Enhancing Water Resistance and Mechanical Properties of Cemented Soil with Graphene Oxide.

Although cemented soil as a subgrade fill material can meet certain performance requirements, it is susceptible to capillary erosion caused by groundwater. In order to eliminate the hazards caused by capillary water rise and to summarize the relevant laws of water transport properties, graphene oxide (GO) was used to improve cemented soil. This paper conducted capillary water absorption tests, unconfined compressive strength (UCS) tests, softening coefficient tests, and scanning electron microscope (SEM) tests on cemented soil using various contents of GO. The results showed that the capillary water absorption capacity and capillary water absorption rate exhibited a decreasing and then increasing trend with increasing GO content, while the UCS demonstrated an increasing and then decreasing trend. The improvement effect is most obvious when the content is 0.09%. At this content, the capillary absorption and capillary water absorption rate were reduced by 25.8% and 33.9%, respectively, and the UCS at 7d, 14d, and 28d was increased by 70.32%, 57.94%, and 61.97%, respectively. SEM testing results demonstrated that GO reduces the apparent void ratio of cemented soil by stimulating cement hydration and promoting ion exchange, thereby optimizing the microstructure and improving water resistance and mechanical properties. This research serves as a foundation for further investigating water migration and the appropriate treatment of GO-modified cemented soil subgrade

NaturalVLM: Leveraging Fine-grained Natural Language for Affordance-Guided Visual Manipulation

Enabling home-assistant robots to perceive and manipulate a diverse range of 3D objects based on human language instructions is a pivotal challenge. Prior research has predominantly focused on simplistic and task-oriented instructions, i.e., "Slide the top drawer open". However, many real-world tasks demand intricate multi-step reasoning, and without human instructions, these will become extremely difficult for robot manipulation. To address these challenges, we introduce a comprehensive benchmark, NrVLM, comprising 15 distinct manipulation tasks, containing over 4500 episodes meticulously annotated with fine-grained language instructions. We split the long-term task process into several steps, with each step having a natural language instruction. Moreover, we propose a novel learning framework that completes the manipulation task step-by-step according to the fine-grained instructions. Specifically, we first identify the instruction to execute, taking into account visual observations and the end-effector's current state. Subsequently, our approach facilitates explicit learning through action-prompts and perception-prompts to promote manipulation-aware cross-modality alignment. Leveraging both visual observations and linguistic guidance, our model outputs a sequence of actionable predictions for manipulation, including contact points and end-effector poses. We evaluate our method and baselines using the proposed benchmark NrVLM. The experimental results demonstrate the effectiveness of our approach. For additional details, please refer to https://sites.google.com/view/naturalvlm

ImageManip: Image-based Robotic Manipulation with Affordance-guided Next View Selection

In the realm of future home-assistant robots, 3D articulated object manipulation is essential for enabling robots to interact with their environment. Many existing studies make use of 3D point clouds as the primary input for manipulation policies. However, this approach encounters challenges due to data sparsity and the significant cost associated with acquiring point cloud data, which can limit its practicality. In contrast, RGB images offer high-resolution observations using cost effective devices but lack spatial 3D geometric information. To overcome these limitations, we present a novel image-based robotic manipulation framework. This framework is designed to capture multiple perspectives of the target object and infer depth information to complement its geometry. Initially, the system employs an eye-on-hand RGB camera to capture an overall view of the target object. It predicts the initial depth map and a coarse affordance map. The affordance map indicates actionable areas on the object and serves as a constraint for selecting subsequent viewpoints. Based on the global visual prior, we adaptively identify the optimal next viewpoint for a detailed observation of the potential manipulation success area. We leverage geometric consistency to fuse the views, resulting in a refined depth map and a more precise affordance map for robot manipulation decisions. By comparing with prior works that adopt point clouds or RGB images as inputs, we demonstrate the effectiveness and practicality of our method. In the project webpage (https://sites.google.com/view/imagemanip), real world experiments further highlight the potential of our method for practical deployment

Altered Functional Connectivity in an Aged Rat Model of Postoperative Cognitive Dysfunction: A Study Using Resting-State Functional MRI

BACKGROUND: Postoperative cognitive impairment is a common complication after cardiac and major non-cardiac surgery in the elderly, but its causes and mechanisms remain unclear. The purpose of the current study was to use resting-state functional magnetic resonance imaging (fMRI) to explore changes in the functional connectivity, i.e. the synchronization of low frequency fluctuation (LFF), in an animal model of cognitive impairment in aged rats. METHODS: Aged (22 months) rats were anaesthetized with 40 µg/kg fentanyl and 500 µg/kg droperidol (intraperitoneal) for splenectomy. Cognitive function was assessed using Y maze prior to operation and on postoperative days 1, 3 and 9. To evaluate functional connectivity, resting-state fMRI data were acquired using a 3T MR imaging system with a 4 channel phase array rat head coil. RESULTS: Cognitive function was impaired at postoperative days 1 and 3 compared with preoperative. Significant synchronized LFF was detected bilaterally in the primary somatosensory cortex and hippocampus preoperatively. By contrast, no significant LFF synchronization was detected in the right primary somatosensory cortex and right hippocampus on postoperative days 1 and 3, although the pattern of functional connectivity had become almost normal by day 9. CONCLUSION: Splenectomy performed under neuroleptic anaesthesia triggers a cognitive decline that is associated with altered spontaneous neuronal activity in the cortex and hippocampus

Knowledge Graph Link Prediction Fusing Description and Structural Features

Knowledge graph generally has the problem of incomplete knowledge, which makes link prediction an important research content of knowledge graph. Existing models only focus on the embedding representation of triples. On the one hand, in terms of model input, only the embedding representation of entities and relations is randomly initialized, and the description information of entities and relations is not incorporated, which will lack semantic information; on the other hand, in decoding, the influence of the structural features of the triplet itself on the link prediction results is ignored. Aiming at the above problems, this paper proposes a knowledge graph link prediction model BFGAT (graph attention network link prediction based on fusion of description information and structural features) that integrates description information and structural features. The BFGAT model uses the BERT pretraining model to encode the description information of entities and relations, and integrates the description information into the embedding representation of entities and relations to solve the problem of missing semantic information. In the coding process, graph attention mechanism is used to aggregate the information of adjacent nodes to solve the problem that the target node can obtain more information. The embedding representation of triples is spliced into a matrix in the decoding process, using a method based on CNN convolution pooling to solve the problem of triple structural features. The model is subjected to detailed experiments on the public datasets FB15k-237 and WN18RR, and the experiments show that the BFGAT model can effectively improve the effect of knowledge graph link prediction

Evaluation of installation timing of initial ground support for large-span tunnel in hard rock

In conventional drill and blast tunnelling, initial ground support is placed immediately after the current round is shot before excavation of the next round (i.e. one-round installation method). When tunnelling in hard rock, one-round installation of initial ground support conservatively ensures tunnel integrity, but meanwhile brings up other problems such as over-break at tunnel face, slow excavation rate and so forth. In this study, a large-span tunnel in Class III hard rock was monitored by a network of sensors to investigate tunnel internal forces in three construction scenarios where initial ground supports were placed in different timing and sequence: (1) initial ground support installed immediately after current round (2) support installed after two rounds (3) support installed after three consecutive rounds. The collected field measurements together with construction records were evaluated from three aspects: structural stability, constructability and cost-effectiveness. Results show that the installation of initial ground support after two rounds generally led to the most regular and minimum tunnel internal forces of the three construction scenarios, whilst it managed to minimize under & over-break and allow more space for construction convenience. In the meanwhile, this installation sequence significantly accelerated tunnel advance rate at lower material cost

Social avoidance and social adjustment in Chinese preschool migrant children: the moderating role of teacher–child relationships

ObjectivesThis study aimed to explore the moderating role of teacher–child relationships in the relations between social avoidance and social adjustment (i.e., prosocial behavior, peer exclusion, and anxious-fearful behavior) in Chinese migrant preschoolers.MethodsParticipants were 148 migrant children aged 4–6 years (82 boys, Mage = 62.32, SD = 6.67) attending kindergartens in Shanghai, People's Republic of China. Mothers reported children's social avoidance, and teachers rated teacher–child relationships and children's social adjustment.ResultsResults indicated that social avoidance was positively related to peer exclusion and negatively related to prosocial behavior. Teacher–child relationships moderated those associations. Specifically, teacher–child closeness buffered the relationship between social avoidance and peer exclusion, whereas teacher–child conflict exacerbated the relations between social avoidance and peer exclusion and anxious-fearful behavior.ConclusionThe current finding informs us of the importance of improving teacher–child closeness and reducing teacher–child conflict to buffer the negative adjustment among socially avoidant young children who migrated from rural-to-urban China. The findings also highlight the importance of considering the meaning and implication of social avoidance for migrant preschoolers in Chinese culture

Association analysis of dopaminergic degeneration and the neutrophil-to-lymphocyte ratio in Parkinson’s disease

IntroductionPeripheral inflammatory responses are suggested to play a major role in the pathophysiology of Parkinson’s disease (PD). The neutrophil-to-lymphocyte ratio (NLR), a new recognized biomarker, can reflect peripheral inflammation in PD. However, the association between the NLR and dopaminergic degeneration in PD remains unclear.MethodsIn this retrospective study, 101 enrolled PD patients were categorized into early-stage and advanced-stage PD based on the Hoehn and Yahr (HY) scale. We evaluated the clinical characteristics, peripheral immune profile, and 11C-CFT striatal dopamine transporter (DAT) binding levels. Linear regression analyses were employed to assess the associations between NLR and striatal DAT levels at different stages in PD patients.ResultsCovariate-controlled regression analysis revealed that higher NLR was significantly associated with lower DAT levels in the caudate (β = −0.27, p = 0.003) and the putamen (β = −0.27, p = 0.011). Moreover, in the early-stage PD subgroup, a similar association was observed (caudate: β = −0.37, p = 0.013; putamen: β = −0.45, p = 0.005). The lymphocytes count was correlated positively with the striatal DAT levels in the Spearman correlation analysis whether in total patients (caudate: ρ = 0.25, p = 0.013; putamen: ρ = 0.22, p = 0.026) or in the early-stage subgroup (caudate: ρ = 0.31, p = 0.023, putamen: ρ = 0.34, p = 0.011).ConclusionDopaminergic degeneration is associated with peripheral inflammation in PD. The NLR, a widely used inflammatory marker, may have the potential to reflect the degree of dopaminergic degeneration in individuals with early-stage PD

3D Alternating Direction TV-Based Cone-Beam CT Reconstruction with Efficient GPU Implementation

Iterative image reconstruction (IIR) with sparsity-exploiting methods, such as total variation (TV) minimization, claims potentially large reductions in sampling requirements. However, the computation complexity becomes a heavy burden, especially in 3D reconstruction situations. In order to improve the performance for iterative reconstruction, an efficient IIR algorithm for cone-beam computed tomography (CBCT) with GPU implementation has been proposed in this paper. In the first place, an algorithm based on alternating direction total variation using local linearization and proximity technique is proposed for CBCT reconstruction. The applied proximal technique avoids the horrible pseudoinverse computation of big matrix which makes the proposed algorithm applicable and efficient for CBCT imaging. The iteration for this algorithm is simple but convergent. The simulation and real CT data reconstruction results indicate that the proposed algorithm is both fast and accurate. The GPU implementation shows an excellent acceleration ratio of more than 100 compared with CPU computation without losing numerical accuracy. The runtime for the new 3D algorithm is about 6.8 seconds per loop with the image size of 256×256×256 and 36 projections of the size of 512×512