locomotion process of the chain-type soft robot is analyzed to provide a general rhythmic control principle for modular soft robots. A vision sensor is adopted to control the locomotion and manipulation processes of the modular soft robot in a closed loop. The experimental results indicate that the modular soft robot put forward in this paper has both locomotion and manipulation abilities.

The current morphing technologies are mostly regarded as auxiliary tools, providing additional control torques to enhance the flight maneuverability of unmanned aerial vehicles (UAVs), and they cannot exist independently of the traditional control surfaces. In this paper, we propose a tandem-wing micro aerial vehicle (MAV) with multiple variable-sweep wings, which can reduce the additional inertia forces and moments and weaken the dynamic coupling between longitudinal and lateral motion while the MAV morphs symmetrically for pitch control or asymmetrically for roll control, thereby flying without the traditional aileron and elevator. First, load experiments were conducted on the MAV to verify the structural strength of the multiple variable sweep wings, and the control moments caused by the morphing of the MAV were presented through numerical simulations. Then, the effects caused by symmetric and asymmetric morphing were investigated via dynamic response simulations based on the Kane dynamic model of the MAV, and the generated additional inertia forces and moments were also analyzed during morphing. Finally, dynamic response experiments and open-loop flight experiments were conducted. The experimental results demonstrated that the morphing mode in this study could weaken the coupling between the longitudinal and lateral dynamics and that it was feasible for attitude control without the traditional aileron and elevator while flying.

The nation’s transportation systems are complex and are some of the highest valued and largest public assets in the United States. As a result of repeated natural hazards and their significant impact on transportation functionality and the socioeconomic health of communities, transportation resilience has gained increasing attention in recent years. Previous studies on transportation resilience have heavily emphasized network functionality during and/or following a scenario hazard event by implicitly assuming that sufficient knowledge of structural capacity and environmental/service conditions is available at the time of an extreme event. However, such assumptions often fail to consider uncertainties that arise when an extreme hazard event occurs in the future. Thus, it is essential to quantify and reduce uncertainties to better prepare for extreme events and accurately assess transportation resilience. To this end, this paper proposes a dynamic Bayesian network-based resilience assessment model for a large-scale roadway network that can explicitly quantify uncertainties in all phases of the assessment and investigate the role of inspection and monitoring programs in uncertainty reduction. Specifically, the significance of data reliability is investigated through a sensitivity analysis, where various sets of data having different reliabilities are used in updating system resilience. To evaluate the effectiveness of the model, a benchmark problem involving a highway network in South Carolina, USA is utilized, showcasing the systematic quantification and reduction of uncertainties in the proposed model. The benchmark problem result shows that incorporating monitoring and inspection data on important variables could improve the accuracy of predicting the seismic resilience of the network. It also suggests the need to consider equipment reliability when designing monitoring and inspection programs. With the recent development of a wide range of monitoring and inspection techniques, including nondestructive testing, health monitoring equipment, satellite imagery, LiDAR, etc., these findings can be useful in assisting transportation managers in identifying necessary equipment reliability levels and prioritizing inspection and monitoring efforts.

A novel approach was developed to detect bed fluidity in gas–solid fluidized beds using diffuse reflectance near-infrared (NIR) spectroscopy. Because the flow dynamics of gas and solid phases are closely associated with the fluidization state, the fluidization quality can be evaluated through hydrodynamic characterization. In this study, the baseline level of NIR spectra was used to quantify the voidage of the fluidized bed. Two indicators derived from the NIR baseline fluctuation profiles were investigated to characterize bed fluidity, named bubble proportion and skewness. To establish a robust fluidity evaluation method, the relationships between the indicators and bed fluidity were investigated under different conditions firstly, including static bed height and average particle size. Then, a generalized threshold was identified to distinguish poor and good bed fluidity, ensuring that the probability of the α- and β-errors was less than 15% regardless of material conditions. The results show that both indicators were sensitive to changes in bed fluidity under the investigated conditions. The indicator of skewness was qualified to detect bed fluidity under varied conditions with a robust threshold of 1.20. Furthermore, the developed NIR method was successfully applied to monitor bed fluidity and for early warning of defluidization in a laboratory-scale fluidized bed granulation process.

Robot measurement systems with a binocular planar structured light camera (3D camera) installed on a robot end-effector are often used to measure workpieces’ shapes and positions. However, the measurement accuracy is jointly influenced by the robot kinematics, camera-to-robot installation, and 3D camera measurement errors. Incomplete calibration of these errors can result in inaccurate measurements. This paper proposes a joint calibration method considering these three error types to achieve overall calibration. In this method, error models of the robot kinematics and camera-to-robot installation are formulated using Lie algebra. Then, a pillow error model is proposed for the 3D camera based on its error distribution and measurement principle. These error models are combined to construct a joint model based on homogeneous transformation. Finally, the calibration problem is transformed into a stepwise optimization problem that minimizes the sum of the relative position error between the calibrator and robot, and analytical solutions for the calibration parameters are derived. Simulation and experiment results demonstrate that the joint calibration method effectively improves the measurement accuracy, reducing the mean positioning error from over 2.5228 mm to 0.2629 mm and the mean distance error from over 0.1488 mm to 0.1232 mm.

In recent years, unmanned aerial vehicle (UAV) remote sensing systems have advanced rapidly, enabling the effective assessment of crop growth through the processing and integration of multimodal data from diverse sensors mounted on UAVs. UAV-derived multimodal data encompass both multi-source remote sensing data and multi-source non-remote sensing data. This study employs Image Guided Filtering Fusion (GFF) to obtain high-resolution multispectral images (HR-MSs) and selects three vegetation indices (VIs) based on correlation analysis and feature reduction in HR-MS for multi-source sensing data. As a supplement to remote sensing data, multi-source non-remote sensing data incorporate two meteorological conditions: temperature and precipitation. This research aims to establish remote sensing quantitative monitoring models for four crucial growth-physiological indicators during rapeseed (Brassica napus L.) seedling stages, namely, leaf area index (LAI), above ground biomass (AGB), leaf nitrogen content (LNC), and chlorophyll content (SPAD). To validate the monitoring effectiveness of multimodal data, the study constructs four model frameworks based on multimodal data input and employs Support Vector Regression (SVR), Partial Least Squares (PLS), Backpropagation Neural Network (BPNN), and Nonlinear Model Regression (NMR) machine learning models to create winter rapeseed quantitative monitoring models. The findings reveal that the model framework, which integrates multi-source remote sensing data and non-remote sensing data, exhibits the highest average precision (R2 = 0.7454), which is 28%, 14.6%, and 3.7% higher than that of the other three model frameworks, enhancing the model’s robustness by incorporating meteorological data. Furthermore, SVR consistently performs well across various multimodal model frameworks, effectively evaluating the vigor of rapeseed seedlings and providing a valuable reference for rapid, non-destructive monitoring of winter rapeseed.

With the use of collaborative robots in intelligent manufacturing, human–robot interaction has become more important in human–robot collaborations. Human–robot handover has a huge impact on human–robot interaction. For current research on human–robot handover, special attention is paid to robot path planning and motion control during the handover process; seldom is research focused on human handover intentions. However, enabling robots to predict human handover intentions is important for improving the efficiency of object handover. To enable robots to predict human handover intentions, a novel human handover intention prediction approach was proposed in this study. In the proposed approach, a wearable data glove and fuzzy rules are firstly used to achieve faster and accurate human handover intention sensing (HIS) and human handover intention prediction (HIP). This approach mainly includes human handover intention sensing (HIS) and human handover intention prediction (HIP). For human HIS, we employ wearable data gloves to sense human handover intention information. Compared with vision-based and physical contact-based sensing, wearable data glove-based sensing cannot be affected by visual occlusion and does not pose threats to human safety. For human HIP, we propose a fast handover intention prediction method based on fuzzy rules. Using this method, the robot can efficiently predict human handover intentions based on the sensing data obtained by the data glove. The experimental results demonstrate the advantages and efficacy of the proposed method in human intention prediction during human–robot handover.

This paper focuses on motion planning for mobile manipulators, which includes planning for both the mobile base and the manipulator. A hierarchical motion planner is proposed that allows the manipulator to change its configuration autonomously in real time as needed. The planner has two levels: global planning for the mobile base in two dimensions and local planning for both the mobile base and the manipulator in three dimensions. The planner first generates a path for the mobile base using an optimized A* algorithm. As the mobile base moves along the path with the manipulator configuration unchanged, potential collisions between the manipulator and the environment are checked using the environment data obtained from the on-board sensors. If the current manipulator configuration is in a potential collision, a new manipulator configuration is searched. A sampling-based heuristic algorithm is used to effectively find a collision-free configuration for the manipulator. The experimental results in simulation environments proved that our heuristic sampling-based algorithm outperforms the conservative random sampling-based method in terms of computation time, percentage of successful attempts, and the quality of the generated configuration. Compared with traditional methods, our motion planning method could deal with 3D obstacles, avoid large memory requirements, and does not require a long time to generate a global plan.

The advancement of mobile internet technology has enabled companies to leverage social media for e-commerce, where some use personified images and language to communicate with consumers. This paper investigates how personified communication affects consumer behavior in social commerce and whether consumers are willing to accept this new form of communication. Specifically, the study explores consumers’ willingness to accept personified communication in social commerce, considering the role of cognitive needs in regulating the internal mechanism. The paper proposes suggestions for enterprises to improve their social media communication and presents an improved model based on the Technology Acceptance Model (TAM). The model introduces perceived interaction as a new independent variable and adds cognitive needs as a regulatory variable, which is more suitable for social commerce. The study conducts a questionnaire survey online and analyzes the data using AMOS and SPSS. The results demonstrate that perceived usefulness and perceived interaction positively impact consumers’ attitudes, which subsequently influences their willingness to purchase. Furthermore, cognitive needs as a regulatory variable significantly affect the path from perceived usefulness to attitude and purchase intention.